growth response of deodar (cedrus deodara), blue pine

GROWTH RESPONSE OF DEODAR (Cedrus deodara), BLUE PINE

(Pinus wallichiana) AND CHIR PINE (Pinus roxburghii) TO CLIMATE

CHANGE IN GALIES FOREST DIVISION – ABBOTTABAD

By

SYED SAID BADSHAH BUKHARI

Department of Environmental Sciences

University of Peshawar

Session: 2009-14

ii

AUTHOR’S DECLARATION

I, Syed Said Badshah Bukhari, hereby state that my PhD thesis titled “GROWTH

RESPONSE OF DEODAR (Cedrus deodara), BLUE PINE (Pinus wallichiana) AND CHIR PINE

(Pinus roxburghii) TO CLIMATE CHANGE IN GALIES FOREST DIVISION - ABBOTTABAD”

is my own work and has not been submitted previously by me for taking any degree

from University of Peshawar or anywhere else in the country/world.

At any time if my statement is found to be incorrect even after my graduation the

university has the right to withdraw my PhD degree.

Syed Said Badshah Bukhari

Dated: 25th September 2018

iii

Anti-Plagiarism Test Certificate (Signed copy attached in the printed copies of the dissertation)

iv

Certificate of Approval (Signed copy attached in the printed copies of the dissertation)

v

DEDICATION

I dedicate this effort, the fruit of my thoughts and study to my parents who always inspired

and supported me to pursue knowledge, truth and virtue and serve the humanity.

vi

ABSTRACT

The present study was conducted to assess climate change and its impacts on growth of

Cedrus deodara, Pinus wallichiana and Pinus roxburghii at Galies Forest Division-

Abbottabad during 1962-2011. The climate parameters of temperature and precipitation

were used to assess climate regimes and climate changes, both on annual and seasonal

basis. Bioclimatic indices regimes and changes therein during 1962-201 were calculated.

The ring-width, early wood and late wood formations, and wood cell diameter and

thickness, were measured for time function analysis and impacts of climate change on

these characteristics.

The findings showed regimes of mean annual maximum temperature of 16.36±0.08 °C,

mean annual minimum temperature 6.08±0.08 °C and mean annual temperature

11.21±0.07 °C, while of precipitation was 889.48±19.43 mm/annum.

Climate Vegetation Productivity Index (CVPI) was ranging from 4,342 to 9,091. The

mean CVPI was calculated at 6,816, which indicated productivity in the range of 163.91-

184.77 cubic feet/acre.

The mean ring-widths of C. deodara, P. wallichiana and P. roxburghii for the time

period of 1962-2011 were 3.08±0.23 mm, 2.54±0.15 mm and 2.62±0.39 mm, with

coefficients of variation of 32.88%, 26.55% and 67.20% respectively. The values of

mean sensitivity of these species for the same period were 0.30±0.11, 0.38±0.11 and

0.29±0.10, with coefficients of variation of 16.56%, 19.50% and 17.53% respectively.

The overall increase in temperature and fluctuations in precipitation affected tree growth,

both in terms of ring-width and intra-ring wood characteristics. The temperature was

found negatively correlated with mean ring-widths and early wood formation in all the

three species, while the correlation with late wood formation and intra-ring wood

characteristics were positive in some cases and negative in others.

Key words: Climate change, Forests, Bioclimatic Indices, Climate Vegetation

Productivity Index, Climate Change Impacts

vii

ACKNOWLEDGEMENT

In the name of Allah, the most merciful and the most benevolent

I bow my head before Allah Almighty Who blessed me with vision, good health,

physical strength and academic vigour to undertake and complete this research work.

The research was conducted under the kind supervision of Professor Dr. S. Shafiqur

Rehman, co-supervision of Professor Dr. Noor Jehan and full assistance of Dr. Ghulam

Ali Bajwa, Coordinator Sericulture, Pakistan Forest Institute.

I am extremely grateful to my supervisor, Professor Dr. S. Shafiqur Rehman and co-

supervisor Professor Dr. Noor Jehan for their motivating spirit, continuous

encouragement and able guidance from conception to completion of this research work.

My special thanks are due for faculty members of Department of Environmental Sciences

and other Departments and Institutes who imparted me basic knowledge, advance

knowledge and scientific training as a PhD scholar, all contributing in undertaking this

research work.

I am highly indebted to Dr. Ghulam Ali Bajwa, Coordinator Sericulture, Pakistan Forest

Institute for his valuable assistance in designing the research study, instrumental

measurement of parameters of interest, data analysis and over all structuring of the thesis.

The technical expertise and assistance provided by Mr. Ghulam Mustafa Nasir, Director,

Forest Products Research Division, and Research Officers and Laboratory staff of

Sericulture Division and Forest Products Research Division are fully acknowledged. I

also wish to express my sincere thanks to Mr. Hakim Shah, Director, Pakistan Forest

Institute, Mr. Muhammad Yousaf Khan, Divisional Forest Officer and colleague

scientists in the Institute who motivated and continuously helped me to fathom the long

academic journey culminating at this thesis.

The research work on the topic involved extensive field visits for collection of cores and

wood discs of sample trees and stumps; I owe special thanks to Mr. Ejaz Qadir, DFO,

Galies Forest Division-Abbottabad and his staff for providing full assistance during the

field visits for collection of the sample wood materials.

Finally, I wish to acknowledge my gratitude to all those individuals and institutions,

researchers and scientists, managers and administrators and my family members,

viii

particularly, my sons, namely, Dr. Syed Kazim Shah Bukhari, Engineer Syed Jafar Shah

Bukhari and Dr. Syed Murtaza Shah Bukhari, and friends who, in several ways,

contributed in undertaking and finishing of this research work.

Syed Said Badshah Bukhari

ix

CONTENTS

AUTHOR’S DECLARATION ii

ANTI-PLAGIARISM TEST CERTIFICATE iii

CERTIFICATE OF APPROVAL iv

DEDICATION v

ABSTRACT vi

ACKNOWLEDGEMENT vii

LIST OF FIGURES xiv

LIST OF TABLES xxii

ABBREVIATIONS xxiv

CHAPTER 1 1

INTRODUCTION 1

1.1 Climate Change 1

1.2 Forest 4

1.3 Climate Change Impacts 5

1.4 Objectives 8

1.5 Scientific Contribution 9

1.6 Limitations of the study 9

CHAPTER 2 11

LITERATURE REVIEW 11

2.1 Climate Change 11

x

2.2 Forest 14

2.2.1 Coniferous Forest 15

2.2.2 Sub-Alpine Forest 15

2.2.3 Dry Temperate Forest 16

2.2.4 Moist Temperate Forest 16

2.2.5 Sub-Tropical Pine Forest 17

2.3 Climate Change and Forest Ecosystems 17

2.4 Climate Change - Growth Response 21

CHAPTER 3 30

MATERIALS AND METHODS 30

3.1 Study Area 30

3.2 Preparation of forest maps 32

3.3 Climate Change Data and Analysis 39

3.4 Bioclimatic Indices 39

3.5 Wood Samples and Measurements 41

3.5.1 Collection and Preparation of Samples 41

3.5.2 Ring-width Measurement 51

3.5.3 Ring-Structure 52

3.6 Time Function and Climate Growth Function 54

3.7 Statistical Design and Analysis 54

CHAPTER 4 56

CLIMATE CHANGE AND BIOCLIMATIC INDICES 56

4.1 Climate 56

4.1.1 Climate Regimes 56

xi

4.1.2 Climate Change Trends 57

4.1.3 Climate Changes 83

4.1.4 Mathematical Expressions of Climate Change

Trends at GFD (1962-2011)

86

4.1.5 Correlation Coefficients Matrix of different Climate

Factors at GFD

87

4.2 Bioclimatic Indices 88

4.2.1 Bioclimatic Indices Regime 88

4.2.2 Changes in Bioclimatic Indices 90

4.2.3 Mathematical Expressions of Changes in

Bioclimatic Indices at GFD (1962-2011)

91

4.3 Climate Vegetation Productivity Index 94

4.4 Discussion 94

CHAPTER 5 100

IMPACTS OF CLIMATE CHANGE ON TREE RINGS AND

RING-WOOD CHARACTERISTICS

100

5.1 Cross-dating of Ring-width data 100

5.2 Standardization of Ring-width data 101

5.3 Mean Sensitivity and Coefficient of Variation 102

5.4 Ring-width and Ring-wood Characteristics of Deodar 103

5.4.1 Time function analysis of Ring-width and Ring-wood

Characteristics of Deodar

103

5.4.2 Mathematical Expressions of Time Function of Ring-

width, Intra- ring wood Formation and Wood Cell Characteristics of

Deodar

113

5.4.3 Decadal changes in Ring-width and Ring-wood

characteristics of Deodar

114

xii

5.4.4 Correlation between Ring-width and Ring-wood

Characteristics of Deodar

117

5.4.5 Impacts of Climate Change on Ring-width of Deodar 119

5.5 Ring-width and Ring-wood Characteristics of Blue pine 140

5.5.1 Time function analysis of Ring-width and Ring-

wood Characteristics of Blue pine

140

5.5.2 Mathematical Expressions of Time Function of

Ring-width, Intra-ring wood Formation and Cell

Characteristics of Blue pine

151



152



155

5.5.5 Impacts of Climate Change on Ring-width of Blue

pine

156

5.5.6 Mathematical Expressions of Impacts of

Temperature and Precipitation on Ring-width of Blue pine

175

5.6 Ring-width and Ring-wood Characteristics of Chir pine 177

5.6.1 Time function analysis of Ring-width and Ring-

wood Characteristics of Chir pine

177

5.6.2 Mathematical Expressions of Time Function of

Ring-width, Intra-ring wood Formation and Cell

Characteristics of Chir pine

187



188



191

5.6.5 Impacts of Climate Change on Ring-width of Chir

pine

192

xiii

5.6.6 Mathematical Expressions of Impacts of

Temperature and Precipitation on Ring-width of Chir pine

212

5.7 Inter-species Comparison of Correlation Coefficients of

Ring-widths of Cedrus deodara, Pinus wallichiana and

Pinus roxburghii with Climate Parameters

214

5.8 Discussion 217

CHAPTER 6 222

SUMMARY, GENERAL CONCLUSIONS AND

RECOMMENDATIONS 222

6.1 Summary 222

6.2 General Conclusions 238

6.3 Recommendations 241

REFERENCES 242

xiv

LIST OF FIGURES

Figure 2 -1 Moist Temperate forest of Galies Forest Division 15

Figure 3 -1 Land cover map of Galies Forest Division 31

Figure 3 -2 Land cover/Forest map of GFD–Abbottabad (Grid 50x50 Km) 34





Figure 3 -7 Core extraction using Presler Borer 43

Figure 3 -8 Samples distribution map of GFD-Abbottabad 48

Figure 3 -9 A magnified section of samples distribution map (1 x 1 km) of

GFD-Abbottabad

49

Figure 3 -10 A magnified section of samples distribution map (0.1 x 0.1

km) of GFD-Abbottabad

50

Figure 3 -11 Preparation of microscopic slides for measuring cell diameter

and cell wall thickness

53

Figure 4 -1 Trend line of Mean Annual Maximum Temp. (°C) vs. Time at

GFD (1962-2011)

59

Figure 4 -2 Trend line of Mean Annual Minimum Temp. (°C) vs. Time at

GFD (1962-2011)

60

Figure 4 -3 Trend line of Mean Annual Temp. (°C) vs. Time at GFD

(1962-2011)

61

Figure 4 - 4 Trend line of Annual Precipitation vs. Time at GFD (1962-

2011)

62

Figure 4 -5 Trend line of Mean Spring Maximum Temp. (°C) vs. Time at

GFD (1962-2011)

63

Figure 4 -6 Trend line of Mean Spring Maximum Temp. (°C) vs. Time at

GFD (1962-2011)

64

xv

Figure 4 -7 Trend line of Mean Spring Temp. (°C) v. Time at GFD (1962-

2011)

65

Figure 4 -8 Trend line of Spring Precipitation vs. Time at GFD (1962-

2011)

66

Figure 4 -9 Trend line of Mean Summer Maximum Temp. (°C) vs. Time at

GFD (1962-2011)

67

Figure 4 -10 Trend line of Mean Summer Minimum Temp. (°C) vs. Time

at GFD (1962-2011)

68

Figure 4 -11 Trend line of Mean Summer Temp. (°C) vs. Time at GFD

(1962-2011)

69

Figure 4 -12 Trend line of Summer Precipitation vs. Time at GFD (1962-

2011)

70

Figure 4 -13 Trend line of Mean Monsoon Maximum Temp. (°C) vs. Time

at GFD (1962-2011)

71

Figure 4 -14 Trend line of Mean Monsoon Minimum Temp. (°C) vs. Time

at GFD (1962-2011)

72

Figure 4 -15 Trend line of Mean Monsoon Temp. (°C) vs. Time at GFD 73

Figure 4 -16 Trend line of Monsoon Precipitation vs. Time at GFD (1962-

2011)

74

Figure 4 -17 Trend line of Mean Autumn Maximum Temp. (°C) vs. Time

at GFD (1962-2011)

75

Figure 4 -18 Trend line of Mean Autumn Minimum Temp. (°C) vs. Time

at GFD (1962-2011)

76

Figure 4 -19 Trend line of Mean Autumn Temp. (°C) vs. Time at GFD

(1962-2011)

77

Figure 4 -20 Trend line of Autumn Precipitation vs. Time at GFD (1962-

2011)

78

Figure 4 -21 Trend line of Mean Winter Maximum Temp. (°C) vs. Time at

GFD (1962-2011)

79

Figure 4 -22 Trend line of Mean Winter Minimum Temp. (°C) vs. Time at

GFD (1962-2011)

80

Figure 4 -23 Trend line of Mean Winter Temp. (°C) vs. Time at GFD

(1962-2011)

81

xvi

Figure 4 -24 Trend line of Winter Precipitation vs. Time at GFD (1962-

2011)

82

Figure 4- 25 Comparison between increases in Maximum Temperature and

Minimum Temperature

85

Figure 4 -26 Trend line of Climate Vegetation Productivity Index at GFD 94

Figure 5 -1 Cross-dating of Ring-width data of Deodar in GFD (1962-

2011)

100

Figure 5 -2 Cross-dating of Ring-width data of Blue pine in GFD (1962-

2011)

101

Figure 5 -3 Cross-dating of Ring-width data of Chir pine in GFD (1962-

2011)

101

Figure 5 -4 Time function of Mean Annual Ring-width of Deodar in GFD

(1962-2011)

105

Figure 5 -5 Time function of Mean Intra-ring Early Wood Formation (%)

of Deodar in GFD (1962-2011)

106

Figure 5 -6 Time function of Mean Intra-ring Late Wood Formation (%) of

Deodar in GFD (1962-2011)

107

Figure 5 -7 Time function of Mean Intra-ring Early Wood Cell Diameter

(µm) of Deodar in GFD (1962-2011)

108

Figure 5 -8 Time function of Mean Intra-ring Early Wood Cell Wall

Thickness (µm) of Deodar in GFD (1962-2011)

109

Figure 5 -9 Intra-ring Early Wood Cell Diameter and Cell Wall Thickness

of Deodar (100x)

110

Figure 5 -10 Time function of Mean Intra-ring Late Wood Cell Diameter

(µm) of Deodar in GFD (1962-2011)

111

Figure 5 -11 Time function of Mean Intra-ring Late Wood Cell Wall

Thickness (µm) of Deodar in GFD (1962-2011)

112

Figure 5 -12 Intra-ring Late Wood Cell Diameter and Cell Wall Thickness

of Deodar (100x)

113

Figure 5 -13 Impact of Mean Annual Maximum Temperature on Ring-

width of Deodar in GFD (1962-2011)

120

Figure 5 -14 Impact of Mean Annual Minimum Temperature on Ring-


121

xvii

Figure 5 -15 Impact of Annual Precipitation on Ring-width of Deodar in

GFD (1962-2011)

122

Figure 5 -16 Impact of Mean Spring Maximum Temperature on Ring-


124

Figure 5 -17 Impact of Mean Spring Minimum Temperature on Ring-width

of Deodar in GFD (1962-2011)

125

Figure 5 -18 Impact of Spring Precipitation on Ring-width of Deodar in

GFD (1962-2011)

126

Figure 5 -19 Impact of Mean Summer Maximum Temperature on Ring-


127

Figure 5 -20 Impact of Mean Summer Minimum Temperature on Ring-


128

Figure 5 -21 Impact of Summer Precipitation on Ring-width of Deodar in

GFD (1962-2011)

129

Figure 5 -22 Impact of Mean Monsoon Maximum Temperature on Ring-


130

Figure 5 -23 Impact of Mean Monsoon Minimum Temperature on Ring-


131

Figure 5 -24 Impact of Monsoon Precipitation on Ring-width of Deodar in

GFD (1962-2011)

132

Figure 5 -25 Impact of Mean Autumn Maximum Temperature on Ring-


133

Figure 5 -26 Impact of Mean Autumn Minimum Temperature on Ring-


134

Figure 5 -27 Impact of Mean Autumn Precipitation on Ring-width of


135

Figure 5 -28 Impact of Mean Winter Maximum Temperature on Ring-


136

Figure 5 -29 Impact of Mean Winter Minimum Temperature on Ring-


137

Figure 5 -30 Impact of Winter Precipitation on Ring-width of Deodar in

GFD (1962-2011)

138

xviii

Figure 5 -31 Time function of Mean Annual Ring-width of Blue pine in

GFD (1962-2011)

142


of Blue pine in GFD (1962-2011)

143

Figure 5 -33 Time function of Mean Intra-ring Late Wood Formation (%)


144

Figure 5 -34 Time function of Mean Intra-Ring Early Wood Cell Diameter

(µm) of Blue pine in GFD (1962-2011)

145


Thickness (µm) of Blue pine

146


of Blue pine (100x)

147


(µm) of Blue pine in GFD (1962-2011)

148


Thickness (µm) of Blue pine in GFD (1962-2011)

149


of Blue pine (100x)

150


width of Blue pine in GFD (1962-2011)

157



158

Figure 5 -42 Impact of Annual Precipitation on Ring-width of Blue pine in

GFD (1962-2011)

159



160



161

Figure 5 -45 Impact of Spring Precipitation on Ring-width of Blue pine in

GFD (1962-2011)

162



163

xix



164

Figure 5 -48 Impact of Summer Precipitation on Ring-width of Blue pine

in GFD (1962-2011)

165



166



167

Figure 5 -51 Impact of Monsoon Precipitation on Ring-width of Blue pine

in GFD (1962-2011)

168



169



170

Figure 5 -54 Impact of Autumn Precipitation on Ring-width of Blue pine in

GFD (1962-2011)

171



172



173

Figure 5 -57 Impact of Winter Precipitation on Ring-width of Blue pine in

GFD (1962-2011)

174

Figure 5 -58 Time function of Mean Annual Ring-Width of Chir pine in

GFD (1962-2011)

178


of Chir pine in GFD (1962-2011)

179

Figure 5 -60 Time function of Mean Intra-ring Late Wood Formation (%)


180

Figure 5 -61 Time function of Mean Intra-ring Early Wood Cell Diameter

(µm) of Chir pine in GFD (1962-2011)

181


Thickness (µm) of Chir pine in GFD (1962-2011)

182

xx


of Chir pine (100x)

183


(µm) of Chir pine in GFD (1962-2011)

184


Thickness (µm) of Chir pine in GFD (1962-2011)

185


of Chir pine (100x)

186


width of Chir pine in GFD (1962-2011)

193



194

Figure 5 -69 Impact of Annual Precipitation on Ring-width of Chir pine in

GFD (1962-2011)

195



197



198

Figure 5 -72 Impact of Spring Precipitation on Ring-width of Chir pine in

GFD (1962-2011)

199



200



201

Figure 5 -75 Impact of Summer Precipitation on Ring-width of Chir pine in

GFD (1962-2011)

202



203



204

Figure 5 -78 Impact of Monsoon Precipitation on Ring-width of Chir pine

in GFD (1962-2011)

205

xxi



206



207

Figure 5 -81 Impact of Autumn Precipitation on Ring-width of Chir pine in

GFD (1962-2011)

208



209



210

Figure 5 -84 Impact of Winter Precipitation on Ring-width of Chir pine in

GFD (1962-2011)

211

xxii

LIST OF TABLES

Table 3 - 1 Site - wise geographical and tree information of Deodar in GFD 43

Table 3 - 2 Site - wise geographical and tree information of Blue pine in

GFD

45

Table 3 - 3 Site - wise geographical and tree information of Chir pine in

GFD

46

Table 4 - 1 Temperature and Precipitation Regimes at GFD (1962-2011) 57

Table 4 - 2 Trend Analysis of Climate Change at GFD (1962-2011) 57

Table 4 - 3 Temperature and precipitation changes at GFD (1962-2011) 84

Table 4 - 4 Mathematical Expressions of Climate Change Trends at GFD

(1962-2011)

86

Table 4 - 5 Correlation Coefficients Matrix among different Climate

Factors at GFD

88

Table 4 - 6 Bioclimatic Indices Regimes at GFD (1962-2011) 88

Table 4 - 7 Changes (%) in Bioclimatic Indices at GFD (1962-2011) 90

Table 4 - 8 Mathematical Expressions of Changes in Bioclimatic Indices at

GFD (1962-2011)

91

Table 5 - 1 (a) Statistics of intra-species variability of annual ring-widths

of Cedrus deodara, Pinus wallichiana and Pinus roxburghii (1962-2011)

102

Table 5 - 1 (b) Statistics of mean sensitivity of mean annual ring-widths of

Cedrus deodara, Pinus wallichiana and Pinus roxburghii for the period

1962-2011

103

Table 5 - 2 Trend Analysis of Ring-width and Ring-wood Characteristics

of Deodar at GFD (1962-2011)

104

Table 5 - 3 Mathematical Expressions of Time Function of Ring-width and

Intra-ring wood Characteristics of Deodar in GFD (1962-2011)

114

Table 5 - 4 Mean Decadal Ring-width and Ring-wood Characteristics of


116

Table 5 - 5 Correlation Coefficients Matrix between Ring-width and Ring-

wood Characteristics of Deodar in GFD (1962-2011)

119

xxiii

Table 5 - 6 Precipitation and Ring-width of Deodar in GFD (1962-2011) 123

Table 5 - 7 Mathematical Expressions of Impacts of Temperature and

Precipitation on Ring-width of Deodar in GFD (1962-2011)

139

Table 5 - 8 Trend Analysis of Ring-width and Ring-wood Characteristics

of Blue pine at GFD (1962-2011)

141

Table 5 - 9 Mathematical Expressions of Time Function of Ring-width and

Intra-ring Wood Characteristics of Blue pine in GFD (1962-2011)

151

Table 5 - 10 Mean Decadal Ring-width and Ring-Wood Characteristics of

Blue pine in GFD (1962-2011)

153

Table 5 - 11 Correlation Coefficients Matrix between Ring-width and

Ring-wood Characteristics of Blue pine in GFD (1962-2011)

156

Table 5 - 12 Precipitation and Ring-width of Blue pine in GFD (1962-

2011)

160

Table 5 - 13 Mathematical Expressions of Impact of Temperature and

Precipitation on Ring-width of Blue pine in GFD (1962-2011)

176

Table 5 - 14 Trend Analysis of Ring-width and Ring-wood characteristics

of Chir pine at GFD (1962-2011)

177

Table 5 - 15 Mathematical Expressions of Time Function of Ring-width

and Intra-ring Wood Characteristics of Chir pine in GFD (1962-2011)

187

Table 5 - 16 Mean Decadal Ring-width and Ring-Wood Characteristics of

Chir pine in GFD (1962-2011)

189

Table 5 - 17 Correlation Coefficients Matrix between Ring-width and

Ring-wood Characteristics of Chir pine in GFD (1962-2011)

192

Table 5 - 18 Impact of Precipitation on Ring-width of Chir pine in GFD

(1962-2011)

196

Table 5 - 19 Mathematical Expressions of Impact of Temperature and

Precipitation on Ring-width of Chir pine in GFD (1962-2011)

212

Table 5 - 20 Inter-species Comparison of Correlation Coefficients of Ring-

widths of Cedrus deodara, Pinus wallichiana and Pinus roxburghii with

Climate Parameters in GFD (1962-2011)

215

xxiv

ABBREVIATIONS

A Annual

AI Aridity Index

ANOVA Analysis of Variance

AP Alpine Pasture

ARM Annual Ring Measuring

Au Autumn

CACTOS California Conifer Timber Output Simulator

CI Confidence Interval

CRU Climate Research Unit

CV Coefficient of Variation

CV Critical value

CVPI Climate Vegetation Productivity Index

DF Dryness Factor

DFO Divisional Forest Officer

DI Dryness Index

DSTBL Dry Sub-Tropical Broad-Leaved

DT Dry Temperate

DTT Dry Tropical Thorn

EW Early wood

EWCD Early wood cell diameter

EWCWT Early wood cell wall thickness

FAO Food and Agriculture Organization of the United Nations

GFD Galies Forest Division

GPS Global Positioning System

HC Humidity Coefficient

HSD Honest Significance Difference

IP Irrigated Plantation

IPCC Intergovernmental Panel on Climate Change

LW Late wood

LWCD Late wood cell diameter

LWCWT Late wood cell wall thickness

xxv

M Monsoon

Max Maximum

Min Minimum

MF Mangrove Forest

MS Mean Sensitivity

MT Moist Temperate

PEI Precipitation Efficiency Index

PFI Pakistan Forest Institute

PI Prediction Interval

r Coefficient of Correlation

R2 Coefficient of determination

RF Rain Factor

RiF River Forest/Riverine Forest

RW Ring-width

S Spring

SA Sub-Alpine

SE Standard Error

STP Sub-Tropical Pine

Su Summer

T Ton

TEI Temperature Efficiency Index

Temp Temperature

USGCRP United States Global Change Research Program

W Winter

µm Micron

1

CHAPTER 1

INTRODUCTION

1.1 Climate Change

Climate change is the most important contemporary environmental issue with global

dimensions. Climate may be defined as an average weather which may be described in

statistical terms of mean and variability of usually surface variables, such as

temperature, precipitation, wind, etc. In the usage of Intergovernmental Panel on

Climate Change (IPCC), climate change refers to a change in the state of the climate

that can be identified by changes in the mean and/or the variability of its properties and

that persists for an extended period, typically decades or longer. It may be due to

natural processes or anthropogenic changes in the composition of the atmosphere or

land use. The United Nations Framework Convention on Climate Change (UNFCCC)

restricts the climate change to “a change of climate which is attributed directly or

indirectly to human activity that alters the composition of the global atmosphere and

which is in addition to natural climate variability observed over comparable time

periods” (Bukhari and Bajwa, 2012).

As per 4th Assessment Report (IPCC, 2007), climate change is manifested in various

forms. The warming of climate system is unequivocal, as is evident from increases in

global average air and ocean temperatures, widespread melting of snow and ice and

rising global average sea level. The 100-year temperature increasing trend over period

of 1906-2005 is 0.74˚C. The linear warming trend over the last 50 years (0.13˚C per

decade) is nearly twice that for the last 100 years (IPCC, 2007). The rise in temperature

is all over the globe and is higher at higher northern latitudes. The rise in global

average sea level was recorded at an average rate of 1.8 mm/year over the time span of

1961 to 2003 and at an average rate of about 3.1 mm/year from 1993 to 2003. The

extent of snow and ice has reduced, with higher rate during summer. From 1906 to

2005, the precipitation trend exhibited significant increase in some regions of the world

and decrease in others, including parts of southern Asia (IPCC, 2007).

Globally, the extent of area affected by drought has increased since 1970s. A number of

extreme weather events, including hot days and nights, heat spells, heavy precipitation

2

and extreme floods have increased frequency, while cold nights and frost have become

less frequent, at wide range of localities.

The research findings indicate that earth’s climate is changing even faster than

previously estimated. With present anthropogenic activities and physical changes

occurring in nature, the mean global temperature on the earth could rise by seven

degree Celsius as compared to pre-industrial era (1750). This temperature increase

would be faster and higher than the one, the earth experienced at the end of last Ice

Age, about 15,000 years ago; i.e., increase of five degrees Celsius over a period of

5,000 years (Vorholz, 2009).

Climate change is reaching a level to threaten lifestyle and livelihoods in multiple

ways. Rising temperature is causing, inter alia, health problems (Gosling et al., 2009),

increase in intense tropical cyclones and rise in sea levels (IPCC, 2007), changes in

agricultural yields and depletion of ocean oxygen (Shaffer et al., 2009), changes in

forest types and composition (Ravindranath et al., 2006), and extinction of animal and

plant species (Thomas et al., 2004). Due to rising temperature many natural habitats are

shifting towards the poles or into higher latitudes. One of the earliest and most powerful

effects of this warming is the melting of snow packs and mountain glaciers which

precipitate as snow and ice in the winter for release during summer (Svendsen and

Künkel, 2009). For example, the snow-packed water reservoirs in the Himalayas are

melting at a rate of 15.0 m per year, the highest rate in the world, due to rising

temperature (Hasnain, 2009). There are several physical (IPCC, 2007; Grunewald et al.,

2009) and anthropogenic activities (Foley et al., 2005; Falcucci et al., 2007; IPCC,

2007; Vorholz, 2009; Bukhari and Bajwa, 2009) that influence the spatio-temporal

changes in climate processes. Among all these external forcing, anthropogenic

activities are dominant cause of recent global warming (Knutson et al., 2006). Climate

change will affect different regions differently, depending on the extent of increase in

temperature and changes in precipitation in the region. Therefore, proper appreciation

of spatio-temporal variability of temperature and precipitation is of high importance for

many applications, including weather forecasting, climate and environmental research,

determining growth period of plants and estimating evapo-transpiration regimes.

3

Among the anthropogenic activities, emission of greenhouse gases (GHGs) is the

dominant climate changing driver, and these emissions have increased 70% between

1970 and 2004 as compared to pre-industrial era. The major share in increase of GHGs

is of Carbon dioxide (CO2). The annual emissions of Carbon dioxide have grown by

about 80%, from 26 to 38 Giga ton (Gt) between 1970 and 2004. The shares of

different GHGs in total emissions during 2004 were: CO2 (fossil fuel use) 56.6%, CO2

(deforestation and decay of biomass) 17.3%, CO2 (other) 2.8%, methane (CH4) 14.3%,

nitrous oxide (N2O) 7.9% and hydro-chlorofluorocarbons, per fluorocarbons and

sulphur hexafluoride 1.1%. As reported by IPCC, 2007, the emissions of GHGs by

sectors were: energy 25.9%, industry 19.4%, forestry 17.4%, agriculture 13.5%,

transport 13.1%, building 8.0%, and waste materials 2.8%. The global atmospheric

concentrations of GHGs have increased significantly since 1750 and reached to high

levels by 2005: CO2 from 280 ppm to 379 ppm, CH4 from 715 ppb to 1774 ppb and

N2O from 270 ppb to 319 ppb. Aerosols, primarily sulphate, organic carbon, nitrate and

dust, cause a cooling effect both through direct radiative forcing and indirect cloud

albedo forcing. The equilibrium climate sensitivity, measured as the equilibrium global

average surface warming, subsequent to doubling of CO2 concentration, is likely to be

in the range of 2 °C to 4.5 °C, with a best estimate of about 3 °C and is very unlikely to

be less than 1.5 °C. Beside average temperature, anthropogenic forcing has increased

the extreme temperatures and sea level and changed the patterns of winds, storms,

precipitation and drought (IPCC, 2007; Bukhari and Bajwa, 2012).

Climate change is a global phenomenon and Pakistan is no exception to it. Pakistan has

experienced gradual increase in temperature over land and sea, fluctuations and

variations in precipitation, increased frequency of extreme climatic events and changes

in wind and storm patterns. Moreover, the exact magnitude and frequency of these

changes are not uniform in time and space and require empirical data for precise

determination and statistical projections (Sheikh et al., 2012).

The emerging climate change scenarios, particularly down-scaled to local forest types,

will likely have adverse effects on biomass production, biodiversity and forest

ecosystem dynamics. These changes will, in turn, reduce economic, social and

ecological services from these forests with dampening effects on living and livelihood of

forest-dependent communities.

4

1.2 Forest

United Nations Framework Convention on Climate Change (UNFCCC) defines forest

as “a minimum area of land of 0.05-1.0 ha with tree crown cover (or equivalent

stocking level) of more than 10-30% with trees potential to reach of minimum height of

2-5 m at maturity, in situ”. A forest may consist either of closed forest formations

where trees of various layers and undergrowth cover a high proportion of the ground or

open forest with substantial openings in the canopy. Young natural stands and all

plantations which have yet to reach a crown density of 10-30% or tree height of 2-5 m

are categorized as forest, like the areas which are temporarily un-stocked as a result of

human intervention, such as harvesting, or natural causes, but which are expected to

revert to forest.

Pakistan has a lower endowment of forest resources with about 4.6 million ha of forest

land and plantations which sum up to about 5.23% of the total land area of the country.

The per capita forest is 0.03 ha which compares unfavorably with an average world per

capita forest endowment of 0.6 ha. The distribution of forests by forest type is: Conifers

42%, Scrub 34%, Irrigated plantations 6%, River forest 6%, Mangroves 11%, Mazri

0.5% and Linear Plantations 0.5%. These forests are not enough to meet the national

demand of timber, fuel wood and wood based products. The wood production in

Pakistan in 2009 was estimated at 3.983 million m3 and the wood consumption at 4.4 20

million m3, thus indicating a large gap of 0.437 million m3 between production and

consumption (Bukhari, 2011).

Coniferous forests are spread over about 1,946,000 ha in Pakistan, including Azad

State of Jammu and Kashmir (AJK) and Northern Areas (Gilgit-Baltistan) (Wani et al.,

2004), and have multiple uses. The dominant species in coniferous biome in

Pakistan are: Deodar (Cedrus deodara), Blue pine (Pinus wallichiana) and Chir

pine (Pinus roxburghii). The contribution of these species in national coniferous

timber production is about 58.0%, while in Khyber Pakhtunkhwa it is about

88.0%, with species-wise distribution: C. deodara 53.0%, P. wallichiana 20.1%

and P. roxburghii 14.9%. These forests are playing a vital role in national economy

and ecology, especially in the Khyber Pakhtunkhwa province of Pakistan. Khyber

5

Pakhtunkhwa nurtures about 1,073,000 ha of coniferous forests (58.2% of total

coniferous forests of the country) which produce about 30.3% of the national timber

output. Apart from production of timber and non-timber forest produce, these forests

are providing livelihoods to summer grazers and dependent communities and serving

as catchment areas of the Indus River System. The region is also important for eco-

tourism due to its spectacular landforms and greenery. The contribution of forestry

sector and allied services to national economy in current national accounting system is

lower due to lack of marketability of forest ecosystem services, but is much higher in

absolute terms. The functioning and productivity of forest ecosystems are highly

dependent on climatic factors. Therefore, any climate change will likely have

significant effects on their productivity, environmental services and contribution to

local and national economy.

It is, therefore, imperative to assess the impacts of climate change on growth of the

dominant species in coniferous as well as other forest types, which will help devising

a strategy for management of these forests for optimum benefits on sustainable basis.

1.3 Climate Change Impacts

Temperature and precipitation, two of the basic climatic factors bound to change with

increased greenhouse gases concentrations, are primary determinants of global

vegetation patterns with significant impacts on forest ecology (including biodiversity),

plant distribution, productivity and health (Spurr and Barnes, 1980; Smith and Tirpak,

1989; Krischbaum et al., 1996, Bukhari and Bajwa, 2012). Increases in temperature

will not necessarily have simple linear impacts on growth of tree species in different

habitats (Carter, 1996). Also, local, regional and global changes in temperature and

precipitation can influence the occurrence, timing, frequency, duration, extent and

intensity of climatic disturbances (Baker, 1995; Turner et al., 1998). Disturbances, both

human-induced and natural, will configure forest ecosystems by influencing their

composition, structure and functional processes.

A variety of factors affects the tree ring-width and intra-ring wood characteristics.

Some of these factors are specific to location of the tree, its age and close surrounding

conditions, while others are related to wider environmental factors, such as,

6

temperature, rainfall and sunshine (Ahmed 1984; Yeh and Wensel 2000; Suarez et al.,

2009). The tree-ring archive reflects a complete history of climate signals of the

environment, emanating and altered by both biotic and abiotic factors. Species that

show a clear climate signal usually live under limiting conditions. Previously, though

based on limited scale dendrological data, usefulness of tree-ring climate proxy had

been demonstrated by Briffa et al. (1984), Pilcher and Baillie (1980a, b) and Hughes et

al. (1982).

The study of intra-ring wood characteristics also provides a promising tool for

investigation in tree biology and climate change (Fonti et al., 2010), and drawing

climate events within the growing seasons (Campelo et al., 2007; Olivar et al., 2012).

These wood characteristics help to assess impacts of climate variability and change on

tree growth and wood structure (Froux et al., 2002; De Micco et al., 2008; Martinez-

Meier et al., 2008; Hoffmann et al., 2011). Some of the quantifiable intra-ring wood

anatomical characteristics, such as early wood formation and late wood formation, are

highly dependent on climate, and facilitate more conclusive growth-climate relationship

compared to total ring-width only (Wimmer and Grabner, 1997; Rigling et al., 2003;

Cherubini et al., 2003; Campelo et al., 2007; Bogino and Bravo, 2009; Vieira et al.,

2009; Battipaglia et al., 2010; Fonti et al., 2010; Lebourgeois et al., 2010). Novak et al.

(2013) studied the impact of climate parameters on tree-ring widths, early wood and

late wood widths, the transition from early to late wood and the occurrence of intra-

annual rings density fluctuations, as well as the presence of resin canals in early and

late woods, in Aleppo pine (Pinus halepensis) from three sites in Spain and one in

Slovenia. He assessed that wood anatomical features provide complementary

information to that contained in tree-ring widths. Since the samples were obtained from

different sites way apart, it is likely that the results may be generalized over the wide

range of the species distribution pointing a useful proxy for studies on a regional scale.

Coniferous forests are primarily managed on the principle of sustained timber

production for which forest managers/policy makers need growth data to monitor

progress towards forest management plans for achieving targets of timber production

along with sustainable forest resource development. Besides, growth responses of

major tree species could also provide early warning signals to forest managers to put in

place additional management and operational plans, policies or societal actions to

7

achieve sustainable forest resource management for providing desired economic and

socio-ecological services.

There are several methods to evaluate climate change growth response of tree species,

including maintaining growth plots and recording data on visible growth parameters,

such as diameter, plant height, etc., over long periods of time. Maintaining such a large

number of research plots for growth parameters over long periods is a difficult and

costly affair. Apart from long duration, taking exhaustive data is labour intensive. In

contrast, measuring Ring-width and Ring structure, i.e., cell wall, cell radial

dimensions, wood density, etc., are quick and accurate methods to assess climate

growth response of tree species (Ahmed et al., 2010).

Dendrology, the science and study of trees and other wooded plants, and its branches,

including dendrochronology, dendroarchaeology, dendroclimatology, dendroecology,

dendrogeomorphology, dendroglaciology, dendrohydrology, dendropyrochronology

and dendroentomology , has emerged as a distinct applied scientific discipline which

relies primarily on study of tree rings to extract information about parameters of

interest. These parameters include the growth dimension of the tracheid cells and its

anatomical characteristics, which in turn determine the forest productivity and quality

of the timber and other forest produce. The dendrological approach provides reliable

and accurate information on these parameters when applied in a rational manner in

suitable cases. This approach has been adopted for research presented in this

dissertation.

What type of a forest crop can be supported by the specific site or locality is determined

by combinations of climatic factors. Various bioclimatic indices have been developed

to correctly express the interactions between climate and life processes of the plants.

Bioclimatic indices are useful tools to explain the spatial distribution of vegetation units

by the combination of different climatic factors (Gavilán, 2005). They are getting more

importance as they promote the transfer of results from climate modelling to land use

and vegetation science.

A major parameter of interest in forestry is the productivity of a forest which is

dependent on maximum sustainable utilization of the environmental resources. When

http://en.wikipedia.org/wiki/Woody_plant

http://en.wikipedia.org/wiki/Woody_plant

8

physiographic and edaphic factors are optimum, the productivity of the site is mainly

determined by suitable combination of climatic factors, for which Climate Vegetation

Productivity Index (CVPI) has been developed by Paterson (1956).

Cedrus deodara, Pinus wallichiana and Pinus roxburghii, along with Picea smithiana

and Abies pindrow, are the dominant species of coniferous biome in mountainous areas

of Pakistan and provide important economic, social and ecological services. Climate

change, with an accelerated trend since the industrial revolution, has the potential to

affect adversely the growth of these species and consequently cause economic, social

and environmental losses. This situation warranted compilation of locality-specific

historic data of climate parameters, its scientific analysis, interpretation and statistical

projections, and impact analysis for the sector of interest. For impact assessment in

forestry sector, tree growth data is the building block which needs to be collected in a

way to cover the entire life span of the tree, through a reliable and easy to handle

method. Ring-width and Ring-structure are dependable bio-tools for estimating growth

and wood quality of forest tree species in response to environmental conditions.

Keeping in view the importance and overall suitability of the tree species: C. deodara,

P. wallichiana and P. roxburghii, representing two coniferous biomes: Moist

Temperate and Sub-Tropical Pine, the present study was conducted in Galies Forest

Division-Abbottabad, to assess the regimes and changes in climate parameters of

temperature and precipitation on annual and seasonal basis, selected bioclimatic indices

and growth response of these species in terms of tree-rings width, early and late wood

formation, and tracheid cells dimensions and cell wall thickness to climate change over

time period of 1962-2011.

1.4 Objectives

This study was primarily aimed to assess the growth response of C. deodara, P.

wallichiana and P. roxburghii to climate change in Galies Forest Division-Abbottabad

over time period of 1962-2011. The specific objectives were set as follows:

1) Analysis of climate change, covering variability of temperature and precipitation

on time scale of annual and five seasons (spring, summer, monsoon, autumn,

winter).

2) Estimation of bioclimatic productivity indices of the study area.

9

3) Assessment of Growth-Response functions of the selected species.

4) Discerning variations and trends in growth of Ring-width and Ring-wood

characteristics of the selected tree species and their association with climate

change.

1.5 Scientific Contribution

The findings of the study were expected to significantly contribute to the scientific

exploration on the subject, particularly understanding the regimes and changes in

climate and bioclimatic indices, and growth response of the selected tree species and

associated anatomical changes in the rings-width, early wood and late wood formation,

ring-cell dimension and cell-wall thickness to climate change in the region focused in

the study. The outcome of the study was further expected to expand the frontiers of

scientific knowledge on the themes covered in this work and enable comparative study

at national, regional and global levels. Besides academic value, the findings would help

and guide forest managers and other stake-holders to design and implement necessary

adaptation strategies to address the negative impacts of climate change on forests.

1.6 Limitations of the study

The methodology adopted for the study had several limitations, enumerated below:

1) There is a lapse rate and local variability of climate with in the study area with a

large altitudinal range, however, the magnitude and impact of the same were

reduced by conducting the study species wise with limited altitudinal ranges and

randomization of the samples across the species altitudinal zones to average out

the variations in data collection and analysis.

2) The climate dataset used for this study was confined to the period 1962-2011 due

to technical and other constraints.

3) Several citations about climate changes were based on the 4th Assessment Report

of IPCC ((IPCC, 2007) and not on the 5th Assessment Report, because at the time

of compilation of this thesis that Report was still under preparation.

10

4) The growth response of tree species is, beside climate change, also affected by

ontogeny and other factors, which creates complication in establishing the causal

relations of the factors of interest. However, ‘standardization’ and appropriate

statistical tools were used to segregate the impacts of climatic variables from

non-climatic factors.

11

CHAPTER 2

LITERATURE REVIEW

2.1 Climate Change

Climate refers to an average of weather or statistical description of mean weather

conditions over a period of several years, typically 2-3 decades, while climate change is

any variation in the climate over a long period of time, but may be restricted only to

those variations which are in excess of natural variability and attributable to human

activities.

McCarthy et al. (2001) reported that the impacts of climate change were noticed both at

global and regional levels. For instance, the rise in mean annual temperature at Western

North America could be 2–5 °C above the range of temperatures that had occurred over

the last 1000 years. The rising temperature would be combined by an increase in winter

precipitation and a decrease in summer precipitation. These changes would

significantly affect human society and ecosystems.

Hulme (2003) reported that the climate of the Earth has never been stable, particularly

during the history and evolution of life on the Earth. The recent glacial periods were 4

°C-5 °C cooler compared to 20th century, while some inter-glacial periods probably

were 1 °C to 2 °C warmer. These climate changes were overwhelmingly natural in

origin and happened on Earth having primitive societies with less population. In fact,

the diurnal and seasonal rhythms of Earth were always regulated by inter-annual, multi-

decadal and millennial variations in climate.

Balling et al. (1998) and Briggs et al. (2005) reported that land cover change and land

degradation either due to anthropogenic activities, deforestation or livestock could

directly increase temperatures. Hulme and Sheard (1999); Johns et al. (2001);

Christensen and Christensen (2003); and Rowell and Jones (2006) reported that the

increase in atmospheric temperature would be compounded with enhanced extreme

weather events (storms, precipitation and droughts), cyclones (hurricanes and

typhoons), etc. The current increasing temperature would affect the global hydrological

12

cycle, and consequently winter temperatures will increase, while frequent and strong

summer droughts will be observed worldwide.

Knutson et al. (2006) reported that among all external forcing, anthropogenic activities

are the dominant cause of recent global warming. Rees and Collins (2006) reported that

the increase in minimum temperature was higher compared to maximum temperature.

Warren et al. (2006) reported that the vulnerable regions and vulnerable communities

would be more prone to drastic impacts of climate change due to its effect as a threat

multiplier combined with inherent vulnerability of insecure communities. The impacts

of climate change would be more severe on poor communities in vulnerable regions.

IPPC (2007) reported that the global surface air temperature had risen by 0.76ºC during

time period of 1850 to 2005. The linear increasing trend of temperature was 0.13 ºC per

decade over the last 50 years and the projected temperature increase is 1.1 ºC to 6.4 ºC

by the end of 21st century. This increase in surface temperature will also result in rise

of sea level. Pyke et al. (2007) reported that land cover and land use were very

important factors which interact with atmospheric conditions to shape the overall

climate. These interactions had great impacts on various ecosystems from regional to

global scales. IPCC (2007) and Grunewald et al. (2009) attributed the climate change,

increasing temperature and precipitation variability, to several physical factors and

Foley et al. (2005); Falcucci et al. (2007); IPCC (2007) and Vorholz (2009) attributed

the same to the anthropogenic activities which affect the spatial and temporal changes

in climate at local and regional levels.

Alavain (2009) reported that water security would be a major conflict issue, both at

inter and intra-regional levels, in water scarcity areas with extreme hydrological

variability and poor endowment of water resources, or inadequate storage capacity,

infrastructure and poor governance. Van de Steeg et al. (2009) and Bukhari and Bajwa

(2009) reported that increased livestock had changed the land cover and land use

pattern. Livestock, besides, were directly responsible for greenhouse gases (18% of all

human-induced greenhouse gases globally) and over-grazing causes deforestation and

deteriorates rangelands. Christy et al. (2001); Houghton et al. (2001); Motha and Baier

(2005); Houghton (2005); Brovkin et al. (2009); Grunewald et al. (2009) and Wu et al.

(2010) reported that out of anthropogenic activities, increasing human population and

13

the activities leading to emission of GHGs were the principal contributors to climate

change. The Economist (2009) reported that besides social problems, changing climate

would cost dearly to global economies. A two-degree increase in global temperature

will devour about one per cent of the world’s total gross domestic product for

undertaking mitigation measures.

Bukhari and Bajwa (2009) reported mean increase in temperature of 0.85 °C (0.77 °C-

0.92 °C) at Peshawar during 1985-2009. The spring season started 15.6 days earlier and

was shortened by 17.8 days. The summer season was extended over seven months

(April-October) with mean maximum temperature >30 °C. There was 30% decrease in

rainfall during recorded time period of 1985-2009. The climate was shifted towards dry

tropical with eight months receiving <25 mm rainfall. The rainfall was reduced

drastically in spring and late summer seasons. Evaporation and wind increased 1.59

times and 1.40 times respectively. The temperature showed negative correlation with

rainfall (r = -0.49) and positive correlation with evaporation (r = 0.78). The range and

coefficient of variation of climate variables indicated higher variability during spring

and autumn seasons. The emerging climate scenario will likely cause multifaceted

effects on vegetative and reproductive growth of plants and habitat characteristics.

Bukhari and Bajwa (2011) studied climate change trends over coniferous forests of

Pakistan for the period 1961-2000 and reported the mean temperature regime between

12.44 °C and 22.54 °C, with the lowest temperature at Alpine Pastures (AP) and the

highest at Sub-Tropical Pine forests (STP). Monsoon was the warmest season followed

by summer. The precipitation regime varied between 266.8 mm and 1071.6 mm. The

highest precipitation was recorded at STP, while the lowest precipitation was at AP.

The highest increase in maximum temperature was 2.03 °C at AP during winter, while

the lowest increase in maximum temperature was 0.08 °C at AP during monsoon. The

highest increase in minimum temperature was 2.61 °C at AP during winter and the

lowest increase in minimum temperature was 0.36 °C at STP monsoon. Temperature

increase was relatively higher at AP compared to other forests types. Temperature

increase during winter was 1-2 °C higher compared to other seasons. Precipitation

decreased by 9.6%, 5.8% and 0.3% at AP, SA and Dry Temperate (DT) respectively,

and increased by 16.7% and 12.3% over Moist Temperate (MT) and STP respectively.

The highest precipitation increase was 71.5% at MT during monsoon, while the highest

14

precipitation decrease was 30% at AP during summer. Precipitation increased at DT

during all seasons, except summer, indicating an elevation-latitudinal movement of

precipitation. The increased temperature and precipitation in MT and DT will likely

enhance plant growth, while the higher temperature increase and precipitation decrease

in AP and SA will have negative effects on plant growth.

Halnaes and Garg (2011) reported that for developing states, the mitigating cost would

be much higher. The unabated increasing temperature would hamper the endeavours to

achieve Millennium Development Goals (MDGs), among which progress on poverty

reduction could slow or even reverse.

Bukhari and Bajwa (2012) reported that mean temperature increase at coniferous

forests was higher compared to other forests types, such as Dry Sub-Tropical Broad-

Leaved (DSTBL), Dry Tropical Thorn (DTT), Irrigated Plantation (IP), Riverine Forest

(RiF) and Mangrove Forest (MF). The results showed increase in mean maximum

temperature between 0.08 °C and 2.03 °C, at coniferous forests during time period of

1961-2000, while the increase in mean minimum temperature was between 0.36 °C and

2.61 °C. Similarly, temperature increase during winter was 1-2 °C higher compared to

other seasons. Precipitation increased by 16.7% and 12.3% at MT and STP

respectively. Contrarily, precipitation decreased by 9.6%, 5.8% and 0.3% at AP, SA

and DT respectively. These climate changes have the potential to affect plant growth as

well as ecological services provided by these forests.

2.2 Forest

Forest may be defined as. –

“A land area of more than 0.5 ha, with a tree canopy cover of more than

10%, which is not primarily under agricultural or other specific non-forest

land use. In case of young forest or where tree growth is climatically

suppressed, the trees should be capable of reaching a height of 5.0 m in situ,

and of meeting the canopy cover requirement (FAO, 2005)”.

15

In Pakistan, forests are divided into nine (09) types depending on vegetation type.

Among these forest types, coniferous forests are the most important, both in the context

of economy and ecology.

2.2.1 Coniferous Forest

In Pakistan, coniferous forests have different biomes: i) Sub-Alpine (SA), ii) Dry

Temperate (DT), iii) Moist Temperate (MT), and iv) Sub-Tropical Pine (STP),

depending upon their geographical locations in terms of altitude and latitude. SA and

DT are located in Greater Himalayan region (winter with snow fall) above 35.25°N,

and MT and STP are present between 33.75°N and 35.25°N in the sub-montane areas

(the monsoon rainfall dominated region) (Bukhari and Bajwa, 2012).

Figure 2-1Moist Temperate forest of Galies Forest Division

2.2.2 Sub-Alpine Forest

Sub-Alpine forests are evergreen formation of coniferous forests located between 3,350

m and 3,800 m elevation in the Himalayas and other mountain ranges, with extensive

covers in Azad Kashmir, Gilgit-Baltistan, Malakand and Hazara Civil Divisions of

Khyber Pakhtunkhwa. Mean annual temperature is about 10ºC and mean monthly

temperature remains below 0ºC for 5-6 months (Champion et al., 1965). The tree

vegetation, such as Himalayan Silver Fir (Abies pindrow) and Blue pine (Pinus

16

wallichiana) are important coniferous species which grow mostly in pure stands with a

lower storey of broad-leaved trees, with Birch (Betula utilis) as the most common

species. Other associates, such as, Prunus spp. and Willow (Salix spp.) and Guch

(Vibernum) bushes complete the vegetation cover. There is a spring flush of herbaceous

flora, including Primula and several composite species. Sub-Alpine forests have

important medicinal plants species, like Aconitum heterophyllum, Chrysanthemum

indicum and Saussurea lappa (Bukhari and Bajwa, 2012).

2.2.3 Dry Temperate Forest

Dry Temperate forests are conspicuous by open evergreen canopies with open scrub

undergrowth, having distribution throughout the dry inner mountain ranges, beyond the

effective reaches of monsoon. DT is primarily located between 1,525 m and 3,350 m

above sea level in Gilgit-Baltistan, Azad Kashmir (Neelum Valley), Khyber

Pakhtunkhwa (Chitral and Kaghan) and Balochistan (Takht-i-Suleman, Shinghar and

Ziarat). The winter season is long and cold with mean temperature between 6ºC and

16ºC. Western disturbances during winter and spring bring considerable snow and

rainfall. The major tree species in DT are: Deodar (Cedrus deodara), Chilgoza (Pinus

gerardiana), Juniper (Juniperus excelsa), Blue pine (Pinus wallichiana), and Spruce

(Picea smithiana). Quercus ilex dominates as pure crop on lower elevations. The

commonly found associates are Fraxinus and Acer spp. DT also contain xerophytic

species, like Daphne, Lonicera, Prunus, Artemisia and Astragalus, and medicinal plant

species, such as, Ephedra nebrodensis, Artemisia maritima, Carum bulbocastanum,

Thymus sp., and Ferula (Bukhari and Bajwa, 2012).

2.2.4 Moist Temperate Forest

Moist Temperate forests extend between 1,375 m and 3,050 m elevation across the

whole stretch of outer ranges of the Himalaya, varying markedly with aspect. MT is

mostly found in Murree, Galies, Kaghan, Dir, Swat and Azad Kashmir. The mean

temperature is about 12.2 °C with mean rainfall of 630-1,500 mm/annum. The major

part of rainfall is received during the monsoon (July to September). The main coniferous

tree species are: Pinus wallichiana, Cedrus deodara, Picea smithiana and Abies

pindrow, while the broad-leaved tree species are: Quercus incana, Q. dilatata and Q.

17

semicarpifolia with Rhododendron arboretum as their commonest associate. The

temperate deciduous tree species, such as Acer, Aesculus, Prunus, Ulmus, Fraxinus,

Corylus and Alnus spp., are found in local consociations. Litsea and Machilus spp. too,

are also found in the moist soil depressions. Shrubs, like Indigofera, Lonicera, Rosa,

Desmodium, Rubus, Viburnum and Strobilanthus spp. and medicinal plant species,

including Punica granatum, Berberis lyceum, Skimmia laureola, Viola serpens,

Dioscorea, Sub-Tropical Pine deltoidea, Valeriana wallichii, Atropa acuminata,

Colchium luteum, Asparagus racemostus, and Mentha piperita are commonly found in

MT (Bukhari and Bajwa, 2012).

2.2.5 Sub-Tropical Pine Forest

Sub-Tropical Pine forests are located between 925 m to 1,675 m elevation and

sometimes ascends up to 2,130 m on ridges with southern exposure. STP is commonly

present in Hazara, Murree and Azad Kashmir. The mean annual temperature range is

between 15ºC and 22ºC, with mean rainfall between 760 mm and 1,270 mm, mainly

falling during monsoon (July and September). Chir pine (Pinus roxburghii) forms

practically whole of the top canopy with Quercus incana as the dominant broad-leaved

associate (Bukhari and Bajwa, 2012).

2.3 Climate Change and Forest Ecosystems

Davis and Botkin (1985) reported that a sustained increase in mean temperature of 1 °C

per annum can introduce considerable changes in the species composition and

distribution. Woodward (1987) reported that growth function and species composition of

forests are highly dependent on surrounding climate. Forest distribution is normally

confined by either limited water availability or extreme temperature. The ratio of actual

evapo-transpiration (the amount allowed by available precipitation) to potential evapo-

transpiration (the amount the atmosphere would take up if soil moisture were not

limiting) determines the maximum leaf area index that can be supported.

Green (1987) reported that the next century will witness the shifting of the potential (or

preferred) geographic ranges of species by approximately 300–500 km, leading to

18

changes and relocation of forest-based industries and substantial socio-economic

impacts.

Pastor and Post (1988) reported that in early Holocene period (about 8,000 years ago)

when the climate became warmer, fire-adapted hardwood species moved northward to

new sites after fire out-breaks, while northern deciduous species, such as sugar maple,

migrated further northward. Forest productivity would increase on soils that retain

adequate moisture. In contrast, productivity would decrease on dry soils where boreal

forest may give way to oak-pine savanna. Rana et al. (1989) estimated carbon

sequestration in central Himalayan temperate forests at 6.3-14 t CO2/ha/yr.

Dobson et al. (1989) reported that reduction in the size of ecosystem has a profound

effect on species abundance, e.g., 10% reduction in ecosystem size will result in loss of

about 50% of species. Based on this projection, it has been estimated that an increase in

temperature of 2 °C would result in 10–50% loss of the animal species currently present

in the Boreal Great Basin mountain ranges.

Brooks et al. (1991) reported that in dry and hot seasons, dry deciduous forests could

burn more frequently and may be perpetually replaced by thorn scrub or savannah

vegetation. Further, changes in forest cover can have profound effects on ground

hydrology, groundwater supplies, surface runoff, sedimentation, and river flows, with

potentially serious socio-economic effects.

Woodward (1987) and Prentice et al. (1992) reported that apart from causing

disturbances in forest ecosystems, climate has a vital role in the survival of many

species, as most species have critical temperature thresholds ranging from +12 °C to –

60 °C. However, many species have narrow ranges of temperature for growth and

reproduction. Groombridge (1992) reported that in general species diversity increases

from temperate to tropical forests.

Melillo et al. (1990) and Dixon et al. (1994) reported that forests are an important part of

the global climatic system, and play a major role in the present and projected future

carbon budget, since they store about 80% of all above ground and 40% of all below

ground terrestrial organic carbon. Thus, forests have a great role in mitigation and

19

adaptation measures against changing climate. Conversely, it is also true that the

changing climate is affecting forests in several ways which are mostly adversely

affecting the forest growth and health. Mitchell et al. (1990) and Greco et al. (1994)

reported that climate-induced desertification problem in semi-arid tropical regions will

become more serious if precipitation declines further.

FAO (1993) reported that the forest ecosystems on the Earth, by and large, have

continued as the least disturbed natural systems from the human influences. They are of

great socio-economic importance, providing timber, pulpwood, fuel wood and many

non-wood products. Globally, forests covered about one-fourth of the Earth’s land

surface in 1990.

Bargali and Singh (1995) reported that Carbon sequestration is directly related with

forest productivity which depends on the age of forest crop. New plantations have more

productivity than natural forests and old plantations and are more efficient in net carbon

sequestration. Mature and old trees have only marginal carbon sequestration due to slow

growth rate and dearth of twigs and leaves Thus, young forest and plantations could help

mitigate greenhouse gas concentration.

Siddiqui et al. (1999) reported that assuming a 0.3 °C rise in temperature and a

precipitation change of 0, +1 and -1% per decade, the three biomes (alpine tundra,

grassland/arid woodlands and deserts) would be reduced in area coverage, and 5 biomes

(cold conifer/mixed woodland, cold conifer/mixed forests, temperate conifer/mixed

forests, warm conifer/ mixed forests, and steppe/arid shrub lands) would increase in area

coverage as a result of climate change.

Spittlehouse and Stewart (2003) reported that the forestry community does need to

evaluate the long-term effects of climate change on forests and determine what the

community should do at present and in future to combat this threat. Forest Managers can

influence the timing and direction of forest adaptation at certain locations, but in most of

the cases the dependent society will have to adjust to the changed forest conditions

arising from natural adaptation. Adapting to climate change with uncertain timing of

impacts requires a set of readily available options. In all such options, a top priority may

20

be given to effective adaptation to emerging forest conditions, along with sustaining the

genetic diversity and resilience of forest ecosystems.

Houghton (2005) and Pachauri and Reisinger (2007) reported that minimizing climate-

based disturbances in forest ecosystems and avoiding deforestation can halt a

considerable emission source of carbon to the atmosphere. Current deforestation and

climate disturbances are releasing about 1,400 to 2,000 Giga ton carbon per year. Millar

et al. (2007) reported that forests which experience frequent disturbances often have

characteristics that increase their resilience against climate disturbances. Fischlin et al.

(2007) reported that globally about 20% to 30% of species (global uncertainty range is

estimated from 10% to 40%, while regional uncertainty from as low as 1% to as high as

80%) will be facing increasingly high risk of extinction by 2100, as global mean

temperature exceed from 2 °C to 3 °C above pre-industrial levels.

USGCRP (2009) reported that climate and climate change affect the composition and

functioning of forest ecosystems and exert great influence in shaping forest health. A

change in climate may aggravate the existing threats to forests, such as pest epidemics,

forest fires, floods, droughts and human interventions. Climate change directly and

indirectly affects the growth and productivity of forests: directly due to changes in

atmospheric Carbon dioxide and climatic factors and indirectly through multiple

complex interactions in forest ecosystems. Climate also affects the frequency and

severity of many forest disturbances.

McKenzie et al. (2009) reported that gradual climate changes, especially atmospheric

changes, result in gradual adaptable changes in ecosystems. However, an acute climate

change, such as high and quick increase in temperature, will cause more frequent and

strong ecological disturbances, which might result in irreversible changes in the

composition and dynamics of forests.

Lasco et al. (2009) reported that climate change and Philippine forests were closely

inter-linked. Climate change was affecting the forests and their capacity to provide

environmental services. Conversely, degradation of the forests caused emission of

Carbon dioxide (CO2) to the atmosphere which contributed to climate change. To

increase the mitigation role of the forests and simultaneously increase their resilience to

21

climate change, suitable policies and programs must be adopted. To be effective, such

policies and programs should be based on scientific research and evidence. Presently, a

number of potential adaptation strategies have been formulated, but are yet to be

adequately tested. The bulk of past researches have focused on the mitigation potential

of terrestrial ecosystems. Huge amount of Carbon is conserved in natural. The stored

Carbon can be emitted to the atmosphere as CO2 gas through deforestation. Sharma et al.

(2010) reported carbon sequestration in coniferous forests of Nepal at 5.12 t

CO2/ha/year.

Ge (2011) studied the effects of climate change and management on the growth of

Norway spruce (Picea abies) in the boreal conditions based on a process-based

ecosystem model simulations, and reported findings on the ways and patterns in which

climate change affected the growth of unmanaged Norway spruce stands in relation to

the water availability, and as how the climate change and varying management regimes

affected the net carbon uptake, total stem wood growth and timber yield of the species at

southern to northern Finland.

Bukhari and Bajwa (2012) reported that the enhanced concentration of CO2 in the

atmosphere seemed to have a profound effect on the biomes area, including shifting of

tree line. The net primary productivity showed an increase in all biomes and scenarios.

However, there is a likelihood of forest dieback and time lag before the prevailing plant

types have enough time to adjust to changed climate and shift to new sites. In the

intervening adjustment period, these species would be vulnerable to ecological and

socio-economic disturbances (e.g., erosion, deforestation, and land-use changes). Thus,

the overall impact of climate change on the forest ecosystems of Pakistan could be

negative.

2.4 Climate Change - Growth Response

The CO2 fertilization effect indicates that the increase of CO2 in the atmosphere

accelerates the rate of photosynthesis in plants and tree growth. However, various

studies have produced divergent evidence, as the effect varies depending on the plant

species, the site, the temperature range, and the availability of water and growth

nutrients.

22

LaMarche et al. (1979) conducted one of the earliest studies which produced evidence

for a possible CO2 fertilization effect in tree rings. The study was based on ring-width

chronologies of high-altitude bristlecone and limber pines in the southwestern United

States, which showed unusually larger growth of ring-widths over the past century. An

explanation was made that the observed enhanced growth was due to CO2 fertilization

as high-altitude plants might be more CO2 limited compared to those at lower altitudes.

However, the study did not present any quantitative modeling to exclude the possible

contribution of favorable climatic change to account for the enhanced growth.

Dieterich and Swetnam (1984) demonstrated that dendrochronological dating was

considerably more reliable and can be used to establish the exact fire-history sequences.

Ahlstrand (1980); Dieterich (1980) and Swetnam and Dieterich (1985) reported the use

of tree-ring data for dating fire scars to reconstruct fire history. Ahlstrand (1980);

Dieterich (1980) and Swetnam and Dieterich (1985) reported that many fire histories

were based upon simple ring counting techniques that could lead to large dating

inaccuracies and uncertain conclusions.

Kienast and Luxmoore (1987) analyzed tree-ring data of naturally grown conifers to

evaluate the possibility of enhanced tree growth due to increased atmospheric CO2.

Samples of tree cores were collected from 34 sites in four different climatic regions in

the northern hemisphere. Growth trends after 1950, when the atmospheric CO2

concentration significantly increased, indicated an increase in ring-widths at eight of

the 34 sites, with moderate temperature or water stress. In four cases the growth

coincided with favorable climatic conditions, while in four cases, the growth increase

exceeded the upper bound expected from CO2 enrichment experiments with seedling

conifer species. Therefore, increased growth in any of the tree-ring chronologies could

not be exclusively attributed to higher atmospheric CO2 concentrations. Jozsa and

Powell (1987) studied representative boreal forest growth and measured ring widths

and density of mature Spruce trees at 11 locations in western Canada. They concluded

that biomass productivity and annual growth layer weights were related to long-term

and yearly climatic variability, but did not present any indication that there was a

systematic growth trend that could be attributed to CO2 fertilization.

23

Schulman (1958; Currey, (1965); Lara and Villalba, (1993) reported that the longevity

of trees (from hundreds to a few thousand years) confounds the detection of

environmental signals. Species with higher longevity possess a greater potential to

record signals over a range of temporal and spatial scales. Suzuki (1990) conducted a

comparative study on the annual ring widths of Abies spectabilis, Pinus wallichiana

and Picea smithiana, from two stands with different crop composition. He reported that

the annual ring widths usually had significant similarities between cores taken from

different trees. These similarities increased with tree size. The climatic change affected

the large trees more strongly than it did the small trees. Annual ring widths were also

correlated with the annual precipitations and its seasonal distribution.

Graumlich (1991) reported that no evidence exist for CO2 fertilization in high-altitude

foxtail pine and other species in the Sierra Nevada. Jacoby et al. (1997) reported that

tree rings provide information about climate change and CO2, but concluded that

overall the present tree-ring evidence for a possible CO2 fertilization effect under

natural environmental conditions seemed very limited.

Bradley and Jones (1992) and Luckman (1996) reported that tree rings act as natural

archives and provide significant proxy data for paleo-environmental studies and

reconstructions. Mann et al. (1998 & 1999) and IPCC (2001) reported that the

information derived from tree-ring series has increasing been used in climate model

validation in the context of global warming assessment. More specifically, the last 1000

years are deemed as a suitable time interval for the assessment of the background

variability in relation to climate change detection and a suitable period in relation to the

life span of several tree species.

Borgaonka et al. (1996) conducted tree‐ring analysis of Cedrus deodara from three

different sites of western Himalaya, including Kufri (Shimla), and reported that

moderately high values of variance exist in the three chronologies which indicate the

high potential of the species for dendroclimatic studies. Response function and

correlation analyses of the tree‐ring‐width data and climate factors showed a significant

negative relationship with summer temperature and positive relationship with summer

precipitation.

24

Yeh and Wensel (2000) observed the variance between actual and predicted growth

rates due to climatic changes for the conifer regions of northern California. They

developed the CACTOS (California conifer timber output simulator) program for

removal of growth variations attributable to biological and cultural factors. The residual

variation was then related with relative precipitation and temperature, while accounting

for effects of elevation, stand density, and species. The results showed that, in addition

to biological and cultural factors, growth variation was related with changes of winter

precipitation and summer temperatures. Winter precipitation and summer temperatures

affected growth in the current and the subsequent years. Further, the relationship

between climate and growth varied with tree densities and species.

Pant et al. (2000) analyzed sample cores of Cedrus deodara collected from two

different sites of western Himalaya by densitometric examination. Data was obtained

for early wood, late wood, minimum, maximum, and mean densities and total ring

width. Most of these variables showed moderately high values of common variance and

signal to noise ratio except latewood and maximum densities. The response function

analyses indicated significant relationships between pre-monsoon summer climate and

early wood density and total ring width.

Dendrochronology normally assumes that once growth trends and disturbance pulses

have been accounted for, climate–growth relationships become age independent.

However, different studies have indicated that tree physiology undergoes changes with

age, which could vary growth‐related climate signals over time. Carrer and Urbinati

(2004) studied the age related consistency of climate-growth responses in tree-ring

series from Larix decidua and Pinus cembra, by comparing their dendrological

statistics. It was found that tree-ring statistics did not change significantly with age in

P. cembra, whereas in L. decidua they appeared to be correlated with age classes. The

response function analysis indicated that climate parameters accounted for a large

fraction of variance in tree ring-widths in both species. Age influence on climate

sensitivity was found uniform.

Goldblum and Rigg (2005) studied the impact of projected future change in monthly

temperature and precipitation compared to General Circulation Model (GCM) monthly

temperature and precipitation conditions for the 2080s on the growth of Sugar maple

25

(Acer saccharum), White spruce (Picea glauca) and Balsam fir (Abies balsamea) at

Ontario, Canada. The sensitivity analysis of the species showed that Sugar maple had

the highest potential for enhanced growth rates under the predicted temperature rise and

altered precipitation pattern. White spruce was assessed likely to show lower increase

in growth, while the balsam fir was likely to confront a decrease in growth potential.

These projected changes would enhance the future status of sugar maple at its northern

limit.

Bouriaud et al. (2005) monitored the intra-annual radial growth variations of two

Norway spruce trees (Picea abies) over a period of four years, at four heights on the

stem, using point dendrometers. The trees were then felled and analyzed for growth and

density parameters. The results indicated that short-term variations in growth rate were

associated with changes in climate parameters and soil water levels. The sensitivity of

radial growth to climate declined with stem height. Wood density responded strongly to

drought events, and was relatively independent of growth rate and climatic conditions

during the early phase of the growing season, but increased with decreasing radial

growth rate later in the growing season.

Khan et al. (2008) conducted dendrochronological study on P. smithiana from District

Dangam of Afghanistan. Twenty eight sample cores were obtained from 15 trees and

cross-dating was done among 24 cores of 12 trees which presented first dated

chronology (1663-2006 AD) from that country. It was indicated that all cores were

highly correlated, indicating similar climatic signals. Ahmed et al. (2009) conducted

dendroclimatic studies on Spruce (Picea smithiana) sample cores obtained from Chera

and Naltar forests, Gilgit Baltistan, Pakistan. He presented six hundred year (1400-2006

AD) chronologies, prepared from highly correlated (r = 0.65 to 0.73) wood samples.

These chronologies were standardized in order to detect long-term climatic trends. The

response function analysis indicated that 37 to 40% variance was due to climatic factors.

Ahmed et al. (2010) studied the core samples of Picea smithiana, Cedrus deodara,

Pinus gerardiana and Juniperus excelsa from seven catchments in the Upper Indus

Basin of Himalayan region of Pakistan, and reported significant inter and intra-species

cross-matching, despite the fact that the samples were obtained from different areas. The

samples of the species showed a high correlation (r = 0.68 to 0.92) with master

chronologies and mean sensitivity in the range of 0.23 to 0.42.

26

Ram et al. (2008) prepared and analyzed tree-ring-width index chronologies of teak

(Tectona grandis) from three sites in Central India. His findings showed existence of

strong correlation among the three site chronologies thus indicated influence of common

climatic signals to the tree growth of the region. Significant positive relationship

between moisture index and tree ring-width variations both during the monsoon months

and on interannual basis indicated the important role of moisture availability at the roots

zone.

Papadopoulos et al. (2009) studied the association between the annual variability of the

Aleppo pine tree ring-widths and the variability of the climate parameters in the Attica

basin for a 45-year period (1959-2003). The results showed that 64% of the total

variance of the tree-rings of the Aleppo pine could be attributed to the common

variability of the climate parameters: precipitation 82.6%, minimum temperature 88.2%

and maximum temperature 88.5% of the total variance of each parameter. Distinct

narrow and wide tree-rings were observed during the years with extreme rainfall or

temperature conditions. The growth of the Aleppo pine showed positive correlations

with the winter and spring precipitations. Conversely, negative correlations were found

with the temperature of the spring months.

Yadav (2009) developed ring-width chronologies of Himalayan pencil juniper

(Juniperus polycarpos), Himalayan pencil cedar (Cedrus deodara) and Chilgoza pine

(Pinus gerardiana) for a millennium and longer from different sites in western

Himalaya. He reported strong precipitation signatures in ring-width measurement series

of these species.

Vaganov et al. (2009) studied correlations between climate and tree ring-width and

anatomy along short to long time scales and examined whether Carbon-13 (13C), a

stable carbon isotope, could be used as an additional parameter to interpret tree-ring

chronologies. The results indicated that climatic variables explained 20% of the

variation in tree ring-width and wood density over consecutive years, while 29-58% of

the variation was explained by autocorrelation between tree rings. The tree ring-width

and 13C values of whole wood were significantly correlated with length of the growing

season, net radiation and vapor pressure deficit. The 13C values were, however, not

27

correlated with precipitation or temperature. A highly significant correlation was also

observed between 13C of the early wood of one year and the late wood of the previous

year, indicating a carry-over effect of the growing conditions of the previous season on

current wood production.

Feliksik and Wilczyński (2009) analyzed the relationships between the diameter

increment of two native species: Norway spruce (Picea abies) and Scots pine (Pinus

sylvestris) and three non-native species: Douglas fir (Pseudotsuga menziesii), Sitka

spruce (P. sitchensis) and Silver fir (Abies alba) in research plot at Poland. The results

indicated that precipitation and temperature of the current growing season and months

preceding that season affected the annual diameter increment of all sampled tree

species. Winter and early spring frosts had a strong negative effect on diameter size.

Norway spruce was found the most resistant to low temperatures but was susceptible to

water deficiency in the soil during spring and summer. The growth of Scots pine was

stimulated by high precipitation in June. The diameter increments of Douglas fir, Sitka

spruce, Scots pine, and Silver fir were more strongly associated with temperature than

precipitation.

Williams et al. (2010) studied the response of trees to inter annual climate variations by

analysis of annual tree ring-width data from 1,097 sites in the continental United States.

A climate-driven statistical growth equation was developed for each site that used

regional climate variables to model ring-width values. These growth models were

applied to predict how tree growth would respond to 21st century climate change, taking

into account four climate projections. The models revealed that productivity of

dominant tree species in the southwestern United States will decrease substantially

during this century, especially in warmer and drier areas. In the northwest, nonlinear

growth relationships with temperature might lead to warming-induced declines in

growth for many trees that historically responded positively to warmer temperatures.

Zafar et al. (2010) investigated the climate sensitivity of tree rings of Picea smithiana

from Haramosh and Bagrot, Gilgit Baltistan, Pakistan. Approximately 550 years

chronology was obtained and quality of cross-dating was found satisfactory by

checking through COFECHA software. ARSTAN program was used to remove non

climatic trends. Mean correlation among samples was high (0.74 to 0.85). Signal

28

strength in Haramosh chronology was found to be higher. The chronology values in

both stands were showing similar trends.

Fernandez et al. (2012) studied the effect of severe drought on growth and anatomy of

pines in stands with different plantation densities, to find the influence of management

practices on the adaptability of the species to climate change. Various growth

parameters were measured in the rings pertaining to 2008-2009, a severe drought year.

It was found that the drought caused decrease in annual growth by 30-38% and 58-65%

with respect to mean growth in previous years and in open versus closed stands

respectively. The higher sensitivity of the latter in this case was opposite to the previous

reports on the same species in similar forests in USA.

(Khan et al. (2013) developed tree-ring-width chronologies covering the past 469 to

595 years of Cedrus deodara from three different sites at Chitral, Hindukush range of

Pakistan, in order to study paleoclimatic records for regions or periods of time for

which no instrumental climatic data was available. Climatic data obtained from the

three weather stations showed strong correlation and was found useful for tree-ring

climate relationships. Correlation function and response function analysis showed that

spring precipitation (March–May) was a critical limiting factor for tree-ring growth,

and temperature prior to November might also play a major role in affecting tree ring-

growth. The results showed that the three sites had continuous relationship which

indicated that only single species from different locations was affected by the same

environmental variables and hence could be used in climate reconstruction in

combination. C. deodara chronologies developed at different locations had several

corresponding narrow and wide marker rings indicating a large macroclimatic response

to regional climatic conditions.

The literature review of dendrological studies on climate change – growth response of

forest tree species reveals distinct pattern of relationships between these variables and

its components, however, the direction and magnitude of the actual and predicted

changes in the morphology, physiology, ring-widths, intra-ring characteristics and

anatomical features of the forest tree species are specific to the species, site and

location, and respond differently to critical climatic factors, its range, intensity and

duration and changes thereof. Therefore, a broad scope is available for conducting

29

research in this field focusing on impact of different climatic factors on growth of

various forest tree species, forest types and ecosystems and changes thereof, at various

locations and geographical regions.

30

CHAPTER 3

MATERIALS AND METHODS

3.1 Study Area

The study was conducted in Galies Forest Division-Abbottabad (GFD), having an

extensive forest cover, comprising legal categories of reserved forests owned by the

Government with no or few public rights and concessions, Guzara forests and scattered

patches of non-designated forests owned by individuals or communities and burdened

with heavy rights and concessions. The reserved forests were located between 33°55´

and 34°20´ N, and 73°15´ and 73°29´ E, (Khan, 1993), while the Guzara forests were

situated between 33°29´ and 34°21´ N, and 72°55´ and 73°29´ E, in northern Pakistan

(Khan, 1987). The reserved forests were spread almost in a continuous stretch with few

blanks, while Guzara forests were in patches with depleted stock and extensive blanks.

Both categories of the forests were organized on territorial basis into Ranges, Blocks and

Compartments to facilitate its scientific management. The management history and

prescriptions for the reserved forests and Guzara forests in the GFD for a period of 20

years were made in separate Working Plans, while the details of silvicultural and other

operations had been properly recorded in the Compartment History Files, maintained for

each compartment. The land cover map of GFD/District Abbottabad is reproduced

below (Figure 1).

31

Figure 3-1 Land cover map of Galies Forest Division

The forest cover of GFD was assessed at 67,173 ha (37.6% of the total district area),

comprising Moist Temperate 46,899 ha (26.3%), Sub-Tropical Chir pine 16,416 (9.2%)

and Sub-Tropical Broad-leaved 3,858 ha (2.1%) (Bukhari et al., 2012). The blue-pine

was the most abundant species, present frequently in pure stands in the Moist

32

Temperate zone, while deodar stands occupied almost the same altitude, but were

mainly confined to southern and western aspects, rocky sites and steep slopes with

well-drained soil. The Chir-pine was present in pure stands at altitude below Moist

Temperate zone and had been managed under Uniform Shelter-wood Compartment

System for more than a century, resulting into even-aged crops in periodic blocks. As

per 1998 census, Abbottabad district had a human population of 880,666, with many

villages and hamlets in close vicinity of the forest areas. The reserved forests and

Guzara forests are vulnerable to natural factors, like snow-fall, drought, frost, fire,

lightening, land-slides, pest attacks and diseases, while the Guzara forests are in

addition severely exposed to anthropogenic factors, like illicit cutting, lopping,

browsing, grass cutting, deliberate fires and encroachment. These forests have

importance for dendro-climatological studies by virtue of location at the boundary

between tropical and temperate continental climatic interaction (Fowler & Archer,

2006).

3.2 Preparation of forest maps

A set of forest maps was prepared by adopting general methodology comprised of

acquisition and processing of satellite data from accessible sources, digitization of

baseline information from General Topographic (GT) sheets, procured from Survey of

Pakistan and visual interpretation of the satellite images.

The SPOT-5 satellite images (2007-08), having 2.5 m ground resolution, were obtained

from Health Department, Khyber Pakhtunkhwa. These images being in raw format were

processed through geo-rectification, geometric corrections and image enhancement

techniques for efficient use. Geo-rectification was used for positional accuracy, relief

displacement and clearing the images from distortion, geometric correction for removing

geometric distortions due to sensor, earth geometry variations and conversion of data to

coordinates (e.g., latitude and longitude) and geometric correction for making the

images compatible with geometry of large scale maps. Different image enhancement

techniques such as Linear Contrast Stretch and Histogram Equalization were used to

improve the visual quality and interpretability of images.

33

Visual interpretation techniques were applied to delineate forest cover and other land

cover types using Arc GIS software. During this process all pixels in image were

categorized into several land cover classes or themes. All the GIS layers of different

land cover were generated in geo-database for storage and manipulation of the requisite

information. ERDAS Imagine software was used for mosaicking and merging of the

images.

Ground trothing / field verification on sampled sites, selected through stratified random

sampling design, was conducted to test the accuracy of the digital estimates of the forest

cover.

GT sheets in 1:50,000 and 1:15,000 scales were obtained from Survey of Pakistan to

extract baseline information which included district boundaries, settlements, road

infrastructure, etc.

Land cover / forest maps of Abbottabad district were prepared and district baseline

information was over-laid. These maps were properly gridded with coordinates

(Latitude/longitude) at 50x50, 25x25, 15x15, 10x10, and 1x1 Sq. Km., each grid cell

measuring 250,000 ha, 62,500 ha, 22,500 ha, 10,000 ha, and 100 ha respectively

(Figure 3.2, 3.3, 3.4, 3.5 and 3.6). These maps were used for data collection on climate

and sampling of trees.

Statistical software Minitab v. 15.1, XLSTAT and MS Office Excel were used for

graphics and database management.

34

Figure 3-2 Land cover/Forest map of GFD–Abbottabad (Grid 50x50 Km)

35


36


37


38


39

3.3 Climate Change Data and Analysis

A gridded map of 0.5 X 0.5 degree (50 km x 50 km) along with climate dataset of

Climate Research Unit (CRU), University of East Anglia, UK, TS v.3.21, reflecting

monthly figures for temperature and precipitation covering the study area, for the

period 1962-2011, was obtained through Global Change Impact Studies Centre

(GCISC), Islamabad. A set of forest maps compatible with the grids pattern of the CRU

map was prepared in the GIS-RS Laboratory of Pakistan Forest Institute, Peshawar for

scaling down the study area, as described in Para 3.2 above. The time period of 1962 -

2011 was focused in the study in view of dataset availability constraint for the study

and time span of prime growth and rotation period of the selected tree species. The

climate parameters of the study area were calculated from the CRU climate dataset by

taking forest area weighted average of the grids covering the study area. The climate

parameters, comprising maximum temperature, minimum temperature, mean

temperature and mean precipitation were used to assess climate regimes along with

standard error (±SE) and climate changes. The climate parameters were calculated both

on vertical (annual) and horizontal (seasonal) basis. Five seasons were marked as spring

(March-April), summer (May-June), monsoon (July-September), autumn (October-

November) and winter (December to February). The monsoon was separated from

summer due to different dynamics in context of temperature, precipitation and plant

growth. Climate data for the study area for a limited period dating back to 1976 were

also available from Kakul Meteorological Observatory, Pakistan Meteorological

Department, located in the study are. The dataset of CRU was compared with the

Observatory data, but no significant variations were observed in the two sets of

corresponding data. Analysis of temperature and precipitation regimes and changing

trends thereof were made by regression analysis and applying Mann Kendall test with

Normal Approximation and Sen’s Slope Estimator method to draw inferences for

meeting the objectives of the study.

3.4 Bioclimatic Indices

What type of a forest crop can be optimally supported by the specific site or locality is

determined not by a single climatic factor, but suitable combinations of climatic factors.

A number of bioclimatic indices have been developed to correctly express the

40

interactions between climate and life processes of the plants. Some of these indices

have since long been in use in forestry in the sub-continent, and were selected for

estimation in this study.

The bioclimatic indices regimes and changes in the indices were calculated. The

indices, namely Temperature Efficiency Index (TEI), Aridity Index (AI), Dryness Index

(DI), Rain Factor (RF), Dryness Factor (DF), Humidity Coefficient (HC) and

Precipitation Efficiency Index (PEI) for the Division were calculated using the

following formulae, as described by Champion et al. (1965), with metric system and

Centigrade scale, except where indicated otherwise:

𝑇𝐸𝐼 = 𝑀𝑎𝑥𝑖𝑚𝑢𝑚 𝑡𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒 (ᴼ𝐹) − 32

4

𝐷𝐼 =𝑀𝑒𝑎𝑛 𝑎𝑛𝑛𝑢𝑎𝑙 𝑝𝑟𝑒𝑐𝑖𝑝𝑖𝑡𝑎𝑡𝑖𝑜𝑛

2𝑥𝑀𝑒𝑎𝑛 𝑎𝑛𝑛𝑢𝑎𝑙 𝑡𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒

𝑅𝐹 = 𝑃𝑟𝑒𝑐𝑖𝑝𝑖𝑡𝑎𝑡𝑖𝑜𝑛

𝑇𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒

DF = 𝑃𝑟𝑒𝑐𝑖𝑝𝑖𝑡𝑎𝑡𝑖𝑜𝑛

𝑇𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒+7

HC = 𝑀𝑒𝑎𝑛 𝑎𝑛𝑛𝑢𝑎𝑙 𝑝𝑟𝑒𝑐𝑖𝑝𝑖𝑡𝑎𝑡𝑖𝑜𝑛

1.07𝑥𝑀𝑒𝑎𝑛 𝑎𝑛𝑛𝑢𝑎𝑙 𝑡𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒

PEI = 11.5𝑥𝑃𝑟𝑒𝑐𝑖𝑝𝑖𝑡𝑎𝑡𝑖𝑜𝑛 (𝑖𝑛𝑐ℎ𝑒𝑠)

𝑀𝑎𝑥𝑖𝑚𝑢𝑚 𝑡𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒 (ᴼ𝐹)−10 𝑥

10

9

41

The potential productivity of the study area was estimated using Climate Vegetation and

Productivity Index (CVPI), following the method as described by Paterson (1956):

10012

Ta

EGPTvCVPI

Where:

Tv = Mean maximum temperature (°C) during the year

Ta = Range between mean maximum temperature (°C) and mean

minimum temperature (°C)

P = Annual precipitation (mm)

G = Growing months, number of months during which the mean

monthly temperature exceeds 3 °C

E = Radiation received at the pole expressed as % age of the

radiation received at the latitude in question

3.5 Wood Samples and Measurements

Sampling of trees and measurement of parameters of interest were made in a planned

manner as described below.

3.5.1 Collection and Preparation of Samples

Based on the information about species and age composition of the forest crop,

obtained from the Working Plans and Compartment History Files, the study area was

divided into two populations comprising Moist Temperate biome and Sub-Tropical

Chir-pine biome. A 2-stage random sampling design was used for samples selection of

twenty trees and twenty stem discs cut at breast height each of C. deodara, P.

wallichiana and P. roxburghii. A forest map of the study area with grid cells measuring

1 x 1 Sq. Km. (100 ha), indicating forest compartment boundaries, was numbered for

each cell and delineated on the basis of information available for the forest

compartments into two populations covering the two biomes: Moist Temperate and

Sub-Tropical Chir pine respectively. The two populations were examined for cells

falling on blanks, or having no tree of over sixty year age, which were excluded from

the sampling frames. The minimum age limit for the sample trees was kept at 60 years

to ensure availability of at least 50 tree-rings in residual chronology after cross-dating.

42

In the first stage sampling twenty cells were selected for P. roxburghii from the

sampling frame of Sub-Tropical Chir pine biome and twenty cells each for C. deodara

and P. wallichiana from the sampling frame of Moist Temperate biome at random

without replacement by draws. The selected cells for C. deodara were compared with

Compartment History File for availability of substantial number of trees of over sixty

year age. In case, this condition was not met, the cell was discarded and substituted by

another cell through a random draw. The process was repeated where needed. The cells

finally selected in the first stage sampling for each of the three species were

superimposed with 0.1 x 0.1 (1.0 ha) grid and each cell was numbered. The second

stage sampling was conducted in a manner analogous to the first stage. The selected 0.1

x 0.1 (1.0 ha) cells were located in the field with the help of compartment boundary

pillars and other conspicuous land feature. Each selected cell area was entered into

from a random point, and the first tree of the relevant species with age of 60 year or

above was selected for analysis. In case, no tree of the requisite species or age was

present in the cell area, that cell was discarded and substituted by another cell through a

random draw. The process was repeated where needed. Circular stem discs of 20 cm

thickness were collected from the stumps of the recently cut trees nearest to the sample

trees for subsequent analysis in the laboratory.

The diameters of the sampled trees along with GPS coordinates and elevation and

general conditions of the sites were recorded on the spot. Increment cores were

extracted using Presler Increment Borer, following the methods as described by Stokes

and Smiley (1968), Ahmed (1984), and Norton and Ogden (1987) (Figure 3.7). The

core samples were preserved in tubes and shifted to the Laboratory. The ring-width was

measured in Annual Ring Measuring Laboratory (ARM Lab.), Pakistan Forest Institute,

Peshawar.

43

Figure 3-7 Core extraction using Presler Borer

Details of sample collection of Deodar, Blue pine and Chir pine are given in Tables 3.1,

3.2 and 3.3 respectively. Samples distribution map of the study area is reproduced in

Figure 3.8 and a magnified version of its section in Figure 3.9. A magnified portion of a

section of the samples distribution map with overlaying grid 0.1 x 0.1 km (1.0 ha) is

reproduced in Figure 3.10.

Table 3-1 Site - wise geographical and tree information of Deodar in GFD

S.

No.

Date GPS

Coordinates

Elevation

(m)

Tree age

(year)

Diameter

(cm)

Forest

Range

Latitude Longitude

1 6-10-12 34° 16'

34.685" N

73° 20'

43.696" E

2299 84 43.4 Thandiani

2 6-10-12 34° 16'

23.173" N

73° 20'

28.269" E


3 6-10-12 34° 16'

23.579" N

73° 20'

45.396" E


4 6-10-12 34° 16'

12.976" N

73° 20'

31.384" E


44

5 6-10-12 34° 16'

9.984" N

73° 20'

37.112" E


6 6-10-12 34° 16'

12.035" N

73° 20'

45.735" E


7 6-10-12 34° 16'

8.906" N

73° 20'

46.479" E


8 6-10-12 34° 15'

57.400" N

73° 20'

45.985" E


9 6-10-12 34° 15'

55.002" N

73° 21'

5.119" E


10 6-10-12 34° 15'

46.295" N

73° 20'

42.392" E

2362 102 58.6 Thandiani

11 6-10-12 34° 15'

42.136" N

73° 21'

15.285" E

2361 116 71.2 Thandiani

12 6-10-12 34° 16'

54.800" N

73° 20'

3.237" E


13 6-10-12 34° 15'

6.253" N

73° 20'

20.807" E


14 6-10-12 34° 15'

13.052" N

73° 21'

2.266" E


15 6-10-12 34° 15'

5.175" N

73° 20'

44.794" E


16 6-10-12 34° 14'

39.161" N

73° 20'

12.999" E


17 6-10-12 34° 14'

51.904" N

73° 20'

5.571" E


18 6-10-12 34° 14'

25.290" N

73° 20'

40.981" E


19 6-10-12 34° 13'

57.591" N

73° 20'

43.867" E


20 6-10-12 34° 13'

40.414" N

73° 20'

38.944" E


45

Table 3-2 Site - wise geographical and tree information of Blue pine in GFD

S.

No

Date GPS

Coordinates

Elevation

(m)

Tree age

(year)

Diameter

(cm)

Forest

Range

Latitude Longitude

1 5-10-12 34° 8'

33.892" N

73° 26'

29.643" E

2095 70 44.6 Bognotar

2 5-10-12 34° 9'

25.114" N

73° 26'

14.026" E

2095 76 58.5 Bognotar

3 5-10-12 34° 7'

54.222" N

73° 23'

2.809" E

2311 106 84.0 Bognotar

4 5-10-12 34° 5'

14.400" N

73° 21'

34.876" E

2329 62 56.2 Bognotar

5 5-10-12 34° 6'

57.411" N

73° 26'

12.620" E

2327 77 48.0 Dongagali

6 6-10-12 34° 6'

18.526" N

73° 25'

31.178" E


7 6-10-12 34° 6'

2.092" N

73° 25'

8.049" E


8 6-10-12 34° 4'

9.976" N

73° 26'

31.767" E

2332 108 59.3 Dongagali

9 6-10-12 34° 2'

42.616" N

73° 24'

6.825" E


10 6-10-12 34° 1'

39.423" N

73° 23'

0.583" E

2335 115 71.0 Dongagali

11 6-10-12 34° 1'

12.553" N

73° 23'

42.695" E

2315 111 56.4 Dongagali

12 6-10-12 34° 0'

4.618" N

73° 23'

26.975" E


13 6-10-12 34° 14'

30.451" N

73° 18'

28.793" E


14 6-10-12 34° 15'

24.700" N

73° 20'

35.825" E

2371 105 58.5 Thandiani

15 6-10-12 33° 59'

53.673" N

73° 22'

49.017" E


46

16 6-10-12 33° 59'

12.313" N

73° 23'

39.204" E


17 6-10-12 34° 2'

53.408" N

73° 25'

41.328" E


18 6-10-12 34° 3'

37.363" N

73° 24'

23.659" E


19 6-10-12 34° 4'

26.106" N

73° 23'

42.609" E

2199 108 55.5 Thandiani

20 6-10-12 34°

4'

6.297" N

73° 25'

12.891" E


Table 3-3 Site - wise geographical and tree information of Chir pine in GFD

S.

No.

Date GPS

Coordinates

Elevation

(m)

Tree age

(year)

Diameter

(cm)

Forest Range

Latitude Longitude

1 5-10-12 34° 5'

35.175" N

73° 21'

11.931" E

2015 87 53.3 Bognotar

2 5-10-12 34° 7'

35.937" N

73° 24'

25.380" E

2015 89 56.2 Bognotar

3 6-10-12 34° 17'

27.910" N

73° 19'

41.993" E


4 6-10-12 34° 17'

57.806" N

73° 20'

24.978" E


5 6-10-12 34° 18'

7.569" N

73° 20'

1.787" E


6 6-10-12 34° 17'

36.793" N

73° 19'

25.872" E


7 6-10-12 34° 18'

7.530" N

73° 20'

15.160" E


8 6-10-12 34° 11'

58.870" N

73° 7'

32.154" E

1510 68 45.1 Abbottabad

47

9 6-10-12 34° 11'

8.366" N

73° 8'

37.620" E


10 6-10-12 34° 11'

52.302" N

73° 7'

49.016" E


11 6-10-12 34° 11'

4.944" N

73° 6'

25.821" E


12 6-10-12 34° 10'

12.968" N

73° 6'

56.169" E


13 6-10-12 34° 11'

46.491" N

73° 7'

13.027" E


14 7-10-12 34° 10'

52.288" N

73° 7'

4.863" E


15 7-10-12 34° 10'

16.465" N

73° 6'

38.620" E


16 7-10-12 34° 11'

32.764" N

73° 7'

33.039" E


17 7-10-12 34° 10'

45.670" N

73° 8'

27.464" E


18 7-10-12 34° 10'

41.087" N

73° 6'

3.702" E


19 7-10-12 34° 11'

42.579" N

73° 9'

3.077" E


20 7-10-12 34° 9'

45.939" N

73° 24'

8.687" E

2029 89 57.5 Bognotar

48

Figure 3-8 Samples distribution map of GFD-Abbottabad

49

Figure 3-9 A magnified section of samples distribution map (1 x 1 km) of GFD-Abbottabad

50

Figure 3-10 A magnified section of samples distribution map (0.1 x 0.1 km) of GFD-Abbottabad

51

3.5.2 Ring-width Measurement

The samples collected were air dried to avoid fungal infection. The cores were glued

into grooved iron mounts with transverse surface of the core upwards. The cores were

surfaced using a razor blade and applying successively finer grades sand paper with

orbital sander. The ring-width was measured using Digital Positiometer with

microcomputer-based measuring system. For each tree the most recently formed 50

rings were measured. Besides ring-width, intra ring early wood formation and late

wood formation were measured on absolute scale and as % age of the total ring-width.

The measurements were recorded up to hundredth of a millimeter. The early wood and

late wood junctions in the ring-widths were identified by ocular observations in change

of colour and size of the tracheid cells.

To identify the exact years of formation of the annual rings of the sampled trees, cross-

dating was done in a manner prescribed by Fritts and Swetnam (1989). The procedure

involved both ring counting and ring-width pattern matching, to ensure against

counting error, or errors, caused by missing or false rings. The cross-dating of the three

selected species was separately done for each species by graphical method, plotting the

ring-widths of the sampled trees of each species on a timeline covering the period

1962–2011. In the first step, graphs were constructed by plotting ring-width data of a

batch of five sampled trees at a time, and checked for synchrony by listing and

comparing the narrow rings or other pattern that were present in each sampled tree. If

any ring was found out of sequence, the samples were examined for counting error,

missing or incomplete or false ring and necessary corrections made. In the second step,

species-wise consolidated graphs were developed, using the ring-width data of all 20

samples of each species, duly corrected in the first step, to depict the cross-dated

picture of each species. Both sets of graphs were generated through the use of the

Minitab software.

The growth potential of the seedling and its capacity to respond to climate change

slowly as the seedling grows, matures and attains a dominant position in the canopy of

the forest. These changes produce a downward trend in ring width and variance that are

due to intrinsic factors such as ontogeny or aging and changes in bole geometry. There

are several methods to standardize growth data as reported by Gonzalez and Eckstein

52

(2003), Murtaugh (2003), Biondi and Qeadan (2008), Rozas et al. (2009), Peters et al.

(2015), however, the most commonly used method of ‘standardization’ as described by

Fritts (1976) was used before applying statistical analyses. The procedure involved the

fitting of a curve or straight line to correspond to the average growth potential as it

changed over time. To correct the series for the intrinsically related decline in mean and

variance and remove its effect, the observed data was divided by the value of the curve

to express it as an index or a percent (x 100) of the potential average growth for that

year. Although other site factors and disturbances can affect the observed data, but such

factors were assumed to be uniform on the sampled sites on the basis of field

observations. The standardized data was used for statistical analyses and drawing

inferences.

Mean sensitivity (MS) i.e., response of trees to growth limiting factors, especially

climatic factors, was computed as the relative difference in width from one ring to the

next by using the formula, as described by Fritts (1976) and Rolland (1993):

1n

1t t1t

t1tx

XX

)X2(XMS

1-n

1

Where:

Xt

Xt+1

=

=

Ring-width at year t

Ring-width at year t+1

n = Total number of rings

3.5.3 Ring-Structure

Apart from ring-width, early wood cell diameter, early wood cell wall thickness, late

wood cell diameter and late wood cell wall thickness were measured using Digital

Compound Microscope linked with computer based measuring system, following the

methods described by Hill (1982); Stuiver et al. (1984), and Berish and Ragsdale

(1985). The measurements were recorded up to hundredth of a micrometer. In order to

determine the radial cell diameter and cell wall thickness, stem discs of respective

species from the sampled sites were used. From the cross surface of the discs, wood

samples were cut keeping in view the presence of whole rings from bark to center of

53

the disc. Radial strips of about 2 cm in width were obtained from each wood sample.

The sample blocks were marked and cut from each strip with the help of knife to avoid

the wastage of any portion in growth ring. Size of each block was kept about 2x2x4

cm3, according to the requirement of sledge microtome and getting length-wise full

sections in order to have all the rings present in a block (Figure 3.11). The sample

blocks prepared from each wooden strip were boiled in water to soften them and get

ready for sectioning with a sledge microtome.

Figure 3-11 Preparation of microscopic slides for measuring cell diameter and cell wall thickness

Cross sections of all the sample blocks in each strip were stained in 0.1% aqueous

solution of Safranin, dehydrated in different grades of alcohol, absolute alcohol and

then Xylol and finally mounted in Canada balsam (mounting medium) to make the

slides. The permanent slides of the cross sections were studied under the Nikon

Eclipse 55i microscope and observations were recorded for the measurement of radial

cell diameter and cell wall thickness in early and late wood for 50 growth rings. Five

random observations were recorded per parameter for each ring. The readings were

taken at two diagonals, mid length and width dimensions and one random point in

54

case of rectangular cells and five random points in case of circular or irregular shaped

cells. Thus, at least 1000 observations were recorded for each wood disc.

3.6 Time Function and Climate Growth Function

The time function of ring-width and ring-wood characteristics of C. deodara, P.

wallichiana and P. roxburghii were studied through regression analysis at 95%

Confidence Interval (CI) and Prediction Interval (PI), using standardized data. The time

functions for early wood and late wood formations were plotted from their mean annual

growth as % age of total annual ring-width. The trends in the time series were assessed

by applying Mann Kendall test with Normal Approximation and using Sen’s Slope

Estimator method. Climate growth functions of the three species were assessed using

standardized tree ring-width data series of the three species and grid mean maximum

temperature, minimum temperature and precipitation data by response function analysis

as described by Fritts (1976).

3.7 Statistical Design and Analysis

Parameters of climate, including maximum temperature, minimum temperature, mean

temperature and precipitation and trends thereof were assessed both on annual and

seasonal basis from the monthly climate data for the study area covering period of

1962-2011 using Mann Kendall test with Normal Approximation and Sen’s Slope

Estimator method and regression analysis. Regression curves for the best fit were

drawn and smoothened by Loess method with 0.5 degree of smoothening and at 2 steps,

and mathematical expressions derived to explain the observed trends. The analysis of

variance (ANOVA) table generated by the regression model was used to determine the

level of significance. The correlation between various components of climate was

assessed using Pearson Correlation Coefficients.

Bioclimatic indices, namely, Temperature Efficiency Index (TEI), Aridity Index (AI),

Dryness Index (DI), Rain Factor (RF), Dryness Factor (DF), Humidity Coefficient

(HC) and Precipitation Efficiency Index (PEI) and Climate Vegetation and Productivity

Index (CVPI) were calculated using the climate data and formulae as described in

55

Chapter 3, Para 3.4 and changes therein highlighted. The significance of the changes in

bioclimatic indices was tested using regression analysis.

The results for climate regime, climate change, bioclimatic indices and changes therein

were discussed for significance and impact on trees growth, forest productivity and

forest management.

Trees growth characteristics, including ring-width, early wood formation, late wood

formation, radial cell diameter and cell wall thickness were measured and analyzed

using mean values of samples of 20 trees of each selected species and applying best fit

regression models. There were four replications comprising five trees each. The impact

of climate change on growth parameters of the three tree species was assessed using a

response function, with climate parameters as independent variable. The significance of

the fitted regression models was assessed from the associated ANOVA table generated

by the software. The decadal changes in Ring-width and Ring-wood characteristics of

the three species were tested by grouping the time period into decades and applying 1-

Way analysis of variance (ANOVA). In case of significant ANOVA, the category means

were separated by applying Tukey’s Honest Significance Difference (HSD) test at p =

0.05. In growth response analysis of the three species, mean annual precipitation, taken

as an independent variable, was clustered on decadal basis and the response variables

were tested for significance using 1-Way analysis of variance (ANOVA). In case of

significant ANOVA, the category means were separated by applying Tukey’s Honest

Significance Difference (HSD) test at p = 0.05. The correlations between climate change

and growth characteristics were assessed using Pearson Correlation Coefficients.

Arc GIS software, ERDAS Imagine software, Minitab v. 15.1, XLSTAT and MS Office

Excel were used for data processing, graphics and manuscript formatting.

56

CHAPTER 4

CLIMATE CHANGE AND BIOCLIMATIC INDICES

4.1 Climate

Climate regimes and changes and trends thereof were assessed at Galies Forest Division

(GFD) for the time period 1962–2011.

4.1.1 Climate Regimes

Mean annual maximum temperature, mean annual minimum temperature and mean

annual temperature at GFD, during the time span of 1962-2011, were 16.36±0.08 °C,

6.08±0.08 °C and 11.21±0.07 °C respectively. The highest mean seasonal maximum

temperature was 23.46±0.08 °C during monsoon, which was marginally higher

compared to 23.09±0.15 °C during summer, while the lowest mean seasonal maximum

temperature was 6.78±0.12 °C during winter. The highest mean seasonal minimum

temperature was 13.12±0.07 °C during monsoon, while the lowest mean seasonal

minimum temperature was 2.01±0.14 °C during winter. The mean seasonal minimum

temperature during summer was slightly lower compared to monsoon. The mean

seasonal minimum temperatures of spring and autumn were nearly equal. The mean

seasonal maximum temperature was 18.27±0.07 °C during monsoon and the mean

seasonal minimum temperature was 2.39±0.12 °C during winter (Table 4.1).

Mean annual precipitation at GFD, during 1962-2011, was 889.48±19.43 mm. The

wettest season was monsoon having mean precipitation of 345.06±13.50 mm/season,

while autumn was the driest season with mean precipitation of 46.67±3.01 mm/season.

The spring and winter were moderately wet with mean precipitation of 198.50±9.68

mm/season and 180.53±8.14 mm/season respectively (Table 4.1).

57

Table 4-1 Temperature and Precipitation Regimes at GFD (1962-2011)

Seasons/

Periods

Climate Parameters (mean annual/seasonal)

Max. Temp.

(°C) ±SE

Min. Temp.

(°C) ±SE

Mean Temp.

(°C) ±SE

Precipitation

(mm) ±SE

Spring 13.70±0.17 4.19±0.15 8.93±0.15 198.50±9.68

Summer 23.09±0.15 11.58±0.14 17.32±0.14 116.63±4.59

Monsoon 23.46±0.08 13.12±0.07 18.27±0.07 345.06±13.50

Autumn 16.01±0.11 4.12±0.11 10.05±0.09 46.67±3.01

Winter 6.78±0.12 -2.01±0.14 2.39±0.12 180.53±8.14

Annual 16.36±0.08 6.08±0.08 11.21±0.07 889.48±19.43

4.1.2 Climate Change Trends

Trend analysis of temperature and precipitation data of GFD for the period 1962-2011

showed varying results. The trends were detected by applying Mann Kendall test with

Normal Approximation and Sen’s Slope Estimator method was used to assess the

magnitude of the detected trends. A summary of the resultant statistics and trends is

reproduced in Table 4.2.

Table 4-2 Trend Analysis of Climate Change at GFD (1962-2011)

Climate Parameters Z-Value p-Value Trend Sen’s Slope

Upward Downward

Annual Max. Temp. 4.350 0.000 1.000 0.022

Annual Min. Temp. 4.584 0.000 1.000 0.026

Annual Mean Temp. 4.417 0.000 1.000 0.025

Annual Precipitation 0.34 0.407 0.593 0.391

Spring Max. Temp. 2.376 0.009 0.971 0.032

58

Spring Min. Temp. 3.463 0.000 1.000 0.039

Spring Mean Temp. 3.061 0.001 0.999 0.034

Spring Precipitation - 0.836 0.799 0.201 0.511

Summer Max.

Temp.

2.083 0.019 0.981 0.022

Summer Min. Temp. 2.560 0.005 0.995 0.024

Summer Mean

Temp.

2.459 0.007 0.993 0.025

Summer

Precipitation

- 0.836 0.799 0.201 -0.264

Monsoon Max.

Temp.

2.041 0.021 0.979 0.013

Monsoon Min.

Temp.

1.573 0.058 0.942

0.008

Monsoon Mean

Temp.

2.208 0.014 0.986

0.010

Monsoon

Precipitation

0.586 0.279 0.721

0.500

Autumn Max. Temp. 0.845 0.199 0.801 0.007

Autumn Min. Temp. 2.509 0.006 0.994 0.019

Autumn Mean

Temp.

2.024 0.021 0.979

0.013

Autumn

Precipitation

0.602 0.273 0.727

0.114

Winter Max. Temp. 4.249 0.000 1.000 0.033

Winter Min. Temp. 5.404 0.000 1.000 0.044

Winter Mean Temp. 5.353 0.000 1.000 0.040

Winter Precipitation 0.405 0.344 0.656 0.290

Increasing trend No trend

Scattered plots were constructed and trend lines were drawn for all climate parameters.

Observations were recorded on conspicuous variations/trends in a particular year or

bracket of years. The curves were smoothened through Loess method with 0.5 degree

of smoothening and at 2 steps to have more insight in the pattern of the climate change

in the study area during the focused period. The details of the analysis from visual

interpretation of the data and scattered plots are reproduced in the following paras.

59

The mean annual maximum temperature exhibited an overall increasing trend during

1962-2011. The highest mean annual maximum temperature was 17.40 °C during 1999,

while the lowest mean annual maximum temperature was 15.20 °C during 1965.

Overall, the mean annual maximum temperature remained higher during 1998-2011,

except 2005, compared to 1972-1994. There were 20 years having mean annual

maximum temperature higher than 16.50°C, while there were three years having mean

annual maximum temperature lower than 15.50 °C (Figure 4.1). The highest variability

in mean annual maximum temperature within a year was recorded during 1968,

followed by 1982 and the lowest variability was recorded during 2011.

Figure 4-1Trend line of Mean Annual Maximum Temp. (°C) vs. Time at GFD (1962-2011)

201020001990198019701960

18.0

17.5

17.0

16.5

16.0

15.5

15.0

Year

Ma

xim

um

Te

mp

. (°

C)

Regression

95% CI

95% PI

60

The mean annual minimum temperature exhibited an overall increasing trend during

1962-2011. The highest mean annual minimum temperature observed was 7.61 °C

during 2001, and the lowest mean annual minimum temperature was 4.96 °C during

1975. The mean annual minimum temperature remained above 6.00 °C during 1998 to

2011, with four years, near the turn of the century, having mean annual minimum

temperature higher than 7.00 °C. (Figure 4.2). The highest variability in mean annual

minimum temperature within a year was during 1968, followed by 1975, while the

lowest variability in mean annual minimum temperature within a year was recorded

during 2004, followed by 1989.

201020001990198019701960

8

7

6

5

4

Year

Me

an

min

imu

m T

em

p.

(°C

)

Regression

95% CI

95% PI

Figure 4-2 Trend line of Mean Annual Minimum Temp. (°C) vs. Time at GFD (1962-2011)

61

The mean annual temperature exhibited an overall increasing trend during 1962-2011.

The highest mean annual temperature observed was 12.26 °C during 1999 and 2001.

The lowest mean annual temperature observed was 10.13 °C during 1965, followed by

10.14 °C during 1968. The mean annual temperature increased steadily, except 1986 to

1992, and remained above 11.50 °C during 1995-2011, except 2005 (Figure 4.3). The

increasing trend of mean annual temperature closely followed the trend of annual

maximum temperature. The highest variability in mean annual temperature within a

year was during 1968, while the lowest variability was recorded during 2004.

201020001990198019701960

13.0

12.5

12.0

11.5

11.0

10.5

10.0

Year

Me

an

Te

mp

. (°

C)

Regression

95% CI

95% PI

Figure 4-3 Trend line of Mean Annual Temp. (°C) vs. Time at GFD (1962-2011)

62

The annual precipitation overall exhibited no trend during 1962-2011. The temporal

distribution showed a flattened normal distribution, with peak values around 1986. The

wettest year was 1983, with annual precipitation of 1146.2±25.01 mm/annum, followed

by 1975. The driest year was 1971, with annual precipitation of 561.2±11.60 mm,

followed by 2000 (Figure 4.4). The average annual precipitation remained around 950

mm/annum during late 1970s to mid-1990s, with lower values on both sides. The

highest variability in annual precipitation within a year was during 2006, followed by

1978. In contrast, the lowest variability in annual precipitation within a year was during

2000, followed by 1974.

201020001990198019701960

1300

1200

1100

1000

900

800

700

600

500

Year

Pre

cip

ita

tio

n (

mm

/an

nu

m)

Regression

95% CI

95% PI

Figure 4-4 Trend line of Annual Precipitation vs. Time at GFD (1962- 2011)

63

The mean spring maximum temperature exhibited an overall increasing trend during

1962-2011. The mean spring maximum temperature during 1962-2011 was

13.70±0.17°C. The highest mean spring maximum temperature was 16.80 °C during

2004, while the lowest mean spring maximum temperature was 11.00 °C during 1983.

There was a steady increase in mean spring maximum temperature during 1970s, and

steeper one during 1997 to 2011, except 2006. Conversely, mean spring maximum

temperature decreased slightly during 1986-1994 (Figure 4.5). The mean spring

maximum temperature nearly followed the pattern of mean annual maximum

temperature.

201020001990198019701960

18

17

16

15

14

13

12

11

10

Year

Sp

rin

g M

ax.

Te

mp

. (°

C)

Regression

95% CI

95% PI

Figure 4-5 Trend line of Mean Spring Maximum Temp. (°C) vs. Time at GFD (1962-2011)

64

The mean spring minimum temperature exhibited an overall increasing trend during

1962-2011. The mean spring minimum temperature during 1962-2011 was

4.19±0.15°C. The highest mean spring minimum temperature was 6.25 °C during 2004,

followed by 6.04 °C during 2008. The lowest mean spring minimum temperature was

2.04 °C during 1965. The mean spring minimum temperature increased steadily during

1962-2011, except a slight decline between 1985 and 1995, and increased considerably

during 1998-2011, except 2010 (Figure 4.6). The increase in mean spring minimum

temperature was slightly higher compared to mean spring maximum temperature.

201020001990198019701960

8

7

6

5

4

3

2

1

Year

Sp

rin

g M

in.

Te

mp

. (°

C)

Regression

95% CI

95% PI

Figure 4-6 Trend line of Mean Spring Maximum Temp. (°C) vs. Time at GFD (1962-2011)

65

The mean spring temperature exhibited an overall increasing trend during 1962-2011.

The mean spring temperature during 1962-2011 was 8.93±0.15 °C. The highest mean

spring temperature was 11.58 °C during 2004, followed by 10.79 °C during 2008. The

lowest mean spring temperature was 6.73 °C during 1965. The mean spring

temperature showed a declining trend during 1985-1995, and a rising trend in years

thereafter (Figure 4.7). The increase in mean spring temperature was slightly higher

compared to mean spring maximum temperature, but lower than mean spring minimum

temperature.

201020001990198019701960

13

12

11

10

9

8

7

6

Year

Sp

rin

g m

ea

n T

em

p.

(°C

)

Regression

95% CI

95% PI

Figure 4-7 Trend line of Mean Spring Temp. (°C) v. Time at GFD (1962-2011)

66

The variability in spring precipitation during 1962-2011 exhibited no overall trend. The

mean spring precipitation during 1962-2011 was 198.50±9.68 mm/season. The highest

spring precipitation was 384.10 mm/season during 1983, while the lowest spring

precipitation was 90.50 mm/season during 1999. An increasing trend in mean spring

precipitation was observed during 1981 to 1992, followed by a steady decline (Figure

4.8).

201020001990198019701960

400

300

200

100

0

Year

Sp

rin

g P

pt.

(m

m/m

on

th)

Regression

95% CI

95% PI

Figure 4-8 Trend line of Spring Precipitation vs. Time at GFD (1962- 2011)

67

The mean summer maximum temperature exhibited an overall increasing trend during

1962-2011. The mean summer maximum temperature during 19622011 was

23.09±0.15°C. The highest mean summer maximum temperature was 25.59 °C during

1982, followed by 25.22 °C during 2001. The lowest mean summer maximum

temperature was 20.61 °C during 1987. There was a steady increase in mean summer

maximum temperature, except a small decline during mid-1980s. There were 11 years

having mean summer maximum temperature higher than 24.00 °C. Conversely, there

were eight years having mean summer maximum temperature lower than 22.00 °C

(Figure 4.9).

201020001990198019701960

26

25

24

23

22

21

20

Year

Su

mm

er

Ma

x. T

em

p.

(°C

)

Regression

95% CI

95% PI

Figure 4-9 Trend line of Mean Summer Maximum Temp. (°C) vs. Time at GFD (1962-2011)

68

The mean summer minimum temperature exhibited an overall increasing trend during

1962-2011. The mean summer minimum temperature during the period was

11.58±0.14°C. The highest mean summer minimum temperature was 14.04 °C during

2000, followed by 13.07 °C during 1984. The lowest mean summer minimum

temperature was 9.44 °C during 1987. The mean summer minimum temperature

increased steadily, except a small decline during 19851994. There were ten years

having mean summer minimum temperature higher than 12.50°C, while there were four

years having mean summer minimum temperature lower than 10.50 °C (Figure 4.10).

The slope of increase in mean summer minimum temperature was relatively higher

compared to mean summer maximum temperature.

201020001990198019701960

14

13

12

11

10

9

Year

Su

mm

er

Min

. T

em

p.

(°C

)

Regression

95% CI

95% PI

Figure 4-10 Trend line of Mean Summer Minimum Temp. (°C) vs. Time at GFD (1962-2011)

69

The mean summer temperature exhibited an overall increasing trend during 1962-2011.

The mean summer temperature during the period was 17.32±0.14°C. The highest mean

summer temperature was 19.50 °C during 2000, followed by 19.4 °C during 2001. The

lowest mean summer temperature was 15.00 °C during 1987. There was a steady

increase in mean summer temperature over the years, with a higher pace after 1995.

There were 11 years having mean summer temperature higher than 18.00°C, while there

were three years having summer temperature lower than 16.00 °C (Figure 4.11). The

increase in mean summer temperature was relatively more even compared to mean

summer maximum temperature and mean summer minimum temperature.

201020001990198019701960

20

19

18

17

16

15

Year

Su

mm

er

Me

an

Te

mp

. (°

C)

Regression

95% CI

95% PI

Figure 4-11 Trend line of Mean Summer Temp. (°C) vs. Time at GFD (1962-2011)

70

The summer precipitation exhibited no trend during 1962-2011. The mean summer

precipitation during the period was 116.63±4.59 mm/season. The highest summer

precipitation was 217.60 mm/season during 1996, while the lowest summer

precipitation was 61.50 mm/season during 2006. An increasing trend in summer

precipitation was observed during late 1990s and early 2000s, followed by a decline.

There were five years having summer precipitation higher than 150.00 mm.

Conversely, there were 14 years having summer precipitation lower than 100.00 mm

(Figure 4.12).

201020001990198019701960

200

150

100

50

Year

Su

mm

er

Pp

t. (

mm

/mo

nth

)

Regression

95% CI

95% PI

Figure 4-12 Trend line of Summer Precipitation vs. Time at GFD (1962-2011)

71

The mean monsoon maximum temperature exhibited an overall increasing trend during

1962-2011. The mean monsoon maximum temperature during the period was

23.46±0.08°C. The highest mean monsoon maximum temperature was 24.80 °C during

1998, followed by 24.60 °C during 2010. The lowest mean monsoon maximum

temperature was 22.10 °C during 1965. There was a steady increase in mean monsoon

maximum temperature, with a steeper pace after 2002. There were nine years having

mean monsoon maximum temperature higher than 24.00 °C. Conversely, there were

two years having mean monsoon maximum temperature lower than 22.50 °C (Figure

4.13). The ranges of mean monsoon maximum temperature and mean summer

maximum temperature were approximately the same.

201020001990198019701960

25.0

24.5

24.0

23.5

23.0

22.5

22.0

Year

Mo

nso

on

Ma

x. T

em

p.

(°C

)

Regression

95% CI

95% PI

Figure 4-13 Trend line of Mean Monsoon Maximum Temp. (°C) vs. Time at GFD (1962-2011)

72

The mean monsoon minimum temperature exhibited no overall trend during 1962-

2011. The mean monsoon minimum temperature during the period was 13.1±0.07 °C.

The highest mean monsoon minimum temperature was 14.40 °C during 1998, followed

by 14.30 °C during 1997. The lowest mean monsoon minimum temperature was 11.80

°C during 1989. The mean monsoon minimum temperature decreased during 1971-

1985 and increased during 1990s. There were five years having mean monsoon

minimum temperature higher than 13.50 °C, while there were four years having mean

monsoon minimum temperature lower than 12.50 °C (Figure 4.14).

201020001990198019701960

14.5

14.0

13.5

13.0

12.5

12.0

Year

Mo

nso

on

Min

Te

mp

. (°

C)

Regression

95% CI

95% PI

Figure 4-14 Trend line of Mean Monsoon Minimum Temp. (°C) vs. Time at GFD (1962-2011)

73

The mean monsoon temperature exhibited an overall increasing trend during 1962-

2011. The mean monsoon temperature during the period was 18.27±0.07°C. The

highest mean monsoon temperature was 19.60 °C during 1998, followed by 19.20 °C

during 1999. The lowest mean monsoon temperature was 17.2 °C during 1965. There

was a decline in mean monsoon temperature during 1978-86, followed by a steady

increase. There were 13 years having mean monsoon temperature higher than 18.50 °C.

Conversely, there were three years having mean monsoon temperature lower than 17.50

°C (Figure 4.15). The changing trend of mean monsoon temperature closely followed

that of mean monsoon minimum temperature.

201020001990198019701960

20.0

19.5

19.0

18.5

18.0

17.5

17.0

Year

Mo

nso

on

Me

an

Te

mp

. (°

C)

Regression

95% CI

95% PI

Figure 4-15 Trend line of Mean Monsoon Temp. (°C) vs. Time at GFD

74

The monsoon precipitation exhibited no overall trend during 1962-2011. The mean

monsoon precipitation during the period was 345.06±13.50 mm/season. The highest

monsoon precipitation was 573.80 mm/season during 2006, while the lowest monsoon

precipitation was 171.20 mm/season during 1963. There was a steep increase in mean

monsoon precipitation during 1970-1984, followed by a gradual decline. There were

eight years having monsoon precipitation higher than 450.00 mm. Conversely, there

were two years having monsoon precipitation lower than 200.00 mm (Figure 4.16).

201020001990198019701960

600

500

400

300

200

100

0

Year

Mo

nso

on

Pp

t (m

m/a

nn

um

)

Regression

95% CI

95% PI

Figure 4-16 Trend line of Monsoon Precipitation vs. Time at GFD (1962-2011)

75

The mean autumn maximum temperature overall exhibited no trend during 1962-2011.

The mean autumn maximum temperature during the period was 16.01±0.11°C. The

highest mean autumn maximum temperature was 18.10 °C during 1998, followed by

17.50 °C during 1979. The lowest mean autumn maximum temperature was 14.40 °C

during 1967. There was an increase in mean autumn maximum temperature during

1970-1978, followed by a declining trend during 1980-1997. There were six years

having mean autumn maximum temperature higher than 17.00 °C and the same number

of years having mean autumn maximum temperature lower than 15.00 °C years (Figure

4.17).

201020001990198019701960

18

17

16

15

14

Year

Au

tum

n M

ax.

Te

mp

. (°

C)

Regression

95% CI

95% PI

Figure 4-17 Trend line of Mean Autumn Maximum Temp. (°C) vs. Time at GFD (1962-2011)

76

The mean autumn minimum temperature exhibited an overall increasing trend during

1962-2011. The mean autumn minimum temperature during the period was

4.12±0.11°C. The highest mean autumn minimum temperature was 5.70 °C during

2001, followed by 5.60 °C during 1977. The lowest mean autumn minimum

temperature was 2.90 °C during 1968. There were wide fluctuations in mean autumn

minimum temperature over the years. The mean autumn minimum temperature

deceased during 1983-1998, followed by a relatively sharp increase. There were eight

years having mean autumn minimum temperature higher than 5.00 °C, while there were

13 years having mean autumn minimum temperature lower than 3.50 °C (Figure 4.18).

201020001990198019701960

6

5

4

3

2

Year

Au

tum

n M

in.

Te

mp

(°C

)

Regression

95% CI

95% PI

Figure 4-18 Trend line of Mean Autumn Minimum Temp. (°C) vs. Time at GFD (1962-2011)

77

The mean autumn temperature exhibited an overall increasing trend during 1962-2011.

The mean autumn temperature during the period was 10.05±0.09 °C. The highest mean

autumn temperature was 11.70 °C during 1998, followed by 11.30 °C during 2006. The

lowest mean autumn temperature was 8.80 °C during 1968. There was a smooth

increasing trend in mean autumn temperature, except a decline during 1980-1991.

There were 13 years having mean autumn temperature higher than 10.50 °C.

Conversely, there were three years having mean autumn temperature lower than 9.00

°C (Figure 4.19). The increase in mean autumn temperature was relatively more even

compared to mean autumn minimum temperature. The changing trend of mean autumn

temperature followed the pattern of mean autumn maximum temperature.

201020001990198019701960

12

11

10

9

8

Year

Au

tum

n M

ea

n T

em

p.

(°C

)

Regression

95% CI

95% PI

Figure 4-19 Trend line of Mean Autumn Temp. (°C) vs. Time at GFD (1962-2011)

78

The autumn precipitation exhibited no overall trend during 1962-2011. The mean

autumn precipitation during the period was 46.67±3.01 mm/season. The highest autumn

precipitation was 105.70 mm/season during 1996, while the lowest autumn

precipitation was 9.00 mm/season during 1974. An increasing trend in autumn

precipitation was observed during 1981-1997, followed by a decline. There were three

years having autumn precipitation higher than 80.00 mm. Conversely, there were five

years having autumn precipitation lower than 20.00 mm (Figure 4.20).

201020001990198019701960

120

100

80

60

40

20

0

Year

Au

tum

n P

pt

(mm

/an

nu

m)

Regression

95% CI

95% PI

Figure 4-20 Trend line of Autumn Precipitation vs. Time at GFD (1962-2011)

79

The mean winter maximum temperature exhibited an overall increasing trend during

1962-2011. The mean winter maximum temperature during the period was 6.78±0.12

°C. The highest mean winter maximum temperature was 8.50 °C during 2009, followed

by 8.30 °C during 2007. The lowest mean winter maximum temperature was 4.00 °C

during 1968. The mean winter maximum temperature increased steadily over the years,

with a higher pace during 20022011. There were eight years each having mean winter

maximum temperature higher than 7.50 °C and lower than 6.00 °C (Figure 4.21).

201020001990198019701960

10

9

8

7

6

5

4

Year

Win

ter

Ma

x. T

em

p.

(°C

)

Regression

95% CI

95% PI

Figure 4-21 Trend line of Mean Winter Maximum Temp. (°C) vs. Time at GFD (1962-2011)

80

The mean winter minimum temperature exhibited an overall increasing trend during

1962-2011. The mean winter minimum temperature during the period was -

2.0±01.14°C. The highest mean winter minimum temperature was 0.40 °C during 2001,

followed by -0.30 °C during 2003. The lowest mean winter minimum temperature was

-4.90 °C during 1968. There was a steady increase in mean winter minimum

temperature during 1962-2011. There were six years having mean winter minimum

temperature higher than -1.00 °C, while there were eight years having mean winter

minimum temperature lower than -3.00 (Figure 4.22).

201020001990198019701960

1

0

-1

-2

-3

-4

-5

Year

Win

ter

Min

. T

em

p.

(°C

)

Regression

95% CI

95% PI

Figure 4-22 Trend line of Mean Winter Minimum Temp. (°C) vs. Time at GFD (1962-2011)

81

The mean winter temperature exhibited an overall increasing trend during 1962-2011.

The mean winter temperature during the period was 2.39±0.12 °C. The highest mean

winter temperature was 4.00 °C during 2009, followed by 3.80 during 2004. The lowest

mean winter temperature was -0.40 °C during 1968. There were ten years having mean

winter temperature higher than 3.00 °C. Conversely, there were four years having mean

winter temperature lower than 1.00 °C (Figure 4.23). The changing trend of mean

winter temperature closely followed that of the mean winter maximum temperature.

201020001990198019701960

5

4

3

2

1

0

Year

Win

ter

Me

an

Te

mp

. (°

C)

Regression

95% CI

95% PI

Figure 4-23 Trend line of Mean Winter Temp. (°C) vs. Time at GFD (1962-2011)

82

The winter precipitation overall exhibited no trend during 1962-2011. The mean winter

precipitation during the period was 180.53±8.14 mm/season. The highest winter

precipitation was 294.40 mm/season during 2005, while the lowest winter precipitation

was 52.50 mm/season during 1989. There were large fluctuations in winter

precipitation over the period 1962-2011. The mean winter precipitation remained

around 175.00 mm for the first 35 years covered under the study, followed by a gradual

increase to above 200.00 mm during the subsequent fifteen years. There were five years

having winter precipitation higher than 250.00 mm. Conversely, there were nine years

having winter precipitation lower than 125.00 mm (Figure 4.24).

201020001990198019701960

300

250

200

150

100

50

Year

Win

ter

Pp

t (m

m/m

on

th)

Regression

95% CI

95% PI

Figure 4-24 Trend line of Winter Precipitation vs. Time at GFD (1962-2011)

83

4.1.3 Climate Changes

The analysis of climate data showed considerable variations in temperature and

precipitation, both on vertical (across years) and horizontal (across seasons) scales,

during 1962-2011. The mean maximum temperature, mean minimum temperature and

mean annual temperature increased by 1.10°C, 1.32 °C and 1.22 °C, and the mean

annual precipitation by 1.39%, during 1962-2011. The changes in these parameters on

seasonal basis varied from season to season. The increases in temperature parameters

on interannual basis were highly significant (p<0.01) and the increase in precipitation

non-significant (p>0.05). The increase in maximum temperature was highly significant

(p<0.01) during winter, significant (p<0.05) during spring, summer, and autumn and

non-significant (p>0.05) during autumn. The increase in minimum temperature was

highly significant (p<0.01) during spring, summer and winter, significant (p<0.05)

during autumn and non-significant (p>0.05) during monsoon. The increase in mean

temperature was highly significant (p<0.01) during spring and winter and significant

(p<0.05) during summer, monsoon and autumn. On seasonal basis, the changes in

precipitation were: significant (p<0.05) decrease of -14.90% in spring, non-significant

(p>0.05) decrease of -9.95% during summer, and significant (p<0.05) increase of

8.94% during monsoon and non-significant (p>0.05) increase of 11.81% and 12.04%

during autumn and winter respectively (Table 4.3). The significance level (probability

of significance) was based on the ANOVA generated by the regression analysis.

Amongst the seasons, the highest increase in mean maximum temperature of 1.73 °C

was recorded during winter and the lowest increase of 0.50 °C during autumn. The

highest increase in mean minimum temperature of 2.37 °C was recorded during winter

and the lowest increase of 0.35 °C during monsoon. The highest increase in mean

seasonal temperature was recorded during winter, followed by spring and summer. The

lowest increase in mean seasonal temperature was recorded during monsoon, followed

by autumn. The increase in mean maximum temperature and mean minimum

temperature during spring and autumn indicated shortening of winter period and

lengthening of summer period.

84

Table 4-3 Temperature and precipitation changes at GFD (1962-2011)

Seasons/

Periods

Climate parameters and precipitation changes

Max. Temp.

(Δ°C)

Min. Temp.

(Δ°C)

Mean Temp.

(Δ°C)

Precipitation

(Δ %)

Spring 1.47* 1.78** 1.64** - 14.90*

Summer 1.04* 1.16** 1.12* - 9.85ns

Monsoon 0.68* 0.35ns 0.54* 8.94*

Autumn 0.50ns 0.92* 0.73* 11.81ns

Winter 1.73** 2.37** 2.08** 12.04ns

Annual 1.10** 1.32** 1.22** 1.39ns

* Significant (p<0.05); ** highly significant (p<0.01); ns= Non-significant (p>0.05)

The analysis showed an overall increase of 1.39% in mean annual precipitation during

1962-2011. The mean seasonal precipitation increased by 8.94%, 11.81%, and 12.04%

during monsoon, autumn and winter respectively. Conversely, the mean seasonal

precipitation decreased by 14.90% and 9.85% during spring and summer respectively.

85

The analysis of temperature data indicated relatively higher increase in mean minimum

temperature compared to mean maximum temperature (Figure 4.25).

Figure 4-25 Comparison between increases in Maximum Temperature and Minimum Temperature

The slope gradient of mean minimum temperature was higher compared to mean

maximum temperature and mean temperature on annual as well as seasonal basis, thus

indicating warming of night temperature and narrowing down diurnal temperature gap.

The narrowing down of the gap between maximum and minimum temperatures was

more pronounced in monsoon. The results also showed higher variability in mean

minimum temperature compared to mean maximum temperature and mean

temperature. Lower fluctuations were recorded in mean maximum temperature, thus

indicating a uniform increase which was also supported by the linear model fit for the

change in mean maximum temperature.

0.00

0.50

1.00

1.50

2.00

2.50

Spring Summer Monsoon Autumn Winter Annual

Season

Max. Temp. Min. Temp.

86

4.1.4 Mathematical Expressions of Climate Change Trends at GFD (1962-2011)

Mathematical expressions of temperature and precipitation changes at GFD during

1962-2011 showed both linear and quadratic behaviors. Climate parameters of annual

minimum temperature, summer maximum temperature, summer minimum temperature,

summer mean temperature, monsoon maximum temperature, autumn maximum

temperature, autumn mean temperature, winter minimum temperature, winter mean

temperature and winter precipitation exhibited linear functions, while the other 14

parameters followed quadratic pattern of regression response. The R2 for linear response

ranged between 0.01 and 0.50, while the R2 for quadratic function ranged between 0.02

and 0.39, thus indicated good fit of models for some climate parameters and poor fit of

models for others, especially precipitation (Table 4.4).

Table 4-4 Mathematical Expressions of Climate Change Trends at GFD (1962-

2011)

Climate Parameters Mathematical Expressions R2 F(1) 2*, (48) 47*

(p)

Annual Max. Temp. Y = 1306 - 1.321 × X

+ 0.0003 × X2

0.39 15.11 (0.000)

Annual Min. Temp. Y = - 47.58 + 0.027 × X 0.44 37.01 (0.000)

Mean Annual Temp. Y = 1133 - 1.155×X + 0.0003 ×

X2

0.43 17.79 (0.000)

Annual Precipitation Y = - 983447 + 990.8 × X

- 0.249 × X2

0.12 3.12 (0.05)

Spring Max. Temp. Y = 3821 - 3.863 × X + 0.001 ×

X2

0.15 4.09 (0.023)

Spring Min. Temp. Y = 2196 - 2.243×X + 0.0006 ×

X2

0.26 8.43 (0.001)

Spring Mean Temp. Y = 3081 - 3.127 × X + 0.001 ×

X2

0.22 6.55 (0.003)

Spring Precipitation Y = -182887 + 185.0 × X

- 0.047 × X2

0.04 0.87 (0.425)

Summer Max. Temp. Y = - 19.08 + 0.021 × X 0.08 4.17 (0.047)

87

Summer Min. Temp. Y = - 35.43 + 0.0237 × X 0.13 7.11 (0.010)

Summer Mean Temp. Y = - 28.02 + 0.0228 × X 0.11 6.00 (0.018)

Summer Precipitation Y = - 53502 + 54.23 × X

- 0.014 × X2

0.02 0.45 (0.643)

Monsoon Max. Temp. Y = - 3.93 + 0.014 × X 0.12 6.21 (0.016)

Monsoon Min. Temp. Y = 613 - 0.611 × X + 0.0002 ×

X2

0.05 1.34 (0.271)

Monsoon Mean Temp. Y = 1241 - 1.242 × X + 0.0003 ×

X2

0.12 3.31 (0.045)

Monsoon Precipitation Y = - 695351 + 699.9 × X

- 0.176 × X2

0.13 3.47 (0.039)

Autumn Max. Temp. Y = - 4.19 + 0.0102 × X 0.04 1.77 (0.190)

Autumn Min. Temp. Y = 1547 - 1.572 × X + 0.0004 ×

X2

0.13 3.63 (0.034)

Autumn Mean Temp. Y = -19.54 + 0.01489 × X 0.11 6.03 (0.018)

Autumn Precipitation Y = -77864 + 78.34 × X

- 0.0197 × X2

0.04 0.86 (0.428)

Winter Max. Temp. Y = 1935 - 1.976 × X + 0.0005 ×

X2

0.36 13.45 (0.000)

Winter Min. Temp. Y = - 98.24 + 0.0484 × X 0.49 46.78 (0.000)

Winter Mean Temp. Y = - 81.97 + 0.0425 × X 0.50 48.29 (0.000)

Winter Precipitation Y = - 649 + 0.4174 × X 0.01 0.54 (0.465)

Values in parenthesis in the top row are degree of freedom for linear equations and with asterisk

for quadratic equations. The (p) values indicate significance levels.

4.1.5 Correlation Coefficients Matrix of different Climate Factors at GFD

The Pearson Correlation Coefficients matrix showed a highly significant (p<0.01)

positive correlation between maximum temperature and mean temperature (r = 0.94)

and minimum temperature and mean temperature (r = 0.97). The correlation of

precipitation with mean temperature, maximum temperature and minimum temperature

was significant but negative (Table 4.5).

88

Table 4-5 Correlation Coefficients Matrix among different Climate Factors at

GFD

Mean Temp Max. Temp. Min. Temp.

Max. Temp. 0.941**

(0.000)

Min. Temp. 0.966**

(0.000)

0.843**

(0.000)

Precipitation - 0.423**

(0.002)

- 0.458**

(0.001)

- 0.353*

(0.012)

Values in parenthesis are p values; * = Significant (p<0.05); ** = highly significant

(p<0.01)

4.2 Bioclimatic Indices

Bioclimatic indices are tools to explain the spatial distribution of vegetation units by the

combination of different climatic factors.

4.2.1 Bioclimatic Indices Regime

The bioclimatic indices regimes were estimated for GFD for the time period of 1962-

2011, with the results reproduced in Table 4.6.

Table 4-6 Bioclimatic Indices Regimes at GFD (1962-2011)

Indices

Seasons

Annual

(A) Spring

(S)

Summer

(Su)

Monsoon

(M)

Autumn

(Au)

Winter

(W)

TEI

±SE

4.02

±0.07

7.79

±0.06

8.22

±0.03

4.52

±0.03

1.07

±0.06

5.04

±0.04

AI

±SE

20.70

±1.24

6.41

±0.27

17.95

±0.73

4.25

±0.29

61.21

±6.05

73.25

±1.82

89

DI

±SE

11.57

±0.71

3.39

±0.14

9.47

±0.38

2.34

±0.16

38.99

±6.48

39.92

±1.01

RF

±SE

23.14

±0.12

6.78

±0.19

18.94

±0.19

4.68

±0.11

77.97

±0.05

79.84

±0.12

DF

±SE

12.70

±0.12

4.82

±0.20

13.68

±0.20

2.75

±0.11

19.46

±0.05

49.00

±0.12

HC

±SE

21.63

±1.30

6.34

±0.31

17.70

±0.79

4.37

±0.30

72.87

±2.76

74.62

±2.43

PEI

±SE

2.67

±0.14

1.11

±0.05

3.17

±0.13

0.59

±0.04

3.47

±0.16

10.63

±0.25

TEI=Temperature Efficiency Index; AI= Aridity Index; DI= Dryness Index; RF= Rain Factor; DF=

Dryness Factor; HC= Humidity Coefficient; PEI= Precipitation Efficiency Index

Temperature Efficiency Index (TEI) varied considerably among seasons with mean

annual TEI of 5.04±0.04. The highest TEI was estimated during monsoon, while the

lowest TEI was during winter. Similarly, Aridity Index (AI) varied among the seasons

with mean annual AI of 73.25±1.82. The highest AI was in winter and the lowest in

autumn. The mean Dryness Index (DI) and mean Rain Factor (RF) were 39.92±1.01

and 79.84±0.12 respectively. The seasonal regimes of DI and RF followed the pattern

of AI. The highest Dryness Factor and the lowest Dryness Factor (DF) were estimated

in winter and autumn respectively. Conversely, DF was higher in monsoon compared to

spring. Humidity Coefficient (HC) varied among seasons with mean annual HC of

74.62±2.43. The highest HC was 72.87±2.76 in winter and the lowest was 4.37±0.30 in

autumn. The mean annual regime of Precipitation Efficiency Index (PEI) was

10.63±0.25. The highest PEI was estimated for winter and the lowest for autumn.

90

4.2.2 Changes in Bioclimatic Indices

The results showed that mean annual TEI increased by 11.53%. The highest increase

was recorded during winter (55.08%), followed by spring (20.21%) and the lowest

during monsoon (2.99%). Conversely, mean annual AI decreased by 7.92%. The

highest decrease in AI was during winter (59.59%), followed by spring (28.03%).

However, AI increased during monsoon (14.18%) and autumn (7.21%). The changes in

DI and RF were almost similar which followed the pattern of AI. The mean annual DF

decreased by (4.95%), with the highest decrease in spring (23.35%), followed by

summer (13.52%), whereas the highest decrease in HC was during winter (42.82%),

followed by spring (29.32%). Conversely, HC increased during monsoon (5.93%) and

autumn (4.24%). The pattern of changes in PEI were similar to DF (Table 4.7). The

results also indicated that mean annual changes in TEI were positive and highly

significant, while for DI, RF and DF negative and significant and AI, HC and PEI both

negative and non-significant. Similarly, the mean seasonal changes in TEI were

significant and highly significant, AI (Monsoon and winter) and DI (Monsoon) were

significant while in other indices were non-significant.

Table 4-7 Changes (%) in Bioclimatic Indices at GFD (1962-2011)

Indices Spring

(S)

Summer

(Su)

Monsoon

(M)

Autumn

(Au)

Winter

(W)

Annual

(A)

TEI 20.21** 6.67* 2.99* 7.54* 55.08** 11.53**

AI - 28.03ns - 14.67 ns 14.18* 7.21 ns - 59.59* - 7.92 ns

DI - 29.33 ns - 14.94 ns 5.93* 4.23 ns - 42.79 ns - 6.40*

RF - 29.29 ns - 14.92 ns 5.93 ns 4.24 ns - 42.62 ns - 8.72*

DF - 23.35 ns - 13.52 ns 6.76 ns 7.34 ns - 11.83 ns - 4.95*

HC - 29.32 ns - 14.93 ns 5.93 ns 4.24 ns - 42.82 ns - 8.72 ns

PEI - 21.30 ns - 12.92 ns 7.12 ns 8.38 ns - 3.59 ns - 3.57 ns

* Significant (p<0.05); ** highly significant (p<0.01); ns= Non-significant (p>0.05)

91

4.2.3 Mathematical Expressions of Changes in Bioclimatic Indices at GFD (1962-

2011)

Mathematical expressions of changes in bioclimatic indices at GFD during 1962-2011

varied in pattern and exhibited linear, quadratic and polynomial forms (Table 4.8). The

annual and seasonal mathematical expressions for TEI were associated with significant

and highly significant higher values of R2, indicating good fit equations, while the

expressions for other indices were mostly non-significant with smaller values of R2, and

indicating poor fit equations.

Table 4-8 Mathematical Expressions of Changes in Bioclimatic Indices at GFD

(1962-2011)

Bioclimatic

Indices Mathematical Expressions R2

F(1,2,3), (48,47,46)*

(p)

ATEI Y = - 17.23 + 0.01121 x X

0.42 34.88 (0.000)

STEI Y = - 25.93 + 0.01508 × X

0.20 11.94 (0.001)

SuTEI Y = - 12.61 + 0.01027 x X

0.11 6.00 (0.018)

MTEI Y = - 1.60 + 0.004944 x X

0.11 5.85 (0.019)

AuTEI Y = - 8.79 + 0.00670 x X

0.11 6.03 (0.018)

WTEI Y = - 36.89 + 0.01911 x X

0.50 48.29 (0.000)

AAI Y = 318.70 - 0.12350 x X

0.02 0.96 (0.333)

SAI Y = 293.60 - 0.13700 x X

0.05 2.64 (0.111)

SuAI Y = 47.55 - 0.020700 x X

0.08 0.25 (0.271)

MAI Y = - 37769 + 38.02 x X - 0.009565 x X2

0.13 6.61 (0.013)

AuAI Y = - 7526 + 7.577 x X - 0.00190 x X2

0.03 1.52 (0.224)

WAI Y = 137713 - 137.6 x X + 0.03436 x X2 0.15 5.63 (0.022)

ADI Y = - 48011 + 48.45 x X - 0.0122 x X2

0.13 5.63 (0.022)

92

SDI Y = 172.9 - 0.081200 x X

0.06 2.82 (0.099)

SuDI Y = - 1542 + 1.567 x X - 0.00039 × X2 0.03 0.26 (0.610)

MDI Y = - 19967 + 20.10 x X - 0.00505 × X2 0.13 6.62 (0.013)

AuDI

Y = 632028 - 956.6 x X + 0.4826 × X2 -

0.00008 × X3

0.62 1.45 (0.234)

WDI Y = 898.3 - 0.43260 x X

0.19 0.93 (0.341)

ARF Y = - 96022 + 96.91 x X - 0.0244 × X2

0.13 5.63 (0.022)

SRF

Y = 4221064 - 6390 x X + 3.224 × X2-

0.00054 × X3

0.93 0.84 (0.365)

SuRF

Y = 1193311 - 1804 x X + 0.9088 × X2-

0.000153 × X3

0.06 1.58 (0.421)

MRF

Y = - 3725827 + 5607 x X - 2.813 × X2+

0.000470 × X3

0.17 2.36 (0.132)

AuRF Y = 1264056 - 1913 x X + 0.9653 × X2-

0.00016 × X3 0.06 1.45 (0.234)

WRF Y = 1797 - 0.86520 ×X 0.02 0.93 (0.341)

ADF Y = - 57298 + 57.79 X - 0.01456 x X2

0.12 5.91 (0.019)

SDF Y = 2161195 - 3271 x X + 1.650 x X2 -

0.000278 × X3 0.06 0.98 (o.327)

SuDF

Y = 837599 - 1266 x X + 0.6379 × X2 -

0.000107 × X3

0.08 1.61 (0.211)

MDF

Y = - 2673684 + 4024 x X - 2.018 × X2+

0.00033 × X3

0.17 2.38 (0.130)

AuDF

Y = 747935 - 1132 x X + 0.5711 x X2 -

0.000096 × X3

0.06 1.52 (0.224)

WDF Y = - 1103090 + 1671 x X - 0.844 x X2 +

0.000142 × X3 0.02 0.28 (0.599)

AHC Y = - 89740 + 90.57 x X - 0.02283 x X2

0.13 1.14 (0.290)

SHC Y = 3944920 - 5972 x X + 3.013 x X2-

0.000507 × X3 0.06 1.62 (0.267)

93

SuHC Y = 1115244 - 1686 x X + 0.8494 x X2-

0.000143 × X3 0.03 1.28 (0.264)

MHC Y = - 3482082 + 5240 x X - 2.629 x X2+

0.000439 × X3 0.17 0.17 (0.680)

AuHC Y = - 1181361 - 1788 x X + 0.9021 x X2 -

0.000152 ×X3 0.01 0.03 (0.859)

WHC Y = 1679 - 0.80860 x X 0.02 0.07 (0.29)

APEI Y = - 12294 + 12.40 x X - 0.003122 x X2

0.12 0.21 (0.650)

SPEI Y = 441981 - 669.0 x X + 0.3375 x X2 -

0.000057× X3 0.07 1.74 (0.193)

SuPEI Y = 191660 - 289.7 x X + 0.1460 x X2 -

0.000057 × X3 0.06 0.99 (0.325)

MPEI Y = - 617185 + 928.8 x X - 0.4659 x X2 +

0.000078 × X3 0.17 0.25 (0.616)

AuPEI Y = 160315 - 242.6 x X + 0.1224 x X2 -

0.000021 × X3 0.06 0.13 (0.723)

WPEI Y = 1602 - 1.607 x X + 0.000404 x X2 0.01 0.05 (0.816)

* Values in parentheses in the top row are degree of freedom for linear, quadratic and polynomial

equations respectively. The (p) values indicate significance levels.

94

4.3 Climate Vegetation Productivity Index

Climate Vegetation Productivity Index (CVPI) during 1962-2011 ranged between 4,342

and 9,091 with mean of 6,816. The highest CVPI was estimated during 2003, while the

lowest CVPI was estimated during 1971. The pattern of CVPI showed significant

increase (F2, 47= 5.34, p<0.01). The mathematical expression (CVPI = -8059491+ 8104

x X- 2.036 x X2; R2= 0.18) showed a quadratic function of CVPI. The maximum CVPI

calculated was for the time period between 1980 and 2000. A declining trend was

observed in CVPI, after 2000 (Figure 4.26).

Figure 4-26 Trend line of Climate Vegetation Productivity Index at GFD

4.4 Discussion

The present results show an increase of 1.10°C, 1.32 °C and 1.22 °C in mean maximum

temperature, mean minimum temperature and mean annual temperature respectively, at

Galies Forest Division-Abbottabad, during 1962-2011. The temperature changes show

an upward trend both horizontally (across seasons) and vertically (across years). The

analysis of data averaged on interannual basis indicates that the highest increase was

recorded in the minimum temperature (1.32 °C), followed by mean temperature

95

(1.22°C) and maximum temperature (1.10°C), while that averaged on seasonal basis

indicates the highest increase in minimum temperature (2.37°C) during winter and the

lowest increase in minimum temperature (0.35°C) during monsoon. By seasons, the

highest increase in temperature (maximum, minimum and mean) is in winter, followed

by spring, thus indicating extending summer time period. The climate change shows a

feedback mechanism with climate parameters. For instance, maximum temperature and

minimum temperature are positively correlated inter se and with mean temperature.

Conversely, precipitation is negatively correlated with temperature. The range of

variation and coefficients of variation indicate a large seasonal volatility of climate.

The present findings of increasing temperature are higher compared to global average

temperature increase of 0.74 °C during 1906-2005 (IPCC, 2007). However, these

findings are broadly in conformity with Bukhari and Bajwa (2009); where they reported

an increase of 0.92 °C and 0.77 °C (mean 0.85 °C) in maximum and minimum

temperatures respectively in Peshawar, during 1985-2009. Similarly, Bukhari and

Bajwa (2011) reported an increase of 0.56 °C to 0.78 °C in mean temperature over

different forest types of Pakistan. The higher increase in temperature towards the end of

20th century and beginning of 21st century is in corroboration with reports of previous

works (Esper et al., 2002; IPCC, 2007). The increasing temperature trends may be

explained in terms of different degrees of albedo, physical nature of soil surface and

anthropogenic activities. There are several physical (IPCC, 2007; Grunewald et al.,

2009) and anthropogenic activities (Foley et al., 2005; Falcucci et al., 2007; Vorholz,

2009) which influence spatio-temporal changes in climate processes at local and

regional levels. Among all these external forcing, anthropogenic activities have been

considered dominant cause of temperature increase (Knutson et al., 2006).

Furthermore, the higher increase in temperature at local level may be explained in

terms of newly emerging urbanization phenomenon, with associated spree of

construction, infrastructure development and deforestation in the area. The local urban

areas act as heat island. Previously, heat island effects have been reported by Trenberth

et al. (2007); Wu et al. (2010). Higher rates of temperature increase under urban

conditions have also been reported in Karachi-Pakistan during 1976-05 (Sajjad et al.,

2009); where they recorded increase of 2.7°C, 1.2 °C and 1.95 °C in maximum

temperature, minimum temperature and mean annual temperature respectively. The

96

present increase in temperature is lower compared to that reported by Sajjad et al.

(2009), except the minimum temperature. The differences in reported increase in

temperature may be, besides other factors, due to the time period analyzed, as Sajjad et

al. (2009) covered time period between 1976-05, while the present study covers time

period of 1962-2011. Similarly, the increase of 1.10 °C in maximum temperature is,

nevertheless, higher compared to previous reports of 0.94 °C in Lahore-Pakistan during

1975-2007.

The present findings also indicate a higher increase in minimum temperature compared

to maximum temperature, thus indicating that nights are becoming warmer at higher

rates compared to days. The highest increase in temperature is during winter, followed

by spring. Higher increase in minimum temperature during winter is bringing an early

start of spring. These findings are in corroboration with Bukhari and Bajwa (2009, 2011

& 2012).

Many parts of the world have experienced changes in global water cycle, such as the

magnitude and timing of runoff and the frequency and intensity of floods and droughts

and rainfall patterns (Jiang et al., 2007). Temperature is a key parameter of the energy

which affects water cycles of the earth-atmosphere system (Behbahani et al., 2009).

The current findings show significant changes in precipitation. There is an overall

increase of 1.39% precipitation at GFD, however, a significant decrease is observed

during spring and summer. The decrease in precipitation and increase in temperature

during spring and summer, signify an inverse relationship between temperature increase

and magnitude of precipitation during these seasons. The drought periods also increase

with a number of years receiving scant or moderate precipitation. These results are

broadly in line with findings reported previously by Grunewald et al. (2009); Liu et al.

(2010).

The recent climate changes at GFD may further be explained in terms of increased

human population, livestock and urban sprawl in the area, especially during summer

and monsoon, in the recent past. These activities, subsequently, are increasing

greenhouse gases (GHGs) in the area. The combination of increased population and

anthropogenic activities influence the biogeochemical processes which might have

changed climate in the Forest Division, because these factors are dominant reasons of

97

climate changes globally (Brovkin et al., 2004; Motha and Baier, 2005; Grunewald et

al., 2009; Houghton, 2005; Wu, et al., 2010).

Land cover and land use are very important factors which interact with atmospheric

conditions to determine the overall climate. These interactions have great impacts on

various ecosystems from regional to global scales (Pyke et al., 2007). Land cover

change and land degradation either due to anthropogenic activities, deforestation or

livestock can directly increase temperatures (Briggs et al., 2005; Balling et al., 1998).

The increased livestock also change the land cover and land use pattern. Livestock,

besides, directly responsible for GHGs (18% of all human-induced GHGs globally)

cause deforestation as well as deteriorate rangelands (Van de Steeg et al., 2009). In

GFD, grazing and deforestation for timber and fuel wood put pressure on forest

resources. These factors, in addition to urban sprawl and road network, have changed

land cover and land use pattern which subsequently may have resulted in climate

changes.

The observed increase in temperature and precipitation, both horizontal and vertical,

will likely have multiple effects, specifically in terms of (i) altered planting seasons due

to early start of spring as well as extended summer seasons, (ii) poor plant growth, (iii)

low survival of newly planted trees in spring and monsoon seasons, (iv) increased

competition for water among different stakeholders (agricultural, forestry, civic

utilities), (v) change in forest types, species composition, geographical relocation of

plant and animal species, (vi) increased and frequent insect pests and diseases

outbreaks, and (vii) escalated wind damage of forests, as reported by Blennow et al.

(2010); Bukhari and Bajwa (2012). These effects would likely lead to increased cost of

forest management and other economic activities in the area.

Overall, these climate changes present a great threat to the present and, to a much

greater extent, to the coming generations. The mitigation of adverse effects of climate

change on future generations requires advance planning because GHGs, especially

carbon dioxide (CO2) is a long-lived atmospheric gas which makes the climate change a

resilient phenomenon. Moreover, the climate change that we are currently experiencing

is primarily the result of emissions from some time in the past, rather than current

emissions (back loaded effect of climate change) and the full cumulative effects of our

98

current emissions will be realized for some time in the future (delayed/deferred effect

of climate change). The resilient and delayed phenomena of climate change have

serious implications for future generations which call the principle of intergenerational

justice into question.

The present findings show an increase in mean annual and seasonal Temperature

Efficiency Indices and decrease in mean annual Aridity Index (AI). Similarly, changes

have occurred in Dryness Index, Rain Factor, Dryness Factor, Humidity Coefficient

and Precipitation Efficiency Index. The Climate Vegetation Productivity Index (CVPI)

of GFD has an observed range of 4,342 to 9,091, with mean of 6,816. The results

indicate an overall increase in CVPI and a close relationship between climate change

and changing CVPI. The increasing temperature shows a negative impact on CVPI,

especially after 2000. Bioclimatic indices are tools to explain the spatio-temporal

distribution of vegetation by the combination of different climatic factors (Baltas,

2007). These findings are increasingly important for future planning and management

of GFD. Previously, these indices were used to transfer the results from climate

modeling to land use and vegetation science, to predict long-term trends in

desertification (Gavilán, 2005), and in the methodology of pollen forecasting

(Valencia-Barrera, et al., 2002). The mean CVPI of 6,816 puts GFD in ideal site class

with productivity in the range of 163.91-184.77 cubic feet per acre, estimated as per

methodology described by Paterson, 1956, Champion et al., 1965. The increasing trend

of CVPI within certain temperature ranges, however, indicates increasing CVPI. This is

an encouraging finding for management of the GFD.

The changes in bioclimatic indices, both horizontal and vertical, indicate changes in

forest growth and productivity. The climate change coupled with bioclimatic indices

during spring is crucial. The spring is a blossom time and, therefore, reflects biological

responses of vegetation towards temperature. Each plant species requires a specific

amount of heat to break winter dormancy and complete a normal annual cycle of

vegetative and reproductive growth (Bukhari and Bajwa, 2009). The increasing

temperature in winter and spring indicates early onset and completion of spring. Earlier

onset of the spring as well as shifting of seasons is in conformity with Bukhari and

Bajwa (2009) and Liu et al. (2010); who reported an early onset of the spring season.

The early start of spring indicates early sprouting of plants, but shortening of this

99

season reduces the flowering period. Apart from this, day length in March and April is

still short which limits the photosynthetic process and subsequently plants are still in

tender stage when exposed to higher temperatures. This will put plants under further

stress. Further, the poor vegetative growth causes inferior reproductive growth

(flowering, quantity and quality of seed) (Bukhari and Bajwa, 2009 & 2011).

Apart from forest growth and productivity, the present findings of changing climate and

bioclimatic indices at GFD indicate changes in vegetation composition. It has been

reported that a change in the mean annual temperature, as small as 1 °C over a

sustained period is sufficient to bring about changes in species composition and

distribution of many tree species (IPCC, 1996). A number of climate-vegetation models

have also shown that certain climatic regimes are associated with particular plant

communities or groups (Holdrige, 1947; Thornthwaite, 1948; Walter, 1985; Whittaker,

1975), and change in the climatic regimes may induce changes in vegetation

composition. The long summer combined with long monsoon may also change the

basic composition of seasonal rhythms and subsequently flora and fauna of GFD. These

seasonal variations might cause extinction of some floral and faunal species by facing

climatic conditions beyond their critical survival ranges. Besides disturbance of

biological processes and biodiversity, the climate change and seasonal variations also

affect a number of physical processes and livelihood activities in the area, particularly

in agriculture, livestock, water supply, housing, construction and tourism sectors.

100

CHAPTER 5

IMPACTS OF CLIMATE CHANGE ON TREE RINGS AND RING-

WOOD CHARACTERISTICS

5.1 Cross-dating of Ring-width data

To establish precise chronology of the sampled trees of the three selected species, cross-

dating was done for the period 1962-2011 as per procedure described by Fritts and

Swetnam (1989). The details of the procedure are given in Chapter 3, Para 3.5.2. The

cross-dating output of Deodar, Blue pine, and Chir pine are presented in Figures 5.1, 5.2

and 5.3 respectively. Each colour-shaded line in the Figures represents ring-width size of

one of the 20 sampled trees of the particular species. The thickness of the lines is

proportional to the annual ring-width size of the sampled trees and the vertical variations

across the lines indicate variability in the year-wise growth of the tree-rings across the

20 sampled trees.

Figure 5-1 Cross-dating of Ring-width data of Deodar in GFD (1962-2011)

101

Figure 5-2 Cross-dating of Ring-width data of Blue pine in GFD (1962-2011)

Figure 5-3 Cross-dating of Ring-width data of Chir pine in GFD (1962- 2011)

5.2 Standardization of Ring-width data

The biological growth trend of ontogeny - decrease in ring-widths with increasing tree

age – and effects of other non-climatic site factors were removed by applying

'standardization' procedure as described by Fritts (1976). The details of the procedure

are given in Chapter 3, Para 3.5.2.

102

5.3 Mean Sensitivity and Coefficient of Variation

A sample of twenty trees each of C. deodara, P. wallichiana and P. roxburghii was

selected in random, and variability of their intra-species annual ring-widths were

assessed, as reflected in Table 5.1(a). The mean annual ring-widths of C. deodara, P.

wallichiana and P. roxburghii for the period 1962-2011 were 3.08±0.23 mm, 2.54±0.15

mm and 2.62±0.39 mm and the variances were 1.03 mm, 0.46 mm and 3.10 mm

respectively. The variability of intra-species annual ring-widths was the highest in P.

roxburghii, followed by C. deodara and P. wallichiana. The variability in samples for

the same species was caused by both climatic and non-climatic factors, like ontogeny,

site disturbances and changes in forest crop conditions. The sensitivity analysis was

done to describe the impacts of climate factors on the growth parameters.

Table 5-1 (a) Statistics of intra-species variability of annual ring-widths of Cedrus

deodara, Pinus wallichiana and Pinus roxburghii (1962-2011)

Statistics

Tree species

C. deodara P. wallichiana P. roxburghii

Mean ring-width (mm) 3.08 2.54 2.62

Standard Error (mm) 0.23 0.15 0.39

Variance (σ2) (mm) 1.03 0.46 3.10

Coefficient of Variation (CV)

(%)

32.88 26.55 67.20

Two important tree-ring measures: mean sensitivity (MS) and coefficient of variation

(CV) were calculated for the means of 20 samples each of the three selected species.

The mean sensitivity was calculated to describe variability of high frequency

component of the ring-width due to climatic fluctuations, while the coefficient of

variation was calculated for low frequency component variability induced either by

climate or by other long term influences. The statistics calculated for Cedrus deodara,

Pinus wallichiana and Pinus roxburghii for the period 1962-2011 are presented in

Table 5.1(b).

103

Table 5-1 (b) Statistics of mean sensitivity of mean annual ring-widths of Cedrus

deodara, Pinus wallichiana and Pinus roxburghii for the period 1962-2011

Statistics

Tree species

C. deodara P. wallichiana P. roxburghii

Mean ring-width (mm) 3.08 2.54 2.62

Mean Sensitivity (MS) 0.30 0.38 0.29

Standard Error (mm) 0.11 0.11 0.10

Variance (σ2) (mm) 0.26 0.25 0.21

Coefficient of Variation (CV)

(%)

16.56 19.50 17.53

The mean sensitivity of mean annual ring-width of C. deodara, P. wallichiana and P.

roxburghii for the period 1961-2011were estimated at 0.30±0.0.11, 0.38±0.0.11 and

0.29±0.0.10 respectively. The highest mean sensitivity of 0.38 was estimated for P.

wallichiana and the lowest of 0.29 for P. roxburghii. The mean annual ring-width for

the period 1962-2011 was relatively larger in C. deodara (3.08 mm) compared to P.

wallichiana (2.54 mm) and P. roxburghii (2.62 mm). The variance of mean annual ring-

widths of C. deodara and P. wallichiana were nearly of equal magnitude, while that of

P. roxburghii was slightly lower compared to the other two species. The highest

coefficient of variation of 19.50% was observed in P. wallichiana and the lowest

coefficient of variation of 17.53% in P. roxburghii. The results of mean sensitivity and

coefficient of variation calculated for the three species indicated enough variability in

growth statistics to enable analysis of its time function, correlation and regression with

climate parameters and changes thereof.

5.4 Ring-width and Ring-wood Characteristics of Deodar

5.4.1 Time function analysis of Ring-width and Ring-wood Characteristics of

Deodar

The time function responses of ring-width and ring-wood characteristics of Deodar

were studied through regression analysis at 95% Confidence Interval (CI) and

Prediction Interval (PI), using the standardized data. The trend in the time series was

104

assessed by applying Mann Kendall test with Normal Approximation and using Sen’s

Slope Estimator method. A summary of the trend analysis is reproduced in Table 5.2.

Table 5-2 Trend Analysis of Ring-width and Ring-wood Characteristics of Deodar

at GFD (1962-2011)

Species /Tree Growth

Characteristics

Z-Value p-Value Trend Sen’s

Slope Upward Downward

Ring-width -6.115 1.000 0.000 0.026

Early wood formation -7.077 1.000 0.000 0.155

Late wood formation 6.608 0.000 1.000 0.153

Early wood cell diameter -1.205 0.886 0.114 0.140

Early wood cell wall

thickness

1.372 0.085 0.915 0.001

Late wood cell diameter 2.627 0.004 0.996 0.012

Late wood cell wall

thickness

4.851 0.000 1.000 0.005

Increasing trend No trend Decreasing trend

The time function analysis of ring-width and ring-wood characteristics of Deodar for

the period 1962-2011 showed highly significant (p<0.01) downward trend in ring-width

and early wood formation, highly significant (p<0.01) upward trend in late wood

formation, late wood cell diameter and late wood cell wall thickness, and no trend in

early wood cell diameter and early wood cell wall thickness.

105

The analysis of time function response of mean annual ring-widths of Deodar for the

period 1962-2011 indicated a wide range of variation in annual ring-widths across the

cores in a quadratic pattern, with an overall decreasing trend. The mean annual ring-

width ranged from 2.24±0.25 mm to 4.37±0.26 mm, with a mean value of 3.08±0.23

mm. The largest mean annual ring-width was recorded during 1962, while the smallest

mean annual ring-width was recorded during 2011. The mean annual ring-width

declined incessantly from 1962 to 1990, remained stable during 1991-2000 and

increased slightly between 2000 and 2011. There were four years having mean annual

ring-widths larger than 4.0 mm, while there were 24 years having mean annual ring-

widths smaller than 3.0 mm (Figure 5.4). The highest variability in mean annual ring-

widths across the cores was recorded during 2000.

Figure 5-4 Time function of Mean Annual Ring-width of Deodar in GFD (1962-2011)

106

The analysis of time function response of mean intra-ring early wood formation of

Deodar for the period 1962-2011 showed a large variation in a linear pattern, with an

overall declining trend. The mean intra-ring early wood formation was 75.64±0.36% of

the mean annual wood formation. The largest mean intra-ring early wood formation

was 80.72±0.88% during 1962, while the smallest mean intra-ring early wood

formation was 71.34±1.51% during 2005. The time function response of mean intra-

ring early wood formation did not follow the pattern of mean annual ring-width. There

were ten years having mean intra-ring early wood formation higher than 78.0%, while

there were eight years having mean intra-ring early wood formation lower than 73.0%

(Figure 5.5). The highest variability in mean intra-ring early wood formation across the

cores was during 2002.

Figure 5-5 Time function of Mean Intra-ring Early Wood Formation (%) of Deodar in GFD (1962-2011)

107

The analysis of time function response of mean intra-ring late wood formation of

Deodar for the period 1962-2011 showed a large variation in a quadratic pattern, with

an overall significant increasing trend. The mean intra-ring late wood formation was

24.53±0.37% of the mean annual wood formation. The largest mean intra-ring late

wood formation was 28.66±2.01% during 2006, while the smallest mean intra-ring late

wood formation was 19.11±0.92% during 1962. The mean intra-ring late wood

formation showed an opposite trend to that of mean intra-ring early wood formation

and mean annual ring-width. The slope gradient of time function of mean intra-ring late

wood formation was relatively smaller compared to time function of mean intra-ring

early wood formation. The mean intra-ring late wood formation increased steadily over

1962-2011, except a slight decline after 2000. There were 25 years having mean intra-

ring late wood formation higher than 25.0%, while there were five years having mean

intra-ring late wood formation lower than 21.0% (Figure 5.6). The highest variability in

mean intra-ring late wood formation across the cores was recorded in 1980.

Figure 5-6 Time function of Mean Intra-ring Late Wood Formation (%) of Deodar in GFD (1962-2011)

108

The mean intra-ring early wood cell diameter of Deodar during 1962-2011 ranged from

33.38±1.10 µm to 37.59±3.15 µm, with a mean of 35.85±0.14 µm. The time function

response of mean intra-ring early wood cell diameter showed a quadratic behavior, with

no significant overall trend. The largest mean intra-ring early wood cell diameter was

recorded during 1963, while the smallest mean intra-ring early wood cell diameter was

recorded during 2002. The mean intra-ring early wood cell diameter decreased

gradually between 1965 and 1990, but increased gradually between 1995 and 2011.

There were eight years each having mean intra-ring early wood cell diameter larger

than 37.0 µm and smaller than 35.0 µm (Figure 5.7). The highest variability in mean

intra-ring early wood cell diameter across the cores was recorded in 1963.

Figure 5-7 Time function of Mean Intra-ring Early Wood Cell Diameter (µm) of Deodar in GFD (1962-2011)

109

The mean intra-ring early wood cell wall thickness of Deodar during 1962-2011 ranged

between 1.93±0.06 µm and 2.33±0.32 µm, with a mean of 2.08±0.01 µm. The time

function response of mean intra-ring early wood cell wall thickness showed a

polynomial pattern, with no significant overall trend. The largest mean intra-ring early

wood cell wall thickness was recorded during 1977, while the smallest mean intra-ring

early wood cell wall thickness was recorded during 1989. The mean intra-ring early

wood cell wall thickness showed an increasing trend during 1962-70 and 2005-2010,

but a decreasing trend during 1980-2004. There were four years having mean intra-ring

early wood cell wall thickness larger than 2.20 µm, while there were five years having

mean intra-ring early wood cell wall thickness smaller than 2.00 µm (Figure 5.8). The

highest variability in mean intra-ring early wood cell wall thickness was recorded in

1977.

Figure 5-8 Time function of Mean Intra-ring Early Wood Cell Wall Thickness (µm) of Deodar in GFD (1962-2011)

110

A microscopic picture (100×) of intra-ring early wood cell diameter and cell wall

thickness of Deodar is depicted below (Figure 5.9).

Figure 5-9 Intra-ring Early Wood Cell Diameter and Cell Wall Thickness of Deodar (100x)

111

The mean intra-ring late wood cell diameter of Deodar during 1962-2011 ranged


function response of mean intra-ring late wood cell diameter showed a quadratic

response, with an overall increasing trend. The largest mean intra-ring late wood cell

diameter was recorded during 2011, while the smallest mean intra-ring late wood cell

diameter was recorded during 1964. The mean intra-ring late wood cell diameter

decreased slightly during 1970-85, but increased steadily after 1990. There were nine

years having mean intra-ring late wood cell diameter larger than 16.0 µm, while there

were five years having mean intra-ring late wood cell wall diameter smaller than 15.0

µm (Figure 5.10). The highest variability in mean intra-ring late wood cell diameter

across the cores was recorded in 1979. The mean intra-ring late wood cell diameter was

significantly smaller compared to mean intra-ring early wood cell diameter.

Figure 5-10 Time function of Mean Intra-ring Late Wood Cell Diameter (µm) of Deodar in GFD (1962-2011)

112

The mean intra-ring late wood cell wall thickness of Deodar during 1962-2011 ranged


function response of mean intra-ring late wood cell wall thickness showed a small

variation in a quadratic pattern, with an overall increasing trend. The largest mean intra-

ring late wood cell wall thickness was recorded during 2003, while the smallest mean

intra-ring late wood cell wall thickness was recorded during 1964. The mean intra-ring

late wood cell wall thickness increased gradually during 1962-2003, but decreased

afterwards. There were 18 years having mean intra-ring late wood cell wall thickness

larger than 3.80 µm, while there were four years having mean intra-ring late wood cell

wall thickness smaller than 3.60 µm (Figure 5.11). The highest variability in mean

intra-ring late wood cell wall thickness across the cores was recorded in 1996. The

mean intra-ring late wood cell wall thickness showed a changing trend which was

opposite to that of mean intra-ring late wood cell diameter. Similarly, the changing

trend of mean intra-ring early cell wall thickness and mean late wood cell wall

thickness followed different patterns. The mean intra-ring late wood cell wall thickness

was significantly (p<0.05) larger compared to mean intra-ring early wood cell wall

thickness. The slope gradient of mean late wood cell wall thickness was considerably

higher compared to that of mean early wood cell wall thickness.

Figure 5-11 Time function of Mean Intra-ring Late Wood Cell Wall Thickness (µm) of Deodar in GFD (1962-2011)

113

A microscopic picture (100×) of intra-ring late wood cell diameter and cell wall

thickness of Deodar is depicted in Figure 5.12.

Figure 5-12 Intra-ring Late Wood Cell Diameter and Cell Wall Thickness of Deodar (100x)

5.4.2 Mathematical Expressions of Time Function of Ring-width, Intra- ring wood

Formation and Wood Cell Characteristics of Deodar

Mathematical expressions of time functions of mean annual ring-width, mean intra-ring

wood formation and wood cell characteristics of Deodar showed a mix of linear,

quadratic and polynomial behaviors. The mean annual ring-width, mean intra-ring early

wood formation, mean intra-ring late wood formation, mean intra-ring late wood cell

diameter and mean intra-ring late wood cell wall thickness showed highly significant

(p<0.01) changes with time. Conversely, temporal changes in mean intra-ring early

wood cell diameter and mean intra-ring early wood cell wall thickness were non-

significant (p>0.05). The R2 ranged between 0.07 and 0.77. The highest R2 value was

estimated for mean intra-ring early wood formation, followed by mean intra-ring late

wood formation. The lowest R2 value was calculated for mean intra-ring early wood

cell wall thickness, followed by mean intra-ring early wood cell diameter. The analysis

indicated that linear model had good fit for time function of mean intra-ring early

wood formation, quadratic model had good fit for mean annual ring-width, mean intra-

ring late wood formation and mean intra-ring late wood cell wall thickness, but poor fit

114

for mean intra-ring early wood cell wall diameter and mean intra-ring early wood cell

thickness. The polynomial model had good fit for time function of early wood cell wall

thickness (Table 5.3).

Table 5-3 Mathematical Expressions of Time Function of Ring-width and Intra-

ring wood Characteristics of Deodar in GFD (1962-2011)

Tree Growth

Characteristics

Mathematical Expressions R2 F(1,2,3), (48,47,46)*

(p)

Ring-width Y = 3357-3.350×X + 0.00084

× X2

0.69 52.08 (0.00)

Early wood formation Y = 380.3 - 0.1534 × X 0.77 162.17(0.00)

Late wood formation Y = -10277 + 10.22 × X

- 0.0025 × X2

0.74 67.55 (0.00)

Early wood cell

diameter

Y = 6343 - 6.343 × X +

0.0016 × X2

0.10 2.73 (0.075)


thickness

Y = -55641+ 83.95 × X -

0.0422 × X2 + 0.00001 × X3

0.07 1.15 (0.340)

Late wood cell

diameter

Y = 2567 - 2.580 × X +

0.0007 × X2

0.20 5.74 (0.006)

Late wood cell wall

thickness

Y = -813.2 + 0.8178 × X -

0.0002 × X2

0.56 29.51 (0.000)

* Values in parentheses in the top row are degree of freedom for linear, quadratic and

polynomial equations respectively. The (p) values indicate significance levels.

5.4.3 Decadal changes in Ring-width and Ring-wood characteristics of Deodar

A highly significant (F4, 15= 400.56; p<0.01) difference was recorded in mean decadal

ring-widths of Deodar, with a decreasing trend, during 1962-2011. The overall

difference in mean decadal ring-widths among the decades was significant (Tukey’s

HSD, CV 0.11; p=0.05). The largest mean decadal ring-width was 3.90±0.01 mm

during 1962-71, which was significantly different from mean decadal ring-width during

1972-81. The difference in mean decadal ring-widths of 1982-91 and 1992-01 was non-

115

significant (p>0.05). The smallest mean decadal ring-width was 2.61±0.01 mm during

2002-11 (Table 5.3).


intra-ring early wood formation of Deodar, with a decreasing trend, during 1962-2011.

The overall difference in mean decadal intra-ring early wood formation among the

decades was significant (Tukey’s HSD, CV 1.49; p=0.05). The largest mean decadal

intra-ring early wood formation was 79.19±0.41% during 1962-71, which was

significantly higher compared to mean decadal intra-ring early wood formation during

1972-81. The smallest mean decadal intra-ring early wood formation was 73.42±0.44%

during 2002-11. The difference in mean decadal intra-ring early wood formation among

decades, 1992-01 and 2001-11, was non-significant. The mean decadal intra-ring early

wood formation followed the pattern of mean decadal ring-widths (Table 5.4).


intra-ring late wood formation of Deodar, with an increasing trend, during 1962-2011.

The overall difference in mean decadal intra-ring late wood formation among the

decades was significant (Tukey’s HSD, CV 1.15; p=0.05). The trend of mean decadal

intra-ring late wood formation followed a pattern opposite to mean decadal intra-ring

ring-width and mean decadal intra-ring early wood formation. The largest mean

decadal intra-ring late wood formation was 27.10±0.32% during 2002-11, which was

not significantly different from 1992-2001. The smallest mean decadal intra-ring late

wood formation was 21.02±0.33% which was significantly lower compared to 1972-81

(Table 5.4).

116

Table 5-4 Mean Decadal Ring-width and Ring-wood Characteristics of Deodar

in GFD (1962-2011)

Decades

/CV

Tree ring Characteristics

RW±SE

(mm)

EW±SE

(%)

LW±SE

(%)

EWCD

±SE

(µm)

EWCWT

±SE

(µm)

LWCD

±SE

(µm)

LWCWT

±SE

(µm)

1962-71 3.90±

0.01 a

79.19±

0.41 a

21.02±

0.33 d

37.16±

0.14 a

1.98±

0.01c

15.54±

0.11b

3.57±

0.01d

1972-81 3.10±

0.01 b

77.03±

0.27 b

23.24±

0.27 c

35.08±

0.23 b

2.05±

0.02 bc

15.31±

0.09 bc

3.73±

0.01c

1982-91 2.92±

0.01 c

75.33±

0.21 c

25.08±

0.09 b

35.08±

0.19 b

2.09±

0.01ab

15.63±

0.13 b

3.78±

0.01bc

1992-01 2.87±

0.05 c

73.50±

0.31 d

26.38±

0.25 a

35.37±

0.33 b

2.10±

0.00 ab

14.83±

0.02 c

3.84±

0.01b

2002-11 2.61±

0.01 d

73.42±

0.44 d

27.10±

0.32 a

35.13±

0.31b

2.15±

0.02 a

16.83±

0.23 a

3.98±

0.03 a

CV 0.11 1.49 1.15 1.09 0.06 0.59 0.07

Mean values within a column sharing same alphabets are not significantly different (Tukey’s

HSD, p=0.05); RW= Ring-width; EW= Early wood; LW= Late wood; EWCD= Early wood cell

diameter; EWCW= Early wood cell wall thickness; LWCD= Late wood cell diameter; LWCW=

Late wood cell wall thickness S


intra-ring early wood cell diameter of Deodar, with an overall decreasing trend, during

1962-2011. The difference in mean decadal intra-ring early wood cell diameter among

the decades was significant (Tukey’s HSD, CV 1.09; p=0.05). The largest mean decadal

intra-ring early wood cell diameter was 37.16±0.14 µm during 1962-71, which was

significantly larger compared to 1972-81. The smallest mean decadal intra-ring early

wood cell diameter was 35.08±0.19 µm during 1982-91. The mean decadal intra-ring

early wood cell diameter did not vary significantly among the decades, 1972-81, 1982-

91, 1992-01 and 2002-11 (Table 5.4).

117


intra-ring early wood cell wall thickness of Deodar, with an increasing trend, during

1962-2011. The difference in mean decadal intra-ring early wood cell wall thickness

among the decades was significant (Tukey’s HSD, CV 0.06; p= 0.05). The largest mean

decadal intra-ring early wood cell wall thickness was 2.15± 0.02 µm during 2002-11,

which was not significantly higher compared to 1982-91 and 1992-01. The smallest

mean decadal intra-ring early wood cell wall thickness was 1.98±0.01 µm during 1962-

71, which was not significantly different from the corresponding values during 1972-81

(Table 5.4).


intra-ring late wood cell diameter of Deodar, with an overall increasing trend, during

1962-2011. The difference in mean decadal intra-ring late wood cell diameter among

the decades was significant (Tukey’s HSD, CV 0.59; p= 0.05). The largest mean

decadal intra-ring late wood cell diameter was 16.83±0.23 µm during 2002-11, followed

by 15.63±0.13 µm during 1982-91. The smallest mean decadal intra-ring late wood cell

diameter was 14.83±0.02 µm during 1992-01. The mean decadal intra-ring late wood

cell diameter did not vary significantly between 1962-71 and 1982-91(Table 5.4).


intra-ring late wood cell wall thickness of Deodar, with an increasing trend, during

1962-2011. The difference in mean decadal intra-ring late wood cell wall thickness

among the decades was significant (Tukey’s HSD, CV 0.07; p= 0.05). The trend of

mean decadal intra-ring late wood cell wall thickness was similar to mean decadal intra-

ring late wood formation. The largest mean decadal intra-ring late wood cell wall

thickness was 3.98±0.03 µm during 2001-11, which was significantly larger compared

to 1992-2001. The smallest mean decadal intra-ring late wood cell wall thickness was

3.57±0.01 µm during 1962-71 (Table 5.4).

5.4.4 Correlation between Ring-width and Ring-wood Characteristics of Deodar

The analysis of Pearson Correlation Coefficients matrix of Deodar growth data for

1962-2011 revealed a highly significant (p<0.01) and positive correlation of mean

annual ring-width with mean intra-ring early wood formation (r = 0.90) and mean intra-

118

ring early wood cell diameter (r = 0.78), but highly significant (p<0.01) and negative

with mean intra-ring early wood cell wall thickness (r = -0.87), mean intra-ring late

wood formation (r = -0.93) and mean intra-ring late wood cell wall thickness (r = -0.93).

The correlation was, however, non-significant (p>0.05) and negative with mean intra-

ring late wood cell diameter (r = -0.31). The correlation of mean intra-ring early wood

formation was highly significant (p<0.01) and positive with mean intra-ring early wood

cell diameter (r = 0.62), but highly significant (p<0.01) and negative with mean intra-

ring early wood cell wall thickness (r = -0.82), mean intra-ring late wood formation (r =

-0.95) and mean intra-ring late wood cell wall thickness (r = -0.86). The correlation of

mean intra-ring early wood formation was non-significant (p>0.05) and negative with

mean intra-ring late wood cell diameter (r = -0.19). The correlation of mean intra-ring

early wood cell diameter was highly significant (p<0.01) and negative with mean intra-


-0.63) and mean intra-ring late wood cell wall thickness (r = 0.61), but non-significant

(p>0.05) and negative with mean intra-ring late wood cell diameter (r = -0.04). The

correlation of mean intra-ring early cell wall thickness was highly significant (p<0.01)

and positive with mean intra-ring late wood formation (r = 0.88) and mean intra-ring

late wood cell wall thickness (r = 0.88), but non-significant (p>0.05) and positive with

mean intra-ring late wood cell diameter (r = 0.40). The correlation of mean intra-ring

late wood formation was highly significant (p<0.01) and positive with mean intra-ring

late wood cell wall thickness (r = 0.91), but non-significant (p>0.05) and positive with

mean intra-ring late wood cell wall diameter (r = 0.29). The correlation of mean intra-

ring late wood cell diameter was significant (p<0.05) and positive with mean intra-ring

late wood cell wall thickness (r = 0.44) (Table 5.5).

119

Table 5-5 Correlation Coefficients Matrix between Ring-width and Ring-wood

Characteristics of Deodar in GFD (1962-2011)

Ring-wood

Characteristics

RW EW EWCD EWCWT LW LWCD

EW 0.90**

(0.000)

EWCD 0.78**

(0.000)

0.62**

(0.003)

EWCWT - 0.87**

(0.000)

- 0.82**

(0.000)

- 0.57**

(0.008)

LW - 0.93**

(0.000)

- 0.95**

(0.000)

- 0.63**

(0.003)

0.88**

(0.000)

LWCD - 0.31ns

(0.191)

- 0.19 ns

(0.419)

- 0.04 ns

(0.870)

0.40 ns

(0.079)

0.29 ns

(0.217)

LWCWT - 0.93**

(0.000)

- 0.86**

(0.000)

- 0.61**

(0.004)

0.88**

(0.000)

0.91**

(0.000)

0.44*

(0.05)

Values in ( ) are p-values; * Significant at 95%; ** Significant at 99%; ns= Non-significant; RW=

Ring-width; EW= Early wood; EWCD= Early wood cell diameter; EWCW= Early wood cell wall

thickness; LW= Late wood; LWCD= Late wood cell diameter; LWCW= Late wood cell wall

thickness

5.4.5 Impacts of Climate Change on Ring-width of Deodar

The impacts of climate change on mean annual ring-widths of Deodar during 1962-2011

were assessed using response functions of ring-widths with temperature (maximum and

minimum) and precipitation, both on annual and seasonal basis.

120

The mean annual maximum temperature showed a significant (F2, 47= 4.78; p<0.05)

impact on mean annual ring-widths of Deodar, exhibiting a quadratic pattern, with an

overall declining trend. Most of the mean annual ring-width responses were observed

between 16.0˚C and 17.0˚C. The mean annual maximum temperature below 15.5˚C

showed positive impact on mean annual ring-width. Similarly, the mean annual

maximum temperature above 17.0˚C also showed slightly positive impact on mean

annual ring-width (Figure 5.13).

Figure 5-13 Impact of Mean Annual Maximum Temperature on Ring-width of Deodar in GFD (1962-2011)

121

The mean annual minimum temperature showed a highly significant (F2, 47= 6.97;

p<0.05) impact on mean annual ring-widths of Deodar, exhibiting a quadratic pattern.

Relatively better growth response (mean annual ring-width >3.50 mm) was observed

between 5.0˚C and 5.5˚C. A positive impact of minimum temperature was also observed

above 7.0˚C (Figure 5.14).

Figure 5-14 Impact of Mean Annual Minimum Temperature on Ring-width of Deodar in GFD (1962-2011)

122

The annual precipitation showed a significant (F1, 48 = 5.60; p<0.05) impact on mean

annual ring-widths of Deodar. The mean annual ring-width decreased with increasing

annual precipitation. A large variation in mean annual ring-width response was noted

across the observed range of precipitation, with growth response more clustered around

annual precipitation range of 800-1100 mm (Figure 5.15).

Figure 5-15 Impact of Annual Precipitation on Ring-width of Deodar in GFD (1962-2011)

123

The 1-way analysis of variance of mean annual ring-width of deodar with mean decadal

precipitation showed that the largest mean annual ring-width was 3.40±0.10 mm when

annual precipitation ranged between 600 mm and 700 mm. The smallest mean annual

ring-width was 2.78±0.09 mm when annual precipitation was higher than 1000 mm

(Table 5.6).

Table 5-6 Precipitation and Ring-width of Deodar in GFD (1962-2011)

Precipitation range

(mm/annum)

Mean Annual Ring-width

(mm)

Standard Error (SE)

501-600 3.46* 0.00

601-700 3.40 0.10

701-800 3.28 0.24

801-900 3.10 0.15

901-1000 3.13 0.12

>1001 2.78 0.09

* Single value

124

The mean spring maximum temperature showed a significant (F1, 48= 3.35; p<0.05)

impact on mean annual ring-widths of Deodar. The mean annual ring-width declined

with increasing mean annual maximum temperature (Figure 5.16).

Figure 5-16 Impact of Mean Spring Maximum Temperature on Ring-width of Deodar in GFD (1962-2011)

125

The mean spring minimum temperature showed a non-significant (F1, 48 = 3.13; p>0.05)

negative impact on mean annual ring-widths of Deodar. The mean annual ring-width

decreased with increasing mean spring minimum temperature. Relatively better growth

response was observed between 2.0˚C and 4.0˚C with mean annual ring-width larger

than 3.0 mm (Figure 5.17).

Figure 5-17 Impact of Mean Spring Minimum Temperature on Ring-width of Deodar in GFD (1962-2011)

126

The spring precipitation showed a non-significant (F2, 47=0.49; p>0.05) positive impact

on mean annual ring-widths of Deodar. There was an overall increase in mean annual

ring-width with increasing spring precipitation (Figure 5.18).

Figure 5-18 Impact of Spring Precipitation on Ring-width of Deodar in GFD (1962-2011)

127

The mean summer maximum temperature showed a non-significant (F3, 46= 0.31;

p>0.05) impact on mean annual ring-widths of Deodar. The response exhibited a

polynomial pattern (Figure 5.19).

Figure 5-19 Impact of Mean Summer Maximum Temperature on Ring-width of Deodar in GFD (1962-2011)

128

The mean summer minimum temperature showed a non-significant (F3, 46= 0.73;

p>0.05) impact on mean annual ring-widths of Deodar. The overall mean annual ring-

width response to summer minimum temperature was negative and exhibited a

quadratic function. Relatively better growth response (mean annual ring-width >3.25

mm) was observed between 9.0˚C and 12.0˚C (Figure 5.20).

Figure 5-20 Impact of Mean Summer Minimum Temperature on Ring-width of Deodar in GFD (1962-2011)

129

The summer precipitation showed a non-significant (F2, 47=1.39; p>0.05) impact on

mean annual ring-widths of Deodar. The mean annual ring-width increased with

increasing precipitation up to 150 mm, followed by dampening and declining pattern,

with a quadratic function (Figure 5.21). The impact of summer precipitation showed a

contrasting effect on mean annual ring-width compared to spring precipitation.

Figure 5-21 Impact of Summer Precipitation on Ring-width of Deodar in GFD (1962-2011)

130

The mean monsoon maximum temperature showed a non-significant (F2, 47=1.04;

p>0.05) impact on mean annual ring-widths of Deodar. The mean annual ring-width

decreased with increasing mean monsoon maximum temperature, except a small

increase during 2000-11. Relatively better growth response (mean annual ring-width

>3.00 mm) was measured at mean monsoon maximum temperature between 22.0˚C and

24˚C (Figure 5.22).

Figure 5-22 Impact of Mean Monsoon Maximum Temperature on Ring-width of Deodar in GFD (1962-2011)

131

The mean monsoon minimum temperature showed a non-significant (F2, 47= 0.76;

p>0.05) impact on mean annual ring-widths of Deodar. The overall impact of

increasing mean monsoon minimum temperature was positive, with a trend contrasting

to that of mean monsoon maximum temperature. Relatively better growth response

(ring-width >3.50 mm) was observed at 13.5˚C (Figure 5.23).

Figure 5-23 Impact of Mean Monsoon Minimum Temperature on Ring-width of Deodar in GFD (1962-2011)

132

The monsoon precipitation showed a non-significant (F1, 48=1.39; p>0.05) impact on

mean annual ring-widths of Deodar. The mean annual ring-width decreased with

increasing monsoon precipitation. Relatively better growth response (mean annual ring-

width >3.5 mm) was observed when monsoon precipitation was ranged between 170

mm/season and 350 mm/season (Figure 5.24). The impact of monsoon precipitation

followed closely the pattern of impact of mean monsoon maximum temperature,

however, the slope gradient of monsoon precipitation was relatively higher compared to

that of mean monsoon maximum temperature.

Figure 5-24 Impact of Monsoon Precipitation on Ring-width of Deodar in GFD (1962-2011)

133

The mean autumn maximum temperature showed a non-significant (F2, 47= 0.82;

p>0.05) impact on mean annual ring-widths of Deodar. The mean annual ring-width

responded positively to increasing autumn maximum temperature. The mean annual

ring-widths were found more clustered around 16.0˚C. Relatively better growth

response (mean annual ring-width >3.25 mm) was observed at 16.0˚C to 17˚C (Figure

5.25).

Figure 5-25 Impact of Mean Autumn Maximum Temperature on Ring-width of Deodar in GFD (1962-2011)

134

The mean autumn minimum temperature showed a highly significant (F2, 47 =5.11;

p<0.01) impact on mean annual ring-widths of Deodar. The mean annual ring-width

decreased slightly with increasing mean autumn minimum temperature. Relatively

better growth response (mean annual ring-width >3.25 mm) was observed at minimum

temperature ranged from 2.0˚C to 3.0˚C (Figure 5.26).

Figure 5-26 Impact of Mean Autumn Minimum Temperature on Ring-width of Deodar in GFD (1962-2011)

135

The autumn precipitation showed a non-significant (F1, 48= 0.76; p>0.05) impact on

mean annual ring-widths of Deodar. The mean annual ring-width decreased marginally

with increasing autumn precipitation. Relatively better growth response (mean annual

ring-width >3.50 mm) was observed when autumn precipitation ranged from 25.0

mm/season to 75.0 mm/season (Figure 5.27).

Figure 5-27 Impact of Mean Autumn Precipitation on Ring-width of Deodar in GFD (1962-2011)

136

The mean winter maximum temperature showed a highly significant (F1, 48= 10.33;


decreased with increasing mean winter maximum temperature. Relatively better growth

response (mean annual ring-width >3.50 mm) was observed at 4.0˚C to 7.0˚C (Figure

5.28).

Figure 5-28 Impact of Mean Winter Maximum Temperature on Ring-width of Deodar in GFD (1962-2011)

137

The mean winter minimum temperature showed a highly significant (F1, 48= 18.02;


decreased with increasing mean winter minimum temperature. Relatively better growth

response (mean annual ring-width >3.50 mm) was observed at - 4.0˚C and - 2.0˚C

(Figure 5.29).

Figure 5-29 Impact of Mean Winter Minimum Temperature on Ring-width of Deodar in GFD (1962-2011)

138

The winter precipitation showed a non-significant (F3, 46=1.32; p>0.05) impact on mean

annual ring-widths of Deodar. The mean annual ring-width showed a polynomial

response. Relatively better growth was found (mean annual ring-width >3.0 mm) when

mean winter precipitation ranged from 50.0 mm/season to 250.0 mm/season (Figure

5.30).

Figure 5-30 Impact of Winter Precipitation on Ring-width of Deodar in GFD (1962-2011)

139

5.4.6 Mathematical Expressions of Impacts of Temperature and Precipitation on

Ring-width of Deodar

Mathematical expressions of impacts of climate change on mean annual ring-widths of

deodar showed linear to polynomial patterns (Table 5.7). The mean annual minimum

temperature showed higher impact on mean annual ring-width (R2 = 0.23) compared to

mean annual maximum temperature (R2 = 0.17). Among the seasons, mean winter

minimum temperature showed the highest impact (R2 = 0.27), followed by mean winter

maximum temperature (R2 = 0.18) and mean autumn minimum temperature (R2 = 0.18).

The impacts of temperature, both maximum and minimum during other seasons, were

marginal. The impacts of mean annual precipitation and mean monsoon precipitation

were significant. The impact of mean monsoon precipitation was higher (R2 = 0.14)

compared to mean annual precipitation (R2 = 0.10). There was no significant difference

in the impacts of other seasonal precipitation.

Table 5-7 Mathematical Expressions of Impacts of Temperature and Precipitation

on Ring-width of Deodar in GFD (1962-2011)

Climate Parameters Mathematical Expressions R2 F(1,2,3), (48,47,46)*

(p)

Mean Max. Temp. Y = 122.3 - 4.31 × X + 0.4292 ×

X2

0.17 4.78 (0.013)

Mean Min. Temp. Y = 19.92 - 5.216 × X + 0.3985

× X2

0.23 6.97 (0.002)

Mean Precipitation Y = 4.150 - 0.0012 × X 0.10 5.60 (0.022)

Spring Max. Temp. Y = 3.951 - 0.06334 × X 0.02 1.16 (0.287)

Spring Min. Temp. Y = 3.586 - 0.1200 × X 0.06 3.13 (0.083)

Spring Precipitation Y = 3.616 - 0.0053 × X +

0.00001 × X2

0.02 0.49 (0.614)

Summer Max. Temp. Y = -324.9 + 42.97 × X -1.871 ×

X2 + 0.0271×X3

0.02 0.31 (0.815)

Summer Min. Temp. Y = 11.83 - 1.432 × X + 0.0581

× X2

0.03 0.80 (0.457)

140

Summer Precipitation Y = 1.831 + 0.019 × X - 0.0001

× X2

0.06 1.39 (0.258)

Monsoon Max. Temp. Y = 78.55 - 6.277 × X + 0.1304

× X2

0.04 1.04 (0.360)

Monsoon Min. Temp. Y = 42.98 - 6.082 × X + 0.232 ×

X2

0.03 0.76 (0.475)

Monsoon

Precipitation

Y = 4.498 - 0.007 × X + 0.0001

× X2

0.14 3.80 (0.030)

Autumn Max. Temp. Y = 30.03 - 3.377 × X + 0.1056

× X2

0.03 0.82 (0.448)

Autumn Min. Temp. Y = 9.774 - 3.186 × X + 0.3662

× X2

0.18 5.11 (0.010)

Autumn Precipitation Y = 3.223 - 0.003 × X 0.02 0.76 (0.388)

Winter Max. Temp. Y = 4.757 - 0.2470 × X 0.18 10.33 (0.002)

Winter Min. Temp. Y = 2.551 - 0.2648 × X 0.27 18.02 (0.000)

Winter Precipitation Y = 3.983 - 0.02 × X + 0.0001 ×

X 2- 0.00001 × X3

0.08 1.32 (0.281)

* Values in parentheses in the top row are degree of freedom for linear, quadratic and

polynomial equations respectively. The (p) values indicate significance levels.

5.5 Ring-width and Ring-wood Characteristics of Blue pine

5.5.1 Time function analysis of Ring-width and Ring-wood Characteristics of Blue

pine

The time function responses of ring-width and ring-wood characteristics of Blue pine

were studied through regression analysis at 95% Confidence Interval (CI) and

Prediction Interval (PI), using the standardized data. The trend in the time series was

assessed by applying Mann Kendall test with Normal Approximation and using Sen’s

Slope Estimator method. A summary of the trend analysis is reproduced in Table 5.8.

141

Table 5-8 Trend Analysis of Ring-width and Ring-wood Characteristics of Blue

pine at GFD (1962-2011)

Increasing trend No trend Decreasing trend

The time function analysis of ring-width and ring-wood characteristics of Blue pine for



formation, significant (p<0.05) downward trend in early wood cell wall thickness and

no trend in early wood cell diameter, late wood cell diameter and late wood cell wall

thickness.


Characteristics

Z-Value p-Value Trend Sen’s Slope

Upward Downward

Ring-width - 4.718 1.000 0.000 - 0.023

Early wood formation - 4.366 1.000 0.000 - 0.092

Late wood formation 4.316 0.000 1.000 0.090

Early wood cell diameter 0.686 0.246 0.754 0.007


thickness

- 2.141 0.984 0.016

- 0.002

Late wood cell diameter 0.234 0.407 0.593 0.001

Late wood cell wall

thickness

- 1.539 0.938 0.061

- 0.002

142

The analysis of time function response of mean annual ring-widths of Blue pine for the

period 1962-2011 indicated a large variation in mean annual ring-widths across the

cores in a quadratic pattern, with an overall decreasing trend. The mean annual ring-

width ranged from 1.85±0.27 to 3.33±0.31 mm, with a mean value of 2.54±0.15. The

largest mean annual ring-width was recorded during 1962, while the smallest mean

annual ring-width was recorded during 2002. The mean annual ring-width was steadily

higher during 1960s, but decreased gradually during 1978-86, and increased again

during 1990s followed by a considerable decrease. There were 13 years having mean

annual ring-width larger than 3.0 mm, while there were nine years having mean annual

ring-width smaller than 2.0 mm (Figure 5.31). The highest variability in mean annual

ring-width across the cores was recorded during 1971.

Figure 5-31 Time function of Mean Annual Ring-width of Blue pine in GFD (1962-2011)

143

The analysis of time function response of mean intra-ring early wood formation of Blue

pine for the period 1962-2011 showed a large variation in a quadratic pattern, with a

declining trend. The mean intra-ring early wood formation was 76.67±0.21% of the

mean annual wood formation. The largest mean intra-ring early wood formation was

78.87±1.51% during 1973, while the smallest early wood formation was 72.12±1.80%

during 2009. The time function response of mean intra-ring early wood formation was

almost similar to that of mean annual ring-width, but with a lower slope gradient. There

were nine years having mean intra-ring early wood formation higher than 78.0%, while

there were ten years having mean intra-ring early wood formation lower than 73.0%

(Figure 5.32). The highest variability in mean intra-ring early wood formation across

the cores was recorded in 1971.

Figure 5-32 Time function of Mean Intra-ring Early Wood Formation (%) of Blue pine in GFD (1962-2011)

144

The analysis of time function response of mean intra-ring late wood formation of Blue

pine for the period 1962-2011 showed a large variation in a quadratic pattern, with an

overall increasing trend. The mean intra-ring late wood formation was 23.37±0.20% of

the mean annual wood formation. The largest mean intra-ring late wood formation was

28.97±1.95% during 2002, while the smallest mean intra-ring late wood formation was

20.53±1.42% during 1976. The time function response of mean intra-ring late wood

formation exhibited opposite trend to that of mean intra-ring early wood formation. The

mean intra-ring late wood formation increased steadily from 1962 onwards, except a

slight decline during 1990s and 2005-08. There were 20 years having mean intra-ring

late wood formation higher than 25.0%, while there were two years having mean intra-

ring late wood formation lower than 21.0% (Figure 5.33). The highest variability in

intra-ring late wood formation across the cores was recorded in 1998.

Figure 5-33 Time function of Mean Intra-ring Late Wood Formation (%) of Blue pine in GFD (1962-2011)

145

The mean intra-ring early wood cell diameter of Blue pine during 1962-2011 ranged

from 40.05±1.48 µm to 45.07±1.08 µm, with a mean of 42.57±0.16 µm. The time

function response of mean intra-ring early wood cell diameter exhibited a polynomial

behavior, with no overall significant trend. The largest mean intra-ring early wood cell

diameter was recorded during 1975, while the smallest mean intra-ring early wood cell

diameter was recorded during 1964. The mean intra-ring early wood cell diameter

increased during 1970-80 and after 2003, but decreased during 1990-2000. There were

46 years having mean intra-ring early wood cell diameter larger than 41.0 µm, while

there were four years having mean intra-ring early wood cell diameter between 40.0

and 41.0 µm (Figure 5.34). The highest variability in mean intra-ring early wood cell

diameter across the cores was recorded in 2009.

Figure 5-34 Time function of Mean Intra-Ring Early Wood Cell Diameter (µm) of Blue pine in GFD (1962-2011)

146

The mean intra-ring early wood cell thickness of Blue pine during 1962-2011 ranged

from 2.25±0.06 µm to 2.57±0.19 µm, with a mean of 2.38±0.01 µm. The time function

response of mean intra-ring early wood cell wall thickness showed a quadratic

behavior, with an overall decreasing trend. The largest mean intra-ring early wood cell

wall was recorded during 1979, while the smallest mean intra-ring early wood cell wall

was recorded during 1964. The mean intra-ring early wood cell wall thickness

decreased slightly during 1985-88. There were five years having mean intra-ring early

wood cell wall thickness larger than 2.5 µm, while there were 11 years having mean

intra-ring early wood cell wall thickness smaller than 2.30 µm (Figure 5.35). The

highest variability in mean intra-ring early wood cell wall thickness was recorded in

1982.

Figure 5-35 Time function of Mean Intra-ring Early Wood Cell Wall Thickness (µm) of Blue pine

147


thickness of Blue pine is depicted in Figure 5.36.

Figure 5-36 Intra-ring Early Wood Cell Diameter and Cell Wall Thickness of Blue pine (100x)

148

The mean intra-ring late wood cell diameter of Blue pine during 1962-2011 ranged


function response of mean intra-ring late wood cell diameter showed a quadratic

behavior, with no overall significant trend. The largest mean intra-ring late wood cell

diameter was recorded during 1966, while the smallest mean intra-ring late wood cell

diameter was recorded during 1990. The mean intra-ring late wood cell diameter

decreased during 1975-95, but increased gradually from 1995 onwards. There were

eight years having mean intra-ring late wood cell diameter larger than 19.0 µm, while

there were 13 years having mean intra-ring late wood cell wall diameter smaller than

18.0 µm (Figure 5.37). The highest variability in mean intra-ring late wood cell

diameter across the cores was recorded in 1991. The mean intra-ring late wood cell

diameter was significantly (p<0.05) smaller compared to mean intra-ring early wood

cell diameter.

Figure 5-37 Time function of Mean Intra-ring Late Wood Cell Diameter (µm) of Blue pine in GFD (1962-2011)

149

The mean intra-ring late wood cell wall thickness of Blue pine during 1962-2011

ranged from 3.90±0.13 µm to 4.30±0.13 µm, with a mean of 4.07±0.01 µm. The time

function response of mean intra-ring late wood cell thickness showed a quadratic

behavior, with no overall significant trend. The largest mean intra-ring late wood cell

wall thickness was recorded during 1976, while the smallest mean intra-ring late wood

cell wall thickness was recorded during 1999. The highest variability in mean intra-ring

late wood cell wall thickness across the cores was recorded in 1987. The mean intra-

ring late wood cell wall thickness showed a slightly declining trend during 1970-2000.

There were seven years having mean intra-ring late wood cell wall thickness larger than

4.3 µm, while there were 12 years having mean intra-ring late wood cell wall thickness

smaller than 4.0 µm (Figure 5.38). The highest variability in mean intra-ring late wood

cell wall thickness across the cores was recorded in 1987. The mean intra-ring late

wood cell wall thickness was significantly (p<0.05) higher compared to mean intra-ring

early wood cell wall thickness.

Figure 5-38 Time function of Mean Intra-ring Late Wood Cell Wall Thickness (µm) of Blue pine in GFD (1962-

2011)

150


thickness of Blue pine is depicted in Figure 5.39

Figure 5-39Intra-ring Late Wood Cell Diameter and Cell Wall Thickness of Blue pine (100x)

151

5.5.2 Mathematical Expressions of Time Function of Ring-width, Intra-ring wood

Formation and Cell Characteristics of Blue pine

Mathematical expressions of mean annual ring-width, mean intra-ring wood formation

and cell characteristics of Blue pine showed a mix of quadratic and polynomial

behaviors of time function. The mean annual ring-width, mean intra-ring early wood

formation and mean intra-ring late wood formation showed highly significant (p<0.01)

changes with time. The mean intra-ring early wood cell diameter, mean intra-ring early

wood cell wall thickness and mean intra-ring late wood cell diameter showed

significant (p<0.05) temporal response. Conversely, temporal change in mean intra-ring

late wood cell wall thickness was non-significant (p>0.05). The R2 ranged between 0.06

and 0.51. The highest R2 value was calculated for mean intra-ring annual ring-width,

while the lowest R2 value was calculated for mean intra-ring late wood intra-ring cell

wall thickness. The models used for time function response indicated good fit of the

models for mean annual ring-width, mean intra-ring wood formation and mean wood

cell characteristics except mean intra-ring late wood cell wall thickness (Table 5.9).


ring Wood Characteristics of Blue pine in GFD (1962-2011)

Tree Growth

Characteristics

Mathematical Expressions R2 F (2,3) (47,46) * (p)

Ring-width Y = 2421 - 2.412 × X +

0.0006 × X2

0.51 24.73 (0.000)

Early wood formation Y = 9697 - 9.591 × X +

0.0024 × X2

0.41 16.48 (0.000)

Late wood formation Y = -12831 + 12.85 × X-

0.003 × X2

0.42 16.89 (0.000)

Early wood cell diameter Y = -1375115 + 2075 × X -

1.044 × X2 + 0.0002 × X3

0.17 3.09 (0.036)


thickness

Y = 172.9 - 0.1698 × X +

0.00004 × X2

0.12 3.26 (0.047)

Late wood cell

diameter

Y = 4400 - 4.413 × X +

0.0011 × X2

0.17 4.84 (0.012)

Late wood cell wall

thickness

Y = 269.6 -0.2660 × X +

0.0001 × X2

0.06 1.51 (0.230)

* Values in parentheses in the top row are degree of freedom for quadratic and polynomial


152

5.5.3 Decadal changes in Ring-width and Ring-wood Characteristics of Blue pine


ring-widths of Blue pine, with a decreasing trend, during 1962-2011. The overall

difference in mean ring-widths among the decades was significant (Tukey’s HSD, CV

0.14; p=0.05). The largest mean decadal ring-width was 3.40±0.04 mm during 1962-71,

which was significantly different from 1972-81. The difference in mean decadal ring-

widths of 1972-81 and 1982-91 was non-significant (p>0.05). The smallest mean

decadal ring-width was 2.06±0.02 mm during 2002-1 (Table 5.10).


intra-ring early wood formation of Blue pine, with a decreasing trend, during 1962-

2011. The overall difference in mean decadal intra-ring early wood formation among

the decades was significant (Tukey’s HSD, CV 0.14; p=0.05). The largest mean

decadal intra-ring early wood formation was 77.37±0.32% during 1962-71, which was

significantly higher compared to mean decadal intra-ring early wood formation during


during 2002-11. The difference in mean decadal intra-ring early wood formation among

decades, 1982-91, 1992-2001, 2001-2011 was non-significant (p>0.05) (Table 5.10).

153

Table 5-10 Mean Decadal Ring-width and Ring-Wood Characteristics of Blue pine

in GFD (1962-2011)

Mean values within a column sharing same alphabets are not significantly different (Tukey’s HSD,

p=0.05); RW= Ring-width; EW= Early wood; LW= Late wood; EWCD= Early wood cell diameter;

EWCW= Early wood cell wall thickness; LWCD= Late wood cell diameter; LWCW= Late wood cell wall

thickness


intra-ring late wood formation of Blue pine, with an increasing trend, during 1962-

2011. The overall difference in mean decadal intra-ring late wood formation among the

decades was significant (Tukey’s HSD, CV 1.02; p=0.05). The trend of mean decadal

intra-ring late wood formation followed a pattern opposite to mean decadal ring-width

and mean decadal intra-ring early wood formation. The largest mean decadal intra-ring

late wood formation was 26.09±0.36 mm during 2002-2011, which was not

significantly different from 1992-2001. The smallest mean decadal intra-ring late wood

formation was 21.92±0.04 mm. The mean decadal intra-ring late wood formation

during 1962-71 was significantly (p<0.05) lower compared to 1972-81 (Table 5.10).


intra-ring early wood cell diameter of Blue pine, with an overall increasing trend,

Decades

/CV

Tree-ring Characteristics

RW±SE

(mm)

EW±SE

(%)

LW±SE

(%)

EWCD

±SE

(µm)

EWC

WT±SE

(µm)

LWCD

±SE

(µm)

LWCWT

±SE

(µm)

1962-71 3.40±

0.04 a

77.37±

0.32 a

21.92±

0.04 d

41.56±

0.08 c

2.51±

0.01 a

17.88±

0.01 c

4.33±

0.04 a

1972-81 2.58±

0.03 b

77.04±

0.36 ab

22.92±

0.16 c

42.10±

0.19b c

2.43±

0.00 b

17.82±

0.02 c

4.10±

0.03 b

1982-91 2.57±

0.04 b

75.32±

0.45 bc

24.31±

0.28 b

42.44±

0.18 b

2.38±

0.01 c

18.07±

0.16 c

4.05±

0.02 bc

1992-01 2.21±

0.03 c

73.65±

0.48 c

25.65±

0.20 a

42.62±

0.10 b

2.35±

0.01 d

18.52±

0.09 b

4.02±

0.01 bc

2002-11 2.06±

0.02 d

73.63±

0.53 c

26.09±

0.36 a

44.02±

0.26 a

2.33±

0.00 d

19.39±

0.02 a

3.92±

0.05 c

CV 0.14 4.37 1.02 0.77 0.02 0.36 0.15

154

during 1962-2011. The difference in mean decadal intra-ring early wood cell diameter

among the decades was significant (Tukey’s HSD, CV 0.77; p=0.05). The largest mean

decadal intra-ring early wood cell diameter was 44.02±0.26 µm during 2002-11, which

was significantly higher compared to 19912001. The smallest mean decadal intra-ring

early wood cell diameter was 41.56±0.08 µm during 1962-71. The mean decadal intra-

ring early wood cell diameter did not vary significantly between decades: 1962-71 and

1972-81, and 1982-91 and 1992-01(Table 5.10).


intra-ring early wood cell wall thickness of Blue pine, with a decreasing trend, during


among the decades was significant (Tukey’s HSD, CV 0.02; p= 0.05). In contrast to

mean decadal intra-ring early wood cell diameter, mean decadal intra-ring early wood

cell wall thickness decreased over time. The largest mean decadal intra-ring early wood

cell wall thickness was 2.51± 0.01 µm, which was significantly higher compared to

1982-91. The smallest mean decadal intra-ring early wood cell wall thickness was

2.33± 0.00 µm during 2002-11. The difference in mean decadal intra-ring early wood

cell wall thickness did not vary significantly between decades 1992-01 and 2002-

11(Table 5.10).


intra-ring late wood cell diameter of Blue pine, with a decreasing trend, during 1962-

2011. The difference in mean decadal intra-ring late wood cell diameter among the

decades was significant (Tukey’s HSD, CV 0.36; p= 0.05). The largest mean decadal

intra-ring mean decadal intra-ring late wood cell diameter was 19.39± 0.02 µm during

2002-11, which was significantly higher compared to 1992-2001. The smallest mean

decadal intra-ring late wood cell diameter was 17.88± 0.01 µm during 1962-71. The

mean decadal intra-ring late wood cell diameter did not vary significantly among the

decades of 1962-71, 1972-81 and 1982-91(Table 5.10).


intra-ring late wood cell wall thickness of Blue pine, with a decreasing trend, during


among the decades was significant (Tukey’s HSD, CV 0.15; p= 0.05). The pattern of

155

change in the mean decadal intra-ring late wood cell wall thickness was similar to the

mean decadal intra-ring early wood cell wall thickness. The largest mean decadal intra-

ring late wood cell wall thickness was 4.33± 0.04 µm during 1962-71, which was

significantly higher compared to 197281. The smallest mean decadal intra-ring late

wood cell wall thickness was 3.92±0.05 µm during 2002-2011. The mean decadal intra-

ring late wood cell wall thickness did not vary among 1982-91, 1992-01 and 2002-2013

(Table 5.10).

5.5.4 Correlation between Ring-width and Ring-wood Characteristics of Blue pine

The analysis of Pearson Correlation Coefficients matrix of Blue pine growth data for

1962-2011 revealed a highly significant (p<0.01) and positive correlation of mean

annual ring-width with mean intra-ring early wood formation (r = 0.80), mean intra-ring

early wood cell wall thickness (r = 0.96) and mean intra-ring late wood cell wall

thickness (r = 0.89). The correlation of mean annual ring-width was highly significant

(p<0.01) and negative with mean intra-ring early wood cell wall diameter (r = -0.80),

mean intra-ring late wood formation (r = -0.89) and mean intra-ring late wood cell

diameter (r = -0.70). The correlation of mean intra-ring early wood formation was

highly significant (p<0.01) and positive with mean intra-ring early wood cell wall

thickness (r = 0.84) and mean intra-ring late wood cell wall thickness (r = 0.69), but

highly significant (p<0.01) and negative with mean intra-ring early wood cell diameter

(r = -0.74), mean intra-ring late wood cell formation (r = -0.91) and mean intra-ring

late wood cell diameter (r = -0.75). The correlation of mean intra-ring early wood cell

diameter was highly significant (p<0.01) and positive with mean intra-ring late wood

formation (r = 0.78) and mean intra-ring late wood cell diameter (r = 0.86), but highly

significant (p<0.01) and negative with mean intra-ring early wood cell wall thickness (r

= -0.81) and mean intra-ring late wood cell wall thickness (r = -0.71). The correlation of

mean intra-ring early wood cell wall thickness was highly significant (p<0.01) and

positive with mean intra-ring late wood cell wall thickness (r = 0.90), but highly

significant (p<0.01) and negative with mean intra-ring late wood formation (r = -0.94)

and mean intra-ring late wood cell diameter (r = -0.73). The correlation of mean intra-

ring late wood formation was highly significant (p<0.01) and positive with mean intra-

ring late wood cell diameter (r = 0.80), but highly significant (p<0.01) and negative

with mean intra-ring late wood cell wall thickness (r = -0.86). The correlation of mean

156

intra-ring late wood cell diameter was highly significant (p<0.01) and negative with

mean intra-ring late wood cell wall thickness (r = -0.68) (Table 5.11).


Characteristics of Blue pine in GFD (1962-2011)

Tree Growth

Characteristics


EW 0.80**

(0.000)

EWCD - 0.80**

(0.000)

- 0.74**

(0.000)

EWCWT 0.96**

(0.000)

0.84**

(0.000)

- 0.81**

(0.000)

LW - 0.89**

(0.000)

- 0.91**

(0.000)

0.78**

(0.000)

- 0.94**

(0.000)

LWCD - 0.70**

(0.000)

- 0.75**

(0.000)

0.86**

(0.000)

- 0.73**

(0.000)

0.80**

(0.000)

LWCWT 0.89**

(0.000)

0.69**

(0.000)

- 0.71**

(0.000)

0.91**

(0.000)

- 0.86**

(0.000)

- 0.68**

(0.001)

Values in ( ) are p-values; ** Significant at 99%; RW= Ring-width; EW= Early wood;

EWCD= Early wood cell diameter; EWCW= Early wood cell wall thickness; LW= Late

wood; LWCD= Late wood cell diameter; LWCW= Late wood cell wall thickness

5.5.5 Impacts of Climate Change on Ring-width of Blue pine

The impacts of climate change on mean annual ring-widths of Blue pine during 1962-

2011 were assessed using response functions of ring-widths with temperature

(maximum and minimum) and precipitation, both on annual and seasonal basis.

157

The mean annual maximum temperature showed a highly significant (F1, 48= 25.10;

p<0.01) impact on mean annual ring-widths of Blue pine, exhibiting a linear pattern

with a declining trend. Most of the ring-width responses were observed between 16.0˚C

and 17.0˚C. The mean annual maximum temperature below 15.5˚C showed positive

impact on mean annual ring-width, while the mean annual maximum temperature

above 17.0˚C showed negative impact on mean annual ring-width (Figure 5.40).

Figure 5-40 Impact of Mean Annual Maximum Temperature on Ring-width of Blue pine in GFD (1962-2011)

158

The mean annual minimum temperature showed a highly significant (F1, 48=18.77;

p<0.01) impact on mean annual ring-widths of Blue pine, exhibiting a linear pattern.

The mean annual ring-width decreased with increasing mean annual minimum

temperature. Relatively better growth response (mean annual ring-width >3.25 mm)

was observed between 5.5˚C and 6.5˚C (Figure 5.41).

Figure 5-41 Impact of Mean Annual Minimum Temperature on Ring-width of Blue pine in GFD (1962-2011)

159

The annual precipitation showed a non-significant (F1, 48= 0.65; p>0.05) impact on

mean annual ring-widths of Blue pine. The mean annual ring-width decreased with

increasing annual precipitation. A large variation in mean annual ring-width response

was noticed across the observed range of precipitation, with relatively better growth

(mean annual ring-width >3.00 mm) when annual precipitation was in the range of 800-

1000 mm (Figure 5.42).

Figure 5-42 Impact of Annual Precipitation on Ring-width of Blue pine in GFD (1962-2011)

160

The 1-way analysis of variance of mean annual ring-width of Blue pine with mean

decadal precipitation showed that the largest mean annual ring-width was 2.64±0.33

mm when annual precipitation was ranged between 600 mm and 700 mm. The smallest

mean annual ring-width was 2.50±0.22 mm when annual precipitation ranged from 701

mm to 800 mm (Table 5.12).

Table 5-12 Precipitation and Ring-width of Blue pine in GFD (1962- 2011)

Precipitation Range

(mm/annum)


(mm)

Standard Error (SE)

501-600 3.25* 0.00

601-700 2.64 0.33

701-800 2.50 0.22

801-900 2.58 0.14

901-1000 2.60 0.12

>1001 2.41 0.13

*Single value

The mean spring maximum temperature showed a highly significant (F1, 48=7.21;

p<0.01) negative impact on mean annual ring-widths of Blue pine. The mean annual

ring-width declined with increasing maximum temperature (Figure 5.43).

Figure 5-43 Impact of Mean Spring Maximum Temperature on Ring-width of Blue pine in GFD (1962-2011)

161

The mean spring minimum temperature showed a highly significant (F1, 48 = 10.04;


ring-width decreased with increasing mean spring minimum temperature. Relatively

better growth response (ring-width >3.0 mm) was observed between 3.0˚C and 4.0˚C

(Figure 5.44).

Figure 5-44 Impact of Mean Spring Minimum Temperature on Ring-width of Blue pine in GFD (1962-2011)

162

The spring precipitation showed a non-significant (F1, 48=0.15; p>0.05) positive

impact on mean annual ring-widths of Blue pine. The mean annual ring-width

increased with increasing spring precipitation. (Figure 5.45).

Figure 5-45 Impact of Spring Precipitation on Ring-width of Blue pine in GFD (1962-2011)

163

The mean summer maximum temperature showed a highly significant (F1, 48=7.84;

p<0.01) negative impact on mean annual ring-widths of Blue pine, in a linear pattern

with declining trend (Figure 5.46).

Figure 5-46 Impact of Mean Summer Maximum Temperature on Ring-width of Blue pine in GFD (1962-2011)

164

The mean summer minimum temperature showed a significant (F1, 48= 6.86; p<0.05)

negative impact on mean annual ring-widths of Blue pine. The mean annual ring-width

decreased with increasing summer minimum temperature. Relatively better growth

response (mean annual ring-width >3.00 mm) was observed between 10.5.˚C and

11.5˚C (Figure 5.47).

Figure 5-47 Impact of Mean Summer Minimum Temperature on Ring-width of Blue pine in GFD (1962-2011)

165


mean annual ring-widths of Blue pine. The mean annual ring-width increased with

increasing summer precipitation. Relatively better growth (mean annual ring-width

>3.25 mm) was observed when summer precipitation was in the range of 100-150

mm/season (Figure 5.48).

Figure 5-48 Impact of Summer Precipitation on Ring-width of Blue pine in GFD (1962-2011)

166

The mean monsoon maximum temperature showed a non-significant (F1, 48=2.43;

p>0.05) impact on mean annual ring-widths of Blue pine. The mean annual ring-width

decreased with increasing mean maximum monsoon temperature. Relatively better

growth response was observed (mean annual ring-width >3.00 mm) at mean monsoon

maximum temperature of 23.0˚C-24.0˚C, with some fluctuations (Figure 5.49).

Figure 5-49 Impact of Mean Monsoon Maximum Temperature on Ring-width of Blue pine in GFD (1962-2011)

167

The mean monsoon minimum temperature showed a non-significant (F1, 48=0.39;

p>0.05) negative impact on mean annual ring-widths of Blue pine. The mean annual

ring-width decreased with increasing mean annual monsoon minimum temperature.

Relatively better growth response (mean annual ring-width >3.25 mm) was observed

between 13.0˚C and 13.5˚C (Figure 5.50).

Figure 5-50 Impact of Mean Monsoon Minimum Temperature on Ring-width of Blue pine in GFD (1962-2011)

168


mean annual ring-widths of Blue pine. The mean annual ring-width decreased slightly

with increasing monsoon precipitation. Relatively better growth response (mean annual

ring-width >3.00 mm) was observed when the monsoon precipitation was in the range

of 250-350 mm/season, with some fluctuations (Figure 5.51).

Figure 5-51 Impact of Monsoon Precipitation on Ring-width of Blue pine in GFD (1962-2011)

169

The mean autumn maximum temperature showed a non-significant (F1, 48=0.55;

p>0.05) impact on mean annual ring-widths of Blue pine. The mean annual ring-width

decreased with increasing mean autumn maximum temperature. The mean annual ring-

width were clustered around 16.0˚C (Figure 5.52).

Figure 5-52 Impact of Mean Autumn Maximum Temperature on Ring-width of Blue pine in GFD (1962-2011)

170

The mean autumn minimum temperature showed a highly significant (F1, 48=10.2;


ring-width decreased with increasing mean autumn minimum temperature. Relatively

better growth response (mean annual ring-width >3.25 mm) was observed between

3.0˚C and 3.5˚C. The mean annual ring-width decreased steadily with mean autumn

minimum temperature above 4.0˚C (Figure 5.53).

Figure 5-53 Impact of Mean Autumn Minimum Temperature on Ring-width of Blue pine in GFD (1962-2011)

171

The autumn precipitation showed a non-significant (F1, 48=0.37; p>0.05) impact on

mean annual ring-widths of Blue pine. The mean annual ring-width decreased

marginally with increasing autumn precipitation. Relatively better growth (mean annual

ring-width >3.00 mm), with few fluctuations, was observed when autumn precipitation

was in the range of 30-60 mm/season (Figure 5.54).

Figure 5-54 Impact of Autumn Precipitation on Ring-width of Blue pine in GFD (1962-2011)

172

The mean winter maximum temperature showed a highly significant (F1, 48=13.03;

p<>0.01) negative impact on mean annual ring-widths of Blue pine. The mean annual

ring-width decreased with increasing mean winter maximum temperature. The mean

annual ring-width was relatively higher (>2.50 mm) at 6.0˚C-7.0˚C, however, it

declined steadily with mean winter maximum temperature higher than 7.5˚C (Figure

5.55).

Figure 5-55 Impact of Mean Winter Maximum Temperature on Ring-width of Blue pine in GFD (1962-2011)

173

The mean winter minimum temperature showed a highly significant (F1, 48=34.28;


ring-width decreased with increasing mean winter minimum temperature. Relatively

better growth response (mean annual ring-width >3.00 mm) was observed between -

3.0˚C and -2.0˚C. The mean annual ring-width decreased steadily with mean winter

minimum temperature above -1.0˚C (Figure 5.56).

Figure 5-56 Impact of Mean Winter Minimum Temperature on Ring-width of Blue pine in GFD (1962-2011)

174

The winter precipitation showed a non-significant (F1, 48=0.18; p>0.05) impact on mean

annual ring-widths of Blue pine. The mean annual ring-width decreased marginally

with increasing winter precipitation. Relatively better growth (mean annual ring-width

>3.00 mm) was observed when winter precipitation was in the range of 150-250

mm/season (Figure 5.57). The impact of winter precipitation on ring-width was almost

similar to that of autumn precipitation.

Figure 5-57 Impact of Winter Precipitation on Ring-width of Blue pine in GFD (1962-2011)

175


Ring-width of Blue pine

Mathematical expressions of impacts of climate change on mean annual ring-width of

Blue pine showed a linear pattern (Table 5.13). The mean annual maximum

temperature (R2 = 0.34) showed a higher negative impact on mean annual ring-width

compared to mean annual minimum temperature (R2 = 0.28). Among the seasons, mean

winter minimum temperature showed the highest (R2= 0.41) impact followed by mean

winter maximum temperature (R2 =0.21), mean autumn minimum temperature (R2 =

0.18), mean spring minimum temperature (R2 = 0.17) and mean summer maximum

temperature (R2 = 0.14). The impacts of mean spring maximum temperature and mean

summer minimum temperature were the same. The impacts of mean monsoon

maximum temperature and mean monsoon minimum temperature and mean autumn

maximum temperature were non-significant. The mean autumn maximum temperature

showed the least impact on mean annual ring-width among all seasons. The impacts of

mean annual precipitation and mean seasonal precipitations were non-significant.

Among the mean seasonal precipitations, the mean autumn precipitation showed the

highest impact on mean annual ring-width.

176

Table 5-13 Mathematical Expressions of Impact of Temperature and

Precipitation on Ring-width of Blue pine in GFD (1962-2011)

Climate Parameters Mathematical

Expressions

R2 F(1), (48)* (p)

Mean Max. Temp. Y = 5.500 - 0.486 × X 0.34 25.10 (0.000)

Mean Min. Temp. Y = 10.57 - 0.4900 × X 0.28 18.77 (0.000)

Mean Precipitation Y = 2.913 - 0.0004 × X 0.01 0.65 (0.425)

Spring Max. Temp. Y = 4.527 - 0.145 × X 0.13 7.21 (0.010)

Spring Min. Temp. Y = 3.365 - 0.196 × X 0.17 10.04 (0.003)

Spring Precipitation Y = 2.464 + 0.0004 × X 0.03 0.15 (0.705)

Summer Max. Temp. Y = 6.456 -0.1695 × X 0.14 7.84 (0.007)

Summer Min. Temp. Y = 4.653 - 0.1823 × X 0.13 6.86 (0.012)

Summer Precipitation Y = 2.346 + 0.0017 × X 0.01 0.60 (0.444)

Monsoon Max. Temp. Y = 6.838 - 0.183 × X 0.05 2.43 (0.126)

Monsoon Min. Temp. Y = 3.791 - 0.095 × X 0.08 0.39 (0.533)

Monsoon Precipitation Y = 2.844 - 0.0009 × X 0.03 1.39 (0.244)

Autumn Max. Temp. Y = 3.621 - 0.0673 × X 0.02 0.55 (0.460)

Autumn Min. Temp. Y = 3.641 - 0.2667 × X 0.18 10.20 (0.002)

Autumn Precipitation Y = 2.63 - 0.002 × X 0.08 0.37 (0.547)

Winter Max. Temp. Y = 4.329 - 0.264 × X 0.21 13.03 (0.001)

Winter Min. Temp. Y = 3.641 - 0.267 × X 0.41 34.28 (0.000)

Winter Precipitation Y = 2.637 - 0.0015 × X 0.04 0.18 (0.675)

* Values in parentheses in the top row are degree of freedom for linear equation. The (p)

values indicate significance levels.

177

5.6 Ring-width and Ring-wood Characteristics of Chir pine

5.6.1 Time function analysis of Ring-width and Ring-wood Characteristics of Chir

pine

The time function responses of ring-width and ring-wood characteristics of Chir pine

were studied through regression analysis at 95% Confidence Interval (CI) and Prediction

Interval (PI), using the standardized data. The trend in the time series was assessed by

applying Mann Kendall test with Normal Approximation and using Sen’s Slope

Estimator method. A summary of the trend analysis is reproduced in Table 5.14.

Table 5-14 Trend Analysis of Ring-width and Ring-wood characteristics of Chir

pine at GFD (1962-2011)


Characteristics

Z-Value p-Value Trend Sen’s

Slope Upward Downward

Ring-width -0.753- 0.774 0.226 -0.013

Early wood formation -0.335 0.631 0.369 -0.004

Late wood formation -0.753 0.774 0.226 -0.013

Early wood cell

diameter

-2.275 0.989 0.011

-0.029


thickness

-0.134 0.553 0.447

0.000

Late wood cell

diameter

-0.452 0.674 0.326

-0.003

Late wood cell wall

thickness

-3.714 1.000 0.000

-0.006

Increasing trend No trend decreasing trend

The time function analysis of ring-width and ring-wood characteristics of Chir pine

for the period 1962-2011 showed highly significant (p<0.01) downward trend in late

wood cell wall thickness and significant (p<0.05) downward trend in early wood cell

diameter and no trend in ring-width, early wood formation, late wood formation,

early wood cell wall thickness and late wood cell diameter.

178

The analysis of time function response of ring-widths of Chir pine for the period 1962-

2011 indicated a wide range of variation in mean annual ring-widths across the cores in

a quadratic pattern, with no overall significant trend. The mean annual ring-width

ranged between 2.00±0.39 mm and 3.66±0.55 mm, with a mean value of 262±0.39 mm.

The largest mean annual ring-width was recorded during 1988, while the smallest mean

annual ring-width was recorded during 2011. The ring-width increased during 1970s

and late 1980s, but declined incessantly from 1995 to 2011. There were ten years

having mean annual ring-width larger than 3.0 mm, while there were 24 years having

mean annual ring-width lower than 2.50 mm (Figure 5.58). The highest variability in

mean annual ring-width across the cores was recorded during 1983.

Figure 5-58 Time function of Mean Annual Ring-Width of Chir pine in GFD (1962-2011)

179

The analysis of time function response of mean intra-ring early wood formation of Chir

pine for the period 1962-2011 showed a highly significant (F2, 47= 10.50; p<0.01)

change in a quadratic pattern, with no overall significant trend. There was an increasing

growth in early wood formation up to 1990, followed by a decline. The mean intra-ring

early wood formation was 66.67±0.21% of mean annual wood formation. The largest

mean early wood formation was 69.95±1.94% during 1988, while the smallest mean

intra-ring early wood formation was 64.22±1.31% during 2002. The time function

response of mean intra-ring early wood formation showed an opposite trend to that of

mean annual ring-width. There were 11 years having mean intra-ring early wood

formation higher than 68.0%, while there were eight years having mean intra-ring early

wood formation lower than 65.0% (Figure 5.59). The highest variability in mean intra-

ring early wood formation across the cores was during 1983.

Figure 5-59 Time function of Mean Intra-ring Early Wood Formation (%) of Chir pine in GFD (1962-2011)

180

The analysis of time function response of mean intra-ring late wood formation of Chir

pine showed a highly significant (F2, 47= 6.79; p<0.01) change in a quadratic pattern,

with no overall significant trend. There was a declining growth in late wood formation

up to 1993, followed by an increasing tendency. The mean intra-ring late wood

formation was 32.97±0.20% of mean annual wood formation. The largest mean intra-

ring late wood formation was 35.58±2.17% during 1983, while the smallest mean intra-

ring late wood formation was 29.92±1.90% during 1988. The mean intra-ring late wood

formation showed a changing trend opposite to that of mean intra-ring early wood

formation. The slope gradient of time function of mean intra-ring late wood formation

was relatively smaller compared to mean intra-ring early wood formation. There were

five years each having mean intra-ring late wood formation higher than 35.0% and

lower than 31.0% (Figure 5.60). The highest variability in mean intra-ring late wood

formation across the cores was recorded in 1983.

Figure 5-60 Time function of Mean Intra-ring Late Wood Formation (%) of Chir pine in GFD (1962-2011)

181

The mean intra-ring early wood cell diameter of Chir pine during 1962-2011 ranged


function response of mean intra-ring early wood cell diameter showed a highly

significant (F2, 47= 5.27; p<0.05) change, with a linear pattern and declining trend. The

largest mean intra-ring early wood cell diameter was recorded during 1974, while the

smallest mean intra-ring early wood cell diameter was recorded during 2001. There

were five years having mean intra-ring early wood cell diameter larger than 53.0 µm,

while there were eight years having mean intra-ring early wood cell diameter smaller

than 50.0 µm (Figure 5.61). The largest variability in mean intra-ring early wood cell

diameter across the cores was recorded in 1982.

Figure 5-61 Time function of Mean Intra-ring Early Wood Cell Diameter (µm) of Chir pine in GFD (1962-2011)

182

The mean intra-ring early wood cell thickness of Chir pine during 1962-2011 ranged

from 2.70±0.08 µm to 2.38±0.08 µm, with a mean of 2.55±0.01 µm. The time function

response of mean intra-ring early wood cell wall thickness showed a non-significant

(F2, 47= 1.60; p>0.05) change, with a quadratic pattern and no overall significant trend.

The largest mean intra-ring early wood cell wall thickness was recorded during 1966,

while smallest mean intra-ring early wood cell wall thickness was recorded during

1975. The mean intra-ring early wood cell wall thickness decreased during 1962-90,

but increased after 2000. There was only one year having mean intra-ring early wood

cell wall thickness larger than 2.7 µm and two years having mean intra-ring early wood

cell wall thickness smaller than 2.40 µm (Figure 5.62). The highest variability in mean

intra-ring early wood cell wall thickness was recorded in 1981.

Figure 5-62 Time function of Mean Intra-ring Early Wood Cell Wall Thickness (µm) of Chir pine in GFD (1962-

2011)

183


thickness of Chir pine is depicted in Figure 5.63.

Figure 5-63 Intra-ring Early Wood Cell Diameter and Cell Wall Thickness of Chir pine (100x)

184

The mean intra-ring late wood cell diameter of Chir pine during 1962-2011 ranged


function response of mean intra-ring late wood cell diameter showed a non-significant

(F2, 47= 0.76; p>0.05) change, with a quadratic response and no overall significant trend.

The largest mean intra-ring late wood cell dia7meter was recorded during 1996, while

the smallest mean intra-ring late wood cell diameter was recorded during 1980. The

mean intra-ring late wood cell diameter decreased gradually during 1962-95, but

increased gradually thereafter. There were 14 years having mean intra-ring late wood

cell diameter larger than 21.0 µm and eight years having mean intra-ring late wood cell

diameter smaller than 21.0 µm (Figure 5.64). The highest variability in mean intra-ring

late wood cell diameter across the cores was recorded in 1984. The mean intra-ring late

wood cell diameter was significantly (p<0.05) smaller compared to mean intra-ring

early wood cell diameter.

Figure 5-64 Time function of Mean Intra-ring Late Wood Cell Diameter (µm) of Chir pine in GFD (1962-2011)

185

The mean intra-ring late wood cell wall thickness of Chir pine during 1962-2011

ranged from 4.56±0.19 µm to 5.24±0.18 µm, with a mean of 4.86±0.02 µm. The time

function response of mean intra-ring late wood cell wall thickness showed a highly

significant (F2, 47= 7.79; p<0.01) change, with a linear function and declining trend. The

largest mean intra-ring late wood cell wall thickness was recorded during 1970, while

smallest mean intra-ring late wood cell wall thickness was recorded during 1984. There

were ten years having mean intra-ring late wood cell wall thickness larger than 5.00 µm

and 12 years having mean intra-ring late wood cell wall thickness smaller than 4.75 µm

(Figure 5.65). The highest variability in mean intra-ring late wood cell wall thickness

across the cores was recorded in 1982. The mean intra-ring late wood cell wall

thickness was significantly (p<0.05) higher compared to mean intra-ring early wood

cell wall thickness. The time function responses of mean intra-ring early and mean

intra-ring late wood cell wall thickness were also different. The slope gradient of mean

intra-ring late wood cell wall thickness was considerably higher compared to mean

intra-ring early wood cell wall thickness.

Figure 5-65Time function of Mean Intra-ring Late Wood Cell Wall Thickness (µm) of Chir pine in GFD (1962-2011)

186


thickness of Chir pine is depicted in Figure 5.66.

Figure 5-66 Intra-ring Late Wood Cell Diameter and Cell Wall Thickness of Chir pine (100x)

187

5.6.2 Mathematical Expressions of Time Function of Ring-width, Intra-ring wood

Formation and Cell Characteristics of Chir pine

Mathematical expressions of time functions of ring-width, intra-ring wood formation

and cell characteristics of Chir pine showed a mix of linear and quadratic behaviors.

The mean annual ring-width, mean intra-ring early wood formation, mean intra-ring

late wood formation, mean intra-ring early wood cell wall thickness, mean intra-ring

late wood cell diameter and mean intra-ring late wood cell wall thickness followed a

quadratic function, while mean intra-ring early wood cell diameter followed a linear

function. The R2 ranged between 0.03 and 0.31. The highest R2 value was estimated for

mean intra-ring early wood formation followed by mean intra-ring late wood cell wall

thickness. The lowest R2 value was calculated for mean intra-ring late wood cell

diameter followed by mean intra-ring early wood cell wall thickness. The results of

time function response indicated good fit of the models for mean annual ring-width,

mean intra-ring early, mean intra-ring late wood formation and mean intra-ring late

wood cell wall thickness. The models were poorly fit for mean intra-ring early wood

cell diameter, mean intra-ring early wood cell wall thickness and mean intra-ring late

wood cell diameter (Table 5.15).


ring Wood Characteristics of Chir pine in GFD (1962-2011)

Tree Growth

Characteristics

Mathematical Expressions R2 F (1) 2, (48) 47 * (p)

Ring-width Y = - 4901 + 4.94 × X +

0.0012 × X2

0.30 9.88 (0.000)

Early wood formation Y = - 17081 + 17.27 × X -

0.0044 × X2

0.31 10.50 (0.000)

Late wood formation Y = 13649 - 13.70 × X +

0.0034 × X2

0.22 6.79 (0.003)

Early wood cell diameter Y = 109.3 - 0.0291 × X 0.10 5.27 (0.026)


thickness

Y = 457.4 - 0.4580 × X +

0.0001 × X2

0.06 1.60 (0.213)

Late wood cell diameter Y = 2425 - 2.418 × X +

0.0006 × X2

0.03 0.76 (0.473)

Late wood cell wall

thickness

Y = 470.5 - 0.4631 × X +

0.0001 × X2

0.25 7.79 (0.001)

* Values in parentheses ( ) in the top row are degree of freedom for linear functions and

outside parenthesis for quadratic functions. The (p) values indicate significance levels.

188

5.6.3 Decadal changes in Ring-width and Ring-wood Characteristics of Chir pine


ring-widths of Chir pine, with an overall irregular declining trend, during 1962-2011.

The difference in mean decadal ring-widths among the decades was significant

(Tukey’s HSD, CV 0.03; p=0.05). The largest mean decadal ring-width was 3.77± 0.01

mm during 1972-81, which was significantly different from mean decadal ring-widths

during 1962-71 and 1982-91. The difference in mean decadal ring-widths of 1992-01

and 2002-11 was significant (p<0.05). The smallest mean decadal ring-width was 2.22±

0.01 mm during 2002-11 (Table 5.16).

An overall highly significant (F4, 15= 29.81; p<0.01) difference was recorded in mean

decadal intra-ring early wood formation of Chir pine, with a decreasing trend, during

1962-2011. The mean decadal intra-ring early wood formation varied significantly on

decadal basis (Tukey’s HSD, CV 1.13; p=0.05). The largest mean decadal intra-ring

early wood formation was 68.23±0.25% during 1962-71, which was significantly

higher compared to mean decadal intra-ring early wood formation measured during


during 2002-11. The mean decadal intra-ring early wood formation during 1992-01 was

not significantly different (p>0.05) from mean decadal intra-ring early wood formation

during 2001-11. The mean decadal intra-ring early wood formation did not follow the

pattern of change in mean decadal ring-width (Table 5.16).


intra-ring late wood formation of Chir pine, with a decreasing trend, during 1962-2011.

The overall difference in mean decadal intra-ring late wood formation among the

decades was significant (Tukey’s HSD, CV: 0.39; p=0.05). The changes in mean

decadal intra-ring late wood formation followed the pattern of changes in mean decadal

intra-ring early wood formation. The largest mean decadal late wood formation was

36.91±0.06% during 1962-71, which was significantly different from mean decadal late

wood formation during 1972-81. The smallest mean decadal late wood formation was

31.73±0.14% during 2002-11 which was not significantly different from 1992-01

(Table 5.16).

189

Table 5-16 Mean Decadal Ring-width and Ring-Wood Characteristics of Chir pine

in GFD (1962-2011)

Decades

/CV

Tree-ring Characteristics

RW±SE

(mm)

EW±SE

(%)

LW±SE

(%)

EWCD

±SE

(µm)

EWCWT

±SE

(µm)

LWCD

±SE

(µm)

LWCWT

±SE

(µm)

1962-71 2.85±

0.01 b

68.23±

0.25 a

36.91±

0.06 a

53.44±

0.25 a

2.46±

0.01 c

21.94±

0.16 b

5.26±

0.01*a

1972-81 3.77±

0.01 a

66.46±

0.36 b

33.03±

0.07 b

52.26±

0.10 b

2.56±

0.02 b

21.02±

0.18bc

4.89±

0.01 b

1982-91 2.64±

0.00 c

66.46±

0.07 b

32.16±

0.02 c

50.59±

0.15 c

2.48±

0.01 c

21.26±

0.11 b

4.82±

0.03bc

1992-01 2.33±

0.02 d

65.27±

0.37 c

31.83±

0.11 cd

51.11±

0.23 c

2.54±

0.00 b

20.58±

0.19 c

4.79±

0.02 c

2002-11 2.22±

0.01 e

64.50±

0.04 c

31.73±

0.14 d

51.34±

0.17 c

2.76±

0.02 a

19.29±

0.11 d

4.57±

0.02 d

CV 0.03 1.13 0.39 0.80 0.02 0.68 0.09

Mean values within a column sharing same alphabets are not significantly different (Tukey’s

HSD, p=0.05); RW= Ring-width; EW= Early wood; LW= Late wood; EWCD= Early wood cell

diameter; EWCW= Early wood cell wall thickness; LWCD= Late wood cell diameter; LWCW=

Late wood cell wall thickness


intra-ring early wood cell diameter of Chir pine, with an overall decreasing trend, during

1962-2011. The difference in mean decadal intra-ring early wood cell diameter among

the decades was significant (Tukey’s HSD, CV: 0.80; p=0.05). The largest mean decadal

intra-ring early wood cell diameter was 53.44±0.25 µm during 1962-71, which was

significantly higher compared to 197281. The smallest mean decadal intra-ring early

wood cell diameter was 51.34±0.17 µm during 2002-11. The mean decadal intra-ring

early wood cell diameter did not vary significantly among 1982-91, 1992-01 and 2002-

11 (Table 5.16).


intra-ring early wood cell wall thickness of Chir pine, with an overall increasing trend,

190

during 1962-2011. The difference in mean decadal intra-ring early wood cell wall

thickness among the decades was significant (Tukey’s HSD, CV: 0.02; p= 0.05). The

largest mean decadal intra-ring early wood cell wall thickness was 2.76±0.02 µm

during 2002-11, which was significantly higher compared to 1992-01. The smallest

mean decadal intra-ring early wood cell wall thickness was 2.46±0.01 µm during 1962-

71 which was significantly different from 1972-81 (Table 5.16).

A highly significant (F4, 15= 40.69; p<0.01) difference was found in mean decadal intra-

ring late wood cell diameter of Chir pine, with an overall decreasing trend, during

1962-2011. The difference in mean decadal intra-ring late wood cell diameter was

significant among decades (Tukey’s HSD, CV 0.68; p= 0.05). The largest mean decadal

intra-ring late wood cell diameter was 21.94±0.16 µm during 1962-71, followed by

21.26±0.11 µm during 1982-91. The smallest mean decadal intra-ring late wood cell

diameter was 19.29±0.11 µm during 2001-11. The mean decadal intra-ring late wood

cell diameter during 2001-11 was significantly different from 1992-01 (Table 5.16).


intra-ring late wood cell wall thickness of Chir pine, with a decreasing trend, during


among the decades was significant (Tukey’s HSD, CV: 0.09; p= 0.05). The largest

mean decadal intra-ring late wood cell wall thickness was 5.26± 0.01 µm during 1962-

71 during 2001-11, which was significantly higher compared to 1972-81. The smallest

mean decadal intra-ring late wood cell wall thickness was 4.57± 0.02 µm during 2002-

11. The mean decadal intra-ring late wood cell wall thickness did not vary between

1972-81 and 1982-91 (Table 5.16). The pattern of change in mean decadal intra-ring

late wood cell wall thickness was similar to mean decadal intra-ring late wood

formation.

191

5.6.4 Correlation between Ring-width and Ring-wood Characteristics of Chir pine

The analysis of Pearson Correlation Coefficients matrix of Chir pine growth data for

1962-2011 revealed a significant (p<0.05) and positive correlation of mean annual ring-

width with mean intra-ring early wood formation (r = 0.47), mean intra-ring early wood

cell diameter (r = 0.45) and mean intra-ring late wood cell diameter (r = 0.46). The

correlation of mean annual ring-width was non-significant (p>0.05) and negative with

mean intra-ring early wood cell wall thickness (r = 0.34), but non-significant (p>0.05)

and positive with mean intra-ring late wood formation (r = 0.31) and mean intra-ring

late wood cell wall thickness (r = 0.42). The correlation of mean intra-ring early wood

formation was highly significant (p<0.01) and positive with mean intra-ring early wood

cell diameter (r = 0.64), mean intra-ring late wood formation (r = 0.83), mean intra-ring

late wood cell diameter (r = 0.82) and mean intra-ring late wood cell wall thickness (r =

0.92), but highly significant (p<0.01) and negative with mean intra-ring early wood cell

wall thickness (r = -0.75). The correlation of mean intra-ring early wood cell diameter

was highly significant (p<0.01) and positive with mean intra-ring late wood formation

(r = 0.86) and mean intra-ring late wood cell wall thickness (r = 0.75), significant

(p<0.05) and positive with mean intra-ring late wood cell diameter (r = 0.46), but non-

significant (p>0.05) and negative with mean intra-ring early wood cell wall thickness (r

= -0.25). The correlation of mean intra-ring early wood cell wall thickness was highly

significant (p<0.01) and negative with mean intra-ring late wood cell wall diameter (r =

-0.90) and mean intra-ring late wood cell wall thickness (r = -0.75), but significant

(p<0.05) and negative with mean intra-ring late wood formation (r = -0.52). The

correlation of mean intra-ring late wood formation was highly significant (p<0.01) and

positive with mean intra-ring late wood cell wall diameter (r = 0.70) and mean intra-

ring late wood cell wall thickness (r = 0.93). The correlation of mean intra-ring late

wood cell diameter was highly significant (p<0.01) and positive with mean intra-ring

late wood cell wall thickness (r = 0.84) (Table 5.17)

192


Characteristics of Chir pine in GFD (1962-2011)

Values in ( ) are p-values; * significant (p<0.05); ** highly significant (p<0.01); RW= Ring-

width; EW= Early wood; EWCD= Early wood cell diameter; EWCW= Early wood cell wall

thickness; LW= Late wood; LWCD= Late wood cell diameter; LWCW= Late wood cell wall

thickness

5.6.5 Impacts of Climate Change on Ring-width of Chir pine

The impacts of climate change on ring-widths of Chir pine during 1962-2011 were

assessed using response functions of ring-widths with temperature (maximum and

minimum) and precipitation, both on annual and seasonal basis.

Tree

Growth

Parameters


EW 0.47*

(0.037)

EWCD 0.45*

(0.049)

0.64**

(0.003)

EWCWT -0.34

(0.146)

-0.75**

(0.000)

-0.25

(0.297)

LW 0.31

(0.179)

0.83**

(0.000)

0.86**

(0.000)

-0.52*

(0.018)

LWCD 0.46*

(0.041)

0.82**

(0.000)

0.46*

(0.039)

-0.90**

(0.000)

0.70**

(0.000)

LWCWT 0.42

(0.069)

0.92**

(0.000)

0.75**

(0.000)

-0.75**

(0.000)

0.93**

(0.000)

0.84**

(0.000)

193

The mean annual maximum temperature showed a highly significant (F2, 47= 3.48;

p<0.05) impact on mean annual ring-widths of Chir pine, exhibiting a quadratic pattern

with a slightly declining trend. Most of the ring-width responses were observed around

16.0˚C. The mean annual maximum temperature of 15.5˚C showed positive impact on

ring-width. (Figure 5.67).

Figure 5-67 Impact of Mean Annual Maximum Temperature on Ring-width of Chir pine in GFD (1962-2011)

194

The mean annual minimum temperature showed a significant (F2, 47= 3.04; p<0.05)

impact on mean annual ring-widths of Chir pine, exhibiting a quadratic pattern.

Overall, the mean annual ring-width decreased with increasing mean annual minimum

temperature. Relatively better growth response (mean annual ring-width >3.0 mm) was

observed between 5.0˚C and 5.5˚C. A positive impact of mean annul minimum

temperature was observed above 5.5˚C and 7.0˚C (Figure 5.68). The mean annual ring-

width of 2.56±0.31 mm at 6.0˚C was significantly (p<0.05) smaller compared to mean

annual ring-width at 5.0˚C. Conversely, mean annual ring-width of 2.57±0.66 mm at

7.0˚C did not differ significantly (p>0.05) from mean annual ring-width at 5.0˚C.

Figure 5-68 Impact of Mean Annual Minimum Temperature on Ring-width of Chir pine in GFD (1962-2011)

195

The annual precipitation showed a non-significant (F3, 46= 2.11; p>0.05) impact on

mean annual ring-widths of Chir pine, in a polynomial pattern. The mean annual ring-

width, as a whole, slightly decreased with increasing annual precipitation. Most of the

mean annual ring-width responses were observed in annual precipitation range of 800-

1000 mm (Figure 5.69).

Figure 5-69 Impact of Annual Precipitation on Ring-width of Chir pine in GFD (1962-2011)

196

The 1-way analysis of variance of mean annual ring-width of deodar with mean decadal

precipitation showed that the largest mean annual ring-width was 3.46±0.40 mm when

annual precipitation was between 501 and 600 mm, followed by mean annual ring-

width 3.28±0.42 mm at annual precipitation of 801-900 mm. The smallest mean annual

ring-width was 2.76±0.40 mm when annual precipitation was between >1000

mm/annum, followed by mean annual ring-width of 2.89±0.39 mm at annual

precipitation range of 901-1000 mm (Table 5.18).

Table 5-18 Impact of Precipitation on Ring-width of Chir pine in GFD (1962-

2011)

Precipitation range

(mm/annum)


(mm)

Standard Error

(SE)

501-600 3.64* 0.00

601-700 3.02 0.77

701-800 3.04 0.49

801-900 3.28 0.42

901-1000 2.89 0.39

>1001 2.76 0.40

*Single value

197

The mean spring maximum temperature showed a significant (F3, 46= 3.45; p<0.05)

impact on mean annual ring-widths of Chir pine. As a whole, mean annual ring-width

slightly increased with increasing mean spring maximum temperature, with a

polynomial function (Figure 5.70).

Figure 5-70 Impact of Mean Spring Maximum Temperature on Ring-width of Chir pine in GFD (1962-2011)

198

The mean spring minimum temperature showed a non-significant (F3, 46 = 1.46; p>0.05)

impact on mean annual ring-widths of Chir pine, in a polynomial pattern and slightly

declining trend. Relatively better growth response was observed around 3.0˚C and

5.0˚C, with mean annual ring-width larger than 3.0 mm (Figure 5.71).

Figure 5-71 Impact of Mean Spring Minimum Temperature on Ring-width of Chir pine in GFD (1962-2011)

199

The spring precipitation showed a non-significant (F3, 46=1.76; p>0.05) impact on mean

annual ring-widths of Chir pine. There was an overall slight increase in mean annual

ring-width with increasing spring precipitation (Figure 5.72).

Figure 5-72 Impact of Spring Precipitation on Ring-width of Chir pine in GFD (1962-2011)

200

The mean summer maximum temperature showed a non-significant (F3, 46= 1.08;

p>0.05) impact on mean annual ring-widths of Chir pine, in polynomial pattern and an

overall slightly declining trend (Figure 5.73).

Figure 5-73 Impact of Mean Summer Maximum Temperature on Ring-width of Chir pine in GFD (1962-2011)

201

The mean summer minimum temperature showed a non-significant (F3, 46= 1.52;

p>0.05) impact on mean annual ring-widths of Chir pine. The mean annual ring-width

response was of polynomial pattern, with an overall declining trend. Relatively better

growth response in terms of mean annual ring-width (mean annual ring-width >2.80

mm) was observed around 11.0˚C (Figure 5.74).

Figure 5-74 Impact of Mean Summer Minimum Temperature on Ring-width of Chir pine in GFD (1962-2011)

202


mean annual ring-widths of Chir pine, in a quadratic pattern and an overall decreasing

trend (Figure 5.75). The overall impact of summer precipitation on mean annual ring-

width was similar to that of spring precipitation.

Figure 5-75 Impact of Summer Precipitation on Ring-width of Chir pine in GFD (1962-2011)

203

The mean monsoon maximum temperature showed a non-significant (F2, 47= 0.42;

p>0.05) impact on mean annual ring-widths of Chir pine, in a polynomial pattern, with

an increasing trend when mean monsoon maximum temperature ranged between 23.0˚C

and 24˚C. A relatively better growth response (mean annual ring-width >2.80 mm) was

measured between mean monsoon maximum temperature of 23.0˚C and 24˚C (Figure

5.76).

Figure 5-76 Impact of Mean Monsoon Maximum Temperature on Ring-width of Chir pine in GFD (1962-2011)

204

The mean monsoon minimum temperature showed a non-significant (F2, 47= 0.52;

p>0.05) impact on mean annual ring-widths of Chir pine, in a linear pattern and

decreasing trend. Relatively better growth response (mean annual ring-width >3.0 mm)

was observed at 13.0˚C to 14.0˚C (Figure 5.77). The trend of impact of mean monsoon

minimum temperature was contrasting with that of mean monsoon maximum

temperature.

Figure 5-77 Impact of Mean Monsoon Minimum Temperature on Ring-width of Chir pine in GFD (1962-2011)

205


mean annual ring-widths of Chir pine, exhibiting a quadratic pattern. As whole there

was an increasing trend in mean annual ring-width with increasing monsoon

precipitation, which followed the impact trend of spring precipitation with lower slope

gradient. Relatively better growth response (mean annual ring-width >3.0 mm) was

observed when monsoon precipitation was ranged between 200 mm/season and 350

mm/season and larger than 500 mm/season (Figure 5.78).

Figure 5-78 Impact of Monsoon Precipitation on Ring-width of Chir pine in GFD (1962-2011)

206

The mean autumn maximum temperature showed a non-significant (F3, 46= 1.41;

p>0.05) impact on mean annual ring-widths of Chir pine, in a quadratic pattern.

Overall, mean annual ring-width slightly declined with increasing mean autumn

maximum temperature. The mean annual ring-widths were found clustered at 16.0˚C.

Relatively better growth response (mean annual ring-width >3.0 mm) was observed at

14.0˚C, 16.0˚C and 17˚C (Figure 5.79).

Figure 5-79 Impact of Mean Autumn Maximum Temperature on Ring-width of Chir pine in GFD (1962-2011)

207

The mean autumn minimum temperature showed a highly significant (F3, 46= 5.28;

p<0.01) impact on mean annual ring-widths of Chir pine, in a weak polynomial pattern.

The mean annual ring-width decreased slightly with increasing mean autumn minimum

temperature. Relatively better growth response (mean annual ring-width >3.0 mm) was

observed between 3.0˚C and 4.0˚C (Figure 5.80). The impact trend of mean autumn

minimum temperature on mean annual ring-width was similar to that of mean autumn

maximum temperature, however, the slope gradient of mean autumn temperature was

smaller.

Figure 5-80 Impact of Mean Autumn Minimum Temperature on Ring-width of Chir pine in GFD (1962-2011)

208

The autumn precipitation showed a non-significant (F3, 46= 2.02; p>0.05) impact on

mean annual ring-widths of Chir pine. The mean annual ring-width decreased marginally

with increasing autumn precipitation, in a polynomial pattern. Relatively better growth

response (mean annual ring-width >3.0 mm) was observed when autumn precipitation

was around 20.0 mm/season, and between 60.0 to 80.0 mm/season (Figure 5.81). In

general, the impact of autumn precipitation on mean annual ring-width followed the

pattern of monsoon precipitation.

Figure 5-81 Impact of Autumn Precipitation on Ring-width of Chir pine in GFD (1962-2011)

209

The mean winter maximum temperature showed a non-significant significant (F3, 46=

1.27; p<0.01) impact on mean annual ring-widths of Chir pine. The overall mean annual

ring-width increased marginally with increasing mean winter maximum temperature, in

a polynomial pattern. The growth was better (mean annual ring-width >3.00 mm)

between 6.0˚C and 7.5˚C (Figure 5.82).

Figure 5-82 Impact of Mean Winter Maximum Temperature on Ring-width of Chir pine in GFD (1962-2011)

210

The mean winter minimum temperature showed a significant (F2, 47= 2.74; p<0.05)

impact on mean annual ring-widths of Chir pine, in a quadratic pattern and overall

slightly declining trend. The mean annual ring-width decreased gradually with mean

winter minimum temperature lower than -3.5˚C and higher than -1.0˚C. Relatively

better growth response (mean annual ring-width >3.0 mm) was observed from -3.0˚C to

-2.0˚C (Figure 5.83). The impact of increasing mean winter minimum temperature was

more profound compared to mean winter maximum temperature.

Figure 5-83 Impact of Mean Winter Minimum Temperature on Ring-width of Chir pine in GFD (1962-2011)

211

The winter precipitation showed a non-significant (F3, 46= 0.31; p>0.05) impact on

mean annual ring-widths of Chir pine. The mean annual ring-width showed a

polynomial response, with a slightly declining trend. Relatively better growth response

(mean annual ring-width >3.0 mm) was found when winter precipitation ranged from

175.0 mm/season to 250.0 mm/season (Figure 5.84).

Figure 5-84 Impact of Winter Precipitation on Ring-width of Chir pine in GFD (1962-2011)

212


Ring-width of Chir pine


Chir pine showed a mix of quadratic and polynomial functions. The mean annual

maximum temperature (R2 = 0.13) showed a higher impact on mean annual ring-width

compared to mean annual maximum temperature (R2 = 0.12) and mean annual

precipitation (R2 = 0.12). Among the seasons, mean autumn minimum temperature

showed the highest (R2 = 0.26) impact, followed by mean spring maximum (R2 = 0.18)

and mean winter minimum temperature (R2 = 0.11). The impacts of mean temperatures,

both maximum and minimum during other seasons, were marginal. The impact of mean

monsoon precipitation (R2 = 0.09) was relatively higher compared to impacts of other

mean seasonal precipitations. The results indicated poor to good fit of the models for

the impacts of maximum temperature, minimum temperature and precipitation on the

ring-width of Chir pine (Table 5.19).

Table 5-19 Mathematical Expressions of Impact of Temperature and Precipitation

on Ring-width of Chir pine in GFD (1962-2011)

Climate Parameters Mathematical Expressions R2 F(2,3), (47,46)* (p)

Mean Max. Temp. Y = - 68.66 + 8.81 × X +

0.272 × X2

0.13 3.48 (0.039)

Mean Min. Temp. Y = 69.40 - 11.40 × X +

0.8812 × X2

0.12 3.04 (0.05)

Mean Precipitation Y = - 37.85 + 0.2680 × X -

0.0003 × X2 + 0.0000 × X3

0.12 2.11 (0.113)

Spring Max. Temp. Y = 417.1 - 82.28 ×X +

5.851 × X2 - 0.1382 × X3

0.18 3.45 (0.024)

Spring Min. Temp. Y = 47.15 - 9.287 × X +

1.947 × X2 - 0.1326 × X3

0.09 1.46 (0.237)

Spring Precipitation Y = 34.42 - 0.01858 × X +

0.0001×X2

0.07 1.76 (0.184)

213

Summer Max. Temp. Y = - 740 + 98.8 × X - 4.193

× X2 + 0.05907 × X3

0.04 1.08 (0.624)

Summer Min. Temp. Y = - 94.5 + 33.41 × X -

2.881 ×X2 + 0.0818 × X3

0.03 1.52 (0.672)

Summer Precipitation Y = 2.458 + 0.005 × X +

0.0031 × X2

0.06 1.51 (0.402)

Monsoon Max. Temp. Y = 6754 - 854.2 × X +

36.18 × X2 - 0.5107 × X3

0.03 0.42 (0.666)

Monsoon Min. Temp. Y = - 0.28 + 0.496 × X -

0.024 × X2

0.02 0.52 (0.855)

Monsoon

Precipitation

Y = 37.90 - 0.0283 × X +

0.0001 × X2

0.09 2.26 (0.116)

Autumn Max. Temp. Y = 1922 - 351.6 × X +

21.76 × X2 - 0.4477 × X3

0.08 1.41 (0.253)

Autumn Min. Temp. Y = 114.4 - 54.70 × X +

11.92 × X2 - 0.8444 × X3

0.26 5.28 (0.003)

Autumn Precipitation Y = 32.56 + 0.0455 × X -

0.0011 × X2 + 0.00001 × X3

0.01 2.02 (0.124)

Winter Max. Temp. Y = 21.22 + 8.33 × X - 1.653

× X2 + 0.0995 × X3

0.08 1.27 (0.297)

Winter Min. Temp. Y = 32.49 + 0.0164 × X2 +

0.1009 × X2

0.11 2.74 (0.075)

Winter Precipitation Y = 3.31 + 0.014 × X -

0.0001 × X2 - 0.000001 × X3

0.09 0.3 (0.736)

* Values in parentheses in the top row are degree of freedom for quadratic and polynomial


214

5.7 Inter-species Comparison of Correlation Coefficients of Ring-widths of Cedrus

deodara, Pinus wallichiana and Pinus roxburghii with Climate Parameters

The mathematical expressions of correlation coefficients of ring-widths of Cedrus

deodara, Pinus wallichiana and Pinus roxburghii with climate parameters at GFD for

the period 1962-2011 (Tables 5.4.6, 5.5.6 and 5.6.6) indicated linear patterns in some

cases and non-linear patterns in others. A matrix of Pearson Correlation Coefficients

drawn for ring-widths of Cedrus deodara, Pinus wallichiana and Pinus roxburghii with

climate parameters at GFD for the period 1962-2011 (Table 5.20) indicated the

comparative direction and strength of the relationships. As the Pearson Correlation

Coefficient predicts the correct direction and strength of only linear relationship of

associated variables, therefore, the interpretation of the matrix were confined to linear

pattern only. The correlation between mean annual ring-width of Deodar with mean

annual precipitation was negative and significant, with winter maximum temperature

and winter minimum temperature negative and highly significant and with summer

maximum temperature and summer minimum temperature negative and non-

significant. The correlation between mean annual ring-width of Blue pine with summer

minimum temperature was negative and significant, with annual maximum

temperature, annual minimum temperature, spring maximum temperature, spring

minimum temperature, summer maximum temperature, winter maximum temperature

and winter precipitation was negative and highly significant, with spring precipitation

and summer precipitation positive and non-significant and with the rest of the climatic

variables negative and non-significant. The correlation between mean annual ring-

width of Chir pine being for non-linear relationships could lead to false results and have

not been interpreted.

215

Table 5-20 Inter-species Comparison of Correlation Coefficients of Ring-widths of

Cedrus deodara, Pinus wallichiana and Pinus roxburghii with Climate Parameters

in GFD (1962-2011)

Climate

parameters

Deodar Blue pine Chir pine

Mean Max. Temp. - 0.302

(0.033)

- 0.530*

(0.000)

- 0.199

(0.166)

Mean Min. Temp. - 0.339

(0.016)

- 0.586*

(0.000)

- 0.211

(0.141)

Mean

Precipitation

- 0.323 *

(0.022)

- 0.115*

(0.425)

0.004

(0.980)

Spring Max.

Temp.

- 0.154 *

(0.283)

- 0.361*

(0.010)

- 0.236

(0.098)

Spring Min.

Temp.

- 0.247 *

(0.083)

- 0.416*

(0.003)

- 0.173

(0.230)

Spring

Precipitation

- 0.034

(0.812)

0.055*

(0.703)

0.153

(0.288)

Summer Max.

Temp.

- 0.077

(0.596)

- 0.375*

(0.007)

- 0.143

(0.323)

Summer Min.

Temp.

- 0.122

(0.398)

- 0.354*

(0.012)

- 0.152

(0.291)

Summer

Precipitation

0.119

(0.412)

0.111*

(0.444)

0.108

(0.455)

Monsoon Max.

Temp.

- 0.172

(0.232)

- 0.219*

(0.126)

- 0.116

(0.421)

216

Monsoon Min.

Temp.

- 0.023

(0.871)

- 0.090*

(0.533)

0.021

(0.887)

Monsoon

Precipitation

- 0.347

(0.014)

- 0.168*

(0.244)

- 0.064

(0.661)

Autumn Max.

Temp.

0.022

(0.881)

- 0.107*

(0.460)

0.039

(0.789)

Autumn Min.

Temp.

- 0.069

(0.632)

- 0.419*

(0.002)

- 0.046

(0.749)

Autumn

Precipitation

- 0.125*

(0.388)

- 0.087*

(0.547)

- 0.074

(0.607)

Winter Max.

Temp.

- 0.421*

(0.002)

- 0.462*

(0.001)

- 0.143

(0.322)

Winter Min.

Temp.

- 0.522*

(0.000)

- 0.645*

(0.000)

- 0.303

(0.032)

Winter

Precipitation

- 0.168

(0.243)

- 0.061*

(0.675)

- 0.075

(0.606)

* Linear relationship. Values in ( ) are p-values; significant (p<0.05); highly significant (p<0.01); non-

significant (p˃0.05)

217

5.8 Discussion

The present study assesses climate change and its impacts on growth, in terms of tree

ring-width and intra-ring wood characteristics, of C. deodara P. wallichiana and P.

roxburghii in Galies Forest Division-Abbottabad. The mean annual ring-widths of C.

deodara P. wallichiana and P. roxburghii, over time period of 1962-2011 were

3.08±0.23 mm, 2.54±0.15 mm and 2.62±0.39 mm respectively. There are several

corresponding narrow and wide marker rings in the species which indicate a growth

response to climatic conditions. Previously, such corresponding narrow and wide marker

rings in C. deodara have been reported from Chitral-Hindukush Range of Pakistan

(Khan et al., 2013) and Aleppo pine in Attica basin (Papadopoulos et al., 2009). The

mean sensitivity (MS) ranged from 0.29 to 0.38, while coefficient of variation (CV)

ranged from 17.53% to19.50%. The present mean sensitivity range is broadly in

agreement with mean sensitivity values of 0.23 to 0.42 reported earlier by Ahmed et al.

(2010) for Picea smithiana, C. deodara, Pinus gerardiana and Juniperus excelsa in the

Upper Indus Basin of Himalayan region of Pakistan. However, differences in present

MS values and those reported by Ahmed and colleagues may be due to site variation.

Besides tree species, MS depends on site (Bogino and Brawo, 2009). The present MS

values show considerable variability of high frequency component of the ring-widths

due to climatic fluctuations, while CV shows low frequency variability caused either by

climate or other long term influences. These tree-ring statistics provide adequate

variation to estimate impacts of climate change through correlation function as

suggested by Rolland (1993) and Speer (2010).

A large variation is observed in intra-ring wood characteristics across the tree species.

In Deodar, mean intra-ring early wood formation is 66.67±0.21%, while mean late

intra-ring wood formation is 32.97±0.20%. The mean intra-ring early wood cell

diameter is 42.57±0.16 µm, with a cell wall thickness of 2.38±0.01 µm. The mean

intra-ring late wood cell diameter is 18.28±0.07 µm, with a cell wall thickness of

4.07±0.01 µm. In Blue pine, mean intra-ring early wood formation is 75.64±0.36%,

while mean intra-ring late wood formation is 24.53±0.37%. The mean intra-ring early

wood cell diameter is 35.85±0.14 µm, with a cell wall thickness of 2.08±0.01 µm. The

mean intra-ring late wood cell diameter is 15.56±0.07 µm, with a cell wall thickness of

3.76±0.02 µm. In Chir pine, mean intra-ring early wood formation is 66.67±0.21%,

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while mean intra-ring late wood formation is 32.97±0.20%. The mean intra-ring early

wood cell diameter is 51.50±0.19 µm, with a cell wall thickness of 2.55±0.01 µm. The

mean intra-ring late wood cell diameter is 20.72±0.10 µm, with a cell wall thickness of

4.86±0.02 µm. These results indicate a two way change in the cell anatomy. The cell

diameter decreases along the ring from early wood to late wood, and, conversely, the

cell wall thickness increases along the ring from early wood to late wood. These

findings are in conformity with earlier findings of Olano (2012)

The present findings reveal a decrease in ring-width with time. A similar change pattern

is followed by early wood formation, early wood cell wall thickness and late wood cell

wall thickness, however, early wood cell diameter, late wood formation and late wood

cell diameter proceed in opposite direction. The early wood formation closely follows

ring-width pattern. This relationship of early wood formation and ring-width is the

likely consequence of that early wood constitutes a major part of ring-width, i.e.,

75.64±0.36%, 66.67±0.21%, 66.67±0.21% of ring-width of P. wallichiana, C. deodara

and P. roxburghii respectively. In corroboration with this trend, the present results

indicate positive correlation between ring-width and early wood formation. Similar

correlation pattern between ring-width and early wood formation and changes in cell

diameter and cell wall thickness of early wood and late wood have been reported in

Juniperus thurifera, by Olano (2012). Bouriaud et al. (2005) has also reported the

increase in cell wall thickness (wood density) with decreasing radial growth rate in

Scots pine.

The observed change in ratio between early wood formation and late wood formation

may have significant effects on tree growth and wood quality. In case of larger early

wood formation, it is more likely that the resin canals will occur in early wood and that

the transition from early wood to late wood will be gradual. Such phenomenon was

observed in Scots pine (Novak et al., 2013). Conversely, larger late wood formation

enhances likelihood of L-ring formation. L-ring formation is related to summer stop

and later restart with another growing cycle in autumn, if the conditions are favorable

(Luis et al., 2011). Such favorable conditions provide longer growing season and

consequently increase in late wood formation. The observed increase in temperature

during autumn and late wood formation indicates longer growth period and L-ring

formation. The complex relationship between ring-width and intra-ring wood features

219

suggests that use of combination of ring-width and intra-ring wood characteristics can

help better interpretation of tree-growth related physiological processes than the use of

tree ring-width alone. Moreover, producing different types and forms of cells by tree

species in different time periods on annual basis may also be interpreted as an

important adaptation of trees which helps in maintaining the balance among the

capacity to conduct water, resistance to cavitation and mechanical stability (Novak et

al., 2013). All this could play an important role in acclimatization or/and adaptation to

new changed climatic conditions. The combination of ring-width and intra-ring wood

characteristics can also be used as a climate multi-proxy and to improve the predictions

of the potential impact of climate change on tree growth and survival. This use of

combination of ring-width and intra-ring wood characteristics for better prediction of

impact of climate change on tree growth and survival has formerly been reported by

Wimmer and Grabner (1997); Wimmer et al. (2000); McCarroll et al. (2003);

Rathgeber et al. (2005); Battipaglia et al., (2010); Campelo et al. (2007); Luis et al.

(2011).

The present study indicates that the information archived in tree-ring statistics and

intra-ring wood characteristics are highly sensitive to year-to-year variation in climatic

conditions. However, the tree species respond differently to these conditions. Some

species are sensitive to temperature while others are sensitive to precipitation. The

pattern and extent of such variability and inferences therefrom for Deodar, Blue pine

and Chir pine have been described in paras 5.4.6, 5.5.6 and 5.6.6, with related

mathematical expressions in Tables 5.6, 5.11 and 5.16 respectively. The critical

limiting factor function of seasonal temperatures and precipitations for ring-width (tree

ring-growth) of C. deodara in Chitral-Hindukush Range has also earlier been reported

by Khan et al. (2013). Similarly, Bouriaud et al. (2005) has reported the strong

influence of precipitation on ring-widths of Beech, Oak and Ash, and of maximum

temperature on Scots pine. A positive influence of January and June-August

precipitation was found on radial growth of Quercus ilex subsp. Ballota (Corcuera et

al., 2004). The positive correlation of spring precipitation and negative correlation of

spring temperature with radial growth of Blue pine and Chir pine are supported by

Bogino et al. (2009) and Papadopoulos et al. (2009). The negative correlation between

ring-width and winter precipitation and positive correlation with summer precipitation

are in agreement with Novak et al. (2013).

220

The impact of climate change on radial growth is in array of Blue pine> Deodar> Chir

pine as indicated by their respective mean sensitivity values of 0.30±0.11, 0.38±0.11 and

0.29±0.10. The different levels of association between growth variation in conifers and

climate changes have been substantiated by Yeh and Wensel (2000) where they found

influences of winter precipitation and summer temperature on coniferous species in

northern California. Nonetheless, the influence of different seasonal climate elements is

integrated into tree ring-width, but since such seasonal climate elements are correlated

among themselves differently, the isolation of the individual climate relationship is

difficult to determine.

The present findings indicate that global warming or any other modification in seasonal

climatic regimes, apart from modifying the growth rates, may also induce changes in

wood structure. For instance, the changes in the early wood and/or late wood widths

may induce modifications in the hydraulic and mechanical properties of wood which

consequently may affect water transport and plant survival (Froux et al., 2002; De

Micco et al., 2008).

The present findings of Climate Vegetation Productivity Index combined with ring-

width data can provide reasonably accurate estimates of yield and biomass production of

C. deodara, P. wallichiana, and P. roxburghii in GFD. The dendrochronological

analysis provides accurate basis to predict growth and yield of trees on large scales,

covering several stands or soil conditions, and over long time series. The variations in

ring-width at breast height have been intensively used for assessing stand yield for

research and practical purposes (Telewski et al., 1999; Bouriaud et al., 2005). Tree ring

analysis can also support identification of various climatic factors that have played a

major role in forest growth and biomass production. Any long term cooling, even of

0.5°C, could adversely affect biomass productivity in boreal forests (Parker and Jozsa,

1973). The foremost important assumption in such type of studies is that ring increment

at breast height is an unbiased predictor of tree volume or biomass increment in conifer

species. The negatively influenced ring-width by increasing temperature and great

variation in precipitation may reduce biomass production of three under study conifer

species. Previously, fluctuations had been found in annual growth at an inter-annual time

step in Fagus sylvatica. These fluctuations were influenced by climate during the

221

growing season, particularly drought events. Ring-area increments were more strongly

reduced at breast height compared to upper parts of the tree during dry years (Bouriaud

et al., 2005).

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CHAPTER 6

SUMMARY, GENERAL CONCLUSIONS AND

RECOMMENDATIONS

6.1 Summary

Climate change is the most serious global issue having multifaceted impacts on

environment, socio-economic and economic parameters. The changing climate is also

affecting forest productivity, health and biodiversity. The present study was conducted

during 2009-14 to assess climate change, bioclimatic indices and their impacts on the

growth of three major coniferous species, i.e., C. deodara, P. Wallachia, P. roxburghii

in Galies Forest Division-Abbottabad (Pakistan) during 1962-2011.

Literature review was done to extract the updated research knowledge on the subject,

and identify gaps for undertaking further research.

The materials and methods used were secondary data on climate parameters obtained

from web-based archives of Climate Research Unit, University of East Anglia, UK, and

local Observatory, satellite imageries, General Topographic (GT) sheets procured from

Survey of Pakistan and primary data collected from sampled trees increment cores and

stem discs samples from the study area. The maps were prepared in GIS-RS

Laboratory, Pakistan Forest Institute, Peshawar and the samples collected were studied

and analyzed in Annual Ring Measuring Laboratory (ARM Lab.), Pakistan Forest

Institute, Peshawar, using instruments, including orbital sander, sledge microtome,

Canada balsam, Digital Positiometer with Microcomputer-based measuring system and

digital compound microscope linked with computer based measuring system.

Climate parameters, including maximum temperature, minimum temperature, mean

temperature and precipitation, and trends thereof were assessed, both on annual and

seasonal basis, from the monthly climate data for the study area covering a period of

1962-2011. Bioclimatic indices, namely, TEI, AI, DI, RF, DF, HC and PEI and CVPI

were calculated and changes therein were assessed. Trees growth characteristics,

including ring-width, early wood formation, late wood formation, radial cell diameter

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and cell wall thickness were measured and analyzed using mean values of samples of 20

trees, with four replications comprising five trees of each selected species. The data were

organized on annual, seasonal and decadal basis and analyzed by applying Mann

Kendall test with Normal Approximation and Sen’s Slope Estimator method, regression

analysis, 1-Way analysis of variance (ANOVA, Tukey’s Honest Significance Difference

(HSD) test and Pearson Correlation formula.

Arc GIS software, ERDAS Imagine software Statistical software, Minitab v. 15.1,

XLSTAT and MS Office Excel were used for data processing, graphics and database

management.

The results indicated that mean annual maximum temperature, mean annual minimum

temperature and mean annual temperature at GFD, during the time period of 1962-

2011, were 16.36±0.08 °C, 6.08±0.08 °C and 11.21±0.07 °C respectively. The highest

mean seasonal maximum temperature was 23.46±0.08 °C during monsoon, which was

marginally higher compared to 23.09±0.15 °C during summer, while the lowest mean

seasonal maximum temperature was 6.78±0.12 °C during winter. The highest mean

seasonal minimum temperature was 13.12±0.07 °C during monsoon, while the lowest

mean seasonal minimum temperature was 2.01±0.14 °C during winter. The mean

seasonal minimum temperature during summer was slightly lower compared to

monsoon. The mean seasonal minimum temperatures of spring and autumn were nearly

equal. The mean seasonal maximum temperature was 18.27±0.07 °C during monsoon

and the mean seasonal minimum temperature was 2.39±0.12 °C during winter. Mean

annual precipitation at GFD, during 1962-2011, was 889.48±19.43 mm. The wettest

season was monsoon having mean precipitation of 345.06±13.50 mm/season, while

autumn was the driest season with mean precipitation of 46.67±3.01 mm/season. The

spring and winter were moderately wet with mean precipitation of 198.50±9.68

mm/season and 180.53±8.14 mm/season respectively.

The results of trend analysis of climate change at GFD during 1962– 2011 indicated an

upward trend in mean maximum and mean minimum temperatures on annual scale as

well as on seasonal scales, except monsoon minimum temperature and autumn

maximum temperature which did not exhibit any trend. The mean precipitation on

annual scale and seasonal scales also did not exhibit any trend. The regression analysis

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and associated ANOVA table also produced similar results, except for mean spring

precipitation and mean autumn precipitation where the changes were indicated as

negative and significant and positive and significant respectively.

The results indicated changes, mostly upward, in maximum, minimum and mean

temperature, both on annual and seasonal basis at GFD during 1962-2011. The mean

maximum temperature, mean minimum temperature and mean annual temperature

increased by 1.10 °C, 1.32 °C and 1.22 °C, and the mean annual precipitation by

1.39%, during 1962-2011. The changes in these parameters on seasonal basis varied

from season to season. The increases in temperature parameters on interannual basis

were highly significant (p<0.01) and the increase in precipitation non-significant

(p>0.05). The increase in maximum temperature was highly significant (p<0.01) during

winter, significant (p<0.05) during spring, summer, and autumn and non-significant

(p>0.05) during autumn. The increase in minimum temperature was highly significant

(p<0.01) during spring, summer and winter, significant (p<0.05) during autumn and

non-significant (p>0.05) during monsoon. The increase in mean temperature was

highly significant (p<0.01) during spring and winter, significant (p<0.05) during

summer, monsoon and autumn. On seasonal basis, the changes in precipitation were:

significant (p<0.05) decrease of -14.90% in spring, non-significant (p>0.05) decrease of

-9.95% during summer, and significant (p<0.05) increase of 8.94% during monsoon

and non-significant (p>0.05) increase of 11.81% and 12.04% during autumn and winter

respectively. Among the seasons, the highest increase was 2.37 °C in mean minimum

temperature during winter. The lowest increase was 0.35 °C in mean minimum

temperature during monsoon. The increase in mean minimum temperature was

relatively higher than mean maximum temperature. The increase in mean maximum

temperature and mean minimum temperature during spring and autumn indicated

shortening of winter period and extending summer period. The analysis showed an

overall increase of 1.39% in mean annual precipitation during 1962-2011. The mean

seasonal precipitation increased by 8.94%, 11.81%, and 12.04% during monsoon,

autumn and winter respectively. Conversely, the mean seasonal precipitation decreased

by 14.90% and 9.85% during spring and summer respectively.

Mathematical expressions of temperature and precipitation changes during 1962-2011

showed both linear and quadratic behaviors. The R2 for linear functions ranged between

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0.01 and 0.50, while the R2 for quadratic functions ranged between 0.02 and 0.39, thus

indicating good fit of models for some climate parameters and poor fit of models for

others, especially precipitation.

The Pearson Correlation Coefficients matrix showed a highly significant (p<0.01)

positive correlation between maximum temperature and mean temperature (r =

0.94) and minimum temperature and mean temperature (r = 0.97). The correlation

of precipitation with mean temperature, maximum temperature and minimum

temperature was significant but negative.

The bioclimatic indices regimes varied along years and among seasons. During 1962-

2011, mean annual regime of TEI was 5.04±0.04, while monsoon and winter had the

highest and the lowest regimes of 4.52±0.03 and 1.07±0.06 respectively. The mean

annual regime of AI was 73.25±1.82, while the highest AI regime of 61.21±6.05 was

during winter and the lowest 4.25±0.29 in autumn. The mean annual regime of DI and

mean annual regime of RF were 39.92±1.01 and 79.84±0.12 respectively. The highest

and the lowest regimes of DI and RF followed the pattern of AI. The mean annual

regime of DF was 49.00±0.12, while winter and autumn had the highest and the lowest

DF regimes of 19.46±0.05 and 2.75±0.11 respectively. The DF was higher in monsoon

compared to spring. The HC varied among seasons with mean annual HC of

74.62±2.43. The highest HC was 72.87±2.76 in winter and the lowest was 4.37±0.30 in

autumn. The mean annual regime of PEI was 10.63±0.25. The highest PEI was

estimated for winter and the lowest for autumn.

During 1962-2011, increase in mean annual TEI was 11.53%, with the highest increase

of 55.08% during winter. Conversely, the mean annual AI decreased by 7.92%, with the

highest decrease of -59.59% during winter. The mean annual DI, mean annual RF,

mean annual DF, mean annual HC and mean annual PEI decreased by -6.40%, -8.72%,

-4.95%, -8.72% and -3.57% respectively. The highest decrease in mean annual DI,

mean annual RF and mean annual HC of -42.79%, -42.62% and -42.82% was during

winter and mean annual DF and mean annual PEI of -23.35% and -21.30% was during

spring respectively. The results also indicated that mean annual changes in TEI were

positive and highly significant, while in DI, RF and DF were negative and significant.

The changes in AI, HC and PEI were negative and non-significant. The mean seasonal

226

changes in TEI were positive and significant in summer and autumn and highly

significant in spring and winter. The mean seasonal changes in all other indices were

negative in spring, summer and winter and positive in monsoon and autumn. The mean

seasonal changes in AI during monsoon and winter and in DI during monsoon were

significant, while in all other indices were non-significant.

Mathematical expressions of changes in bioclimatic indices at GFD during 1962-2011

varied in pattern and exhibited linear, quadratic and polynomial forms. The annual and

seasonal mathematical expressions for TEI were associated with significant and highly

significant higher values of R2, indicating good fit of equations, while the expressions

for other indices were mostly non-significant with smaller values of R2, and indicating

poor fit of equations.

The Climate Vegetation Productivity Index (CVPI) ranged between 4,342 and 9,091,

with a mean of 6,816. The highest CVPI was estimated during 2003 and the lowest

during 1971, with an overall increasing trend. The mean CVPI of 6,816 puts GFD in

ideal site class with productivity in the range of 163.91-184.77 cubic feet/acre.

Cross-dating of the samples of the selected trees was done to establish precise

chronology of the annual growth rings for the period 1962-2011. The results were

graphically reproduced, where the thickness of the lines was kept proportional to the

annual ring-width size of the sampled trees and the vertical variations across the lines

indicated variability in the year-wise growth of the tree-rings across the 20 sampled

trees.

The biological growth trend of ontogeny - decrease in ring-widths with increasing tree

age – and effects of other non-climatic site factors were removed by applying

'standardization' procedure, and the standardized data was used for analysis of the

results.

The results of sensitivity analysis revealed that the mean annual ring-widths of C.

deodara, P. wallichiana and P. roxburghii for the period 1962-2011 were 3.08 mm,

2.54 mm and 2.62 mm, the standard error of the mean annual ring-widths were 0.23

mm, 0.15 mm and 0.39 mm and the variances were 1.0 mm, 0.46 mm and 3.10 mm

227

respectively. The variability of intra-species annual ring-widths was the highest in P.

roxburghii, followed by C. deodara and P. wallichiana. The variability in samples for

the same species was caused by both climatic and non-climatic factors, like ontogeny,

site disturbances and changes in forest crop conditions. The sensitivity analysis was

done to describe the impacts of climate factors on the growth parameters. The mean

sensitivity was calculated to describe variability of high frequency component of the

ring-width due to climatic fluctuations, while the coefficient of variation was calculated

for low frequency component variability induced either by climate or by other long

term influences. The values of mean sensitivity of mean annual ring-width of C.

deodara, P. wallichiana and P. roxburghii for the period 1961-2011were estimated at

0.30±0.11, 0.38±0.11 and 0.29±0.10 respectively. The highest mean sensitivity of 0.38

was estimated for P. wallichiana and the lowest of 0.29 for P. roxburghii. The mean

annual ring-width for the period 1962-2011 was relatively larger in C. deodara (3.08

mm) compared to P. wallichiana (2.54 mm) and P. roxburghii (2.62 mm). The variance

of mean annual ring-widths of C. deodara and P. wallichiana were nearly of equal

magnitude, while that of P. roxburghii was slightly lower compared to the other two

species. The highest coefficient of variation of 19.50% was observed in P. wallichiana

and the lowest coefficient of variation of 17.53% in P. roxburghii. The results of mean

sensitivity and coefficient of variation calculated for the three species indicated enough

variability in growth statistics to enable analysis of its time function, correlation and

regression with climate parameters and changes thereof.

The time function analysis of ring-width and ring-wood characteristics of Deodar for



formation, late wood cell diameter and late wood cell wall thickness, and no trend in

early wood cell diameter and early wood cell wall thickness. The mean annual ring-

width showed a quadratic time function, and ranged from 2.24±0.25 mm to 4.37±0.26

mm, with a mean value of 3.08±0.23. The mean intra-ring early wood formation

showed a linear time function, and ranged from 71.34±1.51% to 80.72±0.88%, with a

mean value of 75.64±0.36% of the mean annual wood formation. The mean intra-ring

late wood formation showed a quadratic time function, and ranged from 19.11±0.92%

to 28.66±2.01%, with a mean value of 24.53±0.37% of the mean annual wood

formation. The mean intra-ring early wood cell diameter showed a quadratic time

228

function, and ranged from 33.38±1.10 µm to 37.59±3.15 µm, with a mean value of

35.85±0.14 µm. The mean intra-ring early wood cell wall thickness showed a

polynomial time function, and ranged from 1.93±0.06 µm to 2.33±0.32 µm, with a

mean value of 2.08±0.01 µm. The mean intra-ring late wood cell diameter showed a

quadratic time function, and ranged between 14.60±0.59 µm and 16.86±0.69 µm, with

a mean of 15.56±0.07 µm. The mean intra-ring late wood cell wall thickness showed a

quadratic time function, and ranged from 3.52±0.12 µm to 4.00±0.13 µm, with a mean

of 3.76±0.02 µm.

Mathematical expressions of time functions of mean annual ring-width, mean intra-ring

wood formation and wood cell characteristics of Deodar showed a mix of linear,

quadratic and polynomial behaviors. The mean annual ring-width, mean intra-ring early

wood formation, mean intra-ring late wood formation, mean intra-ring late wood cell

diameter and mean intra-ring late wood cell wall thickness showed highly significant

(p<0.01) changes with time. Conversely, temporal changes in mean intra-ring early

wood cell diameter and mean intra-ring early wood cell wall thickness were non-

significant (p>0.05). The R2 ranged between 0.07 and 0.77. The analysis indicated that

linear model had good fit for time function of mean intra-ring early wood formation,

quadratic model had good fit for mean annual ring-width, mean intra-ring late wood

formation and mean intra-ring late wood cell wall thickness, but poor fit for mean intra-

ring early wood cell wall diameter and mean intra-ring early wood cell thickness. The

polynomial model had good fit for time function of early wood cell wall thickness.

The decadal analysis of ring-width and ring-wood characteristics of Deodar for the

period 1962-2011 showed a highly significant (F4, 15= 400.56; p<0.01) difference in

mean decadal ring-widths, with a decreasing trend. The overall difference in mean

decadal ring-widths among the decades was significant (Tukey’s HSD, CV 0.11;

p=0.05). A highly significant (F4, 15= 51.66; p<0.01) difference was recorded in mean

decadal intra-ring early wood formation, with a decreasing trend. The overall difference

in mean decadal intra-ring early wood formation among the decades was significant

(Tukey’s HSD, CV 1.49; p=0.05). A highly significant (F4, 15= 87.26; p<0.01)

difference was recorded in mean decadal intra-ring late wood formation, with an

increasing trend. The overall difference in mean decadal intra-ring late wood formation

among the decades was significant (Tukey’s HSD, CV 1.15; p=0.05). A highly

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significant (F4, 15= 17.11; p<0.01) difference was recorded in mean decadal intra-ring

early wood cell diameter, with an overall decreasing trend. The difference in mean

decadal intra-ring early wood cell diameter among the decades was significant (Tukey’s

HSD, CV 1.09; p=0.05). A highly significant (F4, 15= 17.11; p<0.01) difference was

recorded in mean decadal intra-ring early wood cell thickness, with an overall

decreasing trend. The difference in mean decadal intra-ring early wood cell diameter



late wood cell diameter, with an overall increasing trend. The difference in mean

decadal intra-ring late wood cell diameter among the decades was significant (Tukey’s

HSD, CV 0.59; p= 0.05). A highly significant (F4, 15= 78.93; p<0.01) difference was

recorded in mean decadal intra-ring late wood cell wall thickness, with an increasing

trend. The difference in mean decadal intra-ring late wood cell wall thickness among

the decades was significant (Tukey’s HSD, CV 0.07; p= 0.05).

The mean annual ring-width of Deodar exhibited a highly significant and positive

correlation with mean intra-ring early wood formation (r = 0.90) and mean intra-ring

early wood cell diameter (r = 0.78), but highly significant and negative with mean intra-


-0.93) and mean intra-ring late wood cell wall thickness (r = -0.93). The correlation

was, however, non-significant and negative with mean intra-ring late wood cell

diameter (r = -0.31). The correlation of mean intra-ring early wood formation was

highly significant and positive with mean intra-ring early wood cell diameter (r = 0.62),

but highly significant and negative with mean intra-ring early wood cell wall thickness

(r = -0.82), mean intra-ring late wood formation (r = -0.95), and mean intra-ring late

wood cell wall thickness (r = -0.86). The correlation of mean intra-ring early wood

formation was non-significant and negative with mean intra-ring late wood cell

diameter (r = -0.19). The correlation of mean intra-ring early wood cell diameter was

highly significant and negative with mean intra-ring early wood cell wall thickness (r =

-0.57), mean intra-ring late wood formation (r = -0.63) and mean intra-ring late wood

cell wall thickness (r = -0.61), but non-significant and negative with mean intra-ring late

wood cell diameter (r = -0.04). The correlation of mean intra-ring early cell wall

thickness was highly significant and positive with mean intra-ring late wood formation

(r = 0.88) and mean intra-ring late wood cell wall thickness (r = 0.88), but non-

230

significant and positive with mean intra-ring late wood cell diameter (r = 0.40). The

correlation of mean intra-ring late wood formation was highly significant and positive

with mean intra-ring late wood cell wall thickness (r = 0.91), but non-significant and

positive with mean intra-ring late wood cell wall diameter (r = 0.29). The correlation of

mean intra-ring late wood cell diameter was significant and positive with mean intra-

ring late wood cell wall thickness (r = 0.44).

The mean annual maximum temperature, mean annual minimum temperature and mean

annual precipitation had significant negative impacts on mean annual ring-width of

Deodar. Similarly, the mean maximum temperature during winter, the mean minimum

temperature during autumn and winter had significant negative impacts on mean annual

ring-width. A large variation in mean annual ring-width response was noted across the

observed range of precipitation, with growth response more clustered around annual

precipitation range of 800-1100 mm. The mean annual precipitation between 600

mm/annum and 700 mm/annum produced the largest mean annual ring-width of

3.40±0.10 mm. The mean precipitation during monsoon (July-September) had a

positive impact on mean annual ring-width. The largest ring-width was 3.47±0.19 mm

when monsoon precipitation ranged from 250 mm/season to 350 mm/season.


deodar showed linear to polynomial patterns. The mean annual minimum temperature

showed higher impact on mean annual ring-width (R2 = 0.23) compared to mean annual

maximum temperature (R2 = 0.17). Among the seasons, mean winter minimum

temperature showed the highest impact (R2 = 0.27), followed by mean winter maximum

temperature (R2 = 0.18) and mean autumn minimum temperature (R2 = 0.18). The

impacts of temperature, both maximum and minimum during other seasons, were

marginal. The impacts of mean annual precipitation and mean monsoon precipitation

were significant. The impact of mean monsoon precipitation was higher (R2 = 0.14)

compared to mean annual precipitation (R2 = 0.10). There was non-significant

difference in the impacts of other seasonal precipitation.

231

The time function analysis of ring-width and ring-wood characteristics of Blue pine for



formation, significant (p<0.05) downward trend in early wood cell wall thickness and

no trend in early wood cell diameter, late wood cell diameter and late wood cell wall

thickness. The mean annual ring-width showed a quadratic time function, and ranged

from 1.85±0.27 to 3.33±0.31 mm, with a mean value of 2.54±0.15. The mean intra-ring

early wood formation showed a quadratic time function, and ranged from 72.12±1.80%

to 78.87±1.51%, with a mean value of 76.67±0.21% of the mean annual wood

formation. The mean intra-ring late wood formation showed a quadratic time function,

and ranged from 20.53±1.42% to 28.97±1.95%, with a mean value of 23.37±0.20 of the

mean annual wood formation. The mean intra-ring early wood cell diameter showed a

polynomial time function, and ranged from 40.05±1.48 µm to 45.07±1.08 µm, with a

mean of 42.57±0.16 µm. The mean intra-ring early wood cell wall thickness showed a

quadratic time function, and ranged from 2.25±0.06 µm to 2.57±0.19 µm, with a mean

of 2.38±0.01 µm. The mean intra-ring late wood cell diameter showed a quadratic time

function, and ranged from 17.42±0.61 µm to 19.55±0.50 µm, with a mean of

18.28±0.07 µm. The mean intra-ring late wood cell wall thickness showed a quadratic

time function, and ranged from 3.90±0.13 µm to 4.30±0.13 µm, with a mean of

4.07±0.01 µm.

Mathematical expressions of mean annual ring-width, mean intra-ring wood formation

and cell characteristics of Blue pine showed a mix of quadratic and polynomial

behaviors of time function. The mean annual ring-width, mean intra-ring early wood

formation and mean intra-ring late wood formation showed highly significant (p<0.01)

changes with time. The mean intra-ring early wood cell diameter, mean intra-ring early

wood cell wall thickness and mean intra-ring late wood cell diameter showed

significant (p<0.05) temporal response. Conversely, temporal change in mean intra-ring

late wood cell wall thickness was not significant (p>0.05). The R2 ranged between 0.06

and 0.51. The highest R2 value was calculated for mean intra-ring annual ring-width,

while the lowest R2 value was calculated for mean intra-ring late wood intra-ring cell

wall thickness. The models used for time function response indicated good fit of the

models for mean annual ring-width, mean intra-ring wood formation and mean wood

cell characteristics except mean intra-ring late wood cell wall thickness.

232

The decadal analysis of ring-width and ring-wood characteristics of Blue pine for the


mean decadal ring-widths, with a decreasing trend. The overall difference in mean

decadal ring-widths among the decades was significant (Tukey’s HSD, CV 0.14;









early wood cell diameter, with an overall increasing trend. The difference in mean


HSD, CV 0.77; p=0.05). A highly significant (F4, 15= 179.39; p<0.01) difference was

recorded in mean decadal intra-ring early wood cell diameter, with an overall


among the decades was significant (Tukey’s HSD, CV 0.02; p= 0.05). A highly


late wood cell diameter, with an overall decreasing trend. The difference in mean

decadal intra-ring late wood cell diameter among the decades was significant (Tukey’s

HSD, CV 0.36; p= 0.05). A highly significant (F4, 15= 19.47; p<0.01) difference was

recorded in mean decadal intra-ring late wood cell wall thickness, with a decreasing

trend. The difference in mean decadal intra-ring late wood cell wall thickness among

the decades was significant (Tukey’s HSD, CV 0.15; p= 0.05).

The mean annual ring-width of Blue pine exhibited a highly significant and positive

correlation with mean intra-ring early wood formation (r = 0.80), mean intra-ring early

wood cell wall thickness (r = 0.96) and mean intra-ring late wood cell wall thickness (r

= 0.89). The correlation was, however, highly significant and negative with mean intra-

ring early wood cell wall diameter (r = -0.80), mean intra-ring late wood formation (r =

-0.89) and mean intra-ring late wood cell diameter (r = -0.70). The correlation of mean

intra-ring early wood formation was highly significant and positive with mean intra-

233

ring early wood cell wall thickness (r = 0.84) and mean intra-ring late wood cell wall

thickness (r = 0.69), but highly significant and negative with mean intra-ring early wood

cell diameter (r = -0.74), mean intra-ring late wood cell formation (r = -0.91) and mean

intra-ring late wood cell diameter (r = -0.75). The correlation of mean intra-ring early

wood cell diameter was highly significant and positive with mean intra-ring late wood

formation (r = 0.78) and mean intra-ring late wood cell diameter (r = 0.86), but highly

significant and negative with mean intra-ring early wood cell wall thickness (r = -0.81)

and mean intra-ring late wood cell wall thickness (r = -0.71). The correlation of mean

intra-ring early wood cell wall thickness was highly significant and positive with mean

intra-ring late wood cell wall thickness (r = 0.90), but highly significant and negative

with mean intra-ring late wood formation (r = -0.94) and mean intra-ring late wood cell

diameter (r = -0.73). The correlation of mean intra-ring late wood formation was highly

significant and positive with mean intra-ring late wood cell diameter (r = 0.80), but

highly significant and negative with mean intra-ring late wood cell wall thickness (r = -

0.86). The correlation of mean intra-ring late wood cell diameter was highly significant

and negative with mean intra-ring late wood cell wall thickness (r = -0.68).

The mean annual maximum temperature and mean annual minimum temperature had

significant negative impacts on mean annual ring-width of Blue pine. By seasons,

maximum temperature and minimum temperature during spring, summer and winter

and minimum temperature during autumn had negative impacts on mean annual ring-

width. The annual precipitation showed a non-significant impact on mean annual ring-

widths of Blue pine. The mean annual ring-width decreased with increasing annual

precipitation. A large variation in mean annual ring-width response was noticed across

the observed range of precipitation, with relatively better growth (mean annual ring-

width >3.00 mm) when annual precipitation was in the range of 800-1000 mm. The

precipitation between 600 mm/annum and 700 mm/annum had the largest mean annual

ring-width of 2.64±0.33 mm.

Mathematical expressions of impacts of climate change on mean annual ring-width of

Blue pine showed a linear pattern. The mean annual maximum temperature (R2 = 0.34)

showed a higher negative impact on mean annual ring-width compared to mean annual

minimum temperature (R2 = 0.28). Among the seasons, mean winter minimum

temperature showed the highest (R2= 0.41) impact followed by mean winter maximum

234

temperature (R2 =0.21), mean autumn minimum temperature (R2 = 0.18), mean spring

minimum temperature (R2 = 0.17) and mean summer maximum temperature (R2 =

0.14). The impacts of mean spring maximum temperature and mean summer minimum

temperature were the same. The impacts of mean monsoon maximum temperature and

mean monsoon minimum temperature and mean autumn maximum temperature were

non-significant. The mean autumn maximum temperature showed the least impact on

mean annual ring-width among all seasons. The impacts of mean annual precipitation

and mean seasonal precipitations were non-significant. Among the mean seasonal

precipitations, the mean autumn precipitation showed the highest impact on mean

annual ring-width.

The time function analysis of ring-width and ring-wood characteristics of Chir pine for

the period 1962-2011 showed highly significant (p<0.01) downward trend in late wood

cell wall thickness and significant (p<0.05) downward trend in early wood cell diameter

and no trend in ring-width, early wood formation, late wood formation, early wood cell

wall thickness and late wood cell diameter. The mean annual ring-width showed a

quadratic time function, and ranged from 2.00±0.39 mm to 3.66±0.55 mm, with a mean

value of 2.62±0.39 mm. The mean intra-ring early wood formation showed a quadratic

time function, and ranged from 64.22±1.31% to 69.95±1.94%, with a mean value of

66.67±0.21%, of mean annual wood formation. The mean intra-ring late wood

formation showed a quadratic time function, and ranged from 29.92±1.90% to

35.58±2.17%, with a mean value of 32.97±0.20% of the mean annual wood formation.

The mean intra-ring early wood cell diameter showed a linear time function, and ranged

from 48.46±1.51 µm to 54.53±1.25 µm, with a mean of 51.50±0.19 µm. The mean

intra-ring early wood cell wall thickness showed a quadratic time function, and ranged

from 2.70±0.08 µm to 2.38±0.08 µm, with a mean of 2.55±0.01 µm. The mean intra-

ring late wood cell diameter showed a quadratic time function, and ranged from

19.39±0.76 µm to 22.12±1.57 µm, with a mean of 20.72±0.10 µm. The mean intra-ring

late wood cell wall thickness showed a linear time function, and ranged from 4.56±0.19

µm to 5.24±0.18 µm, with a mean of 4.86±0.02 µm.

Mathematical expressions of time functions of ring-width, intra-ring wood formation

and cell characteristics of Chir pine showed a mix of linear and quadratic behaviors.

The mean annual ring-width, mean intra-ring early wood formation, mean intra-ring

235

late wood formation, mean intra-ring early wood cell wall thickness, mean intra-ring

late wood cell diameter and mean intra-ring late wood cell wall thickness followed a

quadratic function, while mean intra-ring early wood cell diameter followed a linear

function. The R2 ranged between 0.03 and 0.31. The highest R2 value was estimated for

mean intra-ring early wood formation, followed by mean intra-ring late wood cell wall

thickness. The lowest R2 value was calculated for mean intra-ring late wood cell

diameter followed by mean intra-ring early wood cell wall thickness. The results of

time function response indicated good fit of the models for mean annual ring-width,

mean intra-ring early, mean intra-ring late wood formation and mean intra-ring late

wood cell wall thickness. The models were poorly fit for mean intra-ring early wood

cell diameter, mean intra-ring early wood cell wall thickness and mean intra-ring late

wood cell diameter.

The decadal analysis of ring-width and ring-wood characteristics of Chir pine for the


mean decadal ring-widths, with an irregular declining trend. The overall difference in

mean decadal ring-widths among the decades was significant (Tukey’s HSD, CV 0.03;







among the decades was significant (Tukey’s HSD, CV: 0.39; p=0.05). A highly


early wood cell diameter, with an overall increasing trend. The difference in mean


HSD, CV: 0.80; p=0.05). A highly significant (F4, 15= 475.93; p<0.01) difference was

recorded in mean decadal intra-ring early wood cell diameter, with an overall


among the decades was (Tukey’s HSD, CV: 0.02; p= 0.05). A highly significant (F4,

15= 40.69; p<0.01) difference was recorded in mean decadal intra-ring late wood cell

diameter, with an overall decreasing trend. The difference in mean decadal intra-ring

late wood cell diameter among the decades was significant (Tukey’s HSD, CV 0.68; p=

236

0.05). A highly significant (F4, 15= 151.54; p<0.01) difference was recorded in mean

decadal intra-ring late wood cell wall thickness, with a decreasing trend. The difference

in mean decadal intra-ring late wood cell wall thickness among the decades was

significant (Tukey’s HSD, CV: 0.09; p= 0.05).

The mean annual ring-width of Chir pine exhibited a significant and positive correlation

with mean intra-ring early wood formation (r = 0.47), mean intra-ring early wood cell

diameter (r = 0.45) and mean intra-ring late wood cell diameter (r = 0.46). The

correlation was, however, non-significant and negative with mean intra-ring early wood

cell wall thickness (r = - 0.34), but non-significant and positive with mean intra-ring

late wood formation (r = 0.31) and mean intra-ring late wood cell wall thickness (r =

0.42). The correlation of mean intra-ring early wood formation was highly significant

and positive with mean intra-ring early wood cell diameter (r = 0.64), mean intra-ring

late wood formation (r = 0.83), mean intra-ring late wood cell diameter (r = 0.82) and

mean intra-ring late wood cell wall thickness (r = 0.92), but highly significant and

negative with mean intra-ring early wood cell wall thickness (r = - 0.75). The

correlation of mean intra-ring early wood cell diameter was highly significant and

positive with mean intra-ring late wood formation (r = 0.86) and mean intra-ring late

wood cell wall thickness (r = 0.75), significant and positive with mean intra-ring late

wood cell diameter (r = 0.46), but non-significant and negative with mean intra-ring

early wood cell wall thickness (r = -0.25). The correlation of mean intra-ring early

wood cell wall thickness was highly significant and negative with mean intra-ring late

wood cell wall diameter (r = -0.90) and mean intra-ring late wood cell wall thickness (r

= -0.75), but significant and negative with mean intra-ring late wood formation (r = -

0.52). The correlation of mean intra-ring late wood formation was highly significant

and positive with mean intra-ring late wood cell wall diameter (r = 0.70) and mean

intra-ring late wood cell wall thickness (r = 0.93). The correlation of mean intra-ring

late wood cell diameter was highly significant and positive with mean intra-ring late

wood cell wall thickness (r = 0.84).

The mean annual maximum temperature, mean annual minimum temperature and mean

autumn minimum temperature had significant negative impacts on mean annual ring-

width of Chir pine, while annual precipitation showed a non-significant impact on mean

annual ring-widths in a polynomial pattern. The largest mean annual ring-width was

237

3.35±0.40 mm when annual precipitation was >1,001 mm/annum. Relatively better

growth response of Chir pine (mean annual ring-width >3.0 mm) was observed at

certain points and ranges of annual and seasonal temperatures and precipitation.


Chir pine showed a mix of quadratic and polynomial functions. The mean annual

maximum temperature (R2 = 0.13) showed a higher impact on mean annual ring-width

compared to mean annual maximum temperature (R2 = 0.12) and mean annual

precipitation (R2 = 0.12). Among the seasons, mean autumn minimum temperature

showed the highest (R2 = 0.26) impact, followed by mean spring maximum (R2 = 0.18)

and mean winter minimum temperature (R2 = 0.11). The impacts of mean temperatures,

both maximum and minimum during other seasons, were marginal. The impact of mean

monsoon precipitation (R2 = 0.09) was relatively higher compared to impacts of other

mean seasonal precipitations.

The mathematical expressions of correlation coefficients of ring-widths of Cedrus

deodara, Pinus wallichiana and Pinus roxburghii with climate parameters indicated

linear patterns in some cases and non-linear patterns in others. A matrix of Pearson

Correlation Coefficients drawn for ring-widths of Cedrus deodara, Pinus wallichiana

and Pinus roxburghii with climate parameters indicated the comparative direction and

strength of the relationships. As the Pearson Correlation Coefficient predicts the

direction and strength of only linear relationship of associated variables, therefore, the

interpretation of the matrix were confined to linear pattern only. The correlation

between mean annual ring-width of Deodar with mean annual precipitation was

negative and significant, with winter maximum temperature and winter minimum

temperature negative and highly significant and with summer maximum temperature

and summer minimum temperature negative and non-significant. The correlation

between mean annual ring-width of Blue pine with summer minimum temperature was

negative and significant, with annual maximum temperature, annual minimum

temperature, spring maximum temperature, spring minimum temperature, summer

maximum temperature, winter maximum temperature and winter precipitation was

negative and highly significant, with spring precipitation and summer precipitation

positive and non-significant and with the rest of the climatic variables negative and

238

non-significant. The correlation between mean annual ring-width of Chir pine being for

non-linear relationships could lead to false results and have not been interpreted.

6.2 General Conclusions

Based on the results of this study, the following general conclusions are drawn for

GFD during 1962-2011:

The mean annual maximum temperature, mean annual minimum temperature,

mean annual temperature and mean annual precipitation were 16.36±0.08 °C,

6.08±0.08 °C, 11.21±0.07 °C and 889.48±19.43 mm/annum respectively.

The monsoon was the warmest season, followed by summer, while winter was

the coldest season, followed by autumn.

The mean maximum and mean minimum temperatures, on annual as well as

seasonal scales, exhibited an upward trend, except monsoon minimum

temperature and autumn maximum temperature, which along with mean

precipitation on annual and seasonal scales, did not exhibit any trend.

The regression analysis produced similar results, except for mean spring

precipitation and mean autumn precipitation where the changes were significant

and negative and significant and positive respectively.

The mean maximum temperature, mean minimum temperature and mean

annual temperature increased by 1.10 °C, 1.32 °C and 1.22 °C, and the mean

annual precipitation by 1.39%.

The observed temporal increases in annual temperature parameters were highly

significant and in annual precipitation non-significant.

The highest increase was in mean minimum temperature during winter, and the

lowest in mean minimum temperature during monsoon.

The increase in mean minimum temperature was relatively higher than mean

maximum temperature.

The increase in mean maximum temperature and mean minimum temperature

during spring and autumn indicated shortening of winter period and extending

summer period.

Among the seasons, the highest increase occurred in minimum temperature.

Winter was becoming relatively warmer followed by autumn compared to other

seasons.

239

The mean seasonal precipitation increased during monsoon, autumn and winter,

and decreased during spring and summer.

An overall increase in precipitation was recorded, with the highest increase in

winter followed by autumn and the highest decrease in spring followed by

summer.

The mathematical expressions of temperature and precipitation changes showed

a mix of linear and quadratic behaviors. The values of R2 indicated good fit of

models for some climate parameters and poor fit of models for others, especially

precipitation.

The correlations between maximum temperature and mean temperature and

minimum temperature and mean temperature were highly significant and

positive, and of precipitation with mean temperature, maximum temperature and

minimum temperature were significant and negative.

The bioclimatic indices varied considerably with climate change.

The TEI increased both vertically and horizontally, but the other six indices

decreased vertically and on seasonal basis during spring, summer and winter.

The decrease of the indices in some seasons, particularly spring and summer,

had strong limiting impacts on forest growth, productivity and composition.

The Climate Vegetation Productivity Index (CVPI) ranged between 4,342 and

9,091, with a mean value of 6,816, and exhibited an increasing trend in a

quadratic pattern.

The mean annual ring-widths of C. deodara, P. wallichiana and P. roxburghii

were 3.08±0.23 mm, 2.54±0.15 mm and 2.62±0.39 mm and the coefficients of

variation were 32.88%, 26.55% and 67.20% respectively.

The variability of intra-species annual ring-widths was the highest in P.

roxburghii, followed by C. deodara and P. wallichiana.

The mean sensitivity of mean annual ring-width of C. deodara, P. wallichiana

and P. roxburghii were estimated at 0.30±0.11, 0.38±0.11 and 0.29±0.10

respectively.

The time function analysis of ring-widths and early wood formation of C.

deodara and P. wallichiana and P. roxburghi indicated an overall downward

trends for the first two and no trends for the third one, while late wood

formation showed downward trend for the first two and no trend for the third

240

one. The cell early and late wood diameters and cell wall thickness showed

varying trends: upward, downward and no-trend.

The analysis of time functions of ring-widths and ring-widths characteristics of

the three species showed a mix of linear, quadratic and polynomial patterns,

with a wide range of R2 values, indicating good fit of the models for some cases

and poor fit of the models for others.

The analysis of ring-widths and ring-wood characteristics on inter-decadal basis

indicated highly significant to significant differences amongst the decades in

some cases and non-significant differences in others.

The results indicated correlations between ring-widths and ring-widths

characteristics of the three species, varying in direction, magnitude and

significance levels.

The analysis of impacts of various climate parameters on ring-widths of the

three species indicated a mix of linear, quadratic and polynomial patterns, with

a wide range of R2 values, indicating good fit of the models for some cases and

poor fit of the models for others.

The impact of increasing mean annual precipitation on the ring-widths of the

three species was negative.

The overall increase in temperature and precipitation affected the growth of

ring-width, early wood formation, late wood formation and intra-ring wood

characteristics in different directions and varying magnitudes.

The impact of climate change on radial growth was the highest in Blue pine,

followed by Deodar and Chir pine.

The reduced early wood formation and increased late wood formation had

changed anatomical properties of the wood produced.

The combination of study of ring-width and wood anatomical features can help

better understanding of climate-growth relationship than the use of tree ring-

width alone. Therefore, this combination can be used as a climate multi-proxy

to enhance prediction of potential impacts of climate change on tree growth and

its adaptability and survival.

This study provides a basis for using short-term climate-growth data to make

long-term growth projections with growth adjusted to long-term climate

conditions.

241

6.3 Recommendations

Based on the findings of the present study, the following recommendations are made:

1. The present study has shown considerable changes in climate at GFD and their

impacts on the tree growth and biomass production. Therefore, climate change

estimates and scenarios may be made an integral part of Forest Management

Plans, and future wood volume and yield estimates should be assessed in light of

climate change scenarios.

2. The tree ring and intra-ring wood characteristics are good indicators of yield and

biomass productivity; hence, these parameters should be used in addition to

inventory data for scientific management of forests.

3. The climate change trends in terms of temperature and precipitation regimes

across five seasons may be used as guiding principles in adaption strategies for

scientific forest management and other economic and socio-economic activities in

the area.

4. The observed climate changes and the trends thereof may be factored in planning

and designing of forest fire control systems and integrated pests management

plans.

5. Research studies may be conducted to assess the impacts of climate changes on

silvicultural characteristics of various forest tree species, associate broad-leaved

species, shrubs and ground flora, medicinal plants, and forage grasses and forbs,

shift in tree lines and trans- migration of species in the region.

6. Growth research trials and genetic engineering studies of forest species may be

undertaken to promote new species and varieties with best adaption to emerging

climatic conditions in the area.

7. Research studies may be conducted on the impacts of observed climate changes

on local flora, fauna and biodiversity in the region.

8. The results of this study indicated that Deodar, Blue pine and Chir pine have

high dendro-climatic value. Further research may be conducted on the same

species in other areas.

242

REFERENCES

Ahlstrand, G. M. 1980. Fire history of a mixed conifer forest in Guadalupe Mountains

National Park. Pp: 47. In: Proceed. Fire History Workshop, Oct. 20- 24, 1980,

Tucson, Arizona.

Ahmed, M. 1984. Ecological and Dendrochronological studies on Agathis australis

Salisb-Kauri. Ph.D. Thesis. New Zealand: Auckland University.

Ahmed, M., Khan, N. and Wahab, M. 2009. Dendroclimatic investigation in Pakistan

using Picea Smithiana. Pak. J. Botany 41(5): 2427–2435.

Ahmed, M., Wahab, M., Khan, N., Zafar, M.U. and Palmer, J. 2010. Tree-ring

chronologies from upper Indus Basin of Karakorum Range, Pakistan. Pak. J.

Botany 42: 295-207.

Alavian, V. 2009. Water and Climate Change: Understanding the Risks and Making

Climate-Smart Investment Decisions. Washington: World Bank Group.

Baker, W. 1995. Long-term response of disturbance landscapes to human intervention

and global change. Landscape Ecol. 10:143-159.

Balling, R.C., Michaels, P.J. and Knappenberger, P.C.1998. Analysis of winter and

summer warming rates in gridded temperature time series. Clim Res 9: 175–181.

Baltas, E. 2007. Spatial distribution of climatic indices in northern Greece Meteorol.

Appl. 14: 69–78.

Bargali, S. S., & Singh, S. P. (1995). Dry matter dynamics, storage and flux of nutrients

in an aged Eucalyptus plantation. Oecologia Montana, 4(12), 9-14.

Battipaglia, G., Micco, D.V., Brand, W.A., Linke, P., Aronne, G., Saurer, M. and

Cherubini, P. 2010. Variations of vessel diameter and δ 13C in false rings of

243

Arbutus unedo L. reflect different environmental conditions. New Phytol 188:

1099-1112.

Behbahani, S.M.R., Rahimikhoob, A. and Nazarifar, M.H. 2009. Comparison of some

split window algorithms to estimate land surface temperature from AVHRR data

in Southeastern Tehran, Iran. Desert 14: 157-161.

Berish, C.W. and Ragsdale, H.L.1985. Chronological sequence of element

concentrations in wood of Carya spp. in the southern Appalachian Mountains.

Canadian J. Forest Res. 15: 477-483.

Biondi, F., & Qeadan, F. 2008. A theory-driven approach to tree-ring standardization:

defining the biological trend from expected basal area increment. Tree-Ring

Research, 64(2), 81-96.

Blennow, K., Andersson, M., Bergh, J., Salinas, O., and Olofsson, E. 2010. Potential

climate change impacts on the probability of wind in a south Swedish forest.

Clim. Change 99: 261-278.

Bogino, S. and Bravo, F. 2009. Climate and intra-annual density fluctuations in Pinus

pinaster subsp. mesogeenesis in Spanish woodlands. Can J For Res 39: 1557-

1565.

Borgaonkar, H. P., Pant, G.B., and Kumar, K.R. 1996. Ring- variations in Cederus

deodara and its climatic response over the Western Himalaya. Internat. J.

Climatology, 16(12), 1409-1422.

Bouriaud, O., Leban, J.M., Bert, D. and Deleuze, C. 2005. Intra-annual variations in

climate influence growth and wood density of Norway spruce. Tree Physiology

25: 651-660.

Bradley, R. S. and Jones. P. D. 1992. Climate Since A.D. 1500. Routledge, London, UK.

244

Briffa, K.R., Jones, P.D., Pilcher, J.R. and Hughes, M.K. 1984. Reconstructing summer

temperatures in northern fennoscandinavia back to 1700 AD using tree-ring data

from Scots pine. Arctic and Alpine Res. 20(4): 385394.

Briggs, D.G., Turnblom, E.C. and Bare, B.B. 2005. Non-destructive methods and

process capability analysis to assess conformance of Douglas fir stands to

customer quality specifications. New Zealand J. forestry Sci. 35(2/3): 170– 188.

Brooks, K.N., Folliott, P.F., Gregersen, H.M. and Thames, J.L. 1991. Hydrology and

Management of Watersheds. University of Iowa Press, Ames, I.A, 2nd. (Ed.). P:

392.

Brovkin, V., Raddatz, T., Reick, C.H., Claussen, M. and Gayler, V. 2004. Global

biogeophysical interactions between forest and climate. Geophysical Research

Letters, 36, doi: 10.1029/2009GL037543.

Bukhari, S.S.B. 2011. Participatory Forestry Management Development Trends in

Pakistan. Paper presented at: Regional Conference on Participatory Forest

Management: Bhopal, India.

Bukhari, S.S.B. and Bajwa, G.A. 2009. Temporal Temperature Rise and its Effects on

other Climatic factors. Pak. J. Forestry 58(1): 1-18.

Bukhari, S.S.B. and Bajwa, G.A. 2011. Climate change trends over coniferous forests of

Pakistan. Pak. J. Forestry 61(2): 1-14.

Bukhari, S.S.B. and Bajwa, G.A. 2012. Development of National Response Strategy to

Combat Impacts of Climate Change on Forest of Pakistan. Peshawar: Pakistan

Forest Institute. P. 156.

Bukhari, S.S.B., Haider, A., and Laeeq M.T. 2012. Landcover Atlas of Pakistan.

Pakistan Forest Institute, Peshawar. P. 47.

245

Campelo, F., Nabais, C., Freitas, H. and Gutierrez, E. 2007. Climatic significance of tree

ring-width and intra-annual density fluctuations in Pinus pinea from a dry

Mediterranean area in Portugal. Ann. For. Sci. 64:229-238.

Carrer, M. and Urbinati, C. 2004. Age-Dependent Tree-Ring Growth Responses to

Climate in Larix Decidua and Pinus Cembra. Ecology 85(3): 730-740.

Carter, K.K. 1996. Provenance tests as indicators of growth responses to climate change

in 10 north temperate tree species. Can J. For. Res. 26: 10891095.

Champion, H.G., Seth, S.K. and Khattak, G.M. 1965. Manual of Silviculture. Peshawar:

Pakistan Forest Institute.

Cherubini, P., Gartner, B.L., Tognetti, R., Braker, O.U., Schoch, W. and Innes,

J.L. 2003. Identification, measurement and interpretation of tree rings in woody

species from Mediterranean climates. Biol Rev 78:119-148.

Christensen, S. and Christensen, B.T. 2003. Organic matter available for denitrification

in different soil fractions: effect of freeze/thaw cycles and straw disposal. J. Soil

Science 42: 637-647.

Christy, J.R. et al., 2001. Observed Climate Variability and Change. In: Climate Change

2001: Impacts, Adaptation, and Vulnerability, McCarthy J.J. et al., (Eds.).

Cambridge, IPCC: Cambridge University Press. Pp: 99-181.

Corcuera, L., Camarero, J.J. and Gil-Pelegrn, E. 2004. Effects of a severe drought on

Quercus ilex radial growth and xylem anatomy. Trees 18: 8392.

Currey, D. R. 1965. An ancient bristlecone pine stand in eastern Nevada. Ecology 46:

564-566.

Davis, M.B. and D.B. Botkin, 1985. Sensitivity of cool-temperate forests and their fossil

pollen record to rapid temperature change. Quaternary Research 23: 327-340.

246

De Micco, V., Aronne, G. and Baas, P. 2008. Wood anatomy and hydraulic architecture

of stems and twigs of some Mediterranean trees and shrubs along a mesic–xeric

gradient. Trees 22: 643–655.

Dieterich, J.H. 1980. Chimney Spring forest fire history. Research Paper RM220, US

Department of Agriculture, Forest Service, Rocky Mountains Forest and Range

Experiment Station, Fort Collins, CO.

Dieterich, J.H. and Swetnam, T.W. 1984. Notes: Dendrochronology of a fire scarred

ponderosa pine. Forest Science 30(1): 238-247.

Dixon, R.K., Brown, S., Houghton, R.A., Solomon, A.M., Trexler, M.C. and

Wisniewski, J. 1994. Carbon pools and flux of global forest ecosystems. Science

263: 185-90.

Dobson, A., Jolly, A. and Rubenstein, D. 1989. The greenhouse effect and biological

diversity. Trends in Ecology and Evolution 4(3): 64-68.

Esper, J., Cook, E. R. and Schweingruber, F.H. 2002. Low-Frequency Signals in Long

Tree-Ring Chronologies for Reconstructing Past Temperature Variability. Science

295: 2250-2253.

Falcucci, A., Maiorano, L. and Boitani, L. 2007. Changes in land-use/land-cover

patterns in Italy and their implications for biodiversity conservation. Landsc.

Ecol. 22: 617-631.

FAO, 1993. Forestry Statistics Today for Tomorrow: 1961-1991...2010. Rome.

FAO, 2005. Global Forest Resource Assessment. Rome.

Feliksik, E. and Wilczyński. S. 2009. The Effect of Climate on Tree-Ring Chronologies

of Native and Nonnative Tree Species Growing Under Homogenous Site

Conditions. Geochronometria 33: 49-57.

247

Fernandez, M. E., Gyenge, J. E., Urquiza, M. M. and Varela, S. 2012. Adaptability to

climate change in forestry species: drought effects on growth and wood anatomy

of Ponderosa pines growing at different competition levels. Forest Systems 21(1):

162-173.

Fischlin, A., Midgley, G.F., Price, J.T. et al., 2007. Ecosystems, their properties, goods,

and services. Climate Change 2007: Impacts, Adaptation and Vulnerability. Pp:

211-272. In: 4th Assessment Report of the Intergovernmental Panel on Climate

Change. Parry, M.L., Canziani, O.F., Palutikof, J.P., Van Linden P.J. and Hanson,

C.E. (Eds.). Cambridge University Press.

Foley, J.A., DeFries, R., Asner, G.P., BarFord, C. and Bonan, G. 2005. Global

consequences of land use. Science 309: 570-574.

Fonti, P., Von Arx G., Garcıa-Gonza´lez, I., Eilmann, B., Sass-Klasen, U., Gartner, H.

and Eckstein, D. 2010. Studying global change through investigation of the

plastic responses of xylem anatomy in tree rings. New Phytol.185: 42-53.

Fowler, H.J. and Archer, D.R. 2006. Conflicting signals of climate change in the upper

Indus Basin. J. Climate DN.

Fritts, H.C. 1976. Tree-Rings and Climate. Academic Press, London.

Fritts, H.C. and Swetnam, T.W. 1989. Dendroecology: A Tool for Evaluating Variations

in Past and Present Forest Environment. Advances in Ecological Research. 19

ISBN 0-12-013919-7.

Froux, F., Huc. R., Ducrey, M. and Dreyer, E. 2002. Xylem hydraulic efficiency versus

vulnerability in seedlings of four contrasting Mediterranean tree species (Cedrus

atlantica, Cupressus sempervirens, Pinus halepensis and Pinus nigra). Ann. For.

Sci. 59: 409-418.

248

Gavilán, R.G. 2005. The use of climatic parameters and indices in vegetation

distribution. A case study in the Spanish Sistema Central Internat. J.

Biometeorology 50: 111-120.

Ge, Z.M. 2011. Effects of climate change and management on growth of Norway spruce

in boreal conditions - an approach based on ecosystem model. Dissertations Fore

stales 122: 35.

Goldblum, D. and Rigg, L S. 2005. Tree growth response to climate change at the

deciduous-boreal forest ecotone, Ontario. Can. J. Forest Res.35 (11): 2709-2718.

Gosling, S.N., Lowe, J.A., McGregor, G.R., Pelling, M. and Lalamud, B.D. 2009.

Association between elevated atmospheric temperature and human mortality: a

critical review of the literature. Clim. Change 92: 299-341.

Graumlich, L. J. 1991. Subalpine Tree Growth, Climate, and Increasing CO₂: An

Assessment of Recent Growth Trends. Ecology 72:1–11.

Greco, S., Moss, R.H., Viner, D. and Jenne, R. 1994. Climate Scenarios and

Socioeconomic Projections for IPCC WG II Assessment. IPCC-WMO and UNEP,

Washington, DC, Pp: 67.

Green, D.G., 1987. Pollen evidence for the postglacial origins of Nova Scotia’s forests.

Can. J. Botany 65: 1163-1179.

González, I. G., & Eckstein, D. 2003. Climatic signal of early wood vessels of oak on a

maritime site. Tree Physiology, 23(7), 497-504.

Groombridge, B. 1992. Global Biodiversity: Status of the Earth’s Living Resources.

Chapman and Hall, London. Pp: 585.

Grunewald, K., Scheithauer, J., Monget, J.M. and Brown, D. 2009. Characterization of

contemporary local climate change in the mountains of southwest Bulgaria. Clim.

Change 95: 535-549.

249

Halnaes, K. and Garg, A. 2011. Assessing the role of energy in development and climate

policies-conceptual approach and key indicators” in World Development 39 (6): 987-

1001.

Hasnain, S.I. 2009. Impact of melting glaciers in the Himalayas. Rural 21 43(4): 13-15.

Hill, J.F. 1982. Spacing of parenchyma bands in wood of Carya labra (Mill.) Sweet,

pignut hickory, as an indicator of growth rate and climatic factors. American J.

Bot. 69: 529-537.

Hoffmann, W.A., Marchin, R.E.M., Abit, P. and Lau, O.L. 2011. Hydraulic failure and

tree dieback are associated with high wood density in a temperate forest under

extreme drought. Global Change Biol. 17: 2731-2742.

Holdridge, L.R. 1947. Determination of world plant formations from simple climatic

data. Science 105: 367-368.

Houghton, J.T., Ding, Y., Griggs, D.J., Noguer, M., Van Linden, P.J. and Xiaosu, D.

2001. Climate change 2001: The scientific basis. Intergovernmental Panel on

Climate Change, Cambridge University Press.

Houghton, R.A. 2005. Above ground forest biomass and the global carbon balance.

Global Change Biology 11: 945-958.

Hughes, M.K., Kelly, P.M., Pilcher, J.R. and LaMarche, V.C (Jr.). 1982. Climate from

Tree Rings. Cambridge University Press, P. 233.

Hulme, M., 2003. Global climate change: climates of the future, choices for the present.

Global Climate Change and Biodiversity summary. Pp 2-4.

Hulme, M. and Sheard, N. 1999. Climate change scenarios for Germany. Climatic

Research Unit, Norwich, UK.

250

IPCC, 1996. Climate Change 1995. Impacts, Adaptations and Mitigation of Climate:

Scientific Technical Analyses. Contribution of Working Group II to the Second

Assessment Report of the Intergovernmental Panel on Climate Change (IPCC).

Cambridge University Press, UK: Cambridge.

IPCC, 2001. Observed climate variability and change. Pp: 99-181. In: Climate change

2001: the scientific basis. Contribution of working group. 3rd Assessment Report

of the IPCC. Houghton, J. T., Ding, Y., Griggs, D. J., Noguer, M., Van Linden, P.

J., Maskell, X. D. K. and Johnson, C. A. (Eds.). Cambridge University Press,

Cambridge, UK and New York, USA.

IPCC, 2007. Climate Change 2007: Synthesis Report. 4th Assessment Report of the

Intergovernmental Panel on Climate Change. Pachauri, R.K. and Reisinger, A.

(Eds.). Geneva: IPCC, P. 104.

Jacoby, G. C. and D’arrigo. R.D. 1997. Tree rings, carbon dioxide, and climatic change.

Proc. Natl. Acad. Sci. USA Vol. 94, pp. 8350–8353, August 1997.

Jozsa, L. A., and Powell, J. M. 1987. Some climatic aspects of biomass productivity of

white spruce stem wood. Canadian J. Forestry Research. 17:1075–1079.

Jiang, T., Kundzewicz, Z.W. and Su, B. 2007. Changes in monthly precipitation and

flood hazards in the Yangtze River Basin, China. Internat. J. Climatol. Doi:

10.1002/joc.1635.

Johns, T.C., Gregory, J.M., Ingram, W.J., Johnson, C.E., Jones, A., Lowe, J.A.,

Mitchell, J.F.B., Roberts, D.L., Sexton, D.M.H., Stevenson, D.S., Tett, S.F.B.,

Wood age, M.J. 2001. Anthropogenic climate change for 1860 to 2100 simulated

with the HadCM3 model under updated emission scenarios, Hadley Centre

technical note.

Khan, H.M.R.A. 1987. Revised Working Plan for Galis Guzara Forests (1987-88 to

1996-97). NWFP Forestry Pre-Investment Centre, Peshawar. P: 1

251

Khan, J. 1993. Working Plan for Reserved Forests of Galies Forest Division (1993-94)

to 2012-13). Forest Management Centre, Peshawar. P: 1

Khan, N., Ahmed, M. and Shaukat, S.S. 2013. Climatic signal in tree-ring chronologies

of Cedrus deodara from Chitral Hindukush Range of Pakistan. Pakistan J. Botany

40(3): 1063-1070.

Khan, N., Ahmed, M. and Wahab, M. 2008. Dendroclimatic potential of Picea

smithiana (Wall) Boiss, from Afghanistan. Pakistan J. Botany 40(3): 10631070.

Kienast, F., and Luxmoore, R. J. 1988. Tree-ring analysis and conifer growth responses

to increased atmospheric CO2levels. Ecology 76:487–495.

Knutson, T.R., Delworth, T.L., Dixon, K.W., Held, I.M., Lu, J., Ramaswamy, V. and

Schwarzkopf, M.D. 2006. Assessment of twentieth-century regional surface

temperature trends using the GFDL CM2 coupled models. J. Clim.19: 1624-51.

Krischbaum, M.U.F., Cannell, M.G.R., Cruz, R.V.O., Galinski, W. and Cramer, W.P.

1996. Climate change impacts of forests. In: Climate Change 1995: Impacts,

Adaptations and Mitigation of Climate: Scientific-Technical Analyses.

Contribution of working Group II to the Second Assessment Report of the

Intergovernmental Panel on Climate Change (IPCC), Watson, R.T., Zinyowera,

M.C. and Moss, R.H., (Eds.). Cambridge University Press, UK: Cambridge.

LaMarche, V.C., Holmes, R.L., Dhwoddie, P.W. and Draw L.G. 1979. Tree ring

chronologies of the Southern Hemisphere. Chronology Series V, University of

Arizona, Tucson.

Lara, A. and Villalba, R. 1993. A 3620-year temperature record from Fitzroya

cupressoides tree rings in southern South America. Science 260: 11041106.

Lasco, R. D., Pulhin, F. B., Jaranilla-Sanchez, P. A., Delfino, R. J. P., Gerpacio R. and

Garcia. K. 2009. Climate Change and Forest Ecosystems in the Philippines

252

Vulnerability, Adaptation and Mitigation. J. Environmental Sci. and Manag.

11(1):1-14.

Lebourgeois, F., Rathgeber, C. and Ulrich, E. 2010. Sensitivity of French temperate

coniferous forest to climate variability and extreme events (Abies alba, Picea

abies and Pinus sylvestris). J Veg Sci 21: 364-376.

Liu, B., Henderson, H., Zhang, Y. and Xu, M. 2010. Spatio-temporal change in China’s

climatic growing season: 1955-2000. Clim. Change 99: 93-118.

Luckman, B. H. 1996. Dendrochronology and global change. Pp: 3-24. In: Tree rings,

Environment and Humanity. Proceed. International Con. Dean, J. S. Meko, D. M.

and Swetnam, T. W. (Eds.). Tucson, Arizona. Radiocarbon 1996: 3-24.

Luis, M., Novak, K., Raventos, J., Gricar, J., Prislan. P. and Cufar, K. 2011. Climate

factors promoting intra-annual density fluctuations in Aleppo pine (Pinus

halepensis) from semiarid sites. Dendrochron 29: 163-169.

Mann, M. E., Bradley, R. S. and Hughes, M. K. 1998. Global scale temperature patterns

and climate forcing over the past six centuries. Nature 392: 779787.

Mann, M.E., Bradley, R.S. and Hughes, M. K., 1999. Northern Hemisphere

hTemperatures during the Past Millennium: Inferences, Uncertainties, and

Limitations. Geophysical Research Letters, 26: 759–762.

Martinez-Meier, A., Sanchez, L., Pastorino, M., Gallo L. and Rozenberg, P. 2008. What

is hot in tree rings? The wood density of surviving Douglas-firs to the 2003

drought and heat wave. For. Ecol. Manag. 256: 837-843.

McCarroll, D., Jalkanen, R., Hicks, S., Tuovinen, M., Gagen, M., Pawellek, F., Dieter,

E., Schmitt, U., Autio, J. and Heikkinen, O. 2003. Multiproxy dendroclimatology:

a pilot study in northern Finland. Holocaust 13: 899900.

253

McCarthy, J.J., Canziani, O.F., Leary, N.A., Dokken, D.J. and White, K.S. 2001.

Climate Change 2001: impacts, adaptation, and vulnerability. Intergovernmental

Panel on Climate Change, Cambridge University Press, New York.

McKenzie, D., Peterson, D.L. and Littell, J.J. 2009. Global warming and stress

complexes in forests of western North America. In: Wildland fires and air

pollution. Bytnerowicz, A., Arbaugh, M.J., Riebau, A.R. and Andersen, C. (Eds.).

The Hague, Netherlands: Elsevier Publishers: 317-337.

Melillo, J.M., Callaghan, T.V., Woodward, F.I., Salati, E. and Sinha, S.K. 1990. Effects

on ecosystems. In: Climate Change-The IPCC Scientific Assessment. Report

Prepared for IPCC by Working Group. Houghton, J.T., Jenkins, G.J. and J.J.

Ephraums (Eds.). Cambridge University Press, Cambridge Pp: 283-310.

Millar, C.I., Stephenson, N.L. and Stephens, S.L. 2007. Climate change and forests of

the future: managing in the face of uncertainty. Ecological Applications 17: 2145–

2151.

Mitchell, J.F.B., Manabe, S., Meleshko, V. and Tokioka, T. 1990. Equilibrium climate

change—and its implications for the future. In: Climate Change. The IPCC

Scientific Assessment. Report Prepared for IPCC by Working Group. Houghton,

J.T., Jenkins, G.J. and Ephraums J.J. (Eds.). Cambridge University Press,

Cambridge Pp. 139-173. New York, N.Y.

Motha R. P. and Baier W. 2005. Impacts of present and future climate variability on

agriculture in the temperate regions: North America. Clim. Change 70, 137–

16410.1007/s10584-005-5940-1.

Murtaugh, P. A. 2003. On detecting hump‐shaped relationships in ecology: a bootstrap

test for monotonicity. Environmetrics, 14(6), 611-616.

Norton, D.A. and Ogden, J. 1987. Dendrochronology: a review with emphasis on New

Zealand application. NZ. J. Ecol., 10: 77-95.

254

Novak, K., Luis, M.D., Ravento, J. and Cufar, K. 2013. Climatic signals in tree ring-

widths and wood structure of Pinus halepensis in contrasted environmental

conditions. DOI 10.1007/s00468-013-0845-5.

Olano,55 J.M. 2012. Quantitative tracheid anatomy of Juniperus thurifera reveals a

complex environmental control of wood structure under continental Mediterranean

climate. International J. Plant Sciences 173(2):137-149

Olivar, J., Bogino, S., Spiecker, H. and Bravo, F. 2012. Climate impact on growth

dynamics and intra-annual density fluctuations in Aleppo pine (Pinus halepensis)

tree on different crown classes. Dendrochronologia 30: 35-47.

Pachauri, R.D. and Reisinger, A. 2007. Climate change 2007: Synthesis Report.

Contribution of Working Groups I, II and III to the Fourth Assessment Report of

the Intergovernmental Panel on Climate Change. Geneva, Switzerland:

Intergovernmental Panel on Climate Change. P. 104.

Pant, G. B., Kumar, K. R., Borgaonkar, H. P., Okada, N., Fujiwara, T. and Yamashita,

K. 2000. Climatic response of Cedrus deodara tree-ring parameters from two sites

in the western Himalaya. Canadian J. Forest Research, 30(7): Pp. 1127-1135.

Papadopoulos A. Tolika, K., Pantera, A. and Maheras, P. 2009. Investigation of the

Annual Variability of the Aleppo Pine Tree-Rings Width: The Relationships with

the Climatic Conditions in the Attica Basin. Global NEST J. 11(4): 583-592.

Parker, M.L. and Jozsa, L.A. 1973. X-ray scanning machine for tree ring-width and

density analysis. Wood and Fiber 5: 192-197.

Pastor, J. and Post, W.M. 1988. Response of northern forests to CO2-induced climate

change. Nature 334(6177): 55–58.

Paterson, S.S. 1956. The Forest Area of the World and Its Potential Productivity.

Goteburg, University.

255

Peters, R. L., Groenendijk, P., Vlam, M., & Zuidema, P. A. (2015). Detecting long‐term

growth trends using tree rings: a critical evaluation of methods. Global change

biology, 21(5), 2040-2054.

Pilcher, J. R. and Baillie, M. G. L. 1980a. Eight modem oak chronologies from England

and Scotland. Tree-ring Bulletin 40: 45.

Pilcher, J. R. and Baillie, M. G. L. 1980b. Six modern oak chronologies from Ireland.

Tree-Ring Bulletin 40: 23.

Prentice, I.C. 1992: Climate change and long-term vegetation dynamics. In: Plant

Succession: Theory and Prediction. Glenn-Lewin, D.C., Peet, R.K. and Veblen,

T.T. (Eds.). Chapman and Hall, London Pp: 293-339.

Press Pp: 174.

Pyke, C.R. and Sandy, J. and Andelman, S.J. 2007. Land use and land cover tools for

climate adaptation. Climatic Change 80(3-4): 225-293.

Ram, S., Borgaonkar, H.P. and Sikder, A.B. 2008. Tree-ring analysis of teak (Tectona

grandis L.F.) in central India and its relationship with rainfall and moisture index.

J. Earth Syst. Sci. 117, No. 5, Pp. 637–645.

Rana, B. S., Singh, S., & Singh, R. (1989) Biomass and net primary productivity in

central Himalayan forests along altitudinal gradient. J. Forest Ecology and

Management, 27, 199-218.

Rathgeber, C., Masson, L., Nicolet, A. and Guizot, J. 2005. Bioclimatic model of tree

radial growth: application to French Mediterranean Aleppo pine forest. Trees

19:162-176.

Ravindranath, N.H., Joshi, N.V., Sumkumar, R. and Saxena, A. 2006. Impact of climate

change on forests in India. Current Sci. 90(3): 355-361.

256

Rees, H.G. and Collins, D.N. 2006. Regional difference in response of flow of glacier-

feed Himalayan River to climate warming. Hydrological Processes, 20: 2157-

2169.

Rigling, A., Bruhlhart, H., Braker, O.U., Forster, T. and Schweingruber, F.H. 2003.

Effect of irrigation on diameter growth and vertical resin duct production in Pinus

sylvestris L. on dry sites in the central Alps, Switzerland. For Ecol Manag. 175:

285-296.

Rolland, C. 1993. Tree-ring and climate relationships for Abies alba in the Internal Alps.

Tree-Ring Bulletin 53: 1-11.

Rozas, V., DeSoto, L., & Olano, J. M. (2009). Sex‐specific, age‐dependent sensitivity of

tree‐ring growth to climate in the dioecious tree Juniperus thurifera. New

Phytologist, 182(3), 687-697

Rowell, D.P. and Jones, R.G. 2006. Causes and uncertainty of future summer drying

over Europe. Climate Dynamics 27: 281-299.

Sajjad, S.H., Hussain, B., Khan, M. A., Raza, A., Zaman, B. and Ahmed, I. 2009.

On rising temperature trends of Karachi in Pakistan. Clim. Change 96: 539-547.

Schulman, E. 1958. Bristlecone pine, oldest known living thing. National Geographic

Magazine 113: 354-372.

Shaffer, G., Olsen, S.M. and Pederson, G.O.P. 2009. Long term ocean oxygen depletion

in response to carbon dioxide emissions from fossil fuels. Nature Geoscience 2:

105-109.

Sharma, C., Baduni, N., Gairola, S., Ghildiyal, S. & Suyal, S. (2010). Tree diversity and

carbon stocks of some major forest types of Garhwal Himalaya, India. Forest

Ecology and Management 260, 2170-2179.

257

Sheikh, M.M., Manzoor, N. and Adnan, M. 2012. Precipitation related disasters in

Pakistan, linkage to climate change, risk reduction and possible adaptation

measures. Pp: 33-45. In: Proceed. Second Internat. Disaster Manag. Conf.: Good

Governance in Disaster Risk Reduction and Climate Change Adaptation.

Siddiqui, K. M., Iqbal, M. and Ayaz, M. 1999. Forest ecosystem climate change impact

assessment and adaptation strategies for Pakistan. Climate Res. 12: 195–203.

Smith, J.B. and Tirpak, D.A. 1989. The potential Effects of Global Climate Change on

the United States. EPA-230-05-89-054. Washington: U.S. Environmental

Protection Agency.

Speer, J.H. 2010. Fundamentals of Tree ring research, The University of Arizona Press

Tucson.

Spittlehouse, D. L. and Stewart, R.B. 2003. Adaptation to climate change in forest

management. J. Ecosystems and Management 4(1), http://www.

forrex.org/jem/2003/ vol4/no1/art1.pdf.

Spurr, S.H. and Barnes, B.V. 1980. Forest Ecology, 3rd Ed. New York: John Wiley &

Sons.

Stokes, M.A. and Smiley, T.L. 1968. An Introduction to Tree-Ring Dating.

University of Chicago Press, Chicago.

Stuiver, M., Burk, R.L. and Quay, P.D. 1984. "C/uC ratios in tree rings and the transfer

of biospheric carbon to the atmosphere. J. Geophysical Res. 89: 11731-11748.

Suarez, G., Butler, C.J. and Baillie, M.G.L. 2009. Climate signal in tree-ring

chronologies in a temperate climate: A multi-species approach.

Dendrochronologia 27: 83-98.

258

Suzuki, E. (1990). Dendrochronology in Coniferous forests around Lake Rara, West

Nepal. The botanical magazine-Shokubutsu-gaku-zasshi, 103 (3), 297-312.

Svendsen, M. and Kunkel, N. 2009. Water and adaptation to climate change. Rural21

43(4): 8-12.

Swetnam, T. W. and Dieterich, J. H. 1985. Fire history of ponderosa pine forests in the

Gila Wilderness, New Mexico. Pp: 390-397. In Proceed. Symposium and

Workshop on Wilderness Fire; Nov. 15-18, 1983, Missoula, MT. General

Technical Report INT-182.

Telewski, F.W., Swanson, R. T., Strain, B. R. and Burns, J. M. 1999. Wood properties

and ring-width responses to long-term atmospheric CO2 enrichment in field-grown

loblolly pine (Pinus taeda L.) Plant, Cell and Environment 22, 213-219.

Trenberth, K.E., Jones, P.D., Ambenje, P.G., Roxana Bojariu, R. 2007. Observations:

Surface and Atmospheric Climate Change.

http://www.junksciencwasxe.com/draft_AR4/Ch03_SOD_Text_TSU_FINAL.pdf.

The Economist, 2009. A Bad Climate for Development. http://www. Economist.

Com/node/14447171.

Thomas, C.D., Cameron, A., Green, R.E. and Bakkenes, M. 2004. Extinction risk from

climate change. Nature 427: 145-148.

Thornthwaite, C.W. 1948. An approach toward a rational classification of climate.

Geographical Review 38: 55-94.

Turner, M.G., Baker, W.L., Peterson, C.J. and Peet, R.K. 1998. Factors influencing

succession: Lessons from large, infrequent natural disturbances. Ecosystems 1:

511-523.

http://www.junksciencwasxe.com/draft_AR4/Ch03_SOD_Text_TSU_FINAL.pdf

http://www.junkscience.com/draft_AR4/Ch03_SOD_Text_TSU_FINAL.pdf

http://www/

http://www/

259

USGCRP, 2009. Global Climate Change Impacts in the United States. T.R. Karl, J.M.

Melillo, and T.C. Peterson (Eds.). United States Global Change Research

Program. New York: Cambridge University Press.

Vaganov, E.A., Schulze, E., Skomarkova, M.V., Knohl, A., Brand W.A. and Roscher,

C. 2009. Intra-annual variability of anatomical structure and δ13C values within

tree rings of spruce and pine in alpine, temperate and boreal Europe. Oecologia

161: 729-745.

Valencia-Barrera, R.M., Fernández-González, D. and Comtois P. 2002. Bioclimatic

indices as a tool in pollen forecasting. Int J Biometeorol 46:171-175.

Van de Steeg, J., Notenbaert, A., Herrero, M., and Thornton, P. 2009. Livestock and

climate change. Rural 21, 43(6): 19-21.

Vieira, J., Campelo, F. and Nabais, C 2009. Age-dependent responses of tree ring

growth and intra-annual density fluctuations of Pinus pinaster to Mediterranean

climate. Trees Structure Function 23: 257-265.

Vorholz, F. 2009. Global Climate Protection. Deutschland 5/2009: 8-13.

Walter, H. 1985. Vegetation Systems of the Earth and Ecological Systems of the Geo-

Biosphere. Berlin: Springer-Verlag.

Wani, B.A., Shah, H. and Khan, S. 2004. Forestry Statistics of Pakistan. Peshawar:

Pakistan Forest Institute. P. 50.

Warren, R., Arnell, N., Nicholls, R.J. et al., 2006. Understanding the regional impacts of

climate change, Research report prepared for the Stern Review, Tyndall Centre

Working Paper 90, Norwich, UK, available from

http://www.tyndall.ac.uk/publications/working_papers/working papers.shtml

http://globalchange.gov/what-we-do/assessment/previous-assessments/global-climate-change-impacts-in-the-us-2009






http://www.tyndall.ac.uk/publications/working_papers/working%20papers.shtml




260

Whittaker, R.H. 1975. Communities and Ecosystem. New York: Macmillan.

Williams. A. P., Michaelsen, J., Leavitt W. and Still, J. 2010. Using Tree Rings to

Predict the Response of Tree Growth to Climate Change in the Continental United

States during the Twenty-First Century. Earth Interactions, 14 (19):

Wimmer, R. and Grabner, M. 1997. Effects of climate on vertical resin duct density and

radial growth of Norway spruce (Picea abies (L) Karst). Trees Structure Function

11: 271-276.

Wimmer, R., Strumia, G. and Holawe, F. 2000. Use of false rings in Austrian pine to

reconstruct early growing season precipitation. Can J For Res 30:16911697.

Woodward, F.I., 1987: Climate and Plant Distribution. Cambridge University.

Wu, Z., Zhang, H., Krause, C.M. and Cobb, N.S. 2010. Climate change and human

activities: a case study in Xinjiang, China. Clim. Change 99:457472.

Yadav, R.R. 2009. Tree ring imprints of long-term changes in climate in western

Himalaya, India: Journal of Biosciences, 34:699.

Yeh, H. Y. and Wensel, L.C. 2000. The relationship between tree diameter growth and

climate for coniferous species in northern California. Can. J. For. Res. 30: 1463-

1471.

Zafar, M.S., Ahmed, M., Akbar, F.M. and Hussain, A. 2010. Standardized Tree Ring

Chronologies of Picea smithiana from two new sites of Northern Area Pakistan.

World Applied Sci. J. 1 (12): 1531-1536.

growth response of deodar (cedrus deodara), blue pine

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