PHYTOSOCIOLOGICAL ATTRIBUTES OF DIFFERENT VEGETATIONAL ZONES OF NANDIAR KHUWAR

CATCHMENT AREA

FAIZ UL HAQ

DEPARTMENT OF BOTANY

HAZARA UNIVERSITY MANSEHRA 2015

ii

HAZARA UNIVERSITY MANSEHRA

Department of Botany

PHYTOSOCIOLOGICAL ATTRIBUTES OF DIFFERENT VEGETATIONAL ZONES OF NANDIAR KHUWAR

CATCHMENT AREA

By

Faiz ul Haq

This research study has been conducted and reported as partial fulfillment for

the requirement of PhD degree in Botany awarded by Hazara University

Mansehra, Pakistan.

The Thursday 23, April 2015

iii

PHYTOSOCIOLOGICAL ATTRIBUTES OF DIFFERENT VEGETATIONAL ZONES OF NANDIAR KHUWAR

CATCHMENT AREA

Submitted by FAIZ UL HAQ

Ph.D. Scholar Research Supervisor PROF. DR. HABIB AHMAD

Tamgha-e-Imtiaz

Dean Faculty of Science Hazara University, Mansehra

Co Supervisor DR. ZAFAR IQBAL

Assistant Professor Department of Botany Hazara University, Mansehra

DEPARTMENT OF BOTANY HAZARA UNIVERSITY MANSEHRA

2015

iv

v

vi

DEDICATION

TO MY PARENTS WHO SACRIFICED THEIR LIVES FOR

SAKE OF MY STUDY MY ALLAH ALMIGHTY LIVE LONG

BOTH OF THEM

vii

TABLE OF CONTENTS

Title Page No.

List of Tables ix

List of Figures xi

Acknowledgments xiii

ABSTRACT xiv

Chapter 1 INTRODUCTION 1

1.1 Study Area 1

1.2 Climate 6

1.3 Forest Types 6

1.4 Rock/Minerals 8

1.5 Biodiversity 9

1.6 Phytosociology 10

Chapter 2 REVIEW OF LITERATURE 14

Chapter 3 MATERIALS AND METHODS 30

3.1 General Survey 30

3.2 Plant Collection 30

3.3 Phenology 31

3.4 Life Form 31

3.5 Leaf Size Spectra 32

3.6 Methodology for Phytosociological Attributes 32

3.7 Multivariate Analysis of Ecological Data 34

3.8 Similarity and Dissimilarity Indices 35

3.9 Diversity Index 36

3.10 Species Richness 36

3.11 Environmental and Geographical Data 36

3.12 Edaphic Factors 36

Chapter 4 RESULTS 38

4.1.1 Diversity and Distribution of Plant Species 38

4.1.2 Biodiversity Index 40

viii

4.1.3 Species Richness 40

4.2 Phenology, Life Form and Leaf Spectra 44

4.3 Similarity and Dissimilarity Indices 55

4.4 Multivariate Analysis 57

4.4.1 TWINSPAN Classification 57

4.4.2 Ordination (Bray-Curtis, DCA and CCA) 68

4.5 Phytosociological Attributes of Different Vegetational Zones 79

4.5.1 Phytosociology in Subtropical Zones 79

4.5.2 Mixed Pinus roxburghii and Pinus wallichiana Zones 89

4.5.3 Moist temperate pure Pinus wallichiana Zones 97

4.5.4 Mixed Coniferous Forests 107

4.5.5 Pure Abies pindrow and Picea smithiana Forests 115

4.5.6 Phytosociology of Alpine Zones 123

4.6 Ordination of Samples on the bases of Microclimatic Data 130

4.7 Ordination of Samples on the bases of Edaphic Factors 139

4.8 Dominance Diversity Curves 147

4.9 Medicinal Flora of the Study Area 151

4.10 Exotic Flora of Nandiar Khuwar Catchment 163

4.11 Market Survey of Important Plant Species 163

4.12 Conservation Status of Plant Species 165

Chapter 5 DISCUSSION 172

RECOMMENDATIONS 185

REFERENCES 188

ix

LIST OF TABLES

Title Page No.

4.1.1 Share of various plant groups 39

4.1.2 Dominant families of the study area 39

4.1.3 Diversity index and species richness 41

4.2.1 Phenology, life form and leaf spectra 46

4.3.1 Similarity and dissimilarity indices among vegetation zones 54

4.3.2 Similarity and dissimilarity indices among different communities 56

4.4.1 Number of species and IVI contribution of biological spectra of

different plant communities

64

4.4.2 Number of species and IVI contribution of leaf size spectra of

different plant communities

65

4.4.3 Regression of stands in species space on 17 parameters 76

4.4.4 The correlations and biplot scores for 17 parameters. 77

4.5.5 The correlation among environmental variables 78

4.5.1 The IVI contribution of biological spectrum of subtropical forests 82

4.5.2 The IVI contribution of leaf size spectra of subtropical forests 82

4.5.3 The similarity and dissimilarity indices of subtropical forests 82

4.5.4 The IVI contribution of biological spectrum of mixed Pinus Pinus

forests

91

4.5.5 The IVI contribution of leaf size spectra of mixed Pinus Pinus

forests

91

4.5.6 The similarity and dissimilarity indices of mixed Pinus Pinus forests 91

4.5.7 The IVI contribution of biological spectrum of pure Pinus wallichiana

forests

101

4.5.8 The IVI contribution of leaf size spectra of pure Pinus wallichiana

forests

101

4.5.9 Similarity and dissimilarity indices of pure Pinus wallichiana forests 101

4.5.10 The IVI contribution of biological spectrum of mixed coniferous

forests

110

x

4.5.11 The IVI contribution of leaf size spectra of mixed coniferous forests 110

4.5.12 Similarity and dissimilarity indices of mixed coniferous forests 110

4.5.13 The IVI contribution of biological spectrum of pure Abies and Picea

forests

118

4.5.14 The IVI contribution of leaf size spectra of pure Abies and Picea

forests

118

4.5.15 Similarity and dissimilarity indices of pure Abies and Picea forests 118

4.5.16 The IVI contribution of biological spectrum of alpine scrub zone 125

4.5.17 The IVI contribution of leaf size spectra of alpine scrub zone 125

4.9 Medicinal plants of Nandiar Khuwar catchment area 151

4.10 The exotic/alien flora of Nandiar Khuwar catchment area 163

4.11 Medicinal plants in the local drug market 163

4.12.1 Critically Endangered species of Nandiar Khuwar catchment 171

4.12.2 Endangered species of Nandiar Khuwar catchment 171

xi

LIST OF FIGURES

Title Page No.

1.1 Map of District Battagram 2

1.2 Map of Nandiar Khuwar catchment area 3

4.4.1 TWINSPAN classification 61

4.4.2 Bray-Curtis ordination 71

4.4.3 DCA ordination of species 72

4.4.4 DCA ordination of stands 72

4.4.5 CCA ordination of species and environmental variables 74

4.4.6 CCA ordination of stands and environmental variables 74

4.5.1 TWINSPAN classification of subtropical vegetation 80

4.5.2 Bray-Curtis ordination of the subtropical zone 85

4.5.3 DCA ordination of species of the subtropical zone 85

4.5.4 CCA ordination of stands of the subtropical zone 86

4.5.5 CCA ordination of species of subtropical zone 86

4.5.6 TWINSPAN Classification of mixed Pinus and Pinus forests 90

4.5.7 PCA ordination in Pinus Pinus forests 94

4.5.8 DCA ordination of species of Pinus Pinus forests 94

4.5.9 CCA ordination of stands of Pinus Pinus forests 96

4.5.10 CCA ordination of species of Pinus Pinus forests 96

4.5.11 TWINSPAN classification of pure Pinus wallichiana forests 98

4.5.12 Bray-Curtis ordination of pure Pinus wallichiana forests 103

4.5.13 DCA ordination of species of pure Pinus wallichiana zone 103

4.5.14 CCA ordination of stands of pure Pinus wallichiana zone 104

4.5.15 CCA ordination of species of pure Pinus wallichiana zone 104

4.5.16 TWINSPAN classification of mixed coniferous forests 108

4.5.17 Bray-Curtis ordination of mixed coniferous forests 113

4.5.18 DCA ordination of species of mixed coniferous forests 113

4.5.19 CCA ordination of stands of mixed coniferous forests 114

4.5.20 CCA ordination of species of mixed coniferous forests 114

4.5.21 TWINSPAN classification of pure Abies and Picea forests 117

4.5.22 Bray-Curtis ordination in pure Abies and Picea forests 121

4.5.23 DCA ordination of species of pure Abies and Picea forests 121

4.5.24 CCA ordination stands of pure Abies and Picea forests 122

4.5.25 CCA ordination species of pure Abies and Picea forests 122

4.5.26 TWINSPAN classification of alpine zone 124

4.5.27 Bray-Curtis ordination of alpine zone 127

4.5.28 DCA ordination of species of alpine zone 127

4.5.29 CCA ordination species of alpine zone 129

xii

4.5.30 CCA ordination of stands of alpine zone 129

4.6.1 The correlation of temperature with stands in species space 132

4.6.2 The correlation of wind speed with stands in species space 132

4.6.3 The correlation of humidity with stands in species space 134

4.6.4 The correlation of heat index with stands in species space 134

4.6.5 The correlation of dew point with stands in species space 136

4.6.6 The correlation of wet bulb with stands in species space 136

4.6.7 The correlation of barometric pressure with stands in species space 137

4.6.8 The correlation of altitude with stands in species space. 138

4.6.9 The correlation of density altitude with stands in species space 138

4.7.1 The correlation of soil saturation with stands in species space 143

4.7.2 The correlation of electrical conductivity with stands in species space 143

4.7.3 The correlation of soil pH with stands in species space 144

4.7.4 The correlation of organic matter with stands in species space 144

4.7.5 The correlation of Phosphorous with stands in species space 148

4.7.6 The correlation of Potassium with stands in species space 148

4.7.7 The correlation of slope aspect with stands in species space 149

4.7.8 The correlation of slope angle with stands in species space 149

4.8.1 Abundance diversity curves of species 150

4.8.2 Frequency and rank abundance of species 150

xiii

ACKNOWLEDGEMENTS

I am extremely thankful to Almighty ALLAH, the Sovereign and Beneficent

Who enables me to accomplish excellence in my research endeavors. All

respect and love to Holy Prophet Muhammad (S.A.W.) Who enables us to

recognize our Creator, Who is the ultimate guide in every aspect of our life. I

offer my humble words of gratitude to my parents and brothers for their

guidance, financial support and moral inspiration throughout my academic

career.

I am greatly indebted to my supervisor Prof. Dr. Habib Ahmad TI, Dean

Faculty of Sciences, Hazara University, whose guidance and criticism enabled

me to complete this task. Thanks are due to my Co-supervisor Dr. Zafar Iqbal,

Assistant Professor Department of Botany, Hazara University, for providing

his valuable hours of assistance in conducting and evaluation of the research

findings.

I am also thankful to Prof. Dr. Manzoor Hussain, Chairman Department of

Botany, Hazara University, Mansehra, Dr. Ghulam Mujtaba Shah, Dr. Jan

Alam, Dr. Shujaul Mulk Khan, Dr. Azhar Hussain Shah, Dr. Muhammad Fiaz

and Mr. Abdul Majid for their cooperation, suggestions and identification of

plant species. I am also very thankful to all my family members and relatives,

especially for their cooperation in collection of plants and their preservation,

Phytosociological analysis, collection of information regarding plant uses,

their historical range of distribution and conservation status.

I am thankful to Higher Education Department Government of Khyber

Pakhtunkhwa for providing me opportunity to complete this task. I am also

thankful to my colleagues in Government Degree College Battagram for their

cooperation during research. I am also thankful to my bosom friends Mr.

Rahatullah and Mr. Sajid Haroon for their manual and moral support in every

part of my study.

xiv

ABSTRACT

This dissertation communicates an analytical exploration of the vegetational

profile of Nandiar Khuwar Catchment area, District Battagram, Pakistan. The

Nandiar Khuwar Catchment starting from the alpine pastures in the east and

stretches towards the famous Indus River in the west. The area provides a

variety of geo-climatic regimes within a sharp relief of 525-3817m with total

land area of 1301km2. Based upon physiognomy of the vegetation, the study

area was divided into 80 stands. Sum 324 vascular plants species belong to 97

families were recorded among which, 157 plant species medicinally

important. The most diverse stand was Rajmira followed by Jaro in term of

Shannon Diversity Index and Species Richness. The widely distributed

species in the study area were Fragaria nubicola and Adiantum capillus-veneris

recorded in 53 stands out of 80 stands. With respect to phenology, the

maximum plant species flowered in April-July (68.5%) and maximum plant

species showed fruiting in May-August. Among life form classes,

phanerophytes were dominant with 118 (36.4%) followed by therophytes

group with 82 (24.05%) species. The leaf size spectra were dominated by

microphyll with 137 (40.2%) followed by mesophyll having 103 species i.e.

30.2%. The TWINSPAN classification sorted out vegetation of the area into 13

plant communities. Six sub communities were identified in subtropical zone,

4 in mixed Pinus roxburghii and Pinus wallichiana forests, 5 in pure Pinus

wallichiana forests, 4 in western mixed coniferous forests, 3 in pure Abies

pindrow and Picea smithiana forests and 2 plant communities were identified in

alpine zone. The index of similarity was maximum (35.7%) for Wikstroemia,

Viburnum, Androsace and Juniperus, Sibbaldia, Primula communities.

Ordination analyses of the data provided a compositional response with a

gradient of 6.4 SD units long. The total variance (inertia) in the species data

was 7.07. Bray-Curtis ordination score was maximum for axis 1 (0.96) having

regression coefficient -54.1 and variance in distance were 2.5. Detrended

Correspondence Analysis (DCA ordination) produced a maximum gradient

length of 6.3 recorded for axis 1 with eigenvalue of 0.71. DCA clustered

xv

different species having similar habitats and different stands having common

species. The Canonical Correspondence Analysis (CCA ordination) showed

that plant species and stands were in linear combination with environmental

variables. Acacia modesta and Ficus carica was positively correlated with

temperature. Betula utilis, Juniperus communis, Ciminalis karelinii and Wulfenia

amherstiana species were negatively correlated with temperature.

Conservation status of the plants species recorded for the area showed that 10

species fall under critically endangered and 12 more species fall under

endangered categories. Major threats recorded for the flora were habitat

losses, excessive logging, selective and unscientific collection of herbs, over

grazing, erosion, environmental changes and introduction of exotic taxa. We

concluded that Nandiar Khuwar Catchment has great potential for

conservation of the native species of the Western Himalayan Ecoregion. The

conservation issues needs to be addressed through devising strategies for

protection, recovery and rehabilitation of the threatened species within their

respective stands.

1

Chapter 1

INTRODUCTION

1.1 Study Area

Nandiar Khuwar catchment, District Battagram is located in Khyber

Pakhtunkhwa Province of Pakistan 34° 33’ and 34° 47’ north latitude and 72°

55’ and 73° 14’ east longitude (Haq et al., 2010). It is bounded by the Allai

Valley in the north, Siran Valley in the east, Konsh and Agror Valleys in the

south and Black mountain and Indus River in the west (Ahmad et al., 2010).

The altitudinal range of Nandiar Khuwar catchment is from 525m at Thakot

to 3817m above mean sea level at Malkisar with a total area of 1,301km2 (Haq

et al., 2011).

Baleja Maidan, Birth Maidan, Machai Sar, Malkai Sar, Ganja, Chail Sar,

Ramosay Sar and Gabrai Kandao are the well known mountainous location of

Nandiar Khuwar catchment area (Haq et al., 2011). The important tribes are

Swatis, Khankhel, Akhunkhel, Syeds and Gujars. District Battagram also

inhabit a small population of Kashmiries, Kohistanies and few Sikh families

(Haq et al., 2010).

Agricultural land, wasteland, forest and alpine meadows can be differentiated

in Nandiar Khuwar catchment. The slopes are variable from gentle to

precipitous. Majority of the people are dependent on agriculture as a first

source of livelihood, followed by pasture animal husbandry. They cultivate

rice, maize, wheat, red beans and vegetables (Muhammad, 2003). Beside

agriculture, labor, remittances and livestock are also major sources of income.

The people belonging to this area are generally poor; nearly 51% income

comes from agriculture, 12% from livestock, 9% from forest, 9% from labor,

4% from services and 15% from remittances (Haq et al., 2011).

2

Fig. 1.1: Map of District Battagram showing stands location.

3

Fig. 1.2: Climatic zones recognized in Nandiar Khuwar Catchment area

(Muhammad, 2003).

4

Fig. 1.3: A view of Nandiar Khuwar near Battamori.

Fig. 1.4: A view of Nandiar Khuwar near Battagram.

5

Fig. 1.5: A view of Chailsar, Ramosay sar and Karganja.

Fig. 1.6: A view of Baleja Mountain.

6

1.2 Climate

The vast variation in altitude, slope aspect, disposition of mountain ranges

and prevailing wind currents in the catchment of Nandiar Khuwar reveal that

the climate vary from sub tropical at lower altitude to “alpine” conditions

prevailing in the higher reaches. In subtropical zones the summer is more

severe; however slight relief is provided by occasional shower. The winter is

more severe in higher altitude as compared to lower altitudes (Muhammad,

2003). The exact data of temperature, rainfall, snowfall and humidity of

Nandiar Khuwar catchment District Battagram is not available in Pakistan

Meteorological Department (National Agromet Centre) Islamabad, as per

their reply vide letter No: Agr – 2(2) III/2013/807 Dated: 06/12/2013. There

are marked seasons of rainfall, drought and snowfall. Rainfall caused by the

southwest monsoon, during July to mid of September constitutes almost half

of the annual average precipitation. May, June, October and November brings

drought spell. Snowfall occurs at lower altitudes during the months of

December, January and February. Nandiar Khuwar catchment receives

annual precipitation in the form of snowfall. Snowfall increases from lower

altitude to high altitude (fig. 1.5 and 1.6). In higher altitudinal zones, snowfall

starts during the month of November and stay up to June, however at lower

altitude it stays only for few days (Haq et al., 2011).

1.3 Forest Types

On the basis of available indicator plant species Haq et al. (2010) classified the

vegetational zones of District Battagram into six categories which fall under

Moist Temperate category of the internationally known Western Himalayan

Moist Temperate ecology (Champion et al., 1965). The vegetational zones

include:

1.3.1 Tropical Sub Humid Forest

It is a scrub forest, consisting of dry bushy shrubs and small trees found up to

an elevation of 700m from Thakot to Peshora. The associated species are

7

Acacia modesta, Mallotus philippensis, Albezia lebbeck, Bauhinia variegata,

Dalbergia sissoo, Dodonaea vescosa, Justicia adhatoda, Rubus fructicosus and Mytus

roylianus.

1.3.2 Sub Tropical Pinus roxburghii Forests

Pinus roxburghii forests occur at the altitudinal zone of 530-2050m. The Pinus

roxburghii forests are found both in pure form and mixed with Pinus

wallichiana. The other associated species are Quercus incana, Rhododendron

arboreum, Grewia optiva, Ficus racemosa, Woodfordia fruticosa, Indigofera

heterantha, Berberis lycium, Colebrookea oppositifolia, Zanthoxylum armatum,

Ziziphus oxyphylla, Rosa moschata and Rubus species.

1.3.3 The Moist Temperate Blue Pine Forest

Blue pine (Pinus wallichiana) occurs at the altitudinal zone of 1800 - 2400m.

The crop on the whole varies from pole to sub mature. Scattered matures and

over-mature trees are also there in some compartment of Hill territory. The

other associated species are Juglans regia, Quercus dilatata, Quercus incana,

Rhododendron arboreum, Viburnum cotinifolium, Cotoneaster microphylla,

Cotoneaster nummularia, Sarcococca saligna, Berberis lycium, Indigofera heterantha,

Rubus fructicosus and Rosa moschata.

1.3.4 Mixed Coniferous Forests

It is an important forest type, with Silver Fir (Abies pindrow) and Spruce (Picea

smithiana) as predominant species. Mixed coniferous forests occur between

elevations of 2000- 3050m. The composition of the forest is strongly influenced

by slope aspects. Hot southern slopes contain more of Blue Pine while on

northern aspect Silver Fir is predominant. The forest is generally

heterogeneous in nature having mixed age classes. The other associated

species are Quercus dilatata, Juglans regia, Aesculus indica, and Prunus padus,

Taxus wallichiana, Berberis lycium, Spiraea vaccinifolia, Lonicera quinquelocularis,

8

Rosa moschata, Viburnum species, Podophyllum emodi, Paeonia emodi, Geranium

wallichianum, Skimmia laureola, and Euphorbia species.

1.3.5 Pure Fir and Spruce Forests

Silver Fir (Abies pindrow) and Spruce (Picea smithiana) are usually found on the

elevation ranging from 2200 - 3000m. On cooler aspects they merge with Blue

Pine in the lower reaches. The crop consists of mature and over mature trees.

Old Fir trees are mostly top dry. The other associated species are Juglans regia,

Aesculus indica, Prunus padus, Quercus semicarpifolia, Betula utilis, Berberis

lycium, Indigofera heterantha, Desmodium elegans, Spiraea vaccinifolia, Ranunculus,

Aquilegia, Aconitum, Skimmia, Fragaria, and Geranium species.

1.3.6 Alpine Pastures

The alpine meadow locally known as “Mali” stretches above the tree limit

between 2850 – 3800m above mean sea level. These pastures have excellent

grasses and forbs available during summer season. Alpine pastures of

Nandiar Khuwar catchment include Chailsar, Ramosysar, Ganja, Baleja top,

Alishera and Malkaisar. These pastures support a large number of sheep,

goats and cattle during summer. Some Birch trees and Juniperus communis are

found on steep rocky places. Birch trees are badly loped for fodder by grazers.

The other species included Berberis species, Salix species, Corydalis species,

Potentilla species, Sibbaldia cuneata, Tanacetum dolicophyllum and Ranunculus

palmatifidus.

1.4 Rock/Minerals

Nandiar Khuwar catchment is underlined by metamorphic and plutonic

igneous rocks which are in turn intruded by pegmatite, aplites and quartz

veins. Quaternary alluvium and glacial deposits are common. Low grade

metamorphic rocks like Graphite schist, Re-crystalline lime stone, Amphibole

schist, Quartz-mica Schist and green schist are exposed in the area. Granite,

Ultra mafic and massive amphibolites cover large area. Shale’s occur

9

occasionally in Pinus roxburghii zones. The surface soil is formed by the

weathering of the parent rocks. The top soil is constantly being washed away

by run off from higher slopes. The soil under fir and spruce is deep and quite

rich in humus, whereas it is shallow and poor under pines and scrub zones

including wasteland (Muhammad, 2003).

1.5 Biodiversity

Biodiversity the measure of the health of ecosystems is referring to organisms

found within the living world (Murthy, 2007). It consists of species diversity,

ecosystem diversity and genetic diversity (Ahmad and Khan, 2004).

Biodiversity varies greatly across the globe as well as within regions.

Globally, there is a latitudinal gradient in species diversity. The tropical

regions are rich in biodiversity as compared to Polar Regions (Haq et al.,

2012). Biodiversity is greatly affected by different genetic and environmental

factors such as temperature, precipitation, altitude, soil, geography and the

presence of other species (Tandon, 2005). Every third plant species is hosted

by the mountainous regions of the world (Namgail et al., 2012).

One half of all plant species in the planet grow in hot spots, but not yet

destroyed vegetation of these territories occupies only 2.3 % of the Earth

(Motiekaityte, 2006). About 1% of the known species of the Earth are extinct

due to environmental changes (Sahney et al., 2010; Sahney and Benton, 2008;

Raup, 1994). The Holocene extinction due to habitat destruction by humans

caused loss of genetic diversity (Biodiversity, 2012). Worldwide land cover, is

altered principally by direct human use; through agriculture, pasture,

forestry, and development (Meyer and Turner, 1992).

Pakistan is under severe ecological stress due to its population explosion,

urbanization, deforestation and over exploitation of natural resources (Ali et

al., 2014; Haq, 2012). The natural forests of Pakistan are rapidly declining at a

rate of 4-6% per year, resulting in a decline in population size of both flora

10

and fauna (Haq et al., 2010). The forests of Pakistan require special attention

for the conservation of environment and sustainable utilization of natural

resources (Khan et al., 2014). The decrease in forest cover and associated major

changes in community composition have led to the decline in population size

of many important plant and animal species (Haq et al., 2010).

1.6 Phytosociology

Phytosociology is the study of biocoenosis from a botanical perspective and is

concerned with plant communities, their relationships, structure,

composition, distribution, development and the short-term processes

modifying them (Poore, 1955). Phytosociological surveys help in planning,

management and use of natural resources (Mashwani et al., 2011). The aim of

Phytosociology is to achieve a sufficient empirical model of vegetation using

plant species combinations that characterize univocally vegetation units

which may express largely abstract vegetation concepts or actual readily

recognizable vegetation types (Weber et al., 2000).

Phytosociology is based on associations and an association is the characteristic

combination of plant taxa, habitat features, physiognomy, biogeographical

area, role in ecological succession, historical and paleo-biogeographical

relationships (Khan et al., 2013). Associations with floristic and territorial

affinities can be grouped in larger ecological conceptual units called

"alliances". Similar alliances may be grouped in "orders" and orders in

vegetation "classes". The setting of syntaxa in such a hierarchy makes up the

syntaxonomical system, or the reference model of the given vegetation and

territory. The higher levels of complexity in describing the vegetation units

including vegetation complexes and multivariate statistics were used for

defining syntaxa and their environmental interpretation (Weber et al., 2000;

Poore, 1955).

11

Habitat of species describes the environment over which a species is known to

occur and the type of community that is formed as a result (Whittaker et al.,

1973). Ecological niche is the set of biotic and abiotic conditions in which a

species is able to persist and maintain stable population sizes (Wiens and

Graham, 2005; Whittaker et al., 1973).

It is useful to collect data to describe the population dynamics of each species

in different abiotic conditions (Haq et al., 2012). Present external factors and

historical plant geography are responsible for the determination of a plant

community (Poore, 1955). The availability of suitable habitat determines the

species distribution patterns in habitat structure where ecogenesis and

phylogenesis interact in a complex manner to shape current species

distributions (Thorpe et al., 1994).

The presence or absence of vegetation is controlled by environmental

variables where soil is of high importance in plant growth, and is a function

of climate, organisms, topography, parent material and time while

topography affects soil and climate, in addition to affecting temperature and

evapo-transpiration, makes deeper soil and higher content of organic matter

(Hoveizeh, 1997; Leonard et al., 1984). Certain plants species perform well in a

wide range of environmental conditions while it is impossible for individual

genotypes to perform well across the full range of conditions (Iqbal et al.,

2013). In a community on the basis of similarity in structure and function the

plant can be classified in different life form and leaf size classes which

indicate the adaptation of plants to certain ecological condition (Khan et al.,

2013).

Phenology and climate are related in terms of temperature, rainfall and day

length. Leaf growth, leaf fall, flowering and fruiting of species occur in

specific seasons of the year. The phenology of life form classes vary and are

associated with day length/ temperature. Precipitation and soil affect species

12

richness. Slope aspect, slope angle, altitude, latitude and longitude also

influence species richness. Diversity indices provide composition, rarity and

commonness of species in a community. It reflects how many different species

are in a dataset, and simultaneously takes into account how evenly the basic

entities are distributed among those types. The value of a diversity index is

directly related to number of species and evenness (Khan et al., 2012).

Life form is the indicator of climate (micro and macroclimate) and can be used

in comparing geographically widely distributed plant communities. Life form

is characterized by plant adaptation to certain ecological conditions and

traditionally being used to describe world vegetation type at community

level. The life form and leaf size spectra are the attributes that have been

widely used in vegetation description. Leaf size classes have been found to be

very useful for plant associations. The leaf size knowledge helps in

understanding physiological processes of plants and plant communities.

Climate of a region is characterized by life form while the biological spectrum

of the region exceeds the percentage of the same life form in the normal

biological spectrum. Biotic agencies are the chief causes for changing the

biological spectrum in a given floristic zone (Amjad, 2012).

Phytosociological surveys help in planning, management and exploitation of

natural resources. Phytosociology provides sufficient empirical model of

vegetation using plant species combinations. The spatial pattern of vegetation

composition and the relationship between vegetation composition and

environmental factors can be easily understood with phytosociological study

(Khan et al., 2011). For a sustainable management of Nandiar Khuwar

Catchment area it is necessary to study the effects of climate and edaphic

factors on vegetation, species diversity and distribution in different

vegetational zones of the study area. The information regarding the

phytosociological study of District Battagram is not available, therefore the

present study was proposed with the following objectives.

13

1.7 OBJECTIVES

i. To explore the plant species diversity in various vegetational zones of

Nandiar Khuwar catchment area

ii. To study the role of physiographic and edaphic factors in determining

the vegetation structure of the study area

iii. To assess the plant communities on the basis of altitudinal gradient,

slope aspect and slope angles

iv. To document the economic important plant species and conservation

status of the flora of selected area

14

Chapter 2

REVIEW OF LITERATURE

Biodiversity is referring to organisms found within the living world and

consists of species diversity, ecosystem diversity and genetic diversity

(Ahmad and Khan, 2004). It is the variation of life forms within a species,

ecosystem, biome, or an entire planet and is a measure of the health of

ecosystems (Haq et al., 2010). Biodiversity is greatly affected by different

genetic and environmental factors (Tandon, 2005). Biodiversity is influenced

by land use patterns (Turner et al., 1998). Land use patterns alter relative

abundances of natural habitats, species richness, response of species to habitat

loss and fragmentation (Pearson et al., 1996; Matlack, 1994; Terborgh, 1992;

Walker, 1992).

The planet earth is the homeland of more than 270,000 vascular plant species

which are surviving in various ecosystems (Alam and Ali, 2009). Out of these,

more than 10,000 plant species have been reported in Himalayas (Khuroo et

al., 2007). In the Himalayas about 50% of the potential forest area has been

decreased due to major structural changes resulting in loss of indigenous

plant resources and their traditional knowledge during last hundred years

(Ibrar, 2003).

Phytosociology is concerned with plant communities, their relationships,

structure, composition, distribution, development and the short-term

processes modifying them (Poore, 1955). Phytosociological surveys helps in

planning, management and use of natural resources (Mashwani et al., 2011).

Present external factors and historical plant geography are responsible for the

determination of a plant community (Poore, 1955).

The presence or absence of vegetation is controlled by environmental

variables, where soil is of high importance in plant growth, and is a function

15

of climate, organisms, topography, parent material and time while

topography affects soil and climate, in addition to affecting temperature and

evapo-transpiration, makes deeper soil and higher content of organic matter

(Hoveizeh, 1997; Leonard et al., 1984). Certain plants species perform well in a

wide range of environmental conditions while it is impossible for individual

genotypes to perform well across the full range of conditions. In a community

on the basis of similarity in structure and function the plant can be classified

in different life form and leaf size classes which indicate the adaptation of

plants to certain ecological condition. Phenology and climate are related in

term of temperature, rainfall and day length. Leaf growth, leaf fall, flowering

and fruiting of species occurs in specific season of the year. The phenology of

life form classes vary and are associated with day length/ temperature.

Precipitation and soil affect species richness. Slope aspect, slope angle,

altitude, latitude and longitude also influence species richness (Khan et al.,

2014).

Vujnovic et al. (2002) analyzed the cover of vascular plants, mosses, and

lichens across a range of disturbance levels in 11 remnant grasslands within

the Aspen Parkland Ecoregion of central Alberta, western Canada. Lower

species diversity was found in undisturbed and lightly grazed as well as in

highly disturbed plots. Intermediate levels of disturbance reduced dominance

of Festuca hallii and increased abundance of most other species resulting inthe

highest species diversity. The species richness and diversity of exotic plant

species showed a significant positive relationship with the magnitude of the

disturbance. Zuo et al. (2014) measured the plant species richness, soil

properties and altitude across four spatial scales at three different dune

stabilization stages in Horqin Sandy Land, Northern China. Plant species

richness increased with the increase of spatial scales in each dune stabilization

stage, as well as with the increase of dune stabilization degrees. CCA analysis

showed that plant species richness was significantly and positively correlated

to soil organic carbon and total nitrogen in mobile dune, and significantly and

16

positively correlated to soil organic carbon, total nitrogen, carbon/nitrogen,

very fine sand and silt and clay. No significant correlation between plant

species richness and environmental factors was observed in fixed dune.

Pearson (1979) studied the effects of temperature and moisture on phenology

and productivity of Indian ricegrass. He reported that the growth in Indian

ricegrass commenced in the spring when soil temperatures stayed at 4°C for

at least 3-4 days. Maximum plant size was attained when soils warmed up

early in the spring. Higher soil temperature late in the vegetative phase of

growth delayed anthesis approximately 3 days for each degree Celsius above

10°C. Sundriyal et al. (1987) selected four stations, representing variation in

elevation, slope, exposure, snow cover, vegetation composition and cover at

Tungnath, a high altitude zone in the Garhwal Himalaya. The initial growth

of plants was noticed in early May to mid of June. A considerable difference

in phenological phases of different species was observed including, bud 4 to 6

weeks; flower 3 to 5 weeks; fruit, 5 to 7 weeks. Pangtey et al. (1990) studied

various phenological stages of 184 species of high-altitude plants in the

Pindari glacial moraine area of Kumaun in the Central Himalaya. The

initiation of growth was synchronized with the beginning of spring/or

summer temperature rise and snowmelt. In this high-altitude zone, the peaks

of various phenophases succeeded one after another over about 4 months

from early June to October. It was suggested that the plants complete various

growth cycles within a very short period of favorable conditions to ensure the

survival of their progeny.

Angelova and Tashev (2005) proposed a model for doing a complex analysis

for the flora of Mount Chepan (Bulgaria). 456 species of MtChepan were

classified, according to their levels of distribution, with altitude into 21

groups. Among them 4 groups were wide spread. The distribution of the

remaining 17 group species ranges in spread in altitude, depending on their

life form classes. Huelber et al. (2006) studied the phenological response of

17

snow bed species to snow removal dates in the central Alps, with implication

for climate warming. They concluded that low temperature and the short

growing season in high altitude snow patches in temperate mountains

constrain life cycle and reproduction of snow bed species. Winter

precipitation and temperature are the main factors for determining the

growing season length and are predicted to change with global warming.

Suresh and Paulsamy (2010) recorded the phenological observation and

population density of six uncommon medicinal plant species (Anaphalis

elliptica, Ceropegia pusilla, Hedyotis articularis, Heracleum rigens, Leucas vestita

and Luzula campestris) in four grasslands in Nilgiri Biosphere Reserve,

Western Ghats. All six species exhibited peak bud formation between

February and May and bud break in June. Most of the leaves were produced

in a single flush. Bijalwan et al. (2013) studied the impact of microclimatic

variations on the developmental stages of common alpine plant species at

four primary phenology sites at Dayara meadow of Garhwal Himalayas. The

study revealed that variations in topographical features and environmental

conditions directly influenced phenology of alpine plant species. Site I and IV

showed great variation in the timing of phenological phases whereas, site III

and IV showed approximately similar phenological timing. Anemone obtusiloba

and Anaphalis contorta showed early flowering whereas Aconitum

heterophyllum, Bupleurum longicaule and Parnassia nubicola flowered in late

August and early September.

Cain and Castro (1959) and Shimwell (1971) reported that hemicryptophytes

are characteristic of temperate zones; therophytes of desert climate and

geophytes of the Mediterranean climate. The environmental implication of life

form spectra from India were described by Meher-Homji (1981). The

reassessment of 38 spectra described the therophytic phytoclimate for arid

zones; thero-Chamaephytic type characterizes semi-arid zones and grasslands

of secondary origin. Marshy grasslands showed thero-Hemicryptophytic

18

spectrum, thero-nanophanerophytic and thero-phanerophytic spectra

correspond to the areas of disturbed woodlands, phanerophytic to the better

preserved forests. Nano- chamaephytic plant-climate was recorded for the

thicket-dotted regions of Madras, Mysore. Geo-chamaephytic type prevails in

the Western Himalayas where the mean of the coldest month is below 5°C.

Ram and Arya (1991) analyzed the life form and vegetation of Rudranth an

alpine meadow of Central Himalaya, India. They reported that the

phanerophytes were dominant life form before degradation while the

degraded vegetation supported therophytic and hemicryptophytic types of

vegetation. El-Demerdash et al. (1994) reported the distribution of the plant

communities in Tihamah coastal plains of Jazan region; Saudi Arabia. They

reported eight major community types constitute the major part of the natural

vegetation of the study area. They have discussed the factors affecting the

species distribution and the correlations between the vegetational gradients

and the edaphic variables. The study area was declared as subtropical desert

where therophytes were the most frequent life-form in this region. Barik and

Misra (1998) studied the phenological pattern, life form, plant type and uses

of 80 plant species of grassland of South Orissa. They reported that due to

diverse geo-morphology of this region the climate and altitude have provided

different microhabitats for specific plant growth. The alpine plants were

recorded on exposed dry rocks crevices, ravines and on much fertile loamy

soils constituting the alpine meadows. Bhandari et al. (1999) explored the

floristic composition, biological spectra and diversity of burnt and unburnt

submontane grazing lands of Garhwal Himalaya. Pharswan et al. (2010)

studied the floristic composition and biological spectrum of vegetation in

alpine meadows of Kedarnath Garhwal Himalaya.

Tareen and Qadir (1993) reported the life form and leaf size spectra of 102

plant communities of diverse areas ranging from Harnai, Sinjawi to Duki

regions of Pakistan. Malik et al. (2007) described the life form and leaf size

19

spectra of plant communities harbouring at Ganga Chotti and Bedorii hills.

Sher and Khan (2007) explored the floristic composition, life form and leaf

spectra of 222 plant species Chagharzai valley, District Buner. Ajaib et al.

(2008) explored the biological spectra of Saney Baney Hills District Kotli,

Azad Jammu and Kashmir. They reported that severe deforestation,

overgrazing, soil erosion and human influence reduced the macrophylls and

so therophytes appeared to occupy the vacant niches. Life form and leaf size

spectra of vegetation in Kotli Hills, Azad Jammu and Kashmir were reported

by Amjad (2012). He reported that therophytes are indicators of subtropical

and disturbed vegetation while hemicryptophytes are indicators of humid

condition. The dominance of therophytes reflects that environmental

conditions or human influences are not well suited to the phanerophytes. The

leaf size spectrum of these communities showed that the overall vegetation of

Kotli is dominated by leptophylls and nanophylls. Microphyll is also reported

as a dominant life form in higher altitudes due to low temperature, high

rainfall and moist condition. Generally, lower altitudes support small leaves

in lower belt and large leaves in upper niches.

Yavari et al. (2010) studied the floristic-ecologic data of 11 disperse

populations of Astragalus glaucops as mean endogenous milieou by eco-

phytosociologic method based on similarity and dissimilarity of floristic

composition in Alvand mountain. Malik and Malik (2004) recorded life form

and index of similarity during Monsoon of Kotli Hills, Pakistan. Life form and

index of similarity of plant communities of Sarsawa Hills were reported by

Malik and Ahmed (2006). Nazir and Malik (2006) worked on life form and

index of similarity of plant communities of Sarsawa hills District Kotli.

Kassas and Imam (1954) described the relation of habitat and plant

communities in the Egyptian desert. Batanouny (1979) analyzed the

vegetation pattern and process affected by human impact along the Jeddah-

Mecca road, Saudi Arabia. Batanouny and Baeshin (1983) described the plant

20

communities along Medina-Badr road across the Hejaz Mountains, Saudi

Arabia and recognized twenty three plant communities by species dominance

and habitat features. Among them ten communities were dominated by trees

and shrubs; five were dominated by Acacia spp. and seven communities were

dominated by suffrutescent species and six are dominated by ephemeral

species. The latter communities appear only during the wet season and

disappear at the beginning of summer. The wide variations in topography,

rock types and soil characteristics, have a marked influence on the water

resources and consequently on the vegetation.

Backeus (1992) analyzed the distribution and vegetation dynamics of humid

savannas in Africa and Asia. He concluded that the extension of savannas

under humid climatic conditions and the relation to the distribution of forests

is a function of cultivation, grazing by domestic and wild animals, present

and previous climate, geomorphology and soil characteristics. The established

savannas were maintained by fires. Montane savannas were generally

brought about by man's clearing, cultivation and burning. In humid African

lowland climates forests expand into savannas if not maintained by man. In

montane areas forest expansion may be delayed on degraded soils and when

diaspores were lacking. Blažková (1993) studied the phytosociology in the

lowlands of North Korea and he recorded two new plant communities. The

association of Digitario ciliaris and Zoysietum japonicae with many species of

the C4 photosynthetic pathway, growing on open sunny habitats and the

association of Plantagini asiaticae and Poetum pratensis consisting mostly of

species with the C3 photosynthetic pathway and growing on half shaded or

moist habitats. They are analogous to the communities described from Japan.

Backeus (1993) used data from transects on the shores and draw down area

of a small reservoir in Tanzania with a strongly fluctuating water level to

illuminate a spatial and short term temporal variation in the vegetation of a

border zone. Few perennial species survived the rainy season in the zone

exposed to the fluctuating water level. Most plants were annuals that

21

colonized yearly. The vegetation under the full supply level was sparse and

related to Eriochloetum nubicae and Ecliption albae.

Suzuki and Saenger (1996) studied the phytosociology of mangrove

vegetation in Australia and they have identified 17 plant communities

compared it with East Asia mangrove vegetation. The Lai Chi Wo mangrove

swamp vegetation in Hong Kong was analyzed by Lu Chang-yi et al. (1998).

These swamps have 10 mangrove species comprising 76.9% of the total

number of mangrove species in Hong Kong and were dominated by Aegiceras

corniculatum, Heritiera littoralis, Excoecaria agallocha with importance values of

37.88, 28.19 and 14.33 respectively. The mangrove species diversity and

evenness, as based on the Shannon-Wiener Function, were 1.44 and 62.01%.

The correlation information of the mangrove community was 1.008.

Hargreaves (2008) compared literatures of the international journals from

1998 to 2007 on the subject of phytosociology in Brazil on three fundamental

themes i.e. coverage of various plant physiognomies, representative of

Brazilian biomes and coverage of environmental factors for composition and

structure of plant communities. Ige et al. (2008) reported the phytosociology

of weed flora from three abandoned farm lands within Owo Local

Government area of Ondo State, Nigeria. Korzeniak (2013) studied the

phytosociological database as a tool for synthetic and comprehensive study of

semi-natural meadows in the Polish part of Carpathians.

Champion et al. (1965) and Beg (1975) recognized various types of forests and

different vegetational zones in Pakistan on the basis of temperature and

altitude. Chaudhri (1960) analyzed the vegetation of Kaghan valley and the

vegetation types described were grassland, Pinus excelsa forest, mixed

coniferous forest, Abies pindrow and Abies webbiana forest, Betula utilis forest,

Cedrus deodara forest, Juniperus macropoda forest, and alpine vegetation.

Chaudhri (1961) divided the vegetation of Karachi into three edaphic types.

22

The coastal vegetation consists of three main associations. In the protected

creeks facing the mouth of the rivers in shallow water were found mangrove

vegetation consisting of mainly Avicennia alba. In the muddy coastal swamps

Arthroenemum indicum were the main species. On the coastal sand dunes the

main species were Suaeda monoica, Ipomaea pes-caprae, Aerua pseudo-tomentosa,

Calotropis procera and Tamarix troupii. The vegetation of the calcareous rocks

consists of mainly Commiphora mukul, Grewia villosa, Grewia tenax, Euphorbia

caudicifolia and Acacia senegal. In the valleys between the hills where alluvium

has been deposited over the basic rock by various rivers, the vegetation

consists of Capparis decidua, Prosopis spicigera and Salvadora oleoides.

Qadir et al. (1966) conducted a phytosociological survey of Karachi University

Campus by mean area method and recognized six plant communities in

which xerophytic and deciduous shrubs were predominant. Amin and

Ashfaque (1982) carried out the phytosociological studies of Ayub National

Park, Rawalpindi. A total of 5 community types were recognized: depression

community (Acacia modesta, Cannabis sativa), level ground community (A.

modesta, Cynodon dactylon), foot hill community (A. modesta, Themeda anathera),

hill slopes community (A. modesta, Dodonaea viscose), and hill tops community

(A. modesta, Lantana camara). The ubiquitous presence of A. modesta

regeneration indicates little topographic influence on this species.

Beg and Khan (1984) investigated the dry oak (Quercus baloot) forest zone in

the Swat valley, Pakistan. They reported 3 new plant communities which

were relatively poor in grasses as compare to herbs and shrubs. Ahmed (1986)

carried out a quantaitative survey at 17 locations near the road side on the

Great Silk Road from Gilgit to Passu in the Himalayan range in Pakistan. Six

plant communities were recognized on the basis of species dominance,

importance value and similarity coefficent. The communities were generally

homogenous in nature. The soil of all communities were fine textured and

basic in nature with low fertility levels.

23

Qadri (1986) explored the vegetation of Kotli Hill, Azad Kashmir in his

Phytosociological study. Rashid et al. (1987) reported the Phytosociology of

Attock-Nizampur Hills. Malik and Hussain (1987) explored the vegetation of

Muzaffarabad in their Phytosociological investigation. Ahmed (1988) finds

out plant communities of some northern temperate forests of Pakistan.

Hussain and Shah (1989) recognized eleven non-stratified plant communities

winter in Docut Hills. The original vegetation has been changed to open

grassland where 35 species were recorded. Deforestation and over-grazing

followed by erosion were the major ecological problems. The species diversity

was low due to dormant winter season.

Hussain and Shah (1991) have also studied the phytosociology of vanishing

sub-tropical vegetation of Swat Docut Hills in spring aspect. Hussain and

Illahi (1991) presented ecology and vegetation types for Lesser Himalayan of

Pakistan. Iqbal and Shafiq (1996) recognized six plant community (Suaeda,

Haloxylon, Lasiurus, Prosopis, Aerva and Senna) from halophytes to xerophytes

with disturbed nature. On the basis of Importance Value Index Suaeda

fruticosa was the leading dominant species. Out of thirty six species, only five

species attained highest constancy class III as compared to rest of the species.

All the communities were heterogeneous due to the absence of certain

frequencies classes. Species diversity and community maturity index were

low. It was concluded that certain edaphic and anthropogenic activity were

responsible for variation in the composition and structure of the vegetation.

Hussain et al. (1997) worked on plant communities of Girbanr Hills, District

Swat Pakistan. Chaudhry et al, (2001) recognized five distinct plant

communities on core area, the mountainous region with sand stone and

patches of red sandy clay and two plant communities in the peripheral area in

the phyto-ecological studies conducted in the Chhumbi Surla Wildlife

Sanctuary, Chakwal, Pakistan. They reported that Chrysopogon serrulatus were

the dominant species in all plant communities.

24

Khan and Shaukat (2005) reported the above ground standing phytomass of

some grass-dominated communities of Karachi. Ahmed et al. (2006)

conducted a quantitative phytosociological survey in different climatic zones

of Himalayan forests of Pakistan and reported 24 different communities and 4

monospecific forest vegetations. Siddiqui et al. (2009) conducted a

phytosociological study of Pinus roxburghii forests in Lesser Himalayan and

Hindu Kush range of Pakistan. Thirteen stands were sampled in Mansehra,

Rawalpindi, Islamabad, Swat and Lower Dir between elevations of 750 –

1700m. Out of 13 stands pure Pinus roxburghii forests were recorded in 12

stands. Abbas et al. (2009) analyzed eight vegetative communities in the grey

goral range of Pakistan and Azad Kashmir. The indicator species was Pinus

roxburghii and loss of habitat was mainly responsible for reduction in

population size.

Ahmad et al. (2010) presented the floristic composition and communities of 47

stands of Cedrus deodara forests in the Himalayan range of Pakistan and

identified 7 different plant communities. Quantitative description and

structure of some forsets of Skardu District of Northern areas of Pakistan

were presented by Akbar et al. (2010). The dominant species in these forests

Pinus wallichiana, Juniperus excelsa and Betula utilis. In six stands three plant

communities were recognized which were Pinus, Juniperus community, Betula

utilis, Pinus community and Juniperus, Betula utilis community. The size class

structure shows gaps in each tree species indicating unstable conditions of

these forests due to anthropogenic disturbance. Farooq et al. (2010) recognized

five plant communities of Push Ziarat area (Shawal) in the South Waziristan,

Pakistan. Hussain et al. (2010) reported six plant communities in Central

Karakoram National Park of Northern Areas of Pakistan.

Khan et al. (2010) explored the species composition, diversity, equitability,

richness and concentration of dominance of tree species along an altitudinal

gradient of District Dir Lower Hindukush range of Pakistan. A total of 15

25

stands in Monotheca buxifolia forests were analyzed. Monotheca buxifolia was

the dominant tree species at all locations. Olea ferruginea and Acacia modesta

were reported in four stands as a second dominant species. Species diversity

ranged from 0 to 0.36. Concentration of dominance and equitability values in

some stands of 1 to 1.70 were relatively high due to the presence of single

species in the forests. Saima et al. (2010) discussed the floristic diversity of

Ayubia National park, District Abbottabad, Pakistan.

Brendenkamp et al. (1983) studied the ecological interpretation of plant

communities by classification and ordination of quantitative soil

characteristics. Peter and Erik (1992) studied woody vegetation in 44 sites in

Senegal. Sixteen vegetation types were recognized by TWINSPAN and CCA

by using species composition and density data. They have also used a

supervised and multi-spectral and multi-temporal classification of day and

night, dry season NOAA-AVHRR imagery to identify their distribution with

a classification accuracy ranging from 60-100%.

Velazquez (1994) used multivariate analysis to describe the composition and

distribution of vegetation types on the slopes of the volcanoes Tláloc and

Pelado, Mexico. The multivariate analysis included TWINSPAN, Detrended

Correspondence Analysis and Canonical Correspondence Analysis. These

volcanoes have relatively high α and β diversities. Floristic and

environmental data from 138 relives and seven explanatory environmental

variables were included among which soil moisture and elevation were the

most relevant variables to explain the distribution of the vegetation under

study.

The phytosociology and gradient analyses of a subalpine treed fen in Rocky

Mountain National Park, Colorado were conducted by Johnson (1996). Four

types of vegetation were subjectively defined; these same types were

distinguished by the DCA. Species composition was related to environment

26

using canonical correspondence analysis (CCA). Water-table depth, hummock

height, shading, groundwater temperature, and conductivity were

significantly correlated with species distribution, accounting for 51% of the

total species variance. Univariate regression was used to examine how tree

density varied with environment.

Johnson and Steingraeber (2003) analyzed the vegetation, environment, and

ecological gradients present on three calcareous mires in the South

Parkvalley, Park County, Colorado. Vegetation was classified into four habitat

classes, nine subclasses, and twelve species associations using TWINSPAN.

DCA was used to ordinate vegetation samples along two axes and CCA was

used to directly relate local environmental conditions to vegetation.

Vogiatzakis et al. (2003) explored the environmental factors that determine the

spatial distribution of oro-mediterranean and alti-mediterranean plant

communities in Crete. Classification of the vegetation was based on

TWINSPAN, while detrended correspondence analysis (DCA) and canonical

correspondence analysis (CCA) were used to identify environmental

gradients linked to community distribution. Hemicryptophytes and

chamaephytes were the most frequent, suggesting a typical oro-

mediterranean life form spectrum. The samples were classified into five main

community types and one transitional. The main gradients, identified by

CCA, were altitude and surface cover type in the North-west site, while in the

Central site the gradients were soil formation-development and surface cover

type.

Ghani and Amer (2003) studied the soil-vegetation relationships in a coastal

desert plain of southern Sinai, Egypt. 203 plant species were recorded in 19

sites. Five vegetation groups were recognized by TWINSPAN. DCA and CCA

ordination techniques were used to examine the relationship between the

vegetation and soil parameters. Therophytes and chamaephytes were the

most frequent, denoting a typical desert life-form spectrum. Floristic

27

composition in the different geomorphologic landscape units showed

differences in species richness. Nezerkova and Hejcman (2006) analyzed the

vegetation-environment relationships in Sudanese savannah, Senegal by

using canonical correspondence analysis. Khafagai et al. (2013) conducted the

vegetational survey on Saint Catherine Mountain Egypt. They reported ten

vegetational groups in 45 stands by using TWINSPAN for classification and

CANOCO for ordination.

Han et al. (2014) studied the characteristics of the vegetation and soil on

Mount Sejila in Tibet. Eleven sampling areas were examined, and the

vegetation composition, species diversity, plant biomass and soil properties

were measured in each one. Nowak et al. (2014) explored the geobotanical

investigations conducted in rush vegetation from the Phragmito-Magno-

Caricetea class in the central Pamir-Alai Mts (Tajikistan, Middle Asia). The

analyses classified the vegetation into 28 plant communities, including 26

associations. The vegetation patches occur mainly along the shores of water

bodies and in ditches. Jurišić et al. (2014) analyzed floristic diversity of

Posavina’s floodplain forests in Serbia. TWINSPAN classification and

ordinary Correspondence Analysis were used to detect floristic divergence of

analyzed stands. Both analyses have shown an almost identical result of

floristic composition, where 114 studied samples were grouped into seven

association groups at the third TWINSPAN classification level. ANOSIM

analyses were used to determine the degree of floristic discontinuity, which

was largest between forests of Pedunculate Oak, Hornbeam and Turkey Oak

and forests of Pedunculate Oak and Ash (statistics R = 0.8824 (p<0.001)).

Ahmed (1976) explored the vegetation of Skardu by using multivariate

analysis. Jabeen and Ahmad (2009) analyzed the vegetation of Ayub National

Park Rawalpindi by using multivariate analysis. The research was conducted

to determine the soil-vegetation relationship and quantify the floristic

composition. For classification TWINSPAN and for ordination DCA and CCA

28

were used and as a result four plant communities were recognized in Ayub

National Park Rawalpindi. Saima et al. (2009) explored the vegetation pattern

along a continuous 18 km long transect that crossed a mixed coniferous forest

in Ayubia National Park District Abbottabad. Five different plant associations

were recognized by cluster analysis. DCA and Spearman’s Rank Correlation

Coefficient were used to detect relationship between environmental factors

and species distribution.

Khan et al. (2013) worked on phyto-climatic gradient of vegetation and habitat

specificity in the high elevation western Himalayas by using Detrended

Correspondence Analysis (DCA) and Canonical Correspondence Analysis

(CCA).They reported 198 plant species in different life form classes among

which hemicryptophytes (51%) dominated the area. Phyto-climatic

relationships show that tree species are widely distributed on northern aspect

slopes whilst shrubs are more dominant on southern aspect slopes.

Vegetation structure, composition and diversity of Hub-dam catchment area,

Pakistan was conducted by Shaukat et al. (2014). They had investigated

variance/mean ratio and Morisita’s index for spatial patterns within-

community of plant populations. CA and CCA ordination were used to

analyze the distribution pattern of vegetation composition and the underlying

environmental gradients. Four major plant community types were recognized

by Ward’s cluster analysis. Biological spectrum of Hub dam catchment area

showed dominance of therophytes and chamaephytes.

Leonard et al. (1988) studied the vegetation-soil relationships on arid and

semiarid rangelands. The rangeland plant community distribution and

species composition were related to specific soil properties such as soil

climate, texture, depth, structure, fertility, pH, salinity and toxic influences.

Jensen et al. (1990) analyzed the correlation between soils and sagebrush-

dominated plant communities of northeastern Nevada. The influence of

29

climate and edaphic factors on vegetation cover, species diversity and

distribution on three lava flows of Mount Cameroon were recorded by Fronge

et al. (2011). They reported that the edaphic factors and climate play very vital

roles in the colonization process on Mount Cameroon.

Malik et al. (2007) studied the phytosociological attributes based on

environmental factor such as climate, soil condition, temperature, humidity,

rain fall, wind and biotic factor of different plant communities of Pir Chinasi

hills of Azad Jammu and Kashmir. Khan et al. (2010) sampled eight stands of

Quercus baloot forests for phytosociology, structure and soil characteristics of

Chitral Hindukush range of Pakistan ranging from 1770 −2370m.asl. Shaheen

et al. (2011) studied the patterns of species composition and diversity in the

lesser Himalayan subtropical forests of Kashmir in relation to environmental

variables and underlying anthropogenic influence. Ilyas et al. (2012) reported

vegetation composition and threats to the montane temperate forest

ecosystem of Qalagai hills, Swat, Khyber Pakhtunkhwa, Pakistan and

reported eight stratified plant communities.

Ahmad et al. (2010) worked on the diversity and distribution of vascular

plants of Nandiar Khuwar catchment District Battagram and reported 380

plant species. Haq et al. (2010) explored the species diversity along with

ethnobotanical uses of 402 plant species of Nandiar valley. These included 273

herbs, 77 shrubs, 68 trees, 18 climbers and 3 epiphytes. Haq et al. (2011)

reported 156 medicinal plants including 22 ethno veterinary important plant

species from Nandiar Khuwar catchment District Battagram. The information

regarding the phytosociological study on the plants of District Battagram is

not available.

30

Chapter 3

MATERIALS AND METHODS

3.1 General Survey

The entire Nandiar Khuwar catchment area District Battagram, Pakistan was

surveyed during 2011 – 2014. The study includes mature and least disturbed

vegetation (Laurance, 2004). The selected 80 stands were identified on the

basis of physiognomy, climates, topography, altitudinal variation and floristic

composition (McMahon et al., 2011).

3.2 Plant Collection

The plant specimens were collected from Nandiar Khuwar catchment area.

Field numbers were allotted to the specimens and field data were recorded in

field notebook. Scientific names, vernacular names, family and other relevant

information were recorded properly. The plant material was pressed and

dried by using blotting papers. The specimens were poisoned using mercuric

chloride, copper sulphate and absolute alcohol in the ratio of 1:2gm/L of

alcohol. The poisoned specimens were mounted on standard size herbarium

sheets and preserved in Herbarium Hazara University, for future reference.

The plant species were identified with the help of available literature (Nasir

and Ali, 1970 to 1989; Ali and Nasir, 1990 to 1992; Ali and Qaiser, 1993 to

2009).

Each species was individually evaluated in the field for its use patterns, range

of distribution, present frequency and compared with the known extent and

its normal ecological niche. The number of the plants scored with reference to

its ecological amplitude and calculated historical distribution were compared

with IUCN criteria version 3.1 (IUCN, 2001) for elaborating the conservation

status of the species concerned. Information regarding ethnobotanical uses of

plant species was obtained through semi-structured questionnaires from 320

peoples in different localities of the study area.

31

3.3 Phenology

Different phenological stages of life-cycle were determined in spring, summer

and autumn (Hänninen and Tanino, 2011; Singh and Singh, 2010).

3.4 Life Form

The adaptation of plants to climate and its biological spectrum were classified

into different life form classes as follows after (Raunkiaer, 1934).

3.4.1 Phanerophytes

In this class those species were included which have perennating buds

emerging at least 25 cm above from aerial parts of the plants and mainly

included woody trees and shrubs. They were further subdivided in to

following sub-classes:

Megaphanerophytes are trees over 30m tall.

Mesophanerophytes are trees between 8-30m tall.

Microphanerophytes are trees and shrubs between 2-8m tall.

Nanophanerophytes are shrubs between 25cm-2m tall.

3.4.2 Chamaephytes

Those plants were included in this class which have perennating buds that

lies on the surface of the ground up to 25cm and are mainly woody or semi-

woody perennials under shrubs.

3.4.3 Hemi-cryptophytes

In this class those plants were included where soil and leaves protect

perennating buds and are located on the surface of the ground.

3.4.4 Geophytes

In this class those plants were included where the perennating buds lie below

the ground surface.

32

3.4.5 Therophytes

Those annual species were included in this class which completed their life

history from seed to seed during favorable season of the year. Their only

perennating buds are those of the embryo in seeds, all other organs of the

plant having died.

3.5 Leaf Size Spectra

The leaf size spectra were determined and further classified as described by

Raunkiaer (1934).

Leptophyll (L): The leaf size is 25sq.mm.

Nanophyll (N): The leaf size is 225 sq.mm.

Microphyll (Mi): The leaf size is 2025sq.mm.

Mesophyll (Me): The leaf size is 18225sq .mm.

Macrophyll (Ma): The leaf size is 164025sq.mm

3.6 Methodology for Phytosociological Attributes

A line transect method was used for quantitative sampling (Buckland et al.,

2007), however, in some circumstances, like small survey plots, or when

plants are not easily detected by line transect method other methods were

used (Brown et al., 2011; Singh and Singh 2010). The study sites were

subdivided into stands for phytosociological data and points were taken at

20-meter intervals along 400 meters transects, however, in some area shorter

transects were also applied. The vegetative characteristics (density, relative

density, cover, relative cover, frequency, relative frequency and important

value index) of each stand were recorded. Importance value (Brown and

Curtis, 1952), were used to rank each species and the plant species with the

highest importance value in the stand were considered the dominant species.

The plant community was named on the basis of three dominant species

(Song, 1992).

33

3.6.1 Density

Density refers to the number of individuals of a species counted on a

sampling area and represents the numerical strength of a species in plant

community. It can be calculated as follows:

Density = Number of individuals

Total number of Transects

3.6.2 Relative Density

Relative density is the proportion of a density of a species to that of a stand as

a whole. It can be calculated as:

Relative Density = Density of a species ×100

Total density of all species

3.6.3 Frequency

Frequency is the percentage of sampling stands in which a given species

occurs and is concerned with the uniformity of occurrence of individual of a

species within an area. It can be calculated as follows:

Frequency = Number of transects in which a species occur ×100

Total number of Transects

3.6.4 Relative Frequency

Relative frequency is the proportion of a species to the sum of the frequency

of all the species in the area. It is determined by the following formula:

Relative Frequency = Frequency of a species ×100

Total frequency of all species

3.6.5 Average Cover

Coverage values are the rough measures of the degree of dominance of a

species in its layer. Each layer of vegetation is considered separately. Average

cover of a species can be determined as:

Average Cover = Total cover of a species

Total number of a species

34

3.6.6 Relative Cover

Relative cover of a species is the proportion of the total of a species to the sum

of the cover of all the plants of all species in the area. It can be calculated as

follows:

Relative Cover = Total cover of a species ×100

Total cover of all species

3.6.7 Importance Value Index

In heterogeneous plant community, data of density, cover and frequency of a

species do not give a clear picture about the dominant species. It can be

obtained by adding the values of relative density, relative frequency and

relative cover and dividing it by three will give the Importance value of the

species.

IVI = Relative Density + Relative Cover + Relative Frequency

3

3.7 Multivariate Analysis

Using classification and ordination techniques, multivariate analysis of the

ecological data was used to analyze data resulting from field observations and

experiments. The aim of this method is to find out the complex ecological

problems, such as the variation of biotic communities with environmental

conditions or the response of biotic communities to experimental

manipulation. For multivariate ordination analyses, Canoco 5 version, and

PC-ORD 6 were used. Ordination is the ordering of objects along axes

according to there resemblances. The major objective is to achieve an effective

data reduction, expressing many-dimensional relationships in a small number

of dimensions. This technique amounts to extracting the strongest correlation

structure in the data (using correlation in the broad sense). The correlation

structure is used to position objects in the ordination space. Objects close in

the ordination space are generally more similar than objects distant in the

ordination space.

35

To classify species and samples cluster analysis by TWINSPAN (Hill, 1979b)

was used. TWINSPAN is based on dividing a reciprocal averaging ordination

space. One of the most useful features of TWINSPAN is the final ordered two-

way table. Species names are arrayed along the left side of the table, while

sample numbers are along the top. The pattern of zeros and ones on the right

and bottom sides define the dendrogram of the classifications of species and

samples, respectively. The interior of the table contains the abundance class of

each species in each sample. Abundance classes are defined by pseudospecies

cut levels.

Detrended correspondence analysis (DCA; Hill and Gauch, 1980) is an

eigenanalysis ordination technique based on reciprocal averaging (RA; Hill

1973). DCA ordinates species and samples simultaneously. The DCA analysis

was used to investigate the relationship among vegetation types. Canonical

correspondence analysis (CCA, ter Braak, 1986, 1994) were used in the

ordination of main matrix (by reciprocal averaging) constrained by multiple

regression on variables included in the second matrix. In CCA method the

ordination of samples and species was constrained by their relationships to

environmental variables. Bray-Curtis Ordination (Polar ordination, Beals,

1984; McCune and Beals, 1993; McCune and Grace, 2002) was used for

ordination scores, endpoints, regression coefficient and variance in distance of

different axis.

3.8 Similarity Index

The community similarity was used for the comparison of all communities

within the study area and was calculated by using following formula after

Sorenson (1948).

Similarity Index = 2C ×100

A + B

Dissimilarity Index was calculated by: 100 –SI.

36

3.9 Diversity Index

The diversity index was used for the comparison of plant diversity at various

altitudes in different plant communities. The Shannon diversity index was

calculated using following formula:

Shannon Diversity Index =∑(p sub i × (ln p sub i))

Where p sub i = ni/N

3.10 Species Richness

Species richness is the number of different species represented in an

ecological stand, region or community. The species richness of each plant

community was calculated as per following formula:

Species Richness = Number of species

SQRT of total number of individuals

3.11 Environmental and Geographical Data

The data of slope aspect and slope angle were determined by clinometer. The

data of altitude, latitude and longitude were taken by GPS. The major

environmental conditions such as wind speed, temperature, chill, humidity,

heat index, dew point, wet bulb, barometric pressure and density altitude

were measured with the help of weather station (Kestrel 4000 weather

tracker).

3.12 Edaphic Factors

3.12.1 Soil Collection

Three kilogram soil samples were randomly collected from each stand from a

depth of 0-30cm. The soil samples were stored in polythene bag and labeled.

The soil samples were analyzed for different physico-chemical characteristics.

37

3.12.2 Soil Texture

Soil texture was determined by hydrometer method. The texture class was

determined with the help of textural triangle (Ghani and Amir, 2003).

3.12.3 Electrical Conductivity

The electrical conductivity of each sample was measured with the help of an

Electrical Conductivity meter (Khan et al., 2010).

3.12.4 Soil pH

The pH of each soil sample was determined by the help of pH Meter (Khan et

al., 2010).

3.12.5 Organic Matter

The organic matter concentration of each sample was calculated by Walkley

and Black’s titration method (Fonge et al., 2011).

3.12.6 Potassium and Phosphorus

The Potassium and Phosphorus concentration in each sample was determined

by atomic absorption spectrophotometer (Fonge et al., 2011).

38

Chapter 4

RESULTS

4.1.1 Diversity and Distribution of Plant Species

The study was carriedout to explore the biodiversity and phytosociology in

Nandiar Khuwar catchment area District Battagram Pakistan. A total of 324

vascular plants species belonging to 97 families was recorded in 80 stands

(Table- 4.2.1). 13 plant communities were recognized from sub tropical to

alpine zone of Nandiar Khuwar catchment. In the selected stands

angiosperms were represented by 84 families and 292 (90.1%) species,

gymnosperms were represented by 3 families and 7 (2.1%) species and

pteridophytes were represented by 10 families and 25 (7.7%) species (Table-

4.1.1). The dominant family was Asteraceae contributing 27 species, followed

by Rosaceae having 22 species, Labiatae (Lamiaceae) contributing 20 species;

Papilionaceae contributing 11 species, the Cyperaceae, Poaceae and

Ranunculaceae each contributing 10 species while Apiaceae were represented

by 9 (2.6%) species (Table- 4.1.2).

The maximum diversity index value was 4.18 and species richness value was

0.94 recorded in the moist temperate zone of the study area on north-facing

steep slope. Among life form phanerophytes were dominant by 118 (36.4%)

species, followed by therophytes having 82 (25.3%). The leaf size spectra were

dominated by microphyll with 137 (40.2%) species followed by mesophyll 103

(31.8%) species. The maximum flowering stages were recorded from April to

July (68.51%) while maximum fruiting stages were recorded from May to

August. The maximum frequencies were recorded for Fragaria nubicola (53)

and Adiantum capillus-veneris (53), followed by Pinus wallichiana (50), Viola

canescens (49), Berberis lyceum, Cynodon dactylon, Dryopteris jaxtapostia,

Indigofera heterantha, and Viburnum cotinifolium.

39

Table- 4.1.1 Share of various plant groups of Nandiar Khuwar catchment.

S. No Group Families Species Percentage

1 Angiosperms 84 292 90.12

2 Gymnosperms 3 7 2.16

3 Pteridophytes 10 25 7.71

Table-4.1.2 Dominant vascular plant familiesof Nandiar Khuwar catchment.

S. No Families Species Percentage

1 Asteraceae 27 8.33

2 Rosaceae 22 6.79

3 Labiatae (Lamiaceae) 20 6.17

4 Papilionaceae 11 3.39

5 Cyperaceae 10 3.09

6 Poaceae 10 3.09

7 Ranunculaceae 10 3.09

8 Apiaceae 9 2.78

9 Dryopteridaceae 9 2.78

10 Polygonaceae 6 1.85

40

4.1.2 Biodiversity Index (Shannon Index)

A diversity index is a mathematical measure of species diversity in a

community. Diversity indices provide important information about rarity and

commonness of species in a community. The Shannon diversity index for 80

stands along with altitude, latitude and longitude are presented in table 4.1.3.

The diversity index was recorded in 80 stands of Nandiar Khuwar Catchment

District Battagram between an elevation of 530 – 3780m on different slope

aspects and slope angles. The maximum Shannon diversity index (4.18) was

recorded in Rajmira on north facing moderate slopes at an elevation of 1488m

followed by Jaro (4.13) on north east facing slope at an elevation of 2222m,

Machaisar (3.88) on north facing steep slope at an elevation of 2899m and

Lekoni (3.86) on north east steep slope at an elevation of 2912m. The

minimum diversity index were recorded for the sub locality Kar Ganja (2.64)

on west facing steep slope at an elevation of 3265m due to harsh

environmental conditions, erosion and over grazing. The diversity index was

also low in Basha Khan (2.75) and Kiari (2.86) on west facing moderate steep

slopes.

4.1.3 Species Richness

Species richness is the number of different species represented in ecological

stand, region or community. The species richness data were recorded for 80

stands of Nandiar Khuwar Catchment between an elevation of 530 – 3780m

on different slope aspects and slope angles. The species richness value was

maximum for Rajmira (0.94) at an elevation of 1488m on north facing

moderate slopes, followed by Belandkot (0.77) on north facing moderate steep

slopes at an elevation of 1575m, Jaro (0.75) on north east facing slope at an

elevation of 2222m and Ledai (0.73) on north east facing slope at an elevation

of 2282m. The minimum species richness value was recorded for Basha Khan

(0.28) at an elevation of 1939m and Kiari (0.28) at an elevation of 1918m on

west facing moderate steep slopes. The detail species richness values of 80

stands are presented in table 4.1.3.

41

Table-4.1.3 Diversity index and species richness at different altitudes of Nandiar Khuwar catchment.

S. No

Name of Stands

Altitude Latitude Longitude Shannon Index

Species richness

1. Thakot I 1788 34º46΄.240 72º55΄.881 3.68 0.46

2. Thakot II 1808 34º46΄.187 72º55΄.838 3.46 0.40

3. Chorlangay 2422 34º44΄.467 72º56΄.731 3.58 0.69

4. Peshora 2568 34º42΄.820 72º58΄.131 3.58 0.62

5. Gajikot 3259 34º40΄.783 72º59΄.547 3.50 0.64

6. Shagai 3614 34º42΄.114 72º57΄.704 3.50 0.66

7. Paimal IV 3645 34º44΄.232 72º58΄.252 3.05 0.40

8. Naraza 3724 34º40΄.258 73º02΄.662 3.60 0.61

9. Paimal III 3800 34º43΄.898 72º58΄.532 3.39 0.43

10. Paimal II 3950 34º44΄.717 72º58΄.472 2.83 0.39

11. Lamai 4000 34º39΄.403 73º07΄.210 2.97 0.42

12. Khairabad 4140 34º37΄.100 72º59΄.515 3.21 0.49

13. Gada 4174 34º40΄.934 73º04΄.841 3.78 0.68

14. Nowshera 4224 34º40΄.229 72º59΄.833 3.17 0.41

15. Nili Reen 4316 34º36΄.609 73º05΄.161 2.76 0.45

16. Paimal V 4550 34º44΄.902 72º59΄.555 2.89 0.46

17. Paimal I 4680 34º42΄.057 72º59΄.943 2.49 0.36

18. Deshara 4684 34º35΄.405 73º02΄.384 2.75 0.35

19. Rajmira 4709 34º42΄.250 73º04΄.200 4.18 0.94

20. Paimal Dabrai 4963 34º43΄.907 72º58΄.977 2.79 0.35

21. Shabora I 4963 34º38΄.505 73º01΄.158 3.54 0.67

22. Shabora II 5014 34º39΄.007 73º02΄.061 2.95 0.39

23. Lundai I 5083 34º41΄.304 72º57΄.081 3.17 0.43

24. Nilishung 5089 34º37΄.188 73º06΄.356 2.83 0.39

25. Belandkot 5300 34º35΄.350 72º58΄.899 3.64 0.77

26. Anora 3 5320 34º41΄.382 73º07΄.299 3.53 0.66

27. Batangi 5328 34º41΄.999 73º02΄.295 3.27 0.48

28. Nil Batangi 5500 34º36΄.662 73º06΄.080 2.79 0.40

42

29. Lundai II 5500 34º41΄.009 72º56΄.759 3.88 0.53

30. Kiari 5519 34º35΄.521 73º00΄.317 2.86 0.28

31. Basha Khan 5578 34º35΄.874 73º00΄.305 2.75 0.28

32. Gat 5580 34º43΄.717 73º05΄.428 3.59 0.70

33. Shinglai 5640 34º42΄.198 73º02΄.453 3.47 0.65

34. Anora II 5699 34º41΄.282 73º07΄.525 3.59 0.53

35. Anora I 5704 34º41΄.167 73º07΄.509 3.15 0.47

36. Chapra 5900 34º43΄.200 73º07΄.319 3.36 0.54

37. Jarotia 6012 34º43΄.912 73º05΄.634 3.42 0.55

38. Chapar 6036 34º43΄.820 73º04΄.312 3.60 0.70

39. Habib Banda II 6150 34º43΄.016 73º03΄.333 3.60 0.49

40. Habib Banda I 6200 34º43΄.598 73º03΄.668 3.39 0.52

41. Bach Maidan 6200 34º42΄.004 73º10΄.269 3.38 0.54

42. Hill 6239 34º41΄.173 73º08΄.593 3.10 0.48

43. Jatial 6267 34º39΄.258 73º06΄.562 3.33 0.54

44. Sandawali 6500 34º43΄.294 73º07΄.497 3.47 0.54

45. Sharkolay 6711 34º38΄.565 73º06΄.644 3.16 0.50

46. Riar 6812 34º43΄.385 73º03΄.815 3.62 0.59

47. Doda I 6888 34º43΄.374 73º07΄.756 3.25 0.47

48. Sarmast 6892 34º38΄.642 73º06΄.681 3.17 0.47

49. Mirani Kandao 7034 34º44΄.016 73º03΄.533 3.38 0.57

50. Sheed 7255 34º44΄.074 73º05΄.539 3.41 0.66

51. Jaro 7362 34º43΄.795 73º04΄.353 4.13 0.75

52. Guchai 7367 34º44΄.212 73º10΄.416 3.64 0.57

53. Ledai 7500 34º44΄.443 73º05΄.302 3.57 0.73

54. Terkana 7701 34º41΄.074 73º09΄.280 3.08 0.41

55. Charoona 7746 34º44΄.831 73º05΄.353 3.46 0.56

56. Manra 7772 34º34΄.204 72º57΄.052 3.22 0.50

57. Buch upper 7876 34º42΄.104 73º10΄.569 3.31 0.49

58. Trapa 7895 34º44΄.985 73º05΄.301 3.46 0.62

43

59. Lunda Matra 7940 34º42΄.138 73º10΄.610 3.23 0.49

60. Doba 8000 34º44΄.515 73º03΄.365 3.40 0.47

61. Chail Kambar 8800 34º45΄.063 73º05΄.274 3.71 0.70

62. Gabrai Kandao 8920 34º45΄.002 73º05΄.202 3.79 0.68

63. Mirani I 9060 34º44΄.715 73º04΄.265 3.47 0.68

64. Baleja 9082 34º42΄.238 73º11΄.018 3.05 0.40

65. Chaprai 9092 34º41΄.465 73º10΄.536 3.28 0.45

66. Birth Maidan 9230 34º44΄.150 73º08΄.725 3.26 0.44

67. Harpal 9571 34º44΄.655 73º03΄.122 3.77 0.64

68. Doda II 9735 34º44΄.410 73º08΄.679 3.35 0.43

69. Kachkol 9768 34º42΄.890 73º11΄.863 3.76 0.53

70. Mirani II 9771 34º44΄.805 73º04΄.604 3.72 0.68

71. Machai sar 9820 34º35΄.009 72º54΄.759 3.88 0.53

72. Belmaz 9891 34º45΄.126 73º10΄.165 3.62 0.54

73. Lekoni 9998 34º44΄.687 73º08΄.880 3.86 0.45

74. Karganja L 10022 34º44΄.587 73º08΄.820 2.89 0.32

75. Chail 10138 34º44΄.987 73º04΄.719 3.83 0.64

76. Magrai 10220 34º45΄.003 73º11΄.242 3.44 0.54

77. Shaheed Gali 10238 34º44΄.874 73º08΄.974 2.78 0.31

78. Kar Ganja H 11172 34º45΄.356 73º09΄.372 2.64 0.34

79. Alishera 11863 34º45΄.463 73º11΄.882 2.27 0.35

80. Malkaisar 12400 34º45΄.012 73º11΄.999 2.25 0.33

44

4.2 Phenology, Life form and Leaf Spectra

4.2.1 Phenology

Phenology refers to the appearance of various plants at different seasons of

the year. The timing of different phenological events (flowering) is related to

environmental variables such as temperature. The changes in environments

therefore lead to changes in life-cycle events. Different phenological stages of

life-cycle were determined in spring, summer and autumn in different plant

stands of Nandiar Khuwar catchment District Battagram. The flowering and

fruiting stages of life-cycle of 324 vascular plant species were recorded in

spring, summer and autumn. The maximum flowering stages were recorded

from April-July (68.51%) and fruiting stages were recorded from May-August

(77.53%). The minimum flowering and fruiting stages were recorded during

the months of November, December, January and February. In lower altitudes

and open slopes the blooming of flowers started first while at higher altitudes

and shady places the blooming were delayed. In winter all of the higher

altitudinal zones of Nandiar Khuwar catchment are covered with snow,

therefore the phenology in winter season was excluded however in the

subtropical and temperate zones only few plants were recognized in

flowering and fruiting conditions. The phenology of different plant species

also depends on temperature, sunlight, rainfall, soil moisture and

atmospheric humidity. The details of phenology, life form and leaf spectra of

different plant species of Nandiar Khuwar catchment are presented in table

4.2.1.

4.2.2 Life Form

Life form is the indicator of climate (micro and macroclimate) and can be used

in comparing geographically widely distributed plant communities. Life form

is the characteristic vegetative appearance of the plant body and its longevity.

The general appearance of a community is caused more by the life form of the

most dominant species, then by any other characteristic of the vegetation. Life

form reflects the adaptation of plant species to the climate and the relative

45

proportion of different life form for a given area is called its biological

spectrum. In Nandiar Khuwar catchment out of 324 species the biological

spectra were dominated by phanerophytes contributing 118 (36.41%) species,

indicating that environmental conditions are well suited for phanerophytes.

The phanerophytes are followed by therophytes contributing 82 (25.30%)

species, indicating that in most of the study area severe deforestation,

overgrazing, soil erosion and human influence has reduced the

phanerophytes population and as a result therophytes appeared to occupy the

vacant niches in Nandiar Khuwar catchment. These are followed by

geophytes with 47 (14.50%) species, chamaephytes by 42 (12.96%) species and

hemi-cryptophytes by 35 (10.82%) species. Among phanerophytes the

nanophanerophytes were represented by 55 (16.98%) species,

microphanerophytes with 33 (10.18%) species, mesophanerophytes with 23

(67.09%) species and megaphanerophytes were represented by 7 (2.16%)

species. The detail of life form of different species is presented in table 4.2.1.

4.2.3 Leaf Size Spectra

Leaf size classes have been found to be very useful for plant associations. The

leaf size knowledge helps in understanding physiological processes of plants

and plant communities. There is consistent variation of leaf, leaf size and

texture between individual plant communities and in various climatic

conditions. In Nandiar Khuwar Catchment a total of 324 plant species were

analyzed for leaf size spectra. The leaf size spectra were dominated by

microphyll contributing 137 (40.28%) species, followed by mesophyll

contributing 103 (31.79%) species, nanophyll by 69 (21.29%) species,

macrophyll by 12 (3.70%) species and leptophyll by 02 (0.61%) plant species.

The dominance microphyll and mesophyll indicates that a large part of

Nandiar Khuwar catchment receive a high amount of rain fall, having

moderate temperature and moist condition. The detail of leaf size spectra of

different species are shown in table 4.2.1.

46

Table- 4.2.1 Phenology, Life form and Leaf spectra of different taxa collected from Nandiar Khuwar catchment.

Botanical name Family Flowering /Fruiting

Life Form

Leaf Spectra

Abies pindrow Royle Pinaceae Apr – May MAP Na

Acacia modesta Wall. Mimosaceae Apr – June MIP Mi

Acer cappadocicum Gled. Aceraceae Mar – May MEP Me

Achillea millefolium L. Asteraceae July – Sept CH Mi

Achyranthes aspera L. Amaranthaceae July – Sept TH Mi

Achyranthes bidentata Blume Amaranthaceae July – Sept TH Mi

Adiantum capillus-veneris L. Adiantaceae Sept – Dec GE Mi

Adiantum incisum Forssk Adiantaceae Sept – Dec GE Mi

Adiantum venustum D. Don Adiantaceae Sept – Dec GE Mi

Aegopodium burttii E. Nasir Apiaceae July – Sept TH Mi

Aesculus indica (Wall.ex. Cambess) Hook.f.

Hippocastinaceae June – Sep MEP Mac

Agrimonia eupatoria L. Rosaceae June – Aug TH Me

Ailanthus altissima (Mill.) Swingle Simaroubaceae Apr – May MEP Mac

Ajuga bracteosa Wall.ex Benth Labiatae Mar – June TH Me

Albezia lebbeck (L.) Benth. Mimosaceae Mar – May MEP Me

Alliaria petiolata (M. Bieb.) Cavara. Brassicaceae Apr – June TH Mi

Allium filidens Regel. Alliaceae Apr – June GE Mi

Alnus nitida (Spach.) Endl. Betulaceae Aug – Oct MEP Me

Alotis stoliczkai Clarke Gentianaceae July – Sept TH Na

Anagalis arvensis L. Primulaceae Apr – June TH Na

Anaphalis busa (Buch.-Ham. ex D.Don) DC.

Asteraceae July – Sept TH Na

Andrachne cordifolia Hemsl. Euphorbiaceae Apr – July NAP Mi

Andropogon sp. Poaceae Aug – Sept HC Mi

Androsace hazarica Nasir Primulaceae Apr – June TH Mi

Androsace rotundifolia Hardw. Primulaceae May – July TH Mi

Apluda mutica L. Poaceae July – Oct HC Mi

Aquilegia pubiflora Wall. ex Royle Ranunculaceae May – Aug TH Me

Arabis bijuga Watt. Brassicaceae Mar – May TH Na

Arisaema flavum (Forssk.) Schott Araceae June – Sept GE Mac

Aristida sp. Poaceae July – Oct HC Mi

Artemisia japonica Schmidt Asteraceae July – Sept TH Na

Artemisia roxburghiana Wall. Asteraceae July – Sept GE Me

Artemisia vulgaris L. Asteraceae July – Sept GE Me

Asparagus filicinus Buch. –Ham. ex. D.Don

Asparagaceae May – June GE Mi

Asplenium adiantum-nigrum L. Aspleniaceae Sept – Nov GE Mi

Asplenium cordatum G. Forst. Aspleniaceae Sept – Nov GE Mi

47

Asplenium cunifolium Altunat. Aspleniaceae Sept – Nov GE Mi

Asplenium dalhousiae Hook. Aspleniaceae Sept – Nov HC Mi

Asplenium trichomonas L. Aspleniaceae Sept – Nov GE Mi

Aster himalaicus C.B.Clarke Asteraceae Aug – Oct TH Mi

Astragalus ammophilus Karelin. Papilionaceae Feb – Apr TH Me

Astragalus graveolens Buch. Papilionaceae Mar – Apr TH Me

Astragalus leucocephalus Grah.ex Benth.

Papilionaceae Apr – Aug TH Me

Astragalus sp. Papilionaceae Mar – Apr TH Mi

Bauhinia variegata L. Caesalpinaceae Mar – Apr MIP Me

Berberis lycium Royle Berberidaceae Mar – June NAP Na

Berberis sp. Berberidaceae July – Sept NAP Na

Bergenia ciliata Sternb. Saxifragaceae Apr – June GE Me

Betula utilis D. Don Betulaceae Apr – June MIP Mi

Bistorta amplexicaulis (D. Don) Greene.

Polygonaceae June – Aug HC Me

Bistorta emodi (Meisn) Hara. Polygonaceae July – Sept CH Mi

Bombax ceiba L. Bombaceae Mar – Apr MAP Mac

Bupleurum hazaricum Nasir Apiaceae June – Aug TH Na

Bupleurum longicaule Wall .ex DC. Apiaceae July – Sept TH Na

Caltha alba L. Ranunculaceae Apr – July HC Me

Carex cardiolepis Nees. Cyperaceae Mar – May GE Mi

Carex foliosa D. Don Cyperaceae Mar – June GE Mi

Carex sanguine Boott. Cyperaceae June – Aug GE Mi

Carex serotina Merat. Cyperaceae Apr – Sept GE Mi

Caryopteris grata Benth. Verbenaceae Mar – May NAP Mi

Carpesium abrotanoides L. Asteraceae July – Sept TH Mi

Carpesium nepalense Less. Asteraceae July – Sept TH Mi

Cassia tora L. Caesalpinaceae Aug – Sep TH Mi

Catharanthus roseus G. Don Apocynaceae May – June TH Mi

Cedrus deodara (Roxb. ex D. Don) G. Don

Pinaceae Sept – Oct MEP Na

Celtis australis L. Ulmaceae Apr – June MEP Mi

Cephalanthera longifolia (L.) Fritsch Orchidaceae May – Aug GE Mi

Chaerophyllum sp. Apiaceae Mar – May CH Me

Cheilanthes anceps Blanf. Adianthaceae Sept – Dec HC Mi

Cheilanthus dalhousiae Hook.f. Adianthaceae Sept – Dec HC Mi

Chelanthus sp. Adianthaceae Sept – Dec HC Mi

Chenopodium album L. Chenopodiaceae May – Aug TH Mi

Ciminalis karelinii (Griseb.) Omer Gentianaceae Aug – Sept HC Na

Circium falconeri (Hook. f.) Petrak. Asteraceae June – Sept TH Me

Cirsium vulgare (Savi) Ten. Asteraceae June – Sept TH Me

Clematis connata D.C. Ranunculaceae Aug – Oct MIP Me

48

Clematis grata Wall. Ranunculaceae July – Sept MIP Me

Clematis montana Buch. Ranunculaceae Apr – May MIP Me

Colebrookea oppositifolia Smith Labiatae Apr – May NAP Me

Conyza canadensis L. Cronquist. Asteraceae July – Aug TH Na

Cornus macrophylla Wall. Cornaceae Apr – June MEP Me

Corydalis sp. Fumariaceae May – June TH Me

Cotinus coggyria Scop. Anacardiaceae Apr – June NAP Mi

Cotoneaster integerrima Medic. Rosaceae Apr – May NAP Na

Cotoneaster microphylla Wall. ex Lindl.

Rosaceae Apr – May NAP Na

Cotoneaster nummularia Fish and Mey

Rosaceae Apr – May NAP Na

Cuscuta gigantea Griff. Cuscutaceae Apr – June NAP Aph

Cynodon dactylon (L.) Pers. Poaceae June – Sept CH Na

Cynoglossum lanceolatum Forssk. Boraginaceae June – Aug TH Na

Cyperus iria L. Cyperaceae July – Sept HC Mi

Cyperus longus L. Cyperaceae June – Sept CH Mi

Cyperus niveus Retz. Cyperaceae May – July HC Mi

Cyperus sp. Cyperaceae May – July CH Mi

Cystopteris fragilis (L.) Benth. Aspidaceae May – July GE Mi

Dalbergia sissoo Roxb. Papilionaceae Mar – May MEP Mi

Daphne mucronata Royle Thymelaeaceae Apr – May NAP Na

Daphne papyracea Wall .ex G.Don Thymelaeaceae Nov – Apr NAP Na

Datura innoxia Mill. Solanaceae Apr – Aug TH Me

Datura stramonium L. Solanaceae May – Sept TH Me

Debregessia salcifolia (D. Don) Rendle

Urticaceae Mar – May MIP Me

Delphinum vestitum Boiss. Ranunculaceae Aug – Sept TH Mi

Desmodium elegans DC. Papilionaceae June – Aug NAP Me

Deutzia staminea R. Br .ex Wall. Philadelphaceae Apr – June NAP Mi

Dicliptra bupleorides Nees. Acanthaceae Apr – June TH Mi

Dioscorea deltoidea Wall. ex Griseb. Dioscoreaceae Apr – July HC Mi

Dioscorea melanophyma Prain & Burkill.

Dioscoreaceae Apr – July HC Mi

Diospyros lotus L. Ebenaceae Aug – Sept MEP Me

Dodonaea vescosa (L.) Jacq. Sapindaceae Aug – Feb NAP Mi

Dryopteris blandfordi Hope. Dryopteridaceae Sept – Dec HC Me

Dryopteris jaxtapostia Chirst Dryopteridaceae Sept – Dec HC Me

Dryopteris macdonellii Fraser. Dryopteridaceae Sept – Dec HC Mac

Dryopteris serrate-dentata (Bedd.) Hay

Dryopteridaceae Sept – Dec HC Me

Dryopteris wallichiana (Spring.) Hyl.

Dryopteridaceae Sept – Dec HC Me

Duchesnea indica (Andr.) Teschem. Rosaceae Apr – May CH Me

49

Duhaldea cappa Anderb. Asteraceae June – Sep NAP Me

Ehretia serrata Roxb. Boraginaceae Mar - May MEP Me

Elaeagnus umbellata Thunb. Elagnaceae Mar – June NAP Mi

Eleocharis umiglumis (Link) Schult. Cyperaceae Apr – May HC Na

Elsholtzia fruticosa (D. Don) Rehd. Labiatae Aug – Oct TH Mi

Elsholtzia strobilifera Benth. Labiatae July – Oct HC Mi

Epilobium rhychospermum Hausskn. Onagraceae July – Aug CH Na

Equisetum arvense L. Equisetaceae Sept – Oct GE Mi

Equisetum hiemale L. Equisetaceae Sept – Oct GE Mi

Eucalyptus globulus Labill. Myrtaceae Mar – May MEP Mi

Euphorbia cognata Boiss. Euphorbiaceae May – June CH Na

Euphorbia indica Lam. Euphorbiaceae July – Sept CH Na

Euphorbia wallichii Hook.f. Euphorbiaceae Apr – June CH Na

Ficus benghalensis L. Moraceae Apr – Nov MIP Me

Ficus carica L. Moraceae June – Sept MIP Me

Ficus palmata Forssk. Moraceae June – Sept MIP Me

Ficus racemosa L. Moraceae Apr – July MIP Me

Ficus sarmentosa Buch. Moraceae Apr – Aug NAP Mi

Filipendula vestita Maxim. Rosaceae July – Aug TH Me

Fragaria nubicola (Hook.f.) Lindl. Rosaceae Apr – July TH Mi

Gagea setifolia Baker. Liliaceae Mar – May GE Na

Galium aparine L. Rubiaceae July – Sep TH Na

Gentiana sp. Gentianaceae Apr – July TH Na

Gentianodes pedicellata (D. Don) Omer.

Gentianaceae May – Aug TH Na

Geranium collinum Steph .ex Willd. Geraniaceae July – Sept TH Mi

Geranium lucidum L. Geraniaceae Apr – May TH Mi

Geranium rotundifolium L. Geraniaceae May – Sept GE Me

Geranium wallichianum D. Don Geraniaceae May – Sept GE Me

Geum roylei Bolle. Rosaceae June – Aug CH Mi

Girardinia palmata Blume Urticaceae July – Sept TH Me

Grewia optiva Drum.ex Burret. Tiliaceae Apr – June MIP Me

Gymnosporia royleana Wall.ex Lawson.

Celastraceae Mar – May NAP Mi

Hedera nepalensis K. Koch. Araliaceae May – Aug MIP Mi

Heliotropium cabulicum Bunge. Boraginaceae July – Oct TH Na

Heracleum cachemiricum C.B. Clarke

Apiaceae June – Sep TH Na

Heteropogon contortus (L.) Beauv. Poaceae Aug – Oct HC Mi

Himalrandia tetrasperma (Wall. ex Roxb.) Yamaz.

Rubiaceae Apr – July NAP Na

Hypericum oblongifolium Choisy Guttiferae Feb – Apr NAP Mi

Hypericum perforatum L. Guttiferae May – Aug TH Na

50

Impatiens bicolor Royle Balsaminaceae June – Sept TH Me

Impatiens brachycentra Kar. Balsaminaceae June – Aug TH Mi

Impatiens edgeworthii Hook.f. Balsaminaceae July – Oct TH Mi

Impatiens sulcata Wall. Balsaminaceae July – Sept TH Mi

Indigofera heterantha Well.ex Brandis

Papilionaceae Apr – June NAP Mi

Inula acuminata Royle .ex DC. Asteraceae July – Sept TH Mi

Inula royleana DC. Asteraceae July – Sept GE Me

Isodon coetsa (Buch. Ham .ex D. Don) Kudo

Labiatae Apr – June NAP Mi

Isodon rugosus (Wall.ex Benth.) Codd.

Labiatae Mar – Apr NAP Mi

Jasminum humile L. Oleaceae Apr – June NAP Mi

Jasminum sp. Oleaceae Apr – May NAP Mi

Juglans regia L. Juglandanceae Feb – Apr MEP Me

Juncus sp. Juncaceae Sept – Nov CH Mi

Juniperus communis L. Cupressaceae Apr – June NAP Le

Justicia adhatoda L. Acanthaceae Feb – Apr NAP Me

Lallemantia royleana Benth. Labiatae July – Sept CH Na

Lamium album L. Labiatae Apr – Aug TH Mi

Lathyrus aphaca L. Papilionaceae Apr – May CH Na

Launea procumbens Roxb. Asteraceae Apr – May CH Mi

Leontopodium brachyoctis Gand. Asteraceae July – Sept CH Na

Leucostegia pulchra D. Don Davalliaceae Sept – Dec GE Me

Lindelofia stylosa Brand. Boraginaceae June – Aug TH Na

Litsea sp. Lauraceae Apr – June MIP Me

Lonicera quinquelocularis Hard. Caprifoliaceae Apr – July NAP Mi

Lotus corniculatus L. Papilionaceae Apr – June TH Na

Lygodium hazaricum Haq. Schizaeaceae Sept – Dec NAP Me

Lyonia ovalifolia (Wall.) Drude Ericaceae Apr – May MIP Me

Mallotus philippensis (Lam.) Mull. Euphorbiaceae Feb – Apr MIP Me

Malva sp. Malvaceae Aug – Oct CH Mi

Marrubium vulgare L. Labiatae May – Aug TH Mi

Medicago denticulata Willd. Papilionaceae Apr – June TH Na

Melia azedarach L. Meliaceae Mar – June MIP Me

Michelia sp. Magnoliaceae May – Aug MIP Me

Micromeria biflora (Buch.ex D. Don) Benth.

Labiatae Mar – June TH Le

Myrsine africana L. Myrsinaceae Mar – Apr NAP Na

Nepeta cataria L. Labiatae June – Sept CH Mi

Nerium indicum Mill. Apocynaceae Apr – May NAP Mi

Notholirion thomsonianum (Royle) Stapf.

Liliaceae Mar – Apr GE Mi

51

Oenothera affinis Camb. Onagraceae Apr – Aug TH Mi

Oenothera rosea L. Onagraceae Apr – July TH Na

Olea ferruginea Royle Oleaceae Apr – June MIP Na

Onopordum acanthium Linn. Asteraceae Apr – July TH Mi

Ophiopogon intermedius D. Don Liliaceae June – July CH Mi

Origanum vulgare L. Labiatae June – Sept TH Na

Otostegia limbata (Benth.) Boiss. Labiatae Mar – Apr NAP Na

Oxalis corniculata L. Oxalidaceae Mar – June TH Na

Paeonia emodi Wall. ex Royle Paeoniaceae Apr – June GE Mac

Panicum maximum Jacq. Poaceae Aug – Oct HC Me

Panicum sp. Poaceae Aug – Oct HC Mi

Phlomis bracteosa Royle .ex Benth. Labiatae July – Aug GE Mi

Phlomis rotata Royle ex Benth. Labiatae June – Aug GE Mi

Phytolacca latbenia (Moq.) H. Walt. Phytolaccaceae July – Sept GE Me

Picea smithiana (Wall.) Boiss. Pinaceae Apr – May MAP Na

Picris hieraciodes L. Asteraceae Apr – June HC Mi

Pinus roxburghii Surg. Pinaceae Apr May MAP Na

Pinus wallichiana A.B. Jack. Pinaceae May – June MAP Na

Pistacea integerrima J. L. Stewart Anacardiaceae Mar – Apr MEP Mac

Plantago lanceolata L. Plantaginaceae Apr – Aug TH Mi

Pleurospermum brunonis (D.C.)C.B. Apiaceae July – Aug HC Mi

Poa sp. Poaceae July – Sept HC Na

Podophyllum emodi Wall .ex Hook.f Podophyllaceae Apr – June GE Me

Polygonatum verticillatum (L.) All. Liliaceae Apr – June GE Mi

Polystichum lonchitis (L.) Roth. Dryopteridaceae Sept – Dec GE Mi

Populus ciliata Wall. Salicaceae Mar – Apr MAP Me

Populus euro-americana L. Salicaceae Mar – May MAP Me

Potentilla gerardiana Lindl. Rosaceae May – July HC Me

Potentilla nepalensis Hook.f. Rosaceae July – Aug HC Me

Potentilla sericophylla Parker. Rosaceae Apr – May NAP Mi

Potentilla sp. Rosaceae June – Aug HC Me

Primula denticulata Wight. Primulaceae Apr – June HC Me

Prunella vulgaris L. Labiatae May – Aug CH Mi

Prunus padus Hook.f. Rosaceae Apr – June MEP Me

Pseudognaphalium hypolecum (DC.) O. M.

Asteraceae July – Sept TH Na

Pseudognaphalium luteo album (L.) Hill.

Asteraceae July – Sept TH Na

Pseudomertensia sp. Boraginaceae May – June TH Na

Pteracanthus urticifolius Bremek Acanthaceae July – Oct CH Mi

Pteridium equilinum (L.) Kuhn. Pteridaceae Sept – Dec GE Mac

Pteris cretica L. Pteridaceae Sept – Dec GE Me

Pteris longifolia L. Pteridaceae Sept – Dec GE Me

52

Pycreus flavidus (Retz) T. Koyama Cyperaceae July – Sept HC Mi

Pyrus pashia L. Rosaceae Mar – Apr MIP Mi

Quercus baloot Griff. Fagaceae Apr – May MIP Mi

Quercus dilatata Lindl. Fagaceae Apr – May MEP Mi

Quercus glauca Thunb. Fagaceae Apr – May MEP Me

Quercus incana W. Bartram. Fagaceae Apr – May MEP Me

Quercus semicarpifolia Smith. Fagaceae May – June MEP Me

Ranunculus hirtellus Royle Ranunculaceae May – Aug TH Mi

Ranunculus laetus Wall .ex Hook.f. Ranunculaceae May – Aug TH Mi

Ranunculus palmatifidus Riedl. Ranunculaceae Apr – Aug TH Mi

Rananculus sp. Ranunculaceae May – June TH Mi

Rhamnus virgata Roxb. Rhamnaceae Apr – July MIP Mi

Rheum australe D. Don Polygonaceae June – July HC Me

Rhododendron arboreum Smith. Ericaceae Mar – May MIP Me

Rhus himalica J. D. Hook. Anacardiaceae Aug – Sept MIP Mac

Rhus javanica L. Anacardiaceae Aug – Sept MIP Mac

Ribes sp. Grossulariaceae July – Sept NAP Me

Ricinus communis L. Euphorbiaceae Jan –Mar MIP Mac

Robinia pseudoacacia L. Papilionaceae Mar – May MEP Me

Rosa moschata J. Herm. Rosaceae Apr – June MIP Me

Rosa sp. Rosaceae May – July MIP Me

Roscoea alpina Royle Orchidaceae June – July GE Na

Rubia cordifolia L. Rubiaceae July – Oct TH Na

Rubus ellipticus Smith. Rosaceae Feb – May NAP Me

Rubus fructicosus Hook.f. Rosaceae Apr – June NAP Me

Rubus ulmifolius Schott. Rosaceae May – July NAP Me

Rumex dentatus L. Polygonaceae Apr – June GE Me

Rumex hastatus D. Don Polygonaceae May – July NAP Mi

Rumex nepalensis Spreng. Polygonaceae June – Aug GE Me

Sageretia thea (Osbeck) M.C.Johnst Rhamnaceae Apr – July NAP Na

Salix calyculata Hook.f. Salicaceae June – July NAP Mi

Salix tetrasperma Roxb. Salicaceae Apr – May MEP Mi

Salvia lanata Roxb. Labiatae Apr – June CH Mi

Salvia sp. Labiatae Apr – June HC Me

Sarcococca saligna (Don) Mull. Buxaceae Sept – May NAP Mi

Sassuria sp. Asteraceae Apr – May HC Me

Saxifraga engleriana Smith. Saxifragaceae July – Aug CH Na

Scutellaria chamaedrifolia Hedge. Labiatae May – July CH Na

Sedum ewersii Ledeb. Crassulaceae July – Oct CH Na

Sedum sp. Crassulaceae July – Oct CH Na

Selaginella sanguinolenta Spring. Selaginellaceae July – Aug HC Le

Selinum vaginatum (Edgew.) Apiaceae July – Sept HC Mi

53

Clarke

Senicio sp. Asteraceae July – Sept TH Mi

Sibbaldia cuneata Hornum. Rosaceae June – Aug CH Na

Sium latijugum C.B. Clarke Apiaceae May – July HC Mi

Skimmia laureola DC. Rutaceae Apr – May NAP Mi

Smilax glaucophylla Klotzch Smilicaceae July – Aug NAP Mi

Solanum surattense Burm.f. Solanaceae Apr – May CH Mi

Solena amplexicaulis (Lam.) Gandhi Cucurbitaceae July – Sept GE Mi

Sonchus asper (L.) Hill. Asteraceae Apr – July CH Mi

Sorbaria tomentosa Lindl. Rosaceae June – Aug MIP Me

Spiraea vaccinifolia D. Don Rosaceae Mar – July NAP Mi

Stellaria media (L.) Vill. Caryophyllaceae Mar – Apr CH Na

Swertia paniculata Wall. Gentianaceae Aug – Sept CH Na

Syzygium sp. Myrtaceae Apr – May NAP Mi

Tagetes minuta L. Asteraceae Aug – Sept TH Me

Tanacetum dolicophyllum Kitam Asteraceae July – Sept CH Mi

Taraxiacum officinale Weber Asteraceae Mar – Apr TH Me

Taxus wallichiana Zuce. Taxaceae Mar – May MIP Na

Themeda anathera (Nees.ex Steud.) Hack.

Poaceae Aug – Oct HC Mi

Thlaspi sp. Brassicaceae Apr – June TH Na

Thymus linearis Benth. Labiatae Apr – Sept CH Na

Torilis japonica (Houtt.) DC. Apiaceae June – Aug GE Mi

Trachelospermum lucidum (D. Don) Schum.

Apocynaceae Apr – July NAP Me

Trillium govanianum Wall. ex Royle Liliaceae May – June GE Me

Tulipa stellata Hook.f. Liliaceae Mar – Apr GE Mi

Ulmus villosa Brandes ex Gamble Ulmaceae Jan – Mar MEP Mi

Ulmus wallichiana Planch. Ulmaceae Mar – Apr MEP Mi

Urtica dioica L. Urticaceae Aug – Sept TH Mi

Valeriana himalayana Grub. Valerianaceae May – June CH Na

Valeriana jatamansi Jones. Valerianaceae Mar – June GE Me

Verbascum thapsus L. Scrophulariaceae May – Sept HC Me

Veronica laxa Benth. Scrophulariaceae June – Aug TH Mi

Veronica persica Poir. Scrophulariaceae Apr – May TH Mi

Viburnum cotinifolium D.Don Caprifoliaceae Apr – May MIP Me

Viburnum grandiflorum Wall.ex DC.

Caprifoliaceae Apr – May MIP Me

Viburnum mullaha Buch. - Ham. ex D.Don

Caprifoliaceae June – Aug NAP Me

Viola canescens Wall. Violaceae Mar – Apr TH Me

Viola odorata L. Violaceae Mar – Apr TH Me

Vitex negundo L. Verbenaceae Apr – Oct NAP Me

54

Wikstroemia canescens Wall.ex Meisn.

Thymelaeaceae May – Aug NAP Mi

Withania somnifera (L.) Dunal Solanaceae July – Oct NAP Me

Woodfordia fruticosa (L.) Kurz Lythraceae Feb – Apr NAP Me

Woodwordia sp. Blechnaceae Sept – Dec GE Mac

Wulfenia amherstiana Wall.ex Bth. Scrophulariaceae July – Aug GE Na

Xanthium stromarium L. Asteraceae Aug – Oct TH Me

Xylosma sp. Salicaceae Apr – June NAP Me

Zanthoxylum armatum DC. Rutaceae Apr – May MIP Me

Ziziphus oxyphylla Edgew. Rhamnaceae Apr – May NAP Na

MAP: Megaphanerophytes MEP: Mesophanerophytes MIP: Microphanerophytes NAP: Nanophanerophytes CH: Chamaephytes HC: Hemi-cryptophytes GE: Geophytes TH: Therophytes Ma: Macrophyll Me: Mesophyll Mi: Microphyll Na: Nanophyll Le: Leptophyll Table- 4.3.1 Similarity and dissimilarity indices of different vegetational zones.

STF PPF PF MCF APF AS

STF

29.97 23.81 11.5 8.58 1.03

PPF 70.03

18.79 17.83 12.86 3.59

PF 76.19 81.21

26.69 23.79 3.8

MCF 88.5 82.17 73.31

37.83 17.1

APF 91.42 87.14 76.21 62.17

16.89

AS 98.97 96.41 96.2 82.9 83.11

STF: subtropical forests; PPF: Pinus Pinus forests; PF: Pinus wallichiana forests; MCF: mixed coniferous forests; APF: Abies Picea forests; AS: Alpine scrub zone.

55

4.3 Similarity and Dissimilarity Indices

The community similarity and dissimilarity were used for the comparison of

all communities within the study area. The similarity and dissimilarity indices

were recorded for vegetational zones as well as for plant communities of

Nandiar Khuwar catchment area.

4.3.1 Similarity and Dissimilarity Indices of Vegetational Zones

The maximum similarity index (37.83%) was recorded between mixed

coniferous forests and pure Abies pindrow and Picea smithiana forests, followed

by subtropical forests and Pinus Pinus forests (29.97). The maximum

dissimilarity index (98.97%) was recorded between subtropical forests and

alpine scrub followed by Pinus Pinus forests and alpine scrub (96.41). The

similarity and dissimilarity indices between different vegetational zones are

presented in table 4.3.1.

4.3.2 Similarity and Dissimilarity Indices of Plant Communities

The similarity and dissimilarity indices were also recorded between different

plant communities. The maximum similarity index (35.7%) were recorded for

Wikstroemia, Viburnum, Androsace community and Juniperus, Sibbaldia, Primula

community, followed by Abies, Picea, Pinus community and Pinus, Abies,

Wikstroemia community (32.9%), and Pinus, Quercus, Pinus community and

Pinus, Quercus, Indigofera community, (30.7%). The dissimilarity index of

Juniperus, Sibbaldia, Primula community were 100% with three different plant

communities Acacia, Dodonaea, Dalbergia community, Pinus, Cynodon, Rubus

community, and Quercus, Dodonaea, Myrsine community. The dissimilarity

index were 99.2% between Quercus, Dodonaea, Myrsine community and

Wikstroemia, Viburnum, Androsace community, and between Pinus, Quercus,

Indigofera community and Juniperus, Sibbaldia, Primula community. The detail

of similarity and dissimilarity index of different plant communities are

presented in table 4.3.2.

56

Table- 4.3.2 The similarity and dissimilarity indices of different communities of Nandiar Khuwar catchment area.

ADD PCR QDM PQP PQI PSB PQB APV APQ APP PAW WVA JSP

ADD 26.8 19.6 13.5 13 7.4 5.1 5.4 2.6 2.6 4.5 1 0

PCR 73.2 24.4 20.7 24.2 14.5 10.3 9.8 5.8 8.3 8.1 1.8 0

QDM 80.4 75.6 26.5 27.1 16.9 15.9 9.5 6.7 6.3 9 0.8 0

PQP 86.5 79.3 73.5 30.7 22.8 25.7 14.5 10.9 10.2 12.1 2.3 1.1

PQI 87 75.8 72.9 69.3 25.3 25.7 18.5 14.1 13.9 14.9 4 0.8

PSB 92.6 85.5 83.1 77.2 74.7 29.1 26.3 20.7 17.5 24.8 5.7 2.8

PQB 94.9 89.7 84.1 74.3 74.3 70.9 25.6 18.4 26.4 25.4 4.6 3.2

APV 94.6 90.2 90.5 85.5 81.5 73.7 74.4 27.6 24.9 21.5 10.7 11.1

APQ 97.4 94.2 93.3 89.1 85.9 79.3 81.6 72.4 25.3 21.1 18.9 20.9

APP 97.4 91.7 93.7 89.8 86.1 82.5 73.6 75.1 74.7 32.9 9.3 10.8

PAW 95.5 91.9 91 87.9 85.1 75.2 74.6 78.5 78.9 67.1 9.1 6.5

WVA 99 98.2 99.2 97.7 96 94.3 95.4 89.3 81.1 90.7 90.9 35.7

JSP 100 100 100 98.9 99.2 97.2 96.8 88.9 79.1 89.2 93.5 64.3

ADD:Acacia, Dodonaea, Dalbergia community, PCR:Pinus, Cynodon, Rubus community, QDM: Quercus, Dodonaea, Myrsine community, PQP: Pinus, Quercus, Pinus community, PQI: Pinus, Quercus, Indigofera community, PSB: Pinus, Sarcococca, Berberis, community, PQB: Pinus, Quercus, Berberis community, APV: Abies, Picea, Viburnum community, APQ:Abies, Picea, Quercus community, APP: Abies, Picea, Pinus community, PAW: Pinus, Abies, Wikstroemia community, WVA: Wikstroemia, Viburnum, Androsace community, JSP: Juniperus, Sibbaldia, Primula community.

57

4.4 Multivariate Analysis of Ecological Data of Nandiar Khuwar

Catchment

In multivariate analysis of ecological data of Nandiar Khuwar catchment a

total of 80 stands were used. For the classification of species and samples

simultaneously TWINSPAN were used which is based on dividing reciprocal

averaging ordination space. For ordination Bray-Curtis Ordination (Polar

ordination) were used for ordination scores, endpoints, regression coefficient

and variance in distance of different axis. Detrended correspondence analysis

(DCA) is an Eigenanalysis ordination technique based on reciprocal

averaging. DCA ordinates species and samples simultaneously. Canonical

correspondence analysis (CCA) was used for the ordination of samples and

species constrained by their relationships to environmental variables. Beside

classification and ordination the graph multivariate analysis included Dominance

Curves, species area curves and scatter plot were also used. The details of the

multivariate analyses of ecological data of Nandiar Khuwar catchment are described

below.

4.4.1 TWINSPAN Classification of the Vegetation of Nandiar Khuwar

For the classification of species and samples simultaneously TWINSPAN

(Two-way Indicator Species Analysis) were used. In this classification a total

of 80 stands and 324 species were used. The data were first classified into two

major groups on the basis of presences or absence of indicator species which

is the distinctive feature of this classification. In division 1 the eigenvalue

were 0.6391. The primary indicator species of division 1 were Abies pindrow,

Viola canescens and Berberis lyceum. 53 stands were placed in negative group

(*0) while 27 stands were placed in positive group (*1). In division 2 (53) the

eigenvalue were 0.4590 and the indicator species were Sarcococca saligna. 15

stands were placed in negative group (*00) while 38 stands were placed in

positive group (*01). In division 3 (27) the Eigenvalue were 0.4834 and the

indicator species were Picea smithiana and Fragaria nubicola 1. In this division

22 stands were placed in negative group (*10) while 5 stands were placed in

58

positive group (*11). The data were further refined after successive division

and redivision on the basis of indicator species and a total of 13 major plant

communities were recognized from subtropical to alpine zones of Nandiar

Khuwar catchment District Battagram (figure 4.4.1). These communities are

described as follow.

4.4.1.1 Acacia, Dodonaea, Dalbergia Community

Acacia modesta, Dodonaea vescosa, Dalbergia sissoo community was recorded

between altitudes of 530-700min two stands Thakot I on south facing steep

slope and Thakot II North facing steep slope. In this community 70 species

were recorded. The indicator species of this community was Acacia modesta.

This community was obtained at level 3 and the eigenvalue were 0.44. The

Shannon-Wiener diversity index value was 3.86. The biological spectrum was

dominated by phanerophytes with 32 species followed by therophytes with

14 species, geophytes by 9 species, chamaephytes by 8 species and

hemicryptophytes by 7 species (table- 4.4.1). The leaf size spectra were

dominated by mesophyll with 26 species followed by microphyll with 24

species, nanophyll having 13 species, macrophyll with 4 species and

leptophyll with 3 species (table- 4.4.2). Gravels, rocks and boulders are

common in this area. The soil is shallow, sandy loam; light brown in colour

and slightly basic in nature. The soil saturation and organic matter

concentration are generally low. The micro climatic data show that the

average temperature of this vegetation zone is 33.8ºC. The average

atmospheric humidity was 25.6%.

4.4.1.2 Pinus, Cynodon, Rubus Community

Pinus roxburghii, Cynodon dactylon, Rubus fructicosus community was recorded

in 8 sub-localities i.e. Peshora, Gajikot, Khairabad, Nowshera, Naraza,

Batangi, Paimal I and Nili Sharkolai between elevations of 880 – 1650m. This

community was obtained at level 4 and the eigenvalue were 0.46. In this

community 87 species were recorded. The Shannon-Wiener diversity index

59

value was 3.87. The biological spectrum was dominated by phanerophytes

having 33 plant species, followed by therophytes with 19 species,

hemicryptophytes 13 species, chamaephytes and geophytes each contributing

11 species (table- 4.4.1). The leaf size spectra were dominated by microphyll

contributing 33 species, mesophyll by 31 species, nanophyll by 17 species,

leptophyll by 3 species and macrophyll by 2 species while one species were

aphyllus in nature (table- 4.4.2). On the basis of leaf size spectra the maximum

IVI value were contributed by nanophyll (35.12). Limestone, granite and

sandstones are common in this community. The soil is shallow, calcareous

and sandy. The soil saturation is generally low. The organic matter

concentration ranges from 0.80-1.75%. The soil is acidic in nature and pH

value ranges from 5.15 - 5.90. The average temperature of this community was

27.8ºC. Atmospheric humidity ranges from 18.1% to 48.5%.

4.4.1.3 Quercus, Dodonaea, Myrsine Community

Quercus incana, Dodonaea vescosa, Myrsine africana community was recorded at

Chorlangai, Shagai, Paimal II, Paimal III and Paimal IV between altitudes of

750-1300m. This community was obtained at level 4 with eigenvalue 0.46

contributing 93 species with 3.88 Shannon-Wiener diversity index values.

Biological spectrum was dominated by phanerophytes with 53 species,

followed by therophytes (13), geophytes (11), chamaephytes (9) and

hemicryptophytes by 7 species (table- 4.4.1). The leaf size spectra were

dominated by microphyll having 36 species, followed by mesophyll (32),

nanophyll (17), leptophyll (4) and macrophyll with 4 species (table- 4.4.2). The

rocks, stones and gravels are numerous in this zone. The soil is shallow,

sandy loam, and light brown in colour. The average soil saturation recorded

for this community was 47.8%, organic matter concentration was 1.43% and

pH value was 5.40. The average temperature of this community was 29.5ºC

and atmospheric humidity was 27.8%.

60

Fig. 4.4.1: TWINSPAN classification of the vegetation of Nandiar Khuwar catchment.

61

4.4.1.4 Pinus, Quercus, Pinus Community

Pinus wallichiana, Quercus incana, Pinus roxburghii community was recorded

from Gada, Rajmira, Lundai I, Lundai II, Belandkot, Anora I, Anora II, Anora

III and Shinglai between an altitudinal ranges of 1250–1900m. This

community was obtained at level 4 and the eigenvalue were 0.36. In this

community 145 species were recorded. The Shannon-Wiener diversity index

value calculated for this community was 4.21. The biological spectrum was

dominated by phanerophytes contributing 59 species, geophytes with 32

species, therophytes with 22 species, hemicryptophytes with 20 species and

chamaephytes with 12 species (table- 4.4.1). The leaf size spectra were

dominated by microphyll contributing 61 species, followed by mesophyll

having 46 species, nanophyll having 26 species, macrophyll having 5 species,

leptophyll with 3 species while 2 species were aphyllus in nature (table- 4.4.2).

The soil varies from shallow to deep. Lime stone, sandstone and granite are

found in these zones. The average soil saturation was 45%, organic matter

concentration was1.32, pH value 6.8, temperature 25.4ºC and atmospheric

humidity 42.21%.

4.4.1.5 Pinus, Quercus, Indigofera Community

Pinus wallichiana, Quercus incana, Indigofera heterantha community were

recorded from Deshara, Lamai, Shabora I, Shabora II, Paimal V, Dabrai,

Bashakhan, Kiari, Nil-Reen, Nili-Batangi, Sharkola and Sarmast between

altitudinal zones of 1240 – 2040m. This community was obtained at level 4

and the eigenvalue were 0.32. In this community 99 species were recorded.

The Shannon-Wiener diversity index value calculated for this community was

3.85. The biological spectrum was dominated by phanerophytes (29), followed

by geophytes (22), therophytes (21), hemi-cryptophytes (18) and

chamaephytes (9) (table- 4.4.1). The leaf size spectra were dominated by

microphyll contributing 42 species, followed by mesophyll (30), nanophyll

(20), leptophyll (3) and macrophyll (3) while one species were aphyllus (table-

4.4.2). Rocks and boulders are also common. The soil is deep, loamy and light

62

brown to light dark in colour. The average organic matter concentration was

1.10%, pH values 5. 88, temperature is 25.8ºC and atmospheric humidity

36.4%.

4.4.1.6 Pinus, Sarcococca, Berberis, Community

Pinus wallichiana, Sarcococca saligna, Berberis lyceum community was recorded

from Chapra, Jarotia, Bach Maidan, Hill, Sandawali, Doda I and Terkana

between altitudinal zones of 1750 – 2350m. This community was obtained at

level 4 and the eigenvalue were 0.30. In this community 79 species were

recorded. The Shannon-Wiener diversity index value calculated for this

community was 3.77. The biological spectrum (table- 4.4.1) was dominated by

phanerophytes having 29 plant species. It was followed by hemicryptophytes

and therophytes each contributing 15 species, geophytes by 12 species and

chamaephytes by 8 species. The leaf size spectra (table- 4.4.2) were dominated

by microphyll contributing 34 species contributing 38.73IVI value, followed

by mesophyll (28), nanophyll (12), macrophyll (2), and leptophyll (2) while 1

species was aphyllus. The parent materials consist of mica schist, shale, gneiss

and lime stone. The soil is deep, moist and well drained. Huge rocks and

stones are found in this zone. The soil is deep, moist and loamy in nature.

The average soil saturation value was 52.57%, organic matter concentration

was 1.29%, pH value 6.78, temperature 24ºC and atmospheric humidity

was47%.

4.4.1.7 Pinus, Quercus, Berberis Community

Pinus wallichiana, Quercus incana, Berberis lyceum community were recorded

from Gat, Habib banda I, Habib banda II, Jatial, Chapar, Riar, Mirani kandao,

Sheed, Jaro and Doba between altitudinal zones of 1960–2222m. This

community was obtained at level 4 and the eigenvalue were 0.30. In this

community 127 species were recorded. The Shannon-Wiener diversity index

value of this community was 4.06. The life form (table- 4.4.1) was dominated

by geophytes with 37 species contributing maximum IVI value (20.15),

63

followed by phanerophytes (29), therophytes (28), hemicryptophytes (20) and

chamaephytes (13). The leaf size spectra (table- 4.4.2) were dominated by

microphyll contributing 58 species, followed by mesophyll (37), nanophyll

(28), macrophyll (2) and leptophyll (2). On the basis of leaf size spectra the

maximum IVI value were contributed by micophyll (39.32). The soil is deep,

moist and loamy in nature. The average soil saturation value was 55.6%,

organic matter concentration was 1.48%, pH value 6.05, temperature 24.1ºC

and atmospheric humidity was 54.7%.

4.4.1.8 Abies, Picea, Viburnum Community

Abies pindrow, Picea smithiana, Viburnum cotinifolium community were

recorded from Manra, Bach upper, Lunda Matra, Baleja, Chaprai,

Birthmaidan and Machaisar between altitudinal zones of 2220- 2900m. This

community was obtained at level 4 and the eigenvalue were 0.33. In this

community 96 species were recorded. Shannon-Wiener diversity index value

calculated for this community was 4.07. The biological spectrum (table- 4.4.1)

was dominated by therophytes contributing 24 species, followed by

geophytes (23), phanerophytes (19), chamaephytes (16) and hemicryptophytes

(14). The leaf size spectra (table- 4.4.2) were dominated by microphyll

contributing 36 species, followed by mesophyll (32), nanophyll (22),

macrophyll (4) and leptophyll (2).On the basis of leaf size spectra the

maximum IVI value were contributed by mesophyll (36.49). The soil is deep,

moist and well drained. The parent materials consist of mica schist, shale,

gneiss and lime stone. The average soil saturation value was 58.6%, organic

matter concentration was 1.61%, pH value 6.23, temperature was 20.6ºC and

atmospheric humidity was 47.4%.

64

Table- 4.4.1 Number of species and IVI contribution of life form classes of the plant communities.

Name of community

Phanerophytes Chamaephytes Hemicryptophytes Geophytes Therophytes

No of Species

IVI No of Species

IVI No of Species

IVI No of Species

IVI No of Species

IVI

Acacia, Dodonaea, Dalbergia 32 50.6 8 12.16 7 6.54 9 8.63 14 22.09

Pinus, Cynodon, Rubus 33 39.83 11 10.19 13 17.20 11 7.36 19 25.41

Quercus, Dodonaea, Myrsine 53 70.85 9 6.18 7 8.00 11 4.51 13 10.47

Pinus, Quercus, Pinus 59 55.17 12 9.29 20 10.82 32 13.07 22 11.66

Pinus, Quercus, Indigofera 29 52.24 9 7.91 18 16.25 22 10.97 21 12.63

Pinus, Sarcococca, Berberis 29 47.68 8 9.11 15 15.14 12 15.12 15 12.94

Pinus, Quercus, Berberis 29 47.91 13 9.55 20 7.66 37 20.15 28 14.74

Abies, Picea, Viburnum 19 35.57 16 6.75 14 16.24 23 21.54 24 19.90

Abies, Picea, Quercus 26 40.07 9 5.11 11 11.63 18 20.50 21 22.70

Abies, Picea, Pinus 16 30.52 10 10.52 15 14.59 21 21.84 19 22.54

Pinus, Abies, Wikstroemia 20 35.37 7 10.65 11 12.77 15 27.24 9 13.97

Wikstroemia, Viburnum, Androsace 06 36.88 5 10.74 5 15.76 6 20.03 4 16.60

Juniperus, Sibbaldia, Primula 07 32.68 5 17.76 7 24.63 3 9.76 8 15.17

65

Table- 4.4.2 Number of species and IVI contribution of leaf size spectra of the plant communities.

Name of community

Macrophyll Mesophyll Microphyll Nanophyll Leptophyll

No of Species

IVI No of

Species IVI

No of Species

IVI No of

Species IVI

No of Species

IVI

Acacia, Dodonaea, Dalbergia 12 5.97 26 26.73 24 41.59 12 22.67 2 2.27

Pinus, Cynodon, Rubus 17 1.56 31 26.93 33 31.04 17 35.12 3 4.78

Quercus, Dodonaea, Myrsine 17 2.18 32 37.62 36 34.07 18 22.32 3 3.80

Pinus, Quercus, Pinus 22 1.68 46 35.55 61 34.35 26 23.29 3 3.93

Pinus, Quercus, Indigofera 19 1.00 30 28.65 42 34.38 20 32.36 3 3.20

Pinus, Sarcococca, Berberis 10 5.92 28 25.86 34 38.73 12 27.55 2 1.20

Pinus, Quercus, Berberis 22 2.76 37 29.77 58 39.32 28 25.58 2 1.29

Abies, Picea, Viburnum 18 9.19 32 36.49 36 30.35 22 23.04 2 0.58

Abies, Picea, Quercus 19 6.04 30 38.94 30 26.11 19 27.44 1 1.20

Abies, Picea, Pinus 16 3.73 25 30.19 35 34.52 16 30.52 1 0.73

Pinus, Abies, Wikstroemia 11 9.14 26 30.12 21 33.97 11 26.77 0 0.00

Wikstroemia, Viburnum, Androsace 7 2.14 8 36.57 10 38.68 7 22.63 0 0.00

Juniperus, Sibbaldia, Primula 10 0.00 10 37.13 9 25.34 10 28.64 1 8.90

66

4.4.1.9 Abies, Picea, Quercus Community

Abies pindrow, Picea smithiana, Quercus semicarpifolia community was recorded

from Guchai, Doda II, Kachkol, Belmaz, Lekoni and Magrai between an

altitudinal zone of 2250 – 3000m. This community was obtained at level 4 and

the eigenvalue were 0.33. In this community 85 species were recorded.

Shannon-Wiener diversity index value of this community was 4.02. Biological

spectrum was dominated by phanerophytes having 26 species (table- 4.4.1),

followed by therophytes (21), geophytes (18), hemicryptophytes (11) and

chamaephytes (9). The leaf size spectra were dominated by mesophyll and

microphyll each contributing 30 species, followed by nanophyll (19),

macrophyll (4) and leptophyll (1) while 1 species were aphyllus (table- 4.4.2).

Rocks, stones and gravels are common. The parent materials consist of mica

schist, shale, gneiss and lime stone. The average soil saturation value was

56.5%, soil organic matter concentration was 1.3%, pH value 6.3, temperature

was 19.3ºC and atmospheric humidity was 53.9%.

4.4.1.10 Abies, Picea, Pinus Community

Abies pindrow, Picea smithiana, Pinus wallichiana community was recorded from

Chailkambar, Gabrai kandao, Mirani I, Mirani II, Harpal and Chail between

an altitudinal zone of 2650 – 3000m. This community was obtained at level 4

and the eigenvalue were 0.32. In this community 81 species were recorded.

Shannon-Wiener diversity index value calculated for this community was

4.10. Biological spectra were dominated by geophytes (table- 4.4.1) with 21

species, followed by therophytes (19), phanerophytes (16), hemicryptophytes

(15) and chamaephytes (10). The leaf size spectra were dominated by

microphyll (table- 4.4.2) with 35 species contributing maximum (34.52) IVI

value, followed by mesophyll (25), nanophyll (16), macrophyll (3) and

leptophyll (1) while 1 species were aphyllus. Rocks, stones and gravels are

numerous in this zone. The parent materials consist of mica schist, shale,

gneiss and lime stone. The soil is deep, moist and well drained. The average

soil saturation value were 56.83%, soil organic matter concentration were

67

1.4%, pH value 6.28, temperature were 19.8ºC and atmospheric humidity

were 55.2%.

4.4.1.11 Pinus, Abies, Wikstroemia Community

Pinus wallichiana, Abies pindrow, Wikstroemia canescens community was

recorded from Ledai, Charoona and Trapa between altitudinal zones of 2270 –

2450m. This community was obtained at level 4 and the eigenvalue were 0.32.

In this community 62 species were recorded. Shannon-Wiener diversity index

value was 3.8. Biological spectrum were dominated by phanerophytes

contributing 20 plant species, followed by geophytes with 15 species,

hemicryptophytes with 11 species, therophytes with 9 species and

chamaephytes with 7 species (table- 4.4.1). The leaf size spectra (table- 4.4.2)

were dominated by mesophyll contributing 26 species, followed by

microphyll (21), nanophyll (11) and macrophyll (3). On the basis of leaf size

spectra maximum IVI value were contributed by microphyll (33.97). Rocks,

stones and gravels are numerous in this zone. The parent materials consist of

mica schist, shale, gneiss and lime stone. The soil is deep, moist and well

drained. The average soil saturation was 58%, organic matter concentration

was 1.5%, pH value 6.18, temperature was 22.5ºC and atmospheric humidity

was 48.4%.

4.4.1.12 Wikstroemia, Viburnum, Androsace Community

Wikstroemia canescens, Viburnum cotinifolium, Androsace hazarica community

was recorded from two stands Karganja and Shaheed Gali between altitudinal

zones of 2850 – 3100m. This community was obtained at level 3 and the

eigenvalue were 0.40. In this community 26 species were recorded. Shannon-

Wiener diversity index value was 3.06. Biological spectrum was represented

by phanerophytes and geophytes each having 6 species, hemicryptophytes

and chamaephytes by 5 species each and therophytes with 4 species (table-

4.4.1). The leaf size spectra were dominated by microphyll with 10 species

contributing 38.68 IVI value (table- 4.4.2), followed by mesophyll (8),

68

nanophyll (7) and macrophyll (1). Rocks, stones and gravels are numerous.

The soil is steep and loamy. The average soil saturation was 52.5%, organic

matter concentration was 0.85%, pH value was 6.3, temperature was 17.7ºC

and atmospheric humidity 47.4%.

4.4.1.13 Juniperus, Sibbaldia, Primula Community

Juniperus communis, Sibbaldia cuneata, Primula denticulata community was

recorded from Kar Ganja top, Alishera and Malkaisar between an altitudinal

ranges of 3250 – 3800m. In this community 30 species were recorded and was

obtained at level 3 with eigenvalue 0.40. Shannon-Wiener diversity index

value for this community was 3.20. Biological spectrum was represented by

therophytes with 8 species, phanerophytes and hemicryptophytes each has 7

species, chamaephytes having 5 species and geophytes with 3 species (table-

4.4.1). The leaf size spectra were dominated by mesophyll with 10 species

contributing maximum IVI value (37.13) followed by nanophyll with 10

species, microphyll (9) and leptophyll by single species (table- 4.4.2). Rocks,

stones and gravels are common. The soil is steep and loamy. The average soil

saturation value was 54.3%, organic matter concentration was 0.89%, pH

value was 6.57, temperature was 16.5ºC and atmospheric humidity was

53.9%.

4.4.2 Ordinations of Vegetation of Nandiar Khuwar Catchment

The vegetation data of Nandiar Khuwar catchment were further analyzed by

ordination. Ordination is the ordering of objects along axes according to there

resemblances. The correlation structure is used to positions objects in the

ordination space. Objects close in the ordination space are generally more

similar than objects distant in the ordination space. In present study three

major ordination techniques were used including Bray-Curtis ordination,

DCA and CCA. For classification and ordination the IVI value of 80 stands

were used. These are described below.

69

4.4.2.1 Bray-Curtis Ordination of the over all vegetation of study area

Bray-Curtis Ordination (Polar ordination) was used for ordination scores,

endpoints, regression coefficient and variance in distance of different axis (fig.

4.4.2). The ordination scores (Distances) were from Paimal V (0.000) to Magrai

(0.960) on axes 1. The regression coefficient for this axis were -54.11, variance

in distance from the first end point were 2.53. Axis 1 extracted 21.95% of

original distance matrix. The ordination scores for axis 2 were from Mirani

kandao (0.000) to Peshora (0.904). The regression coefficient for this axis were

-43.54, variance in distance from the first end point were 2.00. Axis 2 extracted

13% of original distance matrix. The ordination scores for axis 3 was from

Doda I (0.000) to Malkaisar (0.826). The regression coefficient for this axis

were -20.60, variance in distance from the first end point were 1.20. Axis 3

extracted 6.45% of original distance matrix.

4.4.2.2 DCA Ordination of the vegetation of study area

The response data are compositional having gradient length 6.4 SD units long

(fig. 4.5.3 and 4.5.4). In DCA ordination with supplementary variables the

maximum gradient length (6.36) were recorded for axis 1 with eigenvalue

0.71. The gradient length for axis 2 was 2.74 with eigenvalue 0.28. The

gradient length for axis 3 was 2.14 with eigenvalue 0.18. Total variance

("inertia") in the species data were 7.07, supplementary variables account for

37.1%. The Pseudo-canonical correlation (suppl.) for axis 1, 2 and 3 were 0.99,

0.51 and 0.70 respectively. The DCA ordination clearly indicates that the

whole data set were dominated by single dominant gradient.

In DCA ordination different species clustered in ordination space showing

correlation. The subtropical species clustered together and having positive

correlation included Plantago lanceolatum, Panicum spp., Heteropogon contortus,

Tulipa stellata, Oxalis corniculata, Micromeria biflora, Pinus roxburghii and Rubus

fructicosus. The species of moist temperate blue pine forests clustered together

and having positive correlation included Pinus wallichiana, Berberis lyceum,

70

Indigofera heterantha, Carex cardiolepis, Adiantum capillus-veneris, Adiantum

venustum, Bergenia ciliata, Dryopteris jaxtapostia, Potentilla gerardiana, Themeda

anathera and Viola canescens.

The species of silver fir and spruce forests clustered together and having

positive correlation included Abies pindrow, Picea smithiana, Pteridium

equilinum, Paeonia emodi, Rumex nepalensis, Bistorta amplexicaulis, Artemisia

roxburghiana, Potentilla nepalensis, Aquilegia pubiflora, Viburnum cotinifolium,

Ranunculus palmatifidus, Skimmia laureola, Androsace hazarica, Caltha alba and

Primula denticulata. Similarly Arisaema flavum, Ranunculus laetus,

Pseudognaphalium hypolecum, Torilis japonica and Viburnum grandiflorum

showed positive correlation. Anaphalis busa, Impatiens edgeworthii, Dryopteris

serrate dentata and Pteracanthus urticifolius clustered together. The species of

alpine and subalpine zone clustered includes Salix calyculata, Juniperus

communis, Tanacetum dolicophyllum and Wikstroemia canescens.

4.4.2.3 CCA Ordination

Canonical correspondence analysis (CCA) was used for the ordination of

samples and species constrained by their relationships to environmental

variables. In CCA ordination the maximum Eigenvalue were recorded for

axis 2 (0.948) followed by axis 1 (0.699) and axis 3 (0.226). The percentage

variance explained for axis 1, 2 and 3 were 9.89%, 16.19% and 19.39%

respectively. The total variance (inertia) in the species data were 7.07,

explanatory variables account for 37.1%. Pseudo-canonical correlation for axis

1, 2 and 3 were 0.99, 0.94 and 0.82 respectively. The permutation test results

for all axes were pseudo-F=2.2, P=0.002. The Pearson correlation for axis 1, 2

and 3 were 0.988, 0.953 and 0.824. The Kendall (Rank) correlation for axis 1, 2

and 3 were 0.887, 0.670 and 0.602. The correlation between sample score for an

axis derived from the species data and the sample scores that are linear

combination of the environmental variable. The CCA ordinations are

presented in fig. 4.4.5 and 4.4.6.

71

Fig. 4.4.2: The distribution of 80 stands on different axis on the basis of Bray-Curtis ordination.

72

Fig. 4.4.3: DCA ordination of species of Nandiar Khuwar catchment.

Fig. 4.4.4: DCA ordination of stands of Nandiar Khuwar catchment.

-1 7

-14

Abi pinAdi cap

Adi inc

Adi ven

Aes indAil alt

Aju bra

Ana bus

And spp

And haz

And rot Aqu pub

Ari fla

Art rox

Asp dal

Ber lyc

Ber cil

Bis ampCal alb

Car carCor spp

Cot cog

Cot num

Cyn dac

Dap muc

Des ele

Deu sta

Dio lot

Dod ves

Dry bla

Dry jax

Dry ser

Dry wal

Duc ind

Eup walFra nub

Fun spp

Gag sat

Ger col

Ger wal

Het con

Hyp cup

Imp edg

Ind het

Jug reg Jun comLon qui

Lyo ova

Mar spp

Mic bif

Myr afr

Ori vul Oxa cor

Pae emo

Pan spp

Pic smi

Pin rox

Pin wal

Pla lan

Poa spp

Pol ver

Pot ger

Pot nepPot spp

Pri den

Pse hyp

Pte urt

Pte equ

Pte cre

Que dil

Que inc

Que sem

Ran lae

Ran pal

Rho arb

Ros mosRub ell

Rub fru

Rum den

Rum has

Rum nep

Sal cal

Sar sal

Sib cun

Ski lau

Spi vac

Tan dol

Tar off

The ana

Thy lin

Tor jap

Tri gov

Tul ste

Val jat

Ver laxVib cot

Vib gra

Vio can

Vio spp

Wik can

73

Among the environmental variables the maximum strength was recorded for

altitude followed by barometric pressure, temperature and density altitude.

The intermediate strength was recorded for phosphorus, atmospheric

humidity, soil saturation, wind speed, heat index and electrical conductivity.

The less strength was recorded for slope angle, wet bulb temperature, pH and

slope aspect. The minimum strength was recorded for potassium, organic

matter and dew point. Wind speed, phosphorus, electrical conductivity and

slope angle are positively correlated with each other. pH and slope aspect are

positively correlated. Altitude, density altitude, soil saturation and

atmospheric humidity showed positive correlation. Organic matter and dew

point are positively correlated in ordination space. Similarly positive

correlation was also shown by wet bulb, temperature, barometric pressure,

heat index and Potassium. CCA ordination also indicates that maximum

stands clustered near average position in ordination space. However few

stands were away from average position. The stands of Juniperus, Sibbaldia,

Primula community and Wikstroemia, Viburnum, Androsace community were

clustered at high wind speed and Phosphorous concentration.

CCA ordination indicates that different species were clustered along different

environmental variables. The species clusters showing positive correlation

with wind speed included Ciminalis karelinii, Berberis sp., and Wulfenia

amherstiana. The species clusters having positive correlation with

phosphorous included Betula utilis, Juniperus communis, Sibbaldia cuneata,

Valeriana himalayana and Aster himalaicus. The species having positive

correlation with electrical conductivity included Salix calyculata, Trillium

govanianum, Buplerum longicaule and Corydalis sp. The species having positive

correlation with slope angle included Caltha alba, Tanacetum dolicophyllum,

Pseudomertensia perviflora, Ranunculus palmatifidus and Primula denticulata. The

species clustered along slope aspect and pH included Artemisia roxburghiana,

Potentilla nepalensis, Paeonia emodi and Skimmia laureola.

74

Fig. 4.4.5: CCA ordination of species and environmental variables.

Fig. 4.4.6: CCA ordination of stands and environmental variables.

-1.0 1.0

-0.4

1.0

Slope Angle

Slope Aspct

Altitude

Barometric Pressure

Density AltitudeTemperature

Wind Speed

Atmospheric Humidity

Heat Index

Dew Point

Wet Bulb

Electrical Conductivity

PH

Soil Organic Matter

Phosphorus

Potassium

Soil Saturation

Abi pin

Aca mod

Ail alt

Aju bra

Alb leb

Aln nit

Ana arv

And haz

Art jap

Art rox

Ast him

Bau var

Ber lyc

Ber spp

Bet uti

Bom cei

Bup lon Cal alb

Cas tor

Cim kar

Col opp

Cor spp

Cot mic

Cyn dac

Dal sisDat alb

Dat str

Dod ves

Euc glo

Eup ind

Fic ben

Fic car

Fic rac

Fra nub

Ger luc

Ger wal

Gre opt

Gym roy

Ind het

Jun spp

Jun com

Jus adh

Lau pro

Mal phi

Med den

Mel aze

Mic bif

Ner ind

Ole fer

Oxa cor

Pae emo

Pic smi

Pin rox

Pin wal

Pis int

Pot nep

Pot spp

Pri den Pse par

Pte equ

Que inc

Ran pal

Ric com

Rub fru

Rum nep

Sal cal

Sar sal

Sib cun

Ski lau

Sol sur

Ste med

Tan dol

Thl spp

Thy lin

Tri gov

Val him

Ver tha

Ver lax

Vib cot

Vio can

Wik can

Woo fru

Wul amh

75

The species having positive correlation with altitude, density altitude,

humidity and soil saturation included Abies pindrow, Geranium wallichianum,

Picea smithiana, Rumex nepalensis, Viburnum cotinifolium, Veronica laxa and

Pteridium equilinum. The species having positive correlation with organic

matter and dew point temperature include Fragaria nubicola, Cynodon dactylon,

Indigofera heterantha, Berberis lyceum, Pinus wallichiana, Quercus incana,

Sarcococca saligna and Viola canescens. The species clustered along wet bulb,

temperature, barometric pressure and heat index included Oxalis corniculata,

Pinus roxburghii, Ficus carica, Micromeria biflora, Rubus fructicosus, Geranium

lucidum and Ajuga bracteosa. The species having positive correlation with

potassium included Alnus nitida, Dodonaea vescosa, Melia azedarach, Grewia

optiva, Albezia lebbeck and Acacia modesta.

Among environmental variable barometric pressure (0.967), temperature

(0.960), heat index (0.499), wet bulb (0.329) and potassium (0.063) were

positively correlated with axis 1. The environmental variables positively

correlated with axis 2 include humidity (0.347), organic matter (0.291),

altitude (0.137), dew point (0.119) and density altitude (0.104). The

environmental variables positively correlated with axis 3 include pH (0.554),

slope angle (0.501), soil saturation (0.425), humidity (0.309), organic matter

(0.279), phosphorous (0.273), electrical conductivity (0.205), dew point (0.193),

wet bulb (0.111), wind speed (0.065) and temperature (0.062). From

correlation data it was concluded that altitude, density altitude, barometric

pressure, temperature, humidity and phosphorous concentration were more

significant as compared to other environmental variables. The regression of

stands in species space on 15 parameters is presented in table 4.4.3, correlation

and biplot score are presented in table 4.4.4 and correlation among

environmental variables are shown in table 4.4.5.

76

Table- 4.4.3 Regression of stands in species space on 17 parameters.

Canonical Coefficients

Standardized Original units

Variables Axis 1 Axis 2 Axis 3 Axis 1 Axis 2 Axis 3 S. Dev

Altitude -0.114 -6.444 -1.505 0.000 -0.009 -0.002 0.688E+03

Barometric pressure

0.537 -6.573 -2.464 0.008 -0.098 -0.037 0.671E+02

Density altitude

0.136 0.040 -1.194 0.000 0.000 -0.002 0.730E+03

Dew point -0.057 0.150 -0.211 -0.013 0.033 -0.047 0.453E+01

Electric Conductivity

0.001 -0.061 0.113 0.008 -0.612 1.130 0.100E+00

Heat Index -0.022 -0.283 0.143 -0.003 -0.043 0.022 0.656E+01

Humidity 0.141 -0.146 0.222 0.011 -0.012 0.018 0.125E+02

Organic matter

0.080 0.121 0.096 0.235 0.352 0.281 0.342E+00

pH 0.072 0.084 0.266 0.109 0.126 0.401 0.663E+00

Phosphorous -0.086 -0.287 0.100 -0.017 -0.058 0.020 0.495E+01

Potassium -0.047 -0.065 -0.025 -0.001 -0.001 0.000 0.788E+02

Saturation -0.079 0.013 0.192 -0.010 0.002 0.025 0.766E+01

Slope angle -0.126 -0.124 0.190 -0.014 -0.013 0.020 0.925E+01

Slope aspect -0.049 0.030 0.004 -0.019 0.012 0.001 0.251E+01

Temperature 0.276 -0.148 0.146 0.071 -0.038 0.038 0.388E+01

Wet bulb 0.029 0.080 0.264 0.007 0.020 0.067 0.396E+01

Wind speed -0.054 -0.097 0.053 -0.157 -0.282 0.155 0.344E+00

77

Table- 4.4.4 The correlations and biplot scores for 17 parameters.

Correlations* Biplot Scores

Variables Axis 1 Axis 2 Axis 3 Axis 1 Axis 2 Axis 3

Altitude -0.974 0.137 -0.009 -0.824 0.094 -0.004

Barometric pressure

0.967 -0.188 -0.003 0.818 -0.129 -0.001

Density altitude

-0.954 0.104 -0.006 -0.807 0.071 -0.003

Dew point -0.004 0.119 0.193 -0.003 0.082 0.095

Electric Conductivity

-0.453 -0.322 0.205 -0.383 -0.221 0.101

Heat Index 0.499 -0.227 -0.056 0.422 -0.156 -0.028

Humidity -0.605 0.347 0.309 -0.512 0.239 0.152

Organic matter -0.018 0.291 0.279 -0.015 0.200 0.137

pH -0.211 -0.054 0.554 -0.178 -0.037 0.272

Phosphorous -0.688 -0.476 0.273 -0.582 -0.327 0.134

Potassium 0.063 -0.174 -0.053 0.053 -0.119 -0.026

Saturation -0.550 0.139 0.425 -0.465 0.095 0.209

Slope angle -0.390 -0.198 0.504 -0.330 -0.136 0.248

Slope aspect -0.173 -0.045 -0.125 -0.146 -0.031 -0.062

Temperature 0.960 -0.198 0.062 0.812 -0.136 0.031

Wet bulb 0.329 -0.076 0.111 0.278 -0.052 0.055

Wind speed -0.513 -0.518 0.065 -0.434 -0.356 0.032

* Correlations are "intra-set correlations" of ter Braak (1986).

78

Table- 4.4.5 The correlation among environmental variables of Nandiar Khuwar catchment.

Altitude

Baro Density altitude

Dew point

E. C. Heat Index

Humidity

Org matt

pH P mg / kg

K mg /kg

Saturation

Slope angle

Slope aspect

Temp Wet bulb

Wind speed

Altitude 1 -0.99 0.98 0.06 0.38 -0.51 0.70 0.07 0.22 0.60 -0.11 0.53 0.31 0.17 -0.96 -0.30 0.44

Barometric pressure

-0.99 1 -0.98 -0.07 -0.36 0.52 -0.71 -0.08 -0.22 -0.58 0.12 -0.53 -0.31 -0.16 0.97 0.30 -0.42

Density altitude

0.98 -0.98 1 0.17 0.40 -0.38 0.68 0.11 0.26 0.61 -0.08 0.50 0.37 0.17 -0.94 -0.17 0.45

Dew point 0.06 -0.07 0.17 1 0.07 0.63 0.38 0.27 -0.03 -0.04 0.08 0.03 0.02 0.15 0.003 0.86 -0.03

E. C 0.38 -0.36 0.40 0.07 1 -0.07 0.17 0.20 0.004 0.44 0.38 0.46 0.16 0.17 -0.37 -0.01 0.30

Heat Index -0.51 0.52 -0.38 0.63 -0.07 1 -0.41 0.21 -0.29 -0.30 0.28 -0.28 -0.23 -0.01 0.56 0.89 -0.19

Humidity 0.70 -0.71 0.68 0.38 0.17 -0.41 1 0.13 0.24 0.30 -0.14 0.43 0.31 0.22 -0.67 -0.04 0.20

Organic matter

0.07 -0.08 0.11 0.27 0.20 0.21 0.13 1 -0.23 -0.08 0.39 0.49 0.02 0.05 -0.03 0.23 -0.28

pH 0.22 -0.22 0.26 -0.03 0.004 -0.29 0.24 -0.23 1 0.40 -0.34 0.07 0.49 -0.09 -0.20 -0.17 0.32

P mg/ kg 0.60 -0.58 0.61 -0.04 0.44 -0.30 0.30 -0.08 0.40 1 -0.18 0.38 0.44 0.11 -0.56 -0.20 0.59

K mg /kg -0.11 0.12 -0.08 0.08 0.38 0.28 -0.14 0.39 -0.34 -0.18 1 0.27 -0.13 0.09 0.11 0.17 -0.19

Saturation 0.53 -0.53 0.50 0.03 0.46 -0.28 0.43 0.49 0.07 0.38 0.27 1 0.12 0.10 -0.51 -0.18 0.09

Slope angle 0.31 -0.31 0.37 0.02 0.16 -0.23 0.31 0.02 0.49 0.44 -0.13 0.12 1 -0.21 -0.30 -0.11 0.29

Slope aspect 0.17 -0.16 0.17 0.15 0.17 -0.01 0.22 0.05 -0.09 0.11 0.09 0.10 -0.21 1 -0.15 0.09 0.07

Temperature -0.96 0.97 -0.94 0.003 -0.37 0.56 -0.67 -0.03 -0.20 -0.56 0.11 -0.51 -0.30 -0.15 1 0.36 -0.39

Wet bulb -0.30 0.30 -0.17 0.86 -0.01 0.89 -0.04 0.23 -0.17 -0.20 0.17 -0.18 -0.11 0.09 0.36 1 -0.15

Wind speed 0.44 -0.42 0.45 -0.03 0.30 -0.19 0.20 -0.28 0.32 0.59 -0.19 0.09 0.29 0.066 -0.39 -0.15 1

79

4.5 Phytosociological Attributes in Different Vegetational Zones

The phytosociological attributes were recorded in different vegetational zones

of Nandiar Khuwar catchment. The vegetational zones of Nandiar Khuwar

catchment were divided in six major vegetation zones from subtropical to

alpine zone on the basis of indicator species. From these six vegetational

zones 80 stands were selected for phytosocialogical analysis on the basis of

physiognomy. TWINSPAN classification was used to classify the entire

vegetation of Nandiar Khuwar catchment and to classify the vegetation of

each zone. In subtropical zones of Nandiar Khuwar catchment 157 species

were recorded from 16 stands. In subtropical vegetational zones six plant

communities were recognized through TWINSPAN. In mixed Pinus roxburghii

and Pinus wallichiana forests 130 species were recorded from 12 stands. In this

vegetational zone four plant communities were recognized. In the moist

temperate pure Pinus wallichiana forests of Nandiar Khuwar catchment 200

species were recorded from 23 stands. Five plant communities were

recognized in the moist temperate pure Pinus wallichiana forests. In western

mixed coniferous forests of Nandiar Khuwar catchment 156 species were

recorded from 15 stands and four plant communities were recognized by

TWINSPAN classification. In pure Abies pindrow and Picea smithiana forests of

Nandiar Khuwar catchment 111 species were recorded from 9 stands and

three plant communities were identified by TWINSPAN. The five stands of

alpine pasture were classified into two plant communities with 37 plant

species. The details of plant communities of different vegetation zones are as

follow:

4.5.1 Phytosociology of Subtropical zone

In the subtropical zone of Nandiar Khuwar catchment 16 stands were selected

between altitudinal zones of 530 – 1850m above mean sea level. From this

zone a total of 157 species were recorded. Six plant communities were

recognized by TWINSPAN. In subtropical vegetation of Nandiar Khuwar

catchment the biological spectrum were dominated by nanophanerophytes

80

with 36 (22.92%) species followed by therophytes contributing 27 (17.19%)

species. The leaf size spectra were dominated by microphyll with 63 (40.12%)

species followed by mesophyll contributing 54 (34.39%) species. The

maximum similarity index (33.61) was recorded between Pinus, Micromeria,

Rubus community and Pinus, Rubus, Cynodon community. The maximum

dissimilarity index (94.91) was recorded between Acacia, Dodonaea, Dalbergia

community and Pinus, Quercus, Eleagnus community (table.4.5.3).

4.5.1.1 TWINSPAN classification

The data obtained from 16 stands in the subtropical vegetational zone of

Nandiar Khuwar catchment were classified by TWINSPAN classification and

a total of six communities were recognized (fig.4.5.1). The subtropical

communities are described below:

Fig. 4.5.1: TWINSPAN classification of subtropical vegetation of Nandiar Khuwar catchment.

81

4.5.1.1 .1 Acacia, Dodonaea, Dalbergia community

Acacia modesta, Dodonaea vescosa, Dalbergia sissoo community was recorded in

two stands Thakot I on south facing steep slope and Thakot II North facing

steep slope between an altitudes of 530-700m. In this community 70 species

were recorded. The indicator species of this community was Acacia modesta.

Among biological spectrum therophytes were dominated by 14 species

contributing 22.09 IVI value (table-4.5.1). Leaf size spectra were dominated by

mesophyll with 26 species while maximum IVI value (41.59) was contributed

by microphyll (table-4.5.2). The dominance of therophytes indicates that the

vegetation of this community is subtropical and disturbed due to soil erosion

and overgrazing.

4.5.1.1 .2 Pinus, Micromeria, Rubus community

Pinus roxburghii, Micromeria biflora, Rubus fructicosus community was recorded

in Peshora, Gajikot and Naraza between elevations of 850– 1250m. In this

community 69 species were recorded. The biological spectrum was

dominated by therophytes with 14 species contributing maximum (26.38) IVI

value (table-4.5.1). Among leaf size spectra mesophyll were dominated with

27 species contributing 34.33 IVI value (table-4.5.2). The dominance of

therophytes indicates disturbed vegetation due to overgrazing and human

interference; however the dominance of mesophyll leaf size spectra indicates

that this community receives a good amount of precipitation and having

moist environmental conditions.

4.5.1.1 .3 Pinus, Rubus, Cynodon community

Pinus roxburghii, Rubus fructicosus, Cynodon dactylon community was recorded

in 4 stands i.e. Batangi, Khaiabad, Nowshera and Paimal I between elevations

of 1200–1650m. In this community a total of 53 species were recorded.

82

Table- 4.5.1 The IVI contribution of Biological spectrum of the subtropical plant communities.

ADD PMR PRC PQE QDQ QSI

Megaphanerophytes 1.31 13.33 13.8 12.37 4.70 3.23

Mesophanerophytes 13.39 8.91 5.74 10.66 24.97 18.20

Microphanerophytes 19.52 4.40 0.97 8.61 8.13 8.40

Nanophanerophytes 16.38 13.01 24.54 29.2 28.37 50.84

Chamaephytes 12.16 13.2 7.99 10.26 7.33 3.41

Hemicryptophytes 6.54 11.03 20.6 14.77 8.35 7.20

Geophytes 8.63 9.74 4.88 0.00 4.84 4.47

Therophytes 22.09 26.38 21.48 14.13 13.31 4.07

Table- 4.5.2 The IVI contribution of leaf size spectra of the subtropical plant communities.

ADD PMR PRC PQE QDQ QSI

Macrophyll 5.97 2.19 1.48 4.46 3.63 0.00

Mesophyll 26.73 34.33 25.91 31.23 36.97 35.17

Microphyll 41.59 28.25 33.02 34.96 33.72 38.96

Nanophyll 22.67 29.07 35.31 29.35 20.67 24.21

Leptophyll 2.27 4.82 4.14 0.00 5.00 1.48

Aphyllus 0.78 1.35 0.14 0.00 0.00 0.00

Table- 4.5.3 The similarity and dissimilarity indices of the subtropical plant communities.

ADD PMR PRC PQE QDQ QSI

ADD 27.34 18.70 5.49 21.48 12.90

PMR 72.66 33.61 10.00 26.35 16.26

PRC 81.30 66.39 18.92 21.21 17.76

PQE 94.51 90.00 81.08 10.00 17.33

QDQ 78.52 73.65 78.79 90.00 27.82

QSI 87.10 83.74 82.24 82.67 72.18 ADD: Acacia, Dodonaea, Dalbergia community; PMR: Pinus, Micromeria, Rubus community; PRC: Pinus, Rubus, Cynodon community; PQE: Pinus, Quercus, Eleagnus community; QDQ: Quercus, Dodonaea, Quercus community; QSI: Quercus, Spiraea, Indigofera community

83

Among biological spectrum therophytes were dominated with 12 species

followed by hemicryptophytes with 11 species while maximum IVI value

(24.54) was contributed by nanophanerophytes (table-4.5.1). The leaf size

spectra were dominated by microphyll with 21 species, followed by

mesophyll with 18 species, while nanophyll with 10 species contributed

maximum (35.31) IVI value (table-4.5.2).). The dominance of therophytes and

hemicryptophytes vegetation indicates disturbed vegetation while leaf size

spectra showed slightly dry environmental conditions.

4.5.1.1 .4 Pinus, Quercus, Eleagnus community

Pinus roxburghii, Quercus incana, Elaegnus umbellata community was recorded

between elevations of 1800– 1900m in Lamai. In this community 21 species

were recorded. Biological spectrum was dominated by nanophanerophytes

with 7 species contributing 29.2 IVI value (table-4.5.1). Leaf size spectra was

dominated by microphyll with 8 species contributing maximum (34.96) IVI

value followed by mesophyll with 7 species (table-4.5.2). The dominance of

nanophanerophytes and microphyll indicates moist environmental

conditions.

4.5.1.1.5 Quercus, Dodonaea, Quercus community

Quercus incana, Dodonaea vescosa, Quercus glauca community was recorded in

Chorlangay, Shagai and Paimal II between elevations of 730–1160m. In this

community a total of 79 species were recorded. Among biological spectrum

nanophanerophytes was dominated with 22 species contributing 28.37 IVI

value (table-4.5.1), followed by therophytes with 13 species. Among leaf size

spectra microphyll was dominated with 30 species, followed by mesophyll

with 27 species contributing 36.97 IVI value (table-4.5.2). The dominance of

nanophanerophytes life form, microphyll and mesophyll leaf size spectra

indicates moist environmental conditions.

84

4.5.1.1.6 Quercus, Spiraea, Indigofera Community

Quercus incana, Spiraea vaccinifolia, Indigofera heterantha community were

recorded between elevations of 1220–1320m in Nilishung Reen, Paimal III and

Paimal IV. In this community 54 species were recorded. Biological spectrum

was dominated by nanophanerophytes with 19 species contributing 50.84 IVI

value (table-4.5.1). Among leaf size spectra microphyll were dominated with

22 species contributing 38.96 IVI value followed by mesophyll with 19 species,

nanophyll with 10 species and leptophyll with 3 plant species (table-4.5.2).

The dominance of nanophanerophytes and microphyll leaf size spectra

indicates good environmental conditions.

4.5.1.2 Ordination of Subtropical Vegetation

The data of subtropical vegetation were further analyzed for Ordination. In

ordination Bray-Curtis ordination, DCA with supplementary variables and

CCA were used.

In Bray-Curtis ordination the ordination scores (Distances) were recorded for

three axis. The ordination score was maximum for axis 2 (0.921). The

ordination scores on axes 1 were from Naraza (0.000) to Paimal III (0.825). The

regression coefficient for axis 1 was -8.81, variance in distance from the first

end point were 0.37. Axis 1 extracted 24.69% of original distance matrix. The

ordination scores for axis 2 was from Paimal I (0.000) to Thakot II (0.921). The

regression coefficient for axis 2 were –11.32, variance in distance from the first

end point were 0.34. Axis 2 extracted 22.89% of original distance matrix. The

ordination scores for axis 3 was from Thakot I (0.000) to Lamai (0.707). The

regression coefficient for axis 3 were -5.92, variance in distance from the first

end point were 0.15. Axis 3 extracted 8.53% of original distance matrix

(fig.4.5.2).

85

Fig 4.5.2: Bray-Curtis ordination of the subtropical zone of the study area.

Fig. 4.5.3: DCA ordination of the subtropical zone of the area.

86

Fig. 4.5.4: CCA ordination of the subtropical zone of the study area.

Fig. 4.5.5: CCA ordination of the subtropical zone of the study area.

87

The response data were compositional and have a gradient 3.3 SD units long,

so the recommended unimodal method (DCA and CCA) were used. In DCA

ordination with supplementary variables the maximum gradient length (3.35)

were recorded for axis 1 with eigenvalue 0.50. The gradient length for axis 2

was 2.38 with eigenvalue 0.30. Total variance ("inertia") in the species data

were 2.92, supplementary variables account for 100%, while the adjusted

explained variation were 0%. The DCA clearly indicates that the whole data

set is dominated by a single dominant gradient. Different stands and species

were clustered in the ordination space as summarized by TWINSPAN

classification.

In CCA ordination the maximum Eigenvalue were recorded for axis 1 (0.50)

followed by axis 2 (0.42) and axis 3 (0.31). The percentage variance explained

for axis 1, 2 and 3 were 17.21%, 31.88% and 42.64% respectively. The total

variance (inertia) in the species data were 2.92, explanatory variables account

for 100% while adjusted explained variation were 0.0%. The pseudo-canonical

correlation for axis 1, 2 and 3 were 0.996, 0.967 and 0.999. The correlation

between sample score for an axis derived from the species data and the

sample scores that are linear combination of the environmental variable. The

permutation test results for all axes were pseudo-F<0.1, P=1.

CCA ordination showed that the different stands of Acacia, Dodonaea,

Dalbergia community clustered at high temperature, barometric pressure,

phosphorus, slope angle and wind speed. Pinus, Micromeria, Rubus

community were positively correlated with dew point, wet bulb, heat index

and pH value. Pinus, Rubus, Cynodon community was positively correlated

with humidity and organic matter. Pinus, Quercus, Eleagnus community was

more associated with slope aspect and high altitude. Quercus, Dodonaea,

Quercus community were recorded at high soil Potassium and soil saturation.

Quercus, Spiraea, Indigofera community were negatively correlated with most

of the environmental variables. These results showed that the particular

88

environmental variable has a great affect on the distribution of different plant

communities in Nandiar Khuwar catchment (fig. 4.5.4).

The results of DCA and CCA ordination of species and environmental

variables showed that different species clustered along different

environmental variables. The maximum strength was recorded for

temperature, barometric pressure, altitude, density altitude, phosphorous and

wind speed. The minimum environmental variable strength was recorded for

organic matter, potassium and soil saturation. Indigofera heterantha and

Berberis lycium were more correlated with high altitudes. Plantago lanceolatum,

Panicum species, Origanum vulgare, Taraxicum officinale were more correlated

with high atmospheric humidity and soil organic matter. Xanthium

stromarium, Ficus carica were more closely correlated with high pH values,

heat index and wet bulb. Similarly Artemisia japonica, Acacia modesta, Albezia

lebbeck were more closely correlated with high barometric pressure,

temperature, wind speed and P mg/kg. These results show that a specific

environmental variable has a great impact on species distribution in different

vegetational zones of the study area (fig. 4.5.5).

89

4.5.2 Mixed Pinus roxburghii and Pinus wallichiana Forests

In mixed Pinus roxburghii and Pinus wallichiana forests a total of 130 plant

species were recorded from 12 stands between an altitudinal zones of 1250 –

2050m above mean sea level. In this vegetational zone four plant communities

were recognized by TWINSPAN classification. In mixed Pinus roxburghii and

Pinus wallichiana forests of Nandiar Khuwar catchment the biological

spectrum were dominated by nanophanerophytes with 25 species followed by

geophytes contributing 24 species. The leaf size spectra were dominated by

microphyll with 58 species followed by mesophyll contributing 42 species.

The maximum similarity index value (32.9) were recorded between Pinus

roxburghii, Pinus wallichiana, Quercus incana Community and Quercus incana,

Pinus roxburghii, Pinus wallichiana Community. The index of dissimilarity was

maximum (82.80) between Pinus roxburghii, Pinus wallichiana Quercus incana

community and Pinus wallichiana, Quercus incana, Fragaria nubicola community

(table-4.5.6).

4.5.2.1 TWINSPAN Classification

The data obtained from 12 stands in the mixed Pinus roxburghii and Pinus

wallichiana forests were analyzed by TWINSPAN classification. Four distinct

plant communities were recognized in this vegetational zone on cut level 2

(fig. 4.5.6). These communities are Pinus, Pinus, Quercus community, Quercus,

Pinus, Pinus community, Quercus, Pinus, Myrsine community and Pinus,

Quercus, Fragaria community.

4.5.2.1.1 Pinus, Pinus, Quercus Community

Pinus roxburghii, Pinus wallichiana, Quercus incana community was recorded in

two stands Gada and Belandkot between an elevation of 1250-1650m. In this

community a total of 83 species were recorded. The biological spectrum was

dominated by nanophanerophytes with 20 species contributing maximum IVI

value (20.72), followed by geophytes with 16 species (table-4.5.4). Among leaf

90

size spectra microphyll was represented by 37 species contributing 37.3 IVI

value, followed by mesophyll with 27 species (table-4.5.5). The dominance of

nanophanerophytes and microphyll indicates that this community receives a

good amount of precipitation, having moist conditions and was less

disturbed.

4.5.2.1.2 Quercus, Pinus, Pinus Community

Quercus incana, Pinus roxburghii, Pinus wallichiana community was recorded

between an elevation of 1450-1800m in three stands Lundai I, Anora II and

Anora III. In this community a total of 72 species were recorded. Biological

spectrum was dominated by nanophanerophytes with 18 species contributing

maximum (26.83) IVI value (table-4.5.4). Among leaf size spectra 26 plant

species were contributed by each mesophyll and microphyll, while maximum

IVI value (37.45) was contributed by mesophyll (table-4.5.5). The dominance

of nanophanerophytes, mesophyll and microphyll indicates that this

community receives a good amount of precipitation, having moist conditions

and was less disturbed.

Fig. 4.5.6: TWINSPAN Classification of mixed Pinus and Pinus forests.

91

Table- 4.5.4 IVI contributed by biological spectrum in plant communities of mixed Pinus Pinus forests.

PPQ QPP QPM PQF

Megaphanerophytes 10.81 11.78 16.64 14.42

Mesophanerophytes 13.68 9.96 11.9 12.32

Microphanerophytes 5.06 5.82 0.86 9.55

Nanophanerophytes 20.72 26.83 25.58 18.91

Chamaephytes 11.74 7.1 7.79 5.98

Hemicryptophytes 7.95 13.98 16.79 10.73

Geophytes 16.56 10.31 9.68 12.97

Therophytes 13.48 14.22 10.7 15.14

Table- 4.5.5 IVI contributed by leaf size spectra in plant communities of mixed Pinus Pinus forests.

PPQ QPP QPM PQF

Macrophyll 1.46 1.41 0.47 3.46

Mesophyll 32.23 37.45 26.94 41.53

Microphyll 37.3 34.14 34.03 27.67

Nanophyll 23.8 23.99 34.86 24.37

Leptophyll 4.26 2.67 3.64 2.98

Aphyllus 0.94 0.34 0 0

Table- 4.5.6 Similarity and dissimilarity indices in plant communities of mixed Pinus Pinus forests.

PPQ QPP QPM PQF

PPQ

32.9 25.49 17.6

QPP 67.1

30.99 25.44

QPM 74.51 69.01

24.11

PQF 82.4 74.56 75.89

PPQ: Pinus, Pinus, Quercus Community; QPP: Quercus, Pinus, Pinus Community; QPM: Quercus, Pinus, Myrsine Community; PQF: Pinus, Quercus, Fragaria Community

92

4.5.2.1.3 Quercus, Pinus, Myrsine Community

Quercus incana, Pinus wallichiana, Myrsine africana community was recorded

from five stands Shabora I, Shabora II, Deshara, Paimal V and Dabrai

between an elevations of 1250-1550m. In this community a total of 70 species

were recorded. The biological spectrum was dominated by hemicryptophytes

with 16 species, geophytes with 15 species and therophytes with 13 species

while maximum IVI value (25.58) was contributed by nanophanerophytes

(table-4.5.4). The leaf size spectra were dominated by microphyll with 30

species, while maximum IVI value (34.86) was contributed by nanophyll

(table-4.5.5). The vegetation of this community was disturbed due to

overgrazing, soil erosion and human impact.

4.5.2.1.4 Pinus, Quercus, Fragaria Community

Pinus wallichiana, Quercus incana, Fragaria nubicola community was recorded

between an elevation of 1800-2050min two stands Lundai II and Sarmast

contributing 42 plant species. Biological spectrum was dominated by

nanophanerophytes with 10 species contributing 18.91 IVI value was

nanophanerophytes (table-4.5.4). Among leaf size spectra mesophyll was

dominated with 18 species contributing 41.53 IVI value. The vegetation of this

community receives good amount of precipitation, having moist conditions,

low temperature and are less disturbed.

4.5.2.2 Ordination of Vegetation of Mixed Pinus Pinus Forests

In Bray-Curtis ordination (ordination of stands in species space) 12 stands and

130 species were analyzed. The ordination scores (Distances) were from

Anora II (0.000) to Deshara (0.746) on axes 1. The regression coefficient for this

axis were -4.74, variance in distance from the first end point were 0.09. Axis 1

extracted 18.14% of original distance matrix. The ordination scores for axis 2

was from Gada (0.000) to Dabrai (0.725). The regression coefficient for this

axis were –6.66, variance in distance from the first end point were 0.08. Axis 2

93

extracted 17.84% of original distance matrix. The ordination scores for axis 3

was from Belandkot (0.000) to Sarmast (0.658). The regression coefficient for

this axis were -5.88, variance in distance from the first end point were 0.07.

Axis 3 extracted 13.68% of original distance matrix.

The response data are compositional and have a gradient 2.4 SD units long, so

the recommended linear method was used. In linear method PCA with

supplementary variables showed that the total variation were 294.10,

supplementary variables account for 100%. The maximum eigenvalue were

recorded for axis 1 with 0.17. The Eigenvalues for axis 2 and axis 3 were 0.15

and 0.14 respectively. The explained variation for Axis 1, 2 and 3 were 17.71,

32.33 and 46.41 respectively. The Pseudo-canonical correlation (suppl.) for all

axis were 0.00. The PCA analysis showed the correlation of different species

in ordination space. The variables in the data set have linear interrelationship.

The correlation is positive when angle is sharp and negative when the angle is

larger than 90. In this analysis it was observed that Pinus wallichiana, Quercus

dilatata, Indigofera heterantha, Pinus roxburghii and Sarcococca saligna were

positively correlated with each other while negatively correlated with

Heteropogon contortus. Similarly Fragaria nubicola, Rhododendron arboreum, Rhus

javanica, Cotinus coggyria and Bergenia ciliata were positively correlated with

each other while negatively correlated with Rubus fructicosus (fig.4.5.7).

In DCA ordination with supplementary variables the maximum gradient

length (2.43) were recorded for axis 1 with eigenvalue 0.35. The gradient

length for axis 2 was 2.07 with eigenvalue 0. 17. Total variance ("inertia") in

the species data were 2.15, supplementary variables account for 100%. The

Pseudo-canonical correlation (suppl.) for all axis were 0.00. In DCA

ordination different species were clustered in ordination space. Bergenia

ciliata, Rhododendron arboreum and Viburnum cotinifolium were more correlated.

Similarly Pinus wallichiana, Pinus roxburghii, Indigofera heterantha and

Sarcococca saligna clustered near each other (fig 4.5.8).

94

Fig. 4.5.7: DCA ordination of mixed Pinus Pinus forests.

Fig. 4.5.8: DCA ordination of mixed Pinus Pinus forests.

0.0 2.5

0.0

2.5

Gada

Shabora I

Paimal V Deshara

Dabrai

Shabora II

Lundai I

Belandkot Anora III

Lundai II

Anora II

Sarmast

95

In CCA ordination the maximum Eigenvalue were recorded for axis 1 (0.35)

followed by axis 2 (0.30) and axis 3 (0.25). The percentage variance explained

for axis 1, 2 and 3 were 16.45%, 30.78% and 42.52% respectively. The total

variance (inertia) in the species data were 2.15, explanatory variables account

for 100%. Pseudo-canonical correlation for all axis were 1.00. The permutation

test results for all axes were pseudo-F<0.1, P=1.

In CCA ordination the maximum strength were recorded for the

environmental variables temperature, barometric pressure, heat index, wet

bulb, dew point, humidity and altitude. The average values of the

environmental variables were recorded for wind speed, slope aspect,

phosphorus and pH. The intermediate strengths were recorded for soil

saturation, slope angle, density altitude, soil organic matter, electrical

conductivity and Potassium. The species of different stands of Pinus, Pinus,

Quercus community were positively correlated with dew point, wet bulb and

electrical conductivity. The species of different stands of Quercus, Pinus, Pinus

community were clustered at almost average position. The species of different

stands of Quercus, Pinus, Myrsine community were negatively correlated with

most of the environmental variables. The species of different stands of Pinus,

Quercus, Fragaria community were clustered at high altitude, soil saturation

and atmospheric humidity (fig.4.5.9 and 4.5.10).

96

Fig. 4.5.9: CCA ordinationof mixed Pinus Pinus forests.

Fig. 4.5.10: CCA ordination of mixed Pinus Pinus forests.

97

4.5.3 Moist temperate pure Pinus wallichiana forests

In the moist temperate pure Pinus wallichiana forests of Nandiar Khuwar

catchment a total of 200 species were recorded from 23 stands between

altitudinal zones of 1800-2400m. Five distinct plant communities were

recognized by TWINSPAN. In over all pure Pinus wallichiana forests of

Nandiar Khuwar catchment the biological spectrum were dominated by

therophytes with 46 species followed by geophytes contributing 44 species.

The leaf size spectra were dominated by microphyll with 89 species followed

by mesophyll contributing 58 species. In unconstrained DCA ordination with

supplementary variables the maximum gradient length (3.51) were recorded

for axis 1. In CCA ordination the maximum strength were recorded for the

environmental variables temperature, barometric pressure, wind speed and

altitude. The maximum index of similarity (28.5) was recorded between Pinus

wallichiana, Sarcococca saligna, Berberis lycium community and Pinus wallichiana,

Quercus incana, Berberis lycium community. The maximum index of

dissimilarity (91.33) were recorded between Pinus wallichiana, Cynodon

dactylon, Themeda anathera Community and Pinus wallichiana, Quercus incana,

Berberis lycium community (table 4.5.9). The IVI contribution of various classes

of biological spectrum in different plant communities of pure Pinus wallichiana

forests are presented in table 4.5.7. The IVI contribution of various classes of

leaf size spectra in different plant communities of pure Pinus wallichiana

forests are presented in table 4.5.8.

4.5.3.1 TWINSPAN Classification of Pure Pinus wallichiana Forests

The data obtained from 23 stands in the moist temperate pure Pinus

wallichiana forests of Nandiar Khuwar catchment were analyzed by

TWINSPAN classification and a total of five distinct plant communities were

recognized. These communities are described below:

98

Fig. 4.5.11: TWINSPAN classification of moist temperate pure Pinus

wallichiana zones

99

4.5.3.1 .1 Pinus, Cynodon, Themeda Community

Pinus wallichiana, Cynodon dactylon, Themeda anathera community was recorded

between an elevation of 1600-1800m in two stands Nili-Sharkolai and Nili-

Batangi. In this community 33 species were recorded. The biological spectrum

was dominated by therophytes with 12 species contributing maximum (26.85)

IVI value (table-4.5.7). Leaf size spectra were dominated by microphyll with

14 species, followed by nanophyll with 10 species contributing 49.75 IVI value

(table-4.5.8). The vegetation of this community is disturbed due to

deforestation and overgrazing. This community receives good amount of

precipitation and having moist environmental conditions.

4.5.3.1 .2 Pinus, Quercus, Spiraea Community

Pinus wallichiana, Quercus incana, Spiraea vaccinifolia community was recorded

from three stands Rajmira, Anora 1 and Shinglai between an elevation of

1450-1850m contributing 107 plant species. In this community biological

spectrum was dominated by nanophanerophytes with 26 species contributing

32.03 IVI value (table-4.5.7). Among leaf size spectra microphyll was

dominated with 45 species contributing 35.87 IVI value (table-4.5.8) followed

by mesophyll with 31 species. Biological spectra and leaf size spectra showed

that the stands of this community is preserved, having good environmental

conditions.

4.5.3.1 .3 Pinus, Dryopteris, Fragaria Community

Pinus wallichiana, Dryopteris jaxtaposita, Fragaria nubicola community was

recorded between an elevation of 1850-2050min four stands i.e. Kiari,

Bashakhan, Sharkolai and Jatial. In this community a total of 58 species were

recorded among which biological spectrum was dominated by geophytes and

therophytes each contributing 15 plant species. Maximum (18.71) IVI value

was contributed by geophytes (table-4.5.7). The dominance of geophytes and

100

therophytes indicates disturbed vegetation due to deforestation and

overgrazing. The leaf size spectra were dominated by microphyll with 23

species contributing maximum 32.81 IVI value followed by mesophyll with 18

species (table-4.5.8). The leaf size spectra showed that this community has

moist environmental conditions.

4.5.3.1 .4 Pinus, Sarcococca, Berberis Community

Pinus wallichiana, Sarcococca saligna, Berberis lycium community was recorded

in six stands Jarotia, Chapra, Sandawali, Hill, Bachmaidan and Terkana

between an elevations of 1750-2350m. In this community 76 species were

recorded. The biological spectrum was dominated by therophytes and

hemicryptophytes each contributing 15 plant species, followed by

nanophanerophytes and geophytes each contributing 11 plant species while

maximum (24.56) IVI value were contributed by nanophanerophytes (table-

4.5.7). Among leaf size spectra microphyll was dominated with 34 species

contributing 38.77 IVI value, followed by mesophyll with 28 species (table-

4.5.8). This community is disturbed due to deforestation, overgrazing and soil

erosion however the environmental conditions are moist and low

temperature.

4.5.3.1 .5 Pinus, Quercus, Berberis Community

Pinus wallichiana, Quercus incana, Berberis lycium community were recorded

between an elevation of 1800-2300m in eight stands Gat, Sheed, Habibbanda I,

Habibbanda II, Chapar, Riar, Mirani kandao and Jaro. In this community a

total of 117 species were recorded among which biological spectrum was

dominated by geophytes with 35 species while maximum (20.98) IVI value

was contributed by nanophanerophytes (table-4.5.7). The leaf size spectra

were dominated by microphyll with 54 species contributing 39.96 IVI value

(table-4.5.7). Overgrazing and deforestation are common in this community.

101

Table-4.5.7 The IVI contribution of Biological spectrum in plant communities of pure Pinus wallichiana forests.

PCT PQS PDF PSB PQB

Megaphanerophytes 16.8 9.75 14.07 13.66 10.16

Mesophanerophytes 1.60 10.52 4.97 4.92 9.70

Microphanerophytes 0.00 6.62 1.74 4.45 8.10

Nanophanerophytes 15.25 32.03 15.95 24.56 20.98

Chamaephytes 11.21 8.96 9.72 9.32 9.51

Hemicryptophytes 20.1 9.73 18.3 15.57 7.22

Geophytes 8.20 14.5 18.71 14.83 19.64

Therophytes 26.85 7.88 16.55 12.7 14.69

Table- 4.5.8 The IVI contribution of leaf size spectra in plant communities of pure Pinus wallichiana forests.

PCT PQS PDF PSB PQB

Macrophyll 0.00 1.99 0.38 5.50 2.98

Mesophyll 9.80 33.86 32.02 26.84 30.03

Microphyll 34.77 35.87 32.81 38.77 39.96

Nanophyll 49.75 21.36 28.55 27.04 24.62

Leptophyll 5.68 4.30 4.63 1.40 1.26

Aphyllus 0.00 2.62 1.61 0.47 1.15

Table- 4.5.9 Similarity and dissimilarity indices in the communities of pure Pinus wallichiana forests.

PCT PQS PDF PSB PQB

PCT

10.00 24.18 13.76 8.67

PQS 90.00

21.82 21.86 25.00

PDF 75.82 78.18

25.37 20.00

PSB 86.24 78.14 74.63

28.50

PQB 91.33 75.00 80.00 71.50

PCT: Pinus, Cynodon, Themeda community; PQS: Pinus, Quercus, Spiraea community; PDF: Pinus, Dryopteris, Fragaria community; PSB: Pinus, Sarcococca, Berberis community; PQB: Pinus, Quercus, Berberis community.

102

4.5.3.2 Ordination of Vegetation of Pure Pinus wallichiana Forests

In Bray-Curtis ordination the ordination scores (distances) were from Riar

(0.000) to Nilishung Sharkolai (0.865) on axes 1. The regression coefficient for

this axis was -12.32, variance in distance from the first end point were 0.53.

Axis 1 extracted 21.26% of original distance matrix. The ordination scores for

axis 2 were from Mirani Kandao (0.000) to Rajmira (0.678). The regression

coefficient for this axis was –6.68, variance in distance from the first end point

was0.26. Axis 2 extracted 8.71% of original distance matrix. The ordination

scores for axis 3 was from Chapar (0.000) to Bashakhan (0.685). The regression

coefficient for this axis was -8.67, variance in distance from the first end point

was0.28. Axis 3 extracted 11.35% of original distance matrix (fig.4.5.12).

The response data were compositional and have a gradient 3.5 SD units long,

so the recommended unimodal method (DCA and CCA) was used. In

unconstrained DCA ordination with supplementary variables the maximum

gradient length (3.51) was recorded for axis 1 with eigenvalue 0.47. The

gradient length for axis 2 was 2.45 with eigenvalue 0.33. Total variance

("inertia") in the species data was 346, supplementary variables account for

82.1%, while adjusted explained variation was 21.3%. The DCA clearly

indicates that the whole data set is dominated by a single dominant gradient.

The pseudo-canonical correlation for axis 1, 2 and 3 were 0.896, 0.972 and

0.869 respectively.

In DCA ordination different species were clustered in ordination space.

Pseudognaphalium hypolecum, Geranium wallichianum and Anaphalis busa

clustered at the top of ordination space and are positively correlated. These

species were negatively correlated with Oxalis corniculata, Heteropogon

contortus, Plantago lanceolatum, Taraxiacum officinale, Indigofera heterantha, Pinus

wallichiana and Fragaria nubicola. Similarly all these species were negatively

correlated with Rosa moschata and Rubus ellipticus (fig.4.5.13).

103

Fig. 4.5.12: Bray-Curtis ordination of moist temperate pure Pinus wallichiana zone.

Fig. 4.5.13: DCA ordination of moist temperate pure Pinus wallichiana

zone.

104

Fig. 4.5.14: CCA ordination of pure Pinus wallichiana zone.

Fig. 4.5.15: CCA ordination of pure Pinus wallichiana zone.

-0.6 1.0

-0.8

0.8

Slope Angle

Slope Aspct

Altitude

Baro

Den Alti

Temp

Wind Speed

Humidity

Heat Index

Dew Point

Wet BulbEC

PHOrg Matt

P mg/kg

K mg/kg

Saturati

Adi cap

Ail alt

Aju bra

All pet

Ana bus

And cor

Asp fil

Ast amoAst spp

Ber cil

Bol bar

Car gra

Cel aus

Cir falCle con

Cle gra

Cot cog

Cot mic

Cot num

Cus gig

Cyn dac

Dap mucDeb sal

Des ele

Deu sta

Dic bupDio lotDio melDry mac

Duh cap

Ela umbEqu arvEqu heiFic carFic palFic sar

Gag sat

Gal apa

Ger wal

Het con

Imp bic

Lon qui

Lot cor

Lyg haz

Mar vul

Mic bif

Mor sppMyr afr

Oen aff

Ole fer

Ooklo

Oxa corPan spp

Pic hie

Pla lan

Poa spp

Pol lon

Pse hyp

Pte cre

Pte urt

Pyr pas

Que dil

Ran lae

Rhu javRob pse

Rub ell

Rub ulm

Rum denRum has

Sag the

Sar sal

Sel san

Smi gla

Sol amp

Spi vac

Tar offTul ste

Ulm spp

Vib cot

Vib mul

Vio can

Woo uniZan arm

105

In CCA ordination in the moist temperate pure Pinus wallichiana forests the

maximum Eigenvalue was recorded for axis 1 (0.43) followed by axis 2 (0.36)

and axis 3 (0.25). The percentage variance explained for axis 1, 2 and 3

was12.56%, 22.98% and 30.28% respectively. The total variance (inertia) in the

species data was 3.46, explanatory variables account for 82.1%, while adjusted

explained variation was 21.3%. Pseudo-canonical correlation for all axes was

1.00. The permutation test results for all axes was pseudo-F=1.4, P=0.006.

In CCA ordination the maximum strength was recorded for the

environmental variables temperature, barometric pressure, wind speed and

altitude. The average values of the environmental variables were recorded for

pH. The intermediate strengths were recorded for all other environmental

variables. Most of the environmental variables were positively correlated with

each other while negatively correlated with barometric pressure, temperature

and wind speed. Most of the stands were found near the average value.

The species of different stands of Pinus, Cynodon, Themeda community were

positively correlated with wind speed. The species of different stands of

Pinus, Quercus, Spiraea community were positively correlated with slope angle

and barometric pressure. The species of different stands of Pinus, Dryopteris,

Fragaria community were clustered at almost average position. The species of

different stands of Pinus, Sarcococca, Berberis community and Pinus, Quercus,

Berberis community were positively correlated with most of the

environmental variables (fig.4.5.14).

The DCA ordination of species and environmental variables and the results

obtained from CCA ordination showed that different species were clustered

along different environmental variables. Different species like Cotoneaster

microphylla, Celtis australis, Cuscuta gigantean, Daphne mucronata, Diospyros

lotus, Elaegnus umbellata, Ficus carica and Ficus palmata etc were positively

correlated with temperature and barometric pressure. Similarly Rumex

106

hastatus, Ajuga bracteosa, Heteropogon contortus etc were positively correlated

with wind speed. Many species were clustered near other environmental

variable. These results showed that a specific environmental variable has a

great impact on species distribution in moist temperate pure Pinus wallichiana

forests (fig.4.5.15).

107

4.5.4 Mixed Coniferous Forests

In western mixed coniferous forests of Nandiar Khuwar catchment 156

species were recorded from 15 stands between altitudinal zones of 2000-

3050m above mean sea level. Four distinct plant communities were

recognized by TWINSPAN. In unimodal method DCA ordination with

supplementary variables the maximum gradient length (2.69) was recorded

for axis 1 with eigenvalue 0.46. In CCA ordination in the western mixed

coniferous forests the maximum Eigenvalue was recorded for axis 1 (0.46) and

maximum strength was recorded for the environmental variables soil

saturation and phosphorus. In over all western mixed coniferous forests of

Nandiar Khuwar catchment the biological spectrum was dominated by

therophytes with 42 species followed by geophytes contributing 35 species.

The leaf size spectra were dominated by microphyll with 61 species followed

by mesophyll contributing 49 species. The maximum index of similarity

(28.57) was recorded between Pinus wallichiana, Abies pindrow, Picea smithiana

community and Pinus wallichiana, Viburnum cotinifolium Abies pindrow

community. The maximum index of dissimilarity (82.44) was recorded

between Pinus wallichiana, Abies pindrow, Picea smithiana community and Abies

pindrow, Pinus wallichiana, Picea smithiana community (table- 4.5.12).

4.5.4.1 TWINSPAN Classification of Mixed Coniferous Forests

The data obtained from 15 stands in the western mixed coniferous forests of

Nandiar Khuwar catchment were analyzed by TWINSPAN classification and

a total of four distinct plant communities were recognized. These

communities are described below:

4.5.4.1.1 Pinus, Abies, Picea Community

Pinus wallichiana, Abies pindrow, Picea smithiana community was recorded in

four stands i.e. Charoona, Harpal, Mirani I, Mirani II and Ledai between an

elevation of 2250-2950m.

108

Fig. 4.5.16: TWINSPAN classification of mixed coniferous forests.

109

In this community 87 species were recorded. The biological spectrum was

dominated by geophytes with 21 species contributing 21.97 IVI value

followed by therophytes with 19 species (table-4.5.10). Leaf size spectra were

dominated by microphyll with 35 species contributing 33.08 IVI value

followed by mesophyll with 30 species (table-4.5.11). The vegetation of this

community is disturbed due to soil erosion, deforestation and overgrazing

however environmental conditions are moist, receiving a good amount of

precipitation.

4.5.4.1.2 Pinus, Viburnum, Abies Community

Pinus wallichiana, Viburnum cotinifolium, Abies pindrow community were

recorded between an elevation of 2000-2900m in five stands Doba, Doda I,

Bach upper, Manra and Machaisar. In this community a total of 95 species

were recorded among which the biological spectrum was dominated by

geophytes with 24 species contributing 30.33 IVI value followed by

therophytes with 22 species (table-4.5.10). Among leaf size spectra microphyll

was dominated with 36 species contributing 29.47 IVI value followed by

mesophyll with 29 species. Overgrazing and deforestation are common in this

zone.

4.5.4.1.3 Abies, Quercus, Picea Community

Abies pindrow, Quercus semicarpifolia, Picea smithiana community was recorded

from four stands Kachkol, Belmaz, Lekoni and Magrai between an elevation

of 2800-3050m. In this community 67 species were recorded. The biological

spectrum was dominated by therophytes with 16 species contributing 22.14

IVI value (table-4.5.10). The leaf size spectra were dominated by mesophyll

with 24 species contributing 39.64 IVI value (table-4.5.11). The vegetation of

this community is disturbed due to overgrazing, deforestation, soil erosion

and land sliding however it receives good amount of precipitation in the form

of rainfall and snowfall.

110

Table-4.5.10 The IVI contribution of Biological spectrum in plant communities of mixed coniferous forests.

PAP PVA AQP APP

Megaphanerophytes 17.25 17.36 15.65 16.73

Mesophanerophytes 5.69 7.55 8.45 7.30

Microphanerophytes 7.55 6.72 4.70 5.29

Nanophanerophytes 3.53 8.86 13.68 9.78

Chamaephytes 11.33 9.06 4.28 4.77

Hemicryptophytes 12.41 11.79 11.12 7.68

Geophytes 21.97 20.33 20.00 21.23

Therophytes 20.26 18.33 22.14 27.13

Table- 4.5.11 The IVI contribution of leaf size spectra in plant communities of mixed coniferous forests.

PAP PVA AQP APP

Macrophyll 4.79 7.32 6.52 5.59

Mesophyll 30.37 29.44 39.64 40.79

Microphyll 33.08 29.47 25.22 25.99

Nanophyll 31.36 31.77 26.83 27.45

Leptophyll 0.00 0.81 1.80 0.00

Aphyllus 0.39 1.19 0.00 0.00

Table- 4.5.12 Similarity and dissimilarity indices in the communities of mixed coniferous forests.

PAP PVA AQP APP

PAP

28.57 20.78 17.56

PVA 71.43

23.46 20.14

AQP 79.22 76.54

27.93

APP 82.44 79.86 72.07

PAP: Pinus, Abies, Picea community; PVA: Pinus, Viburnum, Abies community; AQP: Abies, Quercus, Picea community; APP: Abies, Pinus, Picea community.

111

4.5.4.1.4 Abies, Pinus, Picea Community

Abies pindrow, Pinus wallichiana, Picea smithiana community was recorded in

Guchai between an elevation of 2250-2600m. In this community 44 plant

species were recorded. Biological spectrum was dominated by therophytes

with 15 species having 27.13 IVI value (table-4.5.10). Leaf size spectra were

dominated by mesophyll with 19 species contributing 40.79 IVI value (table-

4.5.11). The environmental conditions of this community are moist and low

temperature however this community was affected overgrazing, deforestation

and land sliding.

4.5.4.2 Ordination of Vegetation of Mixed Coniferous Forests

In Bray-Curtis ordination (ordination of stands in species space) 15 stands and

156 species were analyzed. The ordination scores were from Lekoni (0.000) to

Mirani I (0.752) on axes 1. The regression coefficient for this axis was -8.06,

variance in distance from the first end point were 0.48. Axis 1 extracted

28.82% of original distance matrix. The ordination scores for axis 2 were from

Harpal (0.000) to Doda I (0.752). The regression coefficient for this axis was –

7.13, variance in distance from the first end point was 0.26. Axis 2 extracted

16.09% of original distance matrix. The ordination scores for axis 3 were from

Kachkol (0.000) to Bach upper (0.486). The regression coefficient for this axis

was -0.88, variance in distance from the first end point were 0.19. Axis 3

extracted 7.75% of original distance matrix (fig.4.5.17).

In unimodal method DCA ordination with supplementary variables the

maximum gradient length (2.69) was recorded for axis 1 with eigenvalue 0.46.

The gradient length for axis 2 was 2.39 with eigenvalue 0. 25. Total variance

("inertia") in the species data was2.43, supplementary variables account for

79.2%, while adjusted explained variation were 2.9%. The DCA clearly

indicates that the whole data set is dominated by a single dominant gradient.

112

In DCA ordination different species of western mixed coniferous forests of

Nandiar Khuwar catchment were clustered in ordination space. The species

clustered together having positive correlation included Abies pindrow,

Viburnum cotinifolium, Primula denticulata, Quercus semicarpifolia, Paeonia emodi,

Picea smithiana, Potentilla nepalensis, Wikstroemia canescens, Geranium

wallichianum, Fragaria nubicola, Juglans regia, Pinus wallichiana, Androsace

hazarica and Caltha alba. These species were negatively correlated with Quercus

incana and Themeda anathera. The species of similar stands were clustered in

the ordination space almost same as were classified by TWINSPAN

ordination (fig.4.5.18).

In CCA ordination in the western mixed coniferous forests the maximum

eigenvalue was recorded for axis 1 (0.46) followed by axis 2 (0.32) and axis 3

(0.30). The percentage variance explained for axis 1, 2 and 3 was 18.98%,

32.33% and 44.98% respectively. The total variance (inertia) in the species data

was 2.43, explanatory variables account for 100%. Pseudo-canonical

correlation for all axis was 1.00. The permutation test results for all axes was

pseudo-F<0.1, P=1.

In CCA ordination the maximum strength was recorded for the

environmental variables like soil saturation and phosphorus while minimum

strength was recorded for atmospheric humidity and electrical conductivity.

In CCA ordination the different stands of Pinus, Abies, Picea community were

positively correlated with heat index, wet bulb and dew point. The stands of

Pinus, Viburnum, Abies community were positively correlated with

temperature, potassium, barometric pressure, soil saturation, organic matter

and pH value. The stands of Abies, Quercus, Picea community were strongly

correlated with altitude, density altitude and phosphorus. Abies, Pinus, Picea

community was positively correlated with slope angle (fig.4.5.19).

113

Fig. 4.5.17: Bray-Curtis ordination of mixed coniferous forests.

Fig. 4.5.18: DCA ordination of mixed coniferous forests.

114

Fig. 4.5.19: CCA ordination of mixed coniferous forests.

Fig. 4.5.20: CCA ordination of mixed coniferous forests.

115

The DCA ordination of species and environmental variables and the results

obtained from CCA ordination showed that different species were clustered

along different environmental variables. Themeda anathera and Pteracanthus

urticifolius were positively correlated with high temperature, Dryopteris serrate

dentata was positively correlated with soil organic matter. Androsace

rotundifolia and Paeonia emodi were positively correlated with slope angle.

Ranunculus palmatifidus was positively correlated with phosphorus. Lindelofia

stylosa and Valeriana himalayana were positively correlated with high altitude

and density altitude. Rheum austral and Delphinum vestitum were positively

correlated with slope aspect. Geranium wallichianum was positively correlated

with atmospheric humidity. These results showed that a specific

environmental variable has a great impact on species distribution in western

mixed coniferous forests of Nandiar Khuwar catchment (fig.4.5.20).

4.5.5 Pure Abies pindrow and Picea smithiana Forests

In pure Abies pindrow and Picea smithiana forests of Nandiar Khuwar

catchment a total of 111 species were recorded from 9 stands between

altitudinal zones of 2200-3000m above mean sea level. Three distinct plant

communities were recognized by TWINSPAN. In unimodal method DCA

ordination with supplementary variables the maximum gradient length (2.53)

was recorded for axis 1 with eigenvalue 0.50. In CCA ordination the

maximum eigenvalue was recorded for axis 1 (0.50). In CCA ordination the

maximum strength was recorded for the environmental variables like pH, soil

saturation, organic matter, heat index, wet bulb, dew point, wind speed and

humidity. In over all Pure Abies pindrow and Picea smithiana forests of Nandiar

Khuwar catchment the biological spectrum was dominated by therophytes

with 34 species followed by geophytes contributing 23 species. The leaf size

spectra were dominated by microphyll with 51 species followed by mesophyll

contributing 37 species. The maximum index of similarity (25.27) was

recorded between Abies pindrow, Quercus dilatata, Picea smithiana community

116

and Abies pindrow, Picea smithiana, Paeonia emodi community. The maximum

index of dissimilarity (79.67) was recorded between Picea smithiana, Abies

pindrow, Wikstroemia canescens community and Abies pindrow, Picea smithiana,

Paeonia emodi community (table 4.5.15).

4.5.5.1 TWINSPAN Classification of Pure Abies and Picea Forests

The data obtained from 9 stands in the vegetational zones of pure Abies

pindrow and Picea smithiana forests of Nandiar Khuwar catchment were

classified by TWINSPAN. Three distinct plant communities were recognized

by TWINSPAN. These communities are described below.

4.5.5.1.1 Picea, Abies, Wikstroemia Community

Picea smithiana, Abies pindrow, Wikstroemia canescens community was recorded

in four stands Trapa, Chail Kambar, Gabrai kandao and Chailsar between an

elevation of 2200-3000m. In this community a total of 77 species were

recorded among which the biological spectrum was dominated by geophytes

and therophytes, each group contributing 19 species. On the basis of

biological spectrum the maximum IVI value was contributed by geophytes

(25.73) (table-4.5.13). Among leaf size spectra microphyll was dominated

with 33 species contributing 35.90 IVI value followed by mesophyll with 25

species (table-4.5.14). This community receives a good amount of rainfall and

snowfall. The vegetation is disturbed due to land sliding, overgrazing and

deforestation.

4.5.5.1.2 Abies, Quercus, Picea Community

Abies pindrow, Quercus dilatata, Picea smithiana community was recorded

between an elevation of 2300-2700m in three stands Lunda matra, Baleja and

Chaprai. In this community a total of 46 species were recorded. Biological

spectrum was dominated by hemicryptophytes with 11 species followed by

geophytes with 10 species contributing 21.52 IVI value (table-4.5.13). Leaf size

117

spectra were dominated by mesophyll and microphyll each contributing 20

species. On the basis of leaf size spectra the maximum IVI value were

contributed by mesophyll (40.46) (table-4.5.14). This community is affected by

overgrazing.

4.5.5.1.3 Abies, Picea, Paeonia Community

Abies pindrow, Picea smithiana, Paeonia emodi community was recorded in two

stands Birthmaidan and Doda II between an elevation of 2700-2900m. In this

community 45 species were recorded. The biological spectrum was

dominated by therophytes with 14 species contributing 24.45 IVI value (table-

4.5.13). The leaf size spectra were dominated by microphyll with 20 species

followed by mesophyll with 15 species contributing maximum (33.09) IVI

value (table-4.5.14). Overgrazing and deforestation are common in this

community.

Fig. 4.5.21: TWINSPAN classification of pure Abies and Picea forests.

118

Table- 4.5.13 The IVI of Biological spectrum of pure Abies and Picea forests.

Picea, Abies, Wikstroemia

Abies, Quercus, Picea

Abies, Picea, Paeonia

Megaphanerophytes 13.94 14.05 19.20

Mesophanerophytes 2.41 15.28 0.00

Microphanerophytes 4.73 6.62 5.72

Nanophanerophytes 8.69 1.73 3.61

Chamaephytes 9.61 4.46 8.86

Hemicryptophytes 15.95 20.6 14.55

Geophytes 25.73 21.52 23.63

Therophytes 18.96 15.73 24.45

Table- 4.5.14 The IVI of leaf size spectra of pure Abies and Picea forests.

Picea, Abies, Wikstroemia

Abies, Quercus, Picea

Abies, Picea, Paeonia

Macrophyll 6.45 9.61 7.07

Mesophyll 29.91 40.46 33.09

Microphyll 35.9 34.59 32.63

Nanophyll 26.65 15.34 25.84

Leptophyll 1.09 0 0

Aphyllus 0 0 1.38

Table- 4.5.15 Similarity and dissimilarity indices of Abies - Picea forests.

Picea, Abies, Wikstroemia

Abies, Quercus, Picea

Abies, Picea, Paeonia

Picea, Abies, Wikstroemia

20.33 20.49

Abies, Quercus, Picea 79.67

25.27

Abies, Picea, Paeonia 79.51 74.73

119

4.4.5.2 Ordination of Vegetation of Pure Abies and Picea Forests

In Bray-Curtis ordination three axes were selected. The ordination scores

(Distances) were from Chail kambar (0.000) to Lunda matra (0.721) on axes 1.

The regression coefficient for this axis were -5.02, variance in distance from

the first end point were 0.15. Axis 1 extracted 31.54% of original distance

matrix. The ordination scores for axis 2 were from Gabrai kandao (0.000) to

Doda II (0.585). The regression coefficient for this axis was –4.96, variance in

distance from the first end point was0.07. Axis 2 extracted 16.54% of original

distance matrix. The ordination scores for axis 3 were from Baleja (0.000) to

Birth maidan (0.532). The regression coefficient for this axis was -3.25,

variance in distance from the first end point was0.05. Axis 3 extracted 12.31%

of original distance matrix (fig.4.5.22).

In unimodal method DCA ordination with supplementary variables the

maximum gradient length (2.53) was recorded for axis 1 with eigenvalue 0.50.

The gradient length for axis 2 was 2.40 with eigenvalue 0. 30. Total variance

("inertia") in the species data were 1.93, supplementary variables account for

100%. The DCA clearly indicates that the whole data set is dominated by a

single dominant gradient.

In DCA ordination different species of pure Abies pindrow and Picea smithiana

forests of Nandiar Khuwar catchment were clustered in ordination space.

The maximum correlation was recorded for Skimmia laureola, Androsace

hazarica and Pseudomertensia sp. The strong association was also recorded for

Prunus padus, Primula denticulata, Aesculus indica, Aquilegia pubiflora and

Polygonatum verticillatum. Similarly positive correlation was recorded for Abies

pindrow, Picea smithiana, Paeonia emodi, Viburnum cotinifolium and Rumex

nepalensis (fig.4.5.23).

The above species were softly correlated with Bistorta emodi, Trillidium

govanianum, Quercus semicarpifolia, Quercus dilatata and Geranium wallichianum.

Similarly all the above species were negatively correlated with Alotis stoliczkai,

120

Delphinum vestitum, Geum roylei, Inula acuminata, Inula royleana, Juniperus

communis and Pleurospermum brunonis. The species of similar stands were

clustered in the ordination space almost same as were classified by

TWINSPAN ordination.

In CCA ordination in the pure Abies pindrow and Picea smithiana forests the

maximum eigenvalue was recorded for axis 1 (0.50) followed by axis 2 (0.33)

and axis 3 (0.30). The percentage variance explained for axis 1, 2 and 3

was26.04%, 43.42% and 59.14% respectively. The total variance (inertia) in the

species data was1.93, explanatory variables account for 100%. Pseudo-

canonical correlation for all axis was 1.00. The permutation test results for all

axes were pseudo-F<0.1, P=1.

In CCA ordination the maximum strength was recorded for the

environmental variables pH, soil saturation, organic matter, heat index, wet

bulb, dew point, wind speed and humidity. The minimum strength was

recorded for phosphorus. In CCA ordination the different stands of Picea,

Abies, Wikstroemia community were positively correlated density altitude,

wind speed and dew point. The stands of Abies, Quercus, Picea community

were positively correlated with organic matter, temperature and barometric

pressure. The stands of Abies, Picea, Paeonia community were strongly

correlated with soil saturation and pH value (fig.4.5.24).

The DCA ordination of species and environmental variables and the results

obtained from CCA ordination showed that different species were clustered

along different environmental variables. Alotis stoliczkai, Delphinum vestitum,

Geum roylei, Inula acuminata, Inula royleana, Juniperus communis and

Pleurospermum brunonis were positively correlated with high wind speed,

density altitude and electrical conductivity. Tanacetum dolicophyllum and

Veronica laxa were positively correlated with slope aspect and humidity.

121

Fig. 4.5.22: Bray-Curtis ordination of pure Abies and Picea forests.

Fig. 4.5.23: DCA ordination of pure Abies and Picea forests.

122

Fig. 4.5.24: CCA ordination of pure Abies and Picea forests.

Fig. 4.5.25: CCA ordination of pure Abies and Picea forests.

123

Senicio species and Artemisia roxburghiana were positively correlated with

altitude and pH. Skimmia laureola and Androsace hazarica were positively

correlated with soil saturation. Abies pindrow, Picea smithiana, Paeonia emodi

and Primula denticulata were positively correlated with Potassium and soil

organic matter. Quercus semicarpifolia and Quercus dilatata were positively

correlated with barometric pressure and temperature. These results showed

that a specific environmental variable has a great impact on species

distribution in pure Abies pindrow and Picea smithiana forests of Nandiar

Khuwar catchment (fig.4.5.25).

4.5.6 Phytosociology in Alpine Vegetational Zone

The alpine pasture stretches above the tree limits in the Nandiar Khuwar

catchment between elevations of 2850 – 3800m above mean sea level. In this

zone five stands were selected and were analyzed by TWINSPAN

classification and two plant communities were identified. In Bray-Curtis

ordination maximum ordination scores were recorded on axes 1 from

Malkaisar (0.000) to Karganja (0.574). In unimodal method DCA ordination

with supplementary variables the maximum gradient length (1.67) was

recorded for axis 1 with eigenvalue 0.31. In CCA ordination in alpine pasture

the maximum eigenvalue was recorded for axis 1 (0.31). The biological spectra

of alpine pasture of Nandiar Khuwar catchment were dominated by

hemicryptophytes and therophytes each contributing 8 species indicating

disturbed vegetation due to overgrazing. The leaf size spectra were

dominated by microphyll contributing 12 species indicating moist

environmental conditions. The index of similarity between Wikstroemia,

Viburnum, Androsace community and Juniperus, Sibbaldia, Primula community

was 35.7%.

124

4.5.6.1 TWINSPAN Classification of Alpine Vegetational Zone

The data obtained from 5 stands in the alpine pasture of Nandiar Khuwar

catchment were classified by TWINSPAN. Two distinct plant communities

were recognized by TWINSPAN. These communities are described below.

4.5.6.1.1 Wikstroemia, Viburnum, Androsace Community

Wikstroemia canescens, Viburnum cotinifolium, Androsace community was

recorded from two stands Karganja and Shaheed Gali between altitudinal

zones of 2850–3100m. In this community 26 species were recorded. Biological

spectrum was dominated by geophytes with 6 species contributing 20.03 IVI

value (table-4.5.16). Leaf size spectra were dominated by microphyll with 10

species contributing 38.7 IVI value followed by mesophyll with 8 species

(table-4.5.17). The vegetation of this community is disturbed due to

overgrazing, however environmental conditions are moist and having low

temperature.

Fig. 4.5.26: TWINSPAN classification of alpine zone.

125

Table- 4.5.16 The IVI contribution of various classes of biological spectrum in different plant communities

Wikstroemia, Viburnum, Androsace community

Juniperus, Sibbaldia, Primula community

Individuals IVI Individuals IVI

Megaphanerophytes 01 6.15 00 00

Microphanerophytes 02 10.77 02 7.75

Nanophanerophytes 03 19.96 05 24.93

Chamaephytes 05 10.74 05 17.76

Hemicryptophytes 05 15.76 07 24.63

Geophytes 06 20.03 03 9.76

Therophytes 04 16.60 08 15.17

Table- 4.5.17 The IVI contribution of various classes of leaf size spectra in different plant communities

Wikstroemia, Viburnum, Androsace community

Juniperus, Sibbaldia, Primula community

Individuals IVI Individuals IVI

Macrophyll 01 2.14 00 00

Mesophyll 08 36.57 10 37.13

Microphyll 10 36.68 09 25.34

Nanophyll 07 22.63 10 28.64

Leptophyll 00 00 01 8.90

126

4.5.6.1.2 Juniperus, Sibbaldia, Primula Community

Juniperus communis, Sibbaldia cuneata, Primula denticulata community was

recorded from Kar Ganja top, Alishera and Malkaisar between an altitudinal

zones of 3250–3800m. In this community a total of 30 plant species were

recorded among which biological spectrum was dominated by therophytes

with 8 species while maximum IVI value (24.93) were contributed by

nanophanerophytes (table-4.5.16). The leaf size spectra were dominated by

mesophyll and nanophyll each contributing 10 species followed by

microphyll (9) and leptophyll (1). On the basis of leaf size spectra the

maximum IVI value (37.12) was contributed by mesophyll (table-4.5.17). The

environmental conditions are harsh in this community.

4.5.6.2 Ordination of Alpine Vegetational Zone

In Bray-Curtis ordination 5 stands and 37 species were analyzed. The

ordination scores were from Malkaisar (0.000) to Karganja (0.574) on axes 1.

The regression coefficient for this axis was -3.39, variance in distance from the

first end point was 0.15. Axis 1 extracted 55.99% of original distance matrix.

The ordination scores for axis 2 were from Alishera (0.000) to Shaheed Gali

(0.399) (fig.4.5.27). The regression coefficient for this axis was –2.98, variance

in distance from the first end point was 0.07. Axis 2 extracted 25.81% of

original distance matrix. The ordination scores (Distances) for axis 3 were

from Malkaisar (0.000) to Kar Ganja top (0.328). The regression coefficient for

this axis was -5.10, variance in distance from the first end point was 0.04. Axis

3 extracted 16.22% of original distance matrix.

Response data are compositional and have a gradient 1.7 SD units long, so a

linear method was also used. In linear method PCA with supplementary

variables showed that the total variation was 90.51. The maximum eigenvalue

was recorded for axis 1 with 0.54. The Eigenvalues for axis 2 and axis 3 were

0.23 and 0.19, respectively. The explained variation for Axis 1, 2 and 3 were

54.86, 78.82 and 98.38 respectively.

127

Fig. 4.5.27: Bray-Curtis ordination of alpine zone.

Fig. 4.5.28: DCA ordination of alpine zone.

128

In unimodal method DCA ordination with supplementary variables the

maximum gradient length (1.67) was recorded for axis 1 with eigenvalue 0.31.

The gradient length for axis 2 was 0.44 with eigenvalue 0.008. Total variance

("inertia") in the species data was 0.63, supplementary variables account for

100%. The DCA clearly indicates that the whole data set is dominated by a

single dominant gradient.

In DCA ordination different species of alpine pasture of Nandiar Khuwar

catchment were clustered in ordination space. The maximum correlation was

recorded for Sibbaldia cuneata, Potentilla nepalensis, Juniperus communis,

Valeriana himalayana and Primula denticulata. The correlation was also positive

for Thymus linearis, Aster himalaicus and Betula utilis. All the above species

were weakly correlated with Pseudomertensia, Caltha alba, Trillium govanianum,

Dryopteris jaxtapostia and Pteridium equilinum, while negatively correlated with

Thlaspi spp. and Skimmia laureola.

In CCA ordination in alpine pasture the maximum eigenvalue was recorded

for axis 1 (0.31) followed by axis 2 (0.17) and axis 3 (0.13). The percentage

variance explained for axis 1, 2 and 3 was 49.19%, 76.84% and 98.76%

respectively. The total variance (inertia) in the species data was 0.63,

explanatory variables account for 100%. Pseudo-canonical correlation for all

axis was 1.00. The permutation test results for all axes was pseudo-F<0.1, P=1.

In CCA ordination the maximum strength was recorded for all environmental

variables except slope aspect and slope angle. The stands of Juniperus,

Sibbaldia, Primula community were positively correlated with all

environmental variables except electrical conductivity, slope angle,

temperature and barometric pressure. Similarly one stand of Wikstroemia,

Viburnum, Androsace community was positively correlated with electrical

conductivity, slope angle temperature and barometric pressure while other

stands were negatively correlated with all environmental variables (fig.

4.5.30).

129

Fig. 4.5.29: CCA ordination of alpine zone.

Fig. 4.5.30: CCA ordination of alpine zone.

130

The DCA ordination of species and environmental variables and the results

obtained from CCA ordination showed that different species were clustered

along different environmental variables. Juniperus communis, Sibbaldia cuneata,

Potentilla nepalensis, Buplerum longicaule, Thymus linearis, Aster himalaicus were

positively correlated with different environmental variables like wet bulb,

dew point, density altitude, heat index, altitude, wind speed, pH,

phosphorus, organic matter, soil saturation and potassium. Bistorta emodi and

Adiantum incisum were more or less negatively correlated with all

environmental variables. Abies pindrow, Dryopteris jaxtapostia and Pteridium

equilinum were positively correlated with slope angle and temperature. These

results showed that a specific environmental variable has a great impact on

species distribution in pure Abies pindrow and Picea smithiana forests of

Nandiar Khuwar catchment area (fig.4.5.29).

4.6 Ordination of Samples on the Basis of Microclimatic Data

Microclimate is a local atmospheric zone where climate differs from the

surrounding area and it may be small as a few square feet or as large as many

square miles. The contributing factors to microclimate are the slope or aspect

of an area. The altitude, latitude and longitude were responsible for change in

microclimate. The microclimate has a great impact on vegetation of the study

area. The microclimate of Nandiar Khuwar catchment varies from sub

tropical to alpine zone. The vegetation was denser on north-facing slopes as

compared to south-facing slopes. Different microclimatic parameters like

temperature, wind speed, humidity, heat index, dew point, wet bulb,

barometric pressure, altitude and density altitude have great impact on local

vegetation. The ordinations of stands on the basis of microclimatic parameters

are described below.

4.6.1 Temperature

The air temperature of Nandiar Khuwar catchment is directly correlated with

altitude. The maximum temperature value was 33.9ºC recorded at Thakot

131

while the minimum temperature value was 15.5ºC recorded at Malkaisar. The

maximum temperature value represents the scrub forests at lower altitudes

while the minimum temperature value represents the alpine scrub at higher

altitudes. The correlation of temperature with axis 1 was 0.960, the regression

value was 0.276 and the total standard deviation of the response data was

0.878. The correlation of temperature with axis 2 was -0.198, the regression

value was -0.148 and the total standard deviation of the response data was -

0.156. The correlation of temperature with stands in species space is presented

in figure 4.6.1.

4.6.2 Wind Speed

In Nandiar Khuwar Catchment the average wind speed in different stands

ranged from 0.4m/s in Batangi, Paimal III, Paimal V, Chapar and Doda I to

1.9m/s at Malkaisar. The correlation of wind speed with axis 1 was -0.515, the

regression value was -0.054 and the total standard deviation of the response

data was -0.328. The correlation of wind speed with axis 2 was -0.518, the

regression value was -0.097 and the total standard deviation of the response

data was-0.173. The wind speed has no such great effect on the distribution of

species in different stands. The correlation of wind speed with stands in

species space is presented in figure 4.6.2.

4.6.3 Humidity

The humidity in Nandiar Khuwar catchment ranges from 18.1% measured in

Peshora to 62% in Bach upper. The correlation of humidity with axis 1 was -

0.605, the regression value was 0.141 and the total standard deviation of the

response data was -0.474. The correlation of humidity with axis 2 was -0.347,

the regression value was -0.146 and the total standard deviation of the

response data was 0.255. The correlation of atmospheric humidity with stands

in species space is presented in figure 4.6.3.

132

Fig. 4.6.1: The correlation of temperature with stands in species space.

Fig. 4.6.2: The correlation of wind speed with stands in species space.

133

4.6.4 Heat Index

Heat index is a practical measure of how hot the current combination of

relative humidity and temperature feel to human body. Higher relative

humidity makes it seem hotter because the body’s ability to cool itself by

evaporating perspiration is reduced. The heat index values in the Nandiar

Khuwar catchment ranges from 10.2 in Birth maidan to 38.6 in Khairabad. The

correlation of heat index with axis 1 was 0.499, the regression value was -0.022

and the total standard deviation of the response data was 0.376. The

correlation of heat index with axis 2 was -0.277, the regression value was-

0.283 and the total standard deviation of the response data was -0.125. The

correlation of heat index with stands in species space is presented in figure

4.6.4.

4.6.5 Dew Point

The dew point values in the Nandiar Khuwar catchment ranges from 2.6 in

Manra to 20.5 in Naraza. The correlation of dew point with axis 1 was -0.004,

the regression value was-0.057 and the total standard deviation of the

response data was -0.033. The correlation of temperature with axis 2 was

0.119, the regression value was 0.150 and the total standard deviation of the

response data was 0.134. The dew point temperature of the study area is

comfortable for most of the stands. The adaptation of different plant species

with dew point temperature and relative humidity indicates its ecological

amplitudes. The correlation of dew point with stands in species space is

presented in figure 4.6.5.

4.6.6 Wet Bulb

In the Nandiar Khuwar catchment the wet bulb data ranges from 6.7°C in

Birth Maidan to 23.6°C in Naraza. The wet-bulb temperature was suitable for

all stands of the study area as it is below 35°C. The correlation of wet bulb

with axis 1 was 0.329, the regression value was 0.029 and the total standard

deviation of the response data was -0.206.

134

Fig. 4.6.3: The correlation of Humidity with stands in species space.

Fig. 4.6.4: The correlation of Heat Index with stands in species space.

135

The correlation of temperature with axis 2 was -0.076, the regression value

was 0.080 and the total standard deviation of the response data was -0.005.

The correlation of wet bulb with stands in species space is presented in figure

4.6.6.

4.6.7 Barometric Pressure

The speed at which plants grow is affected by atmospheric pressure

conditions and at 101 kPa pressure each plant will grow at its ideal rate. At

too low atmospheric pressure, a plant cannot survive due to the lack of gas

exchange that can take place. The barometric pressure in different stands of

Nandiar Khuwar catchment ranged from 640.8 at Malkaisar 950.3 at Thakot II.

The barometric pressure has direct effect on the distribution of species. At

high barometric pressure the subtropical scrub forests occurred while at low

barometric pressure the alpine scrub occurred. The correlation of barometric

pressure with axis 1 was 0.967, the regression value was 0.537 and the total

standard deviation of the response data was 0.902. The correlation of

temperature with axis 2 was -0.188, the regression value was -6.573 and the

total standard deviation of the response data was -0.173. The correlation of

barometric pressure with stands in species space is presented in figure 4.6.7.

4.6.8 Altitude

In Nandiar Khuwar Catchment the altitude of different stands ranges from

530m in Thakot 1 to 3780m at Malkaisar. At low altitude the subtropical scrub

forests were identified while at higher altitude the alpine scrub forests were

recognized. The altitude has direct effect on the distribution of different

species of the study area. The correlation of altitude with axis 1 was -0.974, the

regression value was -0.114 and the total standard deviation of the response

data was -0.901.

136

Fig. 4.6.5: The correlation of Dew Point with stands in species space.

Fig. 4.6.6: The correlation of Wet Bulb with stands in species space.

137

The correlation of altitude with axis 2 was 0.137, the regression value was -

6.444 and the total standard deviation of the response data was 0.173. The

correlation of altitude with stands in species space is presented in figure 4.6.8.

4.6.9 Density Altitude

In Nandiar Khuwar catchment density altitude ranged from 1301 in Thakot 1

to 4712 at Malkaisar. In both extremes the scrub forests were recognized. Al

low density altitude the subtropical scrub forests were recognized while at

high density altitude the alpine scrub was identified. The correlation of

density altitude with axis 1 was -0.954, the regression value was0.136 and the

total standard deviation of the response data was -0.825. The correlation of

density altitude with axis 2 was 0.104, the regression value was 0.040 and the

total standard deviation of the response data was 0.122. The correlation of

density altitude with stands in species space is presented in figure 4.6.9.

Fig. 4.6.7: The correlation of Barometric Pressure with stands in species

space.

138

Fig. 4.6.8: The correlation of Altitude with stands in species space.

Fig. 4.6.9: The correlation of Density Altitude with stands in species space.

139

4.7 Ordination of Samples on the Basis of Edaphic Factors

4.7.1 Soil

Soil is the superficial weathered layer of the earth crust with which are mixed

living organisms and products of their decay. Plants obtain nutrients from the

soil. The soil is formed from the parent material. Edaphic factors play an

important role in the local difference of plant communities in Nandiar

Khuwar catchment District Battagram. The top soil is constantly being

washed away by run off from higher slopes. Gravels, stones, rocks and

boulders are common in the study area. The soil analysis showed that the soil

of Nandiar Khuwar catchment is derived from the underlined metamorphic

and plutonic igneous rocks which are in turn intruded by pegmatite, aplites

and quartz veins. Quaternary alluvium and glacial deposits are common. Low

grade metamorphic rocks like graphite schist, re-crystalline lime stone,

amphibole schist, quartz-mica schist and green schist are exposed in the area.

Granite, ultra mafic and massive amphibolites, limestone and sandstones

cover large area. The soil under fir and spruce is deep and quite rich in

humus, where as it is shallow and poor under pines and scrub zones.

4.7.2 Soil Profile

The soil profile also affects the vegetation of the area along with climatic

conditions. The vertical section of the soil showed a definite zonation in

different plant communities. In general soil profile consists of three distinct

horizons i.e. A, B, C and an unaltered zone of parent material known as D

horizon. The size thickness and number of sub horizons are different in

different plant communities.

4.7.3 Soil Texture

The relative proportion of soil particles indirectly affects the plant

communities by bringing about variations in the soil water and soil air. In the

study area the majority of soil particles consist of silica and silicates beside

other particles. Gravels, coarse sand, fine sand silt and clay particles were

140

recognized in different plant communities. Different soil texture classes were

recognized in different communities.

4.7.4 Soil Parameters

In Nandiar Khuwar catchment soil is the mixture of minerals, organic matter,

gases, liquids and a myriad of micro- and macro- organisms that support

plant life. In the study area soil performs four important functions: as a

medium for plant growth, water storage, as a modifier of the atmosphere and

as a habitat for organisms that take part in decomposition and creation of a

habitat for other organisms. Soil is the end product of the influence of the

climate, relief (elevation, orientation, and slope of terrain), biotic activities

(organisms), and parent materials (original minerals) acting over periods of

time.

For optimum plant growth, the generalized content of soil components by

volume should be roughly 50% solids (45% mineral and 5% organic matter),

and 50% voids of which half is occupied by water and half by gas. The

greatest influence on plant nutrition is soil pH, which is a measure of the

hydrogen ion (acid-forming) soil reactivity, and is in turn a function of the soil

materials, precipitation level, and plant root behavior. Soil pH strongly affects

the availability of nutrients. The organic material of the soil has a powerful

effect on its development, fertility, and available moisture. Following water

and soil colloids, organic material is next in importance to soil's formation and

fertility. The topography is characterized by the inclination (slope), elevation,

and orientation of the terrain. Topography determines the rate of

precipitation or runoff and rate of formation or erosion of the surface soil

profile. The topographical setting may either hasten or retard the work of

climatic forces. Steep slopes generally encourage rapid soil loss by erosion

and allow less rainfall to enter the soil before running off. Different soil

parameters were analyzed for each stands.

141

4.7.5 Soil Saturation

Soil saturation is influenced by soil texture and soil structure. The presence of

water, land slopes, impervious subsurface layers, and compacted soil surface

can also affect drainage. Generally soils have 10–30% of the volume composed

of air–filled spaces but the percentage decreases as water content increases.

Excess soil moisture can actually interfere with water uptake by oxygen-

deprived roots. The result ranges from increased stress and reduced growth

to injury, to death of trees or other plants. The soil saturation data recorded

from different stands of Nandiar Khuwar catchment range from 38% in Anora

II and Anora III to 69% in Riar. The correlation of soil saturation with axis 1

was -0.550, the regression value was -0.079 and the total standard deviation of

the response data was -0.369. The correlation of soil saturation with axis 2 was

0.139, the regression value was 0.013 and the total standard deviation of the

response data was 0.149. The correlation of soil saturation with stands in

species space is presented in figure 4.7.1.

4.7.6 Soil Electrical Conductivity

Soil electrical conductivity is an indirect measurement that correlates very

well with several soil physical and chemical properties. Electrical conductivity

is the ability of a material to conduct (transmit) an electrical current and it is

commonly expressed in units of Siemens per meter (S/m). Sands have low

conductivity and clays have high conductivity; soil electrical conductivity

correlates very strongly with particle size and soil texture. Soils prone to

drought or excessive water will show variations in soil texture that can be

delineated using soil electrical conductivity. The soil electrical conductivity

data recorded from different stands of Nandiar Khuwar catchment ranges

from 0.38dS/m in Deshara to 0.80 dS/m in Kar Ganja, Chail and Mirani 1. The

correlation of electrical conductivity with axis 1 was -0.453, the regression value was

0.001 and the total standard deviation of the response data was -0.319. The

correlation of electrical conductivity with axis 2 was -0.322, the regression value was

-0.061 and the total standard deviation of the response data was -0.161. The

142

correlation of soil electrical conductivity with stands in species space is

presented in figure 4.7.2.

4.7.7 Soil pH

The soil pH is a measure of the acidity or basicity in soils and is defined as the

negative logarithm of the activity of hydronium ions in a solution. In water, it

normally ranges from 1 to 14, with 7 being neutral. A pH below 7 is acidic and

above 7 is basic. Soil pH is considered a master variable in soils as it controls

many chemical processes that take place. It specifically affects plant nutrient

availability by controlling the chemical forms of the nutrient. The optimum

pH range for most plants is between 5.5 and 7.0. The pH of soil data recorded

from different stands of Nandiar Khuwar catchment ranges from 5.15 in

Batangi to 7.72 in Chapra and Anora II. The correlation of pH with axis 1 was -

0.211, the regression value was 0.072 and the total standard deviation of the response

data was -0.265. The correlation of pH with axis 2 was -0.054, the regression value

was 0.084 and the total standard deviation of the response data was 0.123. The

correlation of soil pH with stands in species space is presented in figure 4.7.3.

4.7.8 Soil Organic Matter

The organic matter component of soil consists of plant and animal residues at

various stages of decomposition, cells and tissues of soil organisms and

substances synthesized by soil organisms. The presence of soil organic matter

exerts positive effects on soil physical and chemical properties, as well as the

soil’s capacity to provide regulatory ecosystem services. Soil organic matter

acts the major sink and source of soil carbon and generally ranges from 1 to

6% of the total topsoil mass for most upland soils. Soils whose upper horizons

consist of less than 1% organic matter are mostly limited to desert areas. Soils

containing 12-18% organic matter are generally classified as organic soils

(Senesi, et al., 2006).

143

Fig. 4.7.1: The correlation of soil saturation with stands in species space.

Fig. 4.7.2: The correlation of electrical conductivity with stands in species space.

144

Fig. 4.7.3: The correlation of soil pH with stands in species space.

Fig. 4.7.4: The correlation of Organic Matter with stands in species space.

In Nandiar Khuwar catchment the soil organic matter concentration ranges

from 0.73% in Hill and Jatial to 1.92% in Chapar. Out of 80 stands 23 stands

145

has less than 1% of organic matter showing weak soil. 67 stands have the

organic matter concentration more than 1% showing suitable conditions for

most of the species. Due to the difference in the organic matter concentration

the species composition of different stands was different in the study area. The

correlation of organic matter with axis 1 was -0.018, the regression value was 0.080

and the total standard deviation of the response data was -0.045. The correlation of

organic matter with axis 2 was 0.291, the regression value was 0.121 and the total

standard deviation of the response data was 0.187. The correlation of soil organic

matter with stands in species space is presented in figure 4.7.4.

4.7.9 Phosphorous

The soil phosphorous (P) concentration ranges from 1.8mg/kg in Lunda

matra and Chorlangai to 19.50mg/kg in Thakot I. In 30 stands the

phosphorous concentrations were 7.1-14mg/kg indicating suitable soil for

most of the plant species. In 10 stands the soil phosphorous concentration was

more than 14mg/kg indicating that some plant species will grow actively

while others will not. Variation in the concentration of phosphorous in the

soil of various stands results in the difference of species composition. The

correlation of phosphorous with axis 1 was -0.688, the regression value was -

0.086 and the total standard deviation of the response data was -0.493. The

correlation of phosphorous with axis 2 was -0.476, the regression value was -

0.287 and the total standard deviation of the response data was -0.148. The

correlation of soil phosphorous with stands in species space is presented in

figure 4.7.5.

4.7.10 Potassium

The soil potassium (K) concentration ranges from 100mg/kg in Lundai 1 to

400mg/kg in Shagai. In 30 stands the potassium concentration was less than

200mg/kg showing that the soil was suitable for most of the species. In 50

stands the potassium concentration was more than 200mg/kg showing that

the specialized plant species will grow actively. The varied concentration of

146

potassium in the soil of various stands will result in the difference of species

composition of different stands of Nandiar Khuwar catchment. The

correlation of potassium with axis 1 was 0.063, the regression value was -0.047

and the total standard deviation of the response data was 0.006. The

correlation of potassium with axis 2 was -0.174, the regression value was -

0.065 and the total standard deviation of the response data was -0.082. The

correlation of soil potassium with stands in species space is presented in

figure 4.7.6.

4.7.11 Slope Aspect

Slope aspect has direct impact on the diversity and species richness. The

northern aspects were denser as compared to southern aspect. In the study

area 29 stands were on north-facing slopes, 11 stands were on south facing

slopes, 8 stands were on east facing slope, 7 on west facing slope, 2 on north-

east facing slope, 9 on north-west facing slopes, 6 on south-east facing slope

and 8 stands were on south-west facing slope. The correlation of slope aspect

with axis 1 was -0.173, the regression value was -0.049 and the total standard

deviation of the response data was -0.129. The correlation of slope aspect with

axis 2 was -0.045, the regression value was 0.030 and the total standard

deviation of the response data was 0.008. The correlation of slope aspect with

stands in species space is presented in figure 4.7.7.

4.7.12 Slope Angle

Slope angle has also a great effect on the diversity and species richness. The

slope angle of Nandiar Khuwar catchment varies in different stands as well as

in the same stand. However the average slope angles were from 20° to 55°. At

gradual slope the over grazing activity were more as compared to steep slope

of the study area.

The correlation of slope angle with axis 1 was -0.390, the regression value was

-0.126 and the total standard deviation of the response data was -0.314. The

147

correlation of slope angle with axis 2 was -0.198, the regression value was -

0.124 and the total standard deviation of the response data was -0.131. The

correlation of slope angle with stands in species space is presented in figure

4.7.8.

4.8 Dominance Diversity Curves

Dominance diversity curves are used to study the distribution of abundance

among species in a sample. Species-abundance patterns within trophic levels,

taxonomic groups and whole communities provide clues to the nature of the

niche relationships in groups of species that are closely associated ecologically

in the same macro-habitat. In the dominance diversity curves of Nandiar

Khuwar catchment the rank abundance of species were dominated by Pinus

wallichiana, followed by Quercus incana, Indigofera heterantha, Berberis lyceum

and Pinus roxburghii while Sageretia thea and Populus ciliata were at the base of

dominance abundance curves (Fig. 4.8.1).

In dominance diversity curves the maximum frequency value was recorded

for Fragaria nubicola (53) and Adiantum capillus-veneris (53), followed by Pinus

wallichiana (50), Viola canescens (49), Berberis lyceum, Cynodon dactylon,

Dryopteris jaxtapostia, Indigofera heterantha, and Viburnum cotinifolium. The

minimum frequencies (1) were recorded for many species as shown in fig.

4.8.2.

148

Fig. 4.7.5:The correlation of Phosphorous with stands in species space.

Fig. 4.7.6:The correlation of Potassium with stands in species space.

149

Fig. 4.7.7: The correlation of slope aspect with stands in species space.

Fig. 4.7.8: The correlation of slope angle with stands in species space.

150

Fig. 4.8.1: Abundance diversity curves of species of Nandiar Khuwar

catchment.

Fig. 4.8.2: Frequency and Rank abundance of species of Nandiar Khuwar

catchment.

Ind het

Pin w al

Pop cil

Que inc

Sag the

Rank Abun

Lo

g S

um

Adi cap

Ber lyc

Ber cil

Fra nubPin w al

Pop cil

Pte urt

Que inc

Sag the

Vio can

Rank Abun

Fre

q

151

4.9 Medicinal Flora of the Study Area

An ethno medicinal survey was carried out to collect information regarding

the various traditional uses, especially the medicinal plant uses in Nandiar

Khuwar catchment. A total of 157 plant species were reported as locally used

for various medicinal purposes. Majority of the recipes are prepared in the

form of decoction from freshly collected plant parts. Mostly a single species

was used and mainly taken orally. All of these plants are collected from the

wild, 12 of which are reported as scarce locally. The people of study area use

medicinal plants for asthma, cough, tonic, abdominal pain, expectorant,

anthelmintic, carminative, on boils, snakebites, jaundice, diarrhea and

dysentery etc. Among 157 medicinal plants 22 were used for curing livestock.

The detail traditional uses of medicinal plants of Nandiar Khuwar catchment

are given below:

Table- 4.9 Medicinal Plants of Nandiar Khuwar Catchment area.

S. No

Botanical name Local name Local uses

1. Abies pindrow Royle Achal Decoction of the dried shoots and fresh leaves is used in cough, asthma and other chest infection.

2. Acacia modesta Wall. Palosa Gum is used for backache and weakness. It is also given to pregnant and lactating women as tonic.

3. Achillea millefolium L. Qarqara / Dambrai

The whole plant is used as stimulant, tonic, diaphoretic and in fever and cold.

4. Achyranthes aspera L. Geshay Roasted fruits are grinded and are used as expectorants. Juice of leaves and roots are used as anthelmintic.

5. Acorus calamus L. Skhawaja Rhizome is used in dysentery and chronic diarrhea.

6. Adiantum capillus-veneris L.

Babozea Whole plant is used in lowering blood pressure.

7. Adiantum incisum Forssk.

Babozea Fronds are used for skin diseases, for cough and cold.

152

8. Adiantum venustum D. Don

Babozae

The plant is used in combination with other plant species as expectorant, hypothermic, diuretic and in stomachache.

9. Aesculus indica (Wall.ex. Cambess) Hook.f.

Banakor

Powered seeds are traditionally administered to livestock as anthelmintic. Powered seeds are also used for jaundice.

10. Ajuga bracteosa Wall.ex Benth.

Aseelaboti Its decoction is used for curing jaundice, hypertension and very effective in sore throat.

11. Albezia lebbeck (L.) Benth.

Srikh Powdered bark is used in diarrhea and dysentery.

12. Allium filidens Regel. Oogakay

Leaves are bitter in taste and are eaten raw or cooked along with other pot herbs for gastrointestinal disorders especially stomachache.

13. Amaranthus caudatus L.

Chaleray

The decoction of shoots and leaves are used in cough and asthma. The root is boiled with honey and is used as laxative.

14. Amaranthus viridus L. Ganhar It is used as a vegetable and the paste of leaves and roots are applied on boils and scorpion sting.

15. Arisaema flavum (Forssk.) Schott.

Marjarai Fruit is eaten without chewing in cough and cold.

16. Artemisia vulgaris L. Tarkha

Respiratory stimulant, anti mycotic and effective in urinary tract infections. The juice of leaves and inflorescence are used as anthelmintic.

17. Asparagus officinalis Linn.

Tindoray Young shoots are fed to livestock for promoting lactation.

18. Berberis lycium Royle Kwaray

Bark is used as tonic and is effective in nephrological complaints. It is also used as astringent, antiseptic and as bone tonic for healing bone fractures

19. Bergenia ciliata Sternb. Gut panra

The rhizome are crushed and used in stomach and duodenal ulcers. Also used as tonic and in muscular disorders.

20. Betula utilis D. Don. Broj

Bark is used in various recipes and for amulet. Birch bark soaked until moist in water, and then formed into a cast for a broken arm.

153

21. Caesalpinia decapitala (Roth) Alston.

Jara Decoction of young shoots is used as analgesic and antipyretic.

22. Calotropis procera (Wild.) R. Br.

Spalamay Leaves are applied as poultice on dog bitten wounds. Latex is used against ringworm diseases.

23. Caltha alba L. Makan path Flowering shoots is also used as a laxative and diuretic. It is also used for cleaning skin lesions and sores.

24. Cannabis sativa Linn. Bhang Flowering tops are sedative, anodyne and narcotic

25. Cedrella serrata Royle Meem Whole plant is considered as poisonous. The leaf extract is used for curing roundworms.

26. Cedrus deodara (Roxb. ex D.Don) G. Don

Ranzrah The extract of the wood (Ranzrah) is administrated to the livestock as anthelmintic.

27. Cephalanthera longifolia (L.) Fritsch.

The rhizome is considered as promoting lactation in livestock.

28. Celtis australis L. Batkar The fruits are effective in colic and amenorrhea. Decoction from bark is administrated as anti-allergic.

29. Cephalanthera longifolia (L) Fritsch.

The rhizome is considered as promoting lactation in livestock, and is given along Maize flour.

30. Chenopodium album L. Batu It is uses in hepatic disorder and enlarges spleen. Whole plant is used in abdominal pains and as diuretic.

31. Cichorium intybus L. Kasni The roots are washed, boiled and filtrate is kept for whole night in open sky and then used for abdominal pain.

32. Cissampelos pareira L. Gorisum The leaves extract are administrated to livestock for diarrhea treatment.

33. Clematis grata Wall. Chinjanoly The shoots extracts is considered as antimycotic, applied to ring worm and baldness.

34. Clematis montana Buch.

Chinjanoly The decoction of flowers is used in cough.

35. Convolvulus arvensis L. Ellay The roots are dried, powdered and used as purgative.

36. Corydalis stewartii Fedde.

Mamera Floral drops are used for curing eye diseases.

154

37. Cotinus coggyria Scop. Chamyar-lakhta

Leaves are given to livestock against liver fluke.

38. Cotoneaster microphylla Wall. ex Lindl.

Kharawa Fruit are used as expectorant and astringent, also effective in stomachache.

39. Cotoneaster nummalaria Fish.

Mamanra Fruit is edible and are used as astringent.

40. Crataegus songarica C. Koch.

Batsinga Fruits are edible and considered as cardio tonic.

41. Cuscuta gigantea Griff. Akasbail Juice of plant is used as anti poisonous agent.

42. Cynodon dactylon (L.) Pers.

Kabal Fresh leaves are applied on cuts and bleeding wounds, bleeding piles, diuretic, antipyretic and diarrhea.

43. Dalbergia sissoo Roxb. Shawa

Decoction of leaves is bitter, stimulant, used in gonorrhea. Root is astringent. Wood is used as alterative, useful in leprosy, boils and to stop vomiting.

44. Daphne mucronata Royle

Kutilal Seeds and roots are used as anthelmintic.

45. Daphne papyracea Wall.ex G. Don

Jangali Kutilal

The juice of the leaves is used to kill the ecto parasites of livestock.

46. Datura innoxia Mill. Baturai Juice of the leaves is applied to the cutaneous affection of the head. Seeds are employed in fever.

47. Datura stramonium L. Datura Leaves are applied for the softening of the boils. Juice of the flower is used in earache.

48. Debregessia salcifolia (D.Don) Rendle

Ajlai Leaves are antiseptic also used for boils and other swellings.

49. Descurainia sophia (L.) Webb. & Berth.

Khoob kalan

Shoots and seeds are powdered and used for gas trouble and intestinal disorders. The decoction is used as painkiller.

50. Desmodium elegans DC.

Jamkat

Root is carminative, tonic and diuretic. It is used in chronic fever, cough, vomiting, asthma, and in snakebite.

51. Deutzia staminea R. Br .ex Wall.

Boritus The whole plant is used to remove the fleas from houses.

155

52. Dioscorea deltoidea Wall. ex Griseb.

Kanis zela

The powder tuber is mixed with powdered root of Berberis lycium and is used for the treatment of jaundice. The juice is applied in hair to kill lice. Locally whole plant is crushed and used to kill fishes.

53. Diospyros lotus L. Tor amlok

Fruits are carminative, purgative, anti febrile and cause flatulence. Local people boil the fruit in milk and take it for curing of constipation and dysentery.

54. Dodonaea vescosa (L.) Jacq.

Ghwarasky It is used as astringent, anti rheumatic, aromatic, also used in swelling and burns.

55. Dryopteris jaxtapostia Chirst.

Kuanjay The fronds are used as potherb. It is commonly used as vegetable and believed to enhance digestion.

56. Elaeagnus umbellata Thunb.

Ghanamr-anga

Flowers and seeds are stimulant and astringent. Seed oil is used in pulmonary infections.

57. Equisetum arvense L. Bandakay The extract of the whole plant is used in jaundice.

58. Euphorbia indica Lam. Jangali Spalamai

The milky juice is used against ringworm disease.

59. Euphorbia wallichii Hook. f.

Hirvi It is poisonous, highly laxative causes sever diarrhea and dysentery. Used in skin diseases.

60. Ficus carica L. Inzar

Fruit is demulcent. The latex is placed on the spot in which prickle has hidden, the prickle is easily drawn out from the outer skin of the body.

61. Ficus palmata Forssk. Inzar Fruit is laxative and demulcent used in constipation and piles.

62. Ficus racemosa L. Oormal

Leaves infusion is astringent. Root is used in dysentery and in diabetics. Fruits are used as carminative and astringent.

63. Foeniculum vulgare Mill.

Saunf

It is carminative and purgative also used in stomach disorders. The decoction of fruits is given to livestock in fever.

156

64. Fragaria nubicola (Hook.f.) Lindl.

Budhi maiwa

Fruit is carminative. Leaves and fruits are mixed with leaves of Berberis lycium and used in cure of stomach ulcers, also used as antiseptic on the wound externally.

65. Fumaria indica (Husskin) H. N.

Papra

It is used as alterative, diuretic, anthelmintic and also used in diabetes. Decoction is used in constipation.

66. Galium aparine L. Cochna Leaves are used in jaundice.

67. Gentianodes pedicellata (D.Don) Omer.

Nilkant Decoction of root is used for urinary tract infections, also used for stomachic.

68. Geranium wallichianum D.Don

Rattanjot

Powdered root is mixed with sugar and milk and used in backache, gout and is also used in strengthening of the body muscles and bones.

69. Grewia optiva Drum.ex Burret.

Pastawonay

Infusion of the bark is used as astringent. The leaves are given to livestock for increase in milk production.

70. Gymnosporia royleana Wall.ex Lawson.

Sorazghay The fruit is placed in mouth to relive toothache.

71. Hedera nepalensis K. Koch.

Albomour Leaves are used in diabetes. Juice of the leaves is used for the removal of leeches from the nose of livestock.

72. Heliotropium cabulicum Bunge.

Geshay Whole plant is applied on boils and swellings.

73. Hypericum perforatum L.

Shen chai Decoction is used in cold and in cough. It is also used as carminative and stimulant.

74. Impatiens bicolor Royle Bantil It is diuretic, tonic and has cooling effect.

75. Indigofera heterantha Well.ex Brandis

Ghoreja Powdered roots are used as remedy for headache and chest pain.

76. Inula royleana D.C. Kut Plant is considered to be poisonous. Roots are used to control high blood pressure

77. Isodon rugosus (Wall.ex Benth.) Codd

Sperkay The dried leaves are considered useful for toothache.

78. Jasminum humile L. Konkoni Root decoction is used for curing ringworm disease. Flowers are used as astringent and tonic.

157

79. Jasminum officinale L. Chamba

Decoction of leaves and flowers are given to infants during fever and as blood purifier. It is also given to livestock during cough and fever and also to increase milk production.

80. Juglans regia L. Ghuz

Decoction of leaves is used in eczema and intestinal worms. Fruit is alterative in rheumatism. Bark is used as detergent.

81. Juniperus communis L. Gogar It is believed that the smoke of dried leaves cures the effect evil eyes.

82. Justicia adhatoda L. Baiker

Roots and leaves are used in asthma, bronchitis, cough and rheumatism. Decoction of leaves is antispasmodic and expectorant.

83. Lathyrus aphaca L. Kokorbang

Ripe seeds are narcotic, also used for wound healing. Dried roots are mixed with wheat flour is administrated orally to livestock for various body infections.

84. Launea procumbens Roxb.

Shauda pai Powdered made from the leaves is mixed with sugar and used to enhance lactation.

85. Mallotus philippensis (Lam) Mull.

Kambela Glands and hairs on the fruits are used as anthelmintic. Bark is astringent and diuretic.

86. Malva neglecta Wall. Banerak The roots are boiled and mixed with the seeds of Lepidium sativum and used as purgative for young cattle.

87. Marrubium vulgare L. Kharbotay Decoction is made from the young leaves and is used against cough. Sugar is added for enhancing flavor.

88. Melia azedarach L. Bikyana

Bark is cathartic and emetic. Decoction of leaves is used in hysteria. Seeds are used in rheumatism and hypertension. Ripened fruits are used against diabetes.

89. Mentha longifolia (L.) Huds.

Villanay It is carminative and is used in diarrhea, dysentery and stomachache.

90. Mentha spicata L. Podina It is carminative and is used in diarrhea, dysentery and stomachache. Leaves are used for salad, spice etc.

158

91.

Micromeria biflora (Buch.ex D. Don) Benth.

Yakha booti The stem and the leaves of the plant are plucked, chewed and the juice is swallowed to relive abdominal pain.

92. Mirabalis jalpa L. Gule badam

A hot poultice of leaves is used to mature and resolve boils. Leaf juice is used for cleaning and healing wounds.

93. Morchella sp. Gochai Whole plant is used as general body tonic.

94. Morus alba L. Spin toot Fruit is laxative and purgative. Leaves and bark is used as anthelmintic.

95. Morus nigra L. Tor toot

Fruit are laxative, cooling and aromatic. Leaves decoction is used for cleaning throat. Root is anthelmintic and astringent.

96. Myrtus communis L. Manoo Leaves are boiled in water with ghur, and its decoction is used for abdominal pain and diarrhea.

97. Narcissus tazetta L. Gul-e-Nargis

It is used in the treatment of boils and mastitis. The root is emetic and applied on boils and other skin complaints.

98. Nerium indicum Mill. Ganderay The plant is poisonous. Decoction of leaves is used to reduce swilling. Root is used against snakebites.

99. Olea ferruginea Royle Khona Leaves are astringent, antiseptic and diuretic. Locally the leaves are used in soar throat and toothache.

100. Origanum vulgare L. Ishpain Shoot is chewed for toothache. It is also used as flavoring agent.

101. Otostegia limbata (Benth.) Boiss.

Pishkand Dried powdered plant is used in jaundice.

102. Oxalis corniculata L. Zmakay tarookay

Leaves are anti ascorbic, cooling and used in stomach disorder. The plant is mixed with maize flour and used for diarrhea treatment in livestock.

103. Paeonia emodi Wall ex Royle

Mamekh

The infusion of dried flower is used in diarrhea. Rhizome is used to increase milk production in livestock, also used as tonic.

104. Persicaria stagnina Buch. - Ham. ex Meisn

Pulpulak Root is cooling and astringent. Seed is used in colic. The plant is also locally used for killing fishes.

159

105. Pinus roxburghii Surg. Nakhtar Resin of bark (jaula) is stimulant used in ulcer, skin diseases, snakebites and scorpion stings.

106. Pistacea integerrima J. L. Stewart

Shnai Fruits and galls extract are used as tonic and expectorant.

107. Plantago lanceolata L. Chamchi patar

Seeds are used in dysentery and diarrhea. Powdered leaves are used as antiseptic.

108. Plantago major L. Jabai

It is used as astringent, tonic, stimulant, antiseptic, also used in stomach disorders, in fever and dysentery.

109. Platanus orientalis L. Chinar

Bark is useful remedy in diarrhea and dysentery. Fresh leaves bruised and applied to the eye in ophthalmic diseases.

110. Podophyllum emodi Wall .ex Hook.f

Bankakri

Rhizome and root are hepatic stimulant, purgative and emetic. Flower is used for fever and body pain. Rhizome is given to cattle for fever and milk production.

111. Polygonatum verticillatum (L.) All.

Noorealam

Rhizome is mixed with sugar and used for treatment of joint pain, also used as aphrodisiac. The decoction of dried rhizome is administrated to livestock for removal of placenta.

112. Polygonum amplexicaule D. Don

Masloon Rhizome is crushed and mixed with milk to soften mammary gland of livestock and also given in diarrhea.

113. Populus alba L. Bensa / Aspai / Shafeda

The juice of fresh leaves is given to livestock for Mouth and Foot diseases. The branches are supposed to control diseases of rice crop.

114. Portulaca oleracea L. Warkharay

Its leaves are used for external inflammation in the form of poultice and seeds decoction is used as a cooling, demulcent and stomachache.

115. Primula denticulata Wight.

Asal Mamera

Flowers are used ophthalmic and hair tonic

116. Prunus padus Hook.f. Barith Fruits is used as narcotic. The bark infusion is used in the treatment of colds.

117. Pteris cretica L. Qinchi panra

The whole plant is given to livestock during cough.

160

118. Punica granatum L.

Anangoray Decoction of fruit pericarp is used in whooping cough.

119. Pyrus pashia L. Tangai Juice of fruits is used for eyes infections in livestock.

120. Quercus dilatata Lindl. Tor banj

Powdered fruit are used to treat gonorrhea and urinary diseases. It is also astringent and diuretic, used in diarrhea, indigestion and asthma.

121. Quercus incana Bartram.

Spin banj It is used as astringent, diuretic, diarrhea and asthma.

122. Rhododendron arboreum Smith.

Gulamair Flower petals are tonic and leaves are applied in headache.

123. Rhus javanica L. Tetray The fruits are carminative and are recommended in colic.

124. Ricinus communis L. Arind Leaves are emetic, narcotic and purgative. Leaves poultice is applied to swellings. Seed oil is purgative.

125. Rosa moschata Herm. Qurach Decoction of flowers is used in stomach disorder.

126. Rubus ellipticus Smith.

Goraj

The juice and decoction of the root is used in the treatment of fever, gastric troubles, diarrhea and dysentery. A paste of the roots is applied externally to wounds. Both the root and young leaves are used in colic. The juice of the fruit is used in the treatment of fever, colic, cough and sore throat.

127. Rubus fructicosus Hook .f.

Karwara Leaves are used for the treatment of diarrhea, cough and fever. Fruit are used as carminative.

128. Rubus ulmifolius Schott.

Goraj

Fruits are edible and carminative. Unripe fruit are used as tonic and aphrodisiac. Roots and leaves are used for the treatment f skin diseases.

129. Rumex dentatus L. Shalkhay Fresh leaves mixed with wheat are used for treatment of constipation in livestock.

130. Rumex hastatus D.Don Tarukay

It is used as carminative, purgative, astringent and diuretic. Root is used in jaundice. It is also used as antiseptic.

131. Rumex nepalensis Spreng.

Shalkhaey It is diuretic, astringent, purgative and demulcent

161

132. Salix babylonica L. Asela ola Leaves are used in diabetics.

133. Sarcococca saligna (Don) Mull.

Bansatra

Bark of the root is antiseptic and also used as blood purifier. Leaves and shoots are boiled and applied on swollen joints in the form of poultice. The leaves are heated in mustered oil and applied to muscular pain.

134. Silene conoidea L. Mashroa

A paste is prepared by grinding seeds and young leaves which is applied on pimples. This paste is also used for backache.

135. Silene vulgaris (Moench) Garcke.

Mashroa Shoots are used as stomachic and emollient.

136. Silybum marianum Gaertn.

Rejakai Infusion of leaves is used in throat and chest infections. Seeds are expectorant and stimulant.

137. Skimmia laureola DC. Ner Smoke of burned leaves is used in cleaning the nasal tract also used in cough and cold.

138. Solanum nigrum L. Kachmachu Fruit is edible and are used in jaundice.

139. Solanum surattense Burm.f.

Maraghonay

The plant is expectorant, digestive, astringent and diuretic. It is used in asthma, cough, fever and chest pain. Fruits are used in jaundice.

140. Solena amplexicaulis (Lam.) Gandhi

Kakora

The rhizome is crushed and mixed with maize flour and given to livestock for promotion milk production and for fever.

141. Stellaria media (L.) Vill. Larolay It is used in rheumatism, joint diseases and constipation.

142. Swertia paniculata Wall.

Momera The ripe shoots have powder-like substance, which is used for curing eye diseases.

143. Taraxacum officinale Weber.

Hind The root is used in diabetics, jaundice and kidney disorders.

144. Taxus wallichiana Zuce.

Barmi

Bark is used in cancer and pneumonia. Leaves are used in bronchitis, whooping cough and asthma. Fruit is sedative.

162

145. Thymus linearis Benth. Jaman

Decoction of leaves is used for fever, cough and cold. Seeds are used for abdominal pain. Juice from leaves is applied to toothache.

146. Urtica dioica L. Jalbang

Juice of the plant is external irritant. Leaves are mixed with fodder are fed to livestock to increase milk production.

147. Valeriana jatamansi Jones.

Mushkbala Whole plant is fed to livestock to promote milk production.

148. Verbascum thapsus L. Khardhag Leaves and flowers are used in cough, pulmonary diseases, bleeding of bowels and other skin diseases.

149. Verbena officinalis L. Shamakay

Herb is febrifuge and nerve tonic and is used in amenorrhea. It is used in rheumatism and joint diseases. Root is antidote to snakebite.

150. Viburnum cotinifolium D.Don

Bring Fruits are used as general body tonic.

151. Viola canescens Wall. Banafsha Flower and leaves are used in cough, cold and fever. Whole plant is used in jaundice.

152. Vitex negundo L. Marvanday

Fresh roots are used as bandage to relive pain of chest and back. Branches are used as miswak. Leaves are smoked to relive headache.

153. Withania somnifera (L.) Dunal.

Asghand Leaves and roots are used as poultice on swellings. Fruits and seeds are used as diuretic.

154. Xanthium stromarium L.

Desi Arind

Fruit is demulcent and cooling, used in small pox. Leaves decoction is recommended in long standing malarial fever.

155. Zanthoxylum armatum DC.

Dambara

Seed and bark are tonic and aromatic and are used in fever, cholera and dyspepsia. Fruit is used to cure stomachache and toothache.

156. Zizyphus oxyphylla Edgew.

Elanai Root is used to cure jaundice. Fruits are edible and used in gas trouble.

157. Ziziphus oxyphylla Edgew.

Markhanai All parts of the plant are used in diabetics.

163

4.10 Exotic flora of Nandiar Khuwar catchment

Plant species that have been transported by humans from one region to

another are defined as alien or exotic. There were 13 exotic/alien plant species

identified in the study area. These plant species have been introduced in the

Nandiar Khuwar by deliberate attempts of human beings during the recent

past. The introduction have been made from different parts of the world

including; Africa, Australia, Europe, America and other parts of the Asia.

Population size of some of the exotic plant species is increasing at high rate

while other increasing at lower rate. Some exotic species are cultivated for

ornamental purposes restricted to the habitations and waste places. Some of

the exotic plant species reproduce at high rate but are constantly used and

hence seems to be stable. Some of the plant species are cultivated for edible

fruits while other for fuel wood and timber purposes. Tagetus minuta

increasing more rapidly and are considered as ornamental at waste localities.

The alien plant species are presented in table 4.10.

4.11 Market survey of important plant species

Market survey reveals that the inhabitants of the valley use 157 plants as

healing agents for the treatment of different diseases, but only 15 of them are

sold in the local market. A summary of the plants sold in the local market is

presented in Table-10. Mostly, freshly collected plants are sold in the market.

Morchella spp. is also sold in the local market. The highest purchase price

among medicinal plants is that of Morchella species @ Rs. 23500 /kg, Viola

canescens @ Rs. 1300 /kg. Zanthoxylum armatum is sold for Rs. 160 / kg,

Diospyros lotus for Rs. 120 / kg and Geranium wallichianum for Rs. 100 / kg.

164

Table-4.10 The exotic/alien flora of Nandiar Khuwar catchment.

S. No

Botanical name Habit Altitudinal range

1 Acacia farnesiana (L.) Willd. Shrub 2490 – 3515ft

2 Ailanthus altissima (Mill.) Swingle Tree 2154 – 5782ft

3 Broussonetia papyrifera Vent. Tree 1934 – 3981ft

4 Cupressus sempervirens L. Tree 1977– 4892ft

5 Diospyros kaki L. Tree 3131 – 5143ft

6 Eucalyptus globules Labill. Tree 2710 – 4555ft

7 Phleum pratense L. Herb 3405 – 6510ft

8 Populus euro-americana L. Tree 2618– 5567ft

9 Ricinus communis L. Shrub 1920 – 3540ft

10 Robinia pseudoacacia L. Tree 2498 – 6551ft

11 Sapindus mukorossi Gaertn. Tree 3010- 5165ft

12 Tagetus minuta L. Herb 2040 – 5433ft

13 Xanthium stromarium L. Shrub 2034 – 5145ft

Table- 4.11 Medicinal plants in the local drug market.

S. No

Botanical Name Season of collection

Part used Formal material

Price /Kg

1 Adiantum species Mar-Apr Fronds Dried 90

2 Aesculus indica Sep-Oct Fruits Dried 25

3 Berberis lyceum Feb-Mar Root bark Dried 30

4 Bergenia ciliata June-July Rhizome Dried 50

5 Datura stramonium July-Aug Seeds Dried 40

6 Diospyros lotus Nov-Des Fruits Dried 120

7 Dryopteris jaxtapostia Apr –May Fronds Fresh 30

8 Geranium wallichianum May – June Roots Dried 100

9 Mentha longifolia Apr –May Leaves Dried 70

10 Morchella spp. Mar –May Whole Dried 23500

11 Paeonia emodi July – Sept Rhizome Dried 60

12 Skimmia laureola Apr – Oct Leaves Fresh 50

13 Valeriana jatamansi May – June Rhizome Dried 70

14 Viola canescens Mar – Apr Flowers Dried 1300

15 Zanthoxylum armatum July – Aug Fruits Dried 160

165

4.12 Conservation Status of the Most Important Species

In the present study 324 vascular plant species were recorded from Nandiar

Khuwar catchment area. Due to small area size, the criteria published by

IUCN 2001; cannot be applied to determine the conservation status of the

flora of the study area (1,301km2) and there is no previous complete data

regarding deserved species. However the criteria D of IUCN Version 3.1

(IUCN, 2001) can be applied to some of the plant species having very small

population size or very restricted distribution. The reduction in population

size was observed by direct observation. These included a decline in area of

occupancy (AO), extent of occurrence (EO), loss of habitat, actual or potential

level of exploitation, effect of introduced taxa and attack of pathogens.

4.12.1 Critically Endangered Species

There are 10 plant species evaluated and falling in the Critically Endangered

category under criteria D having population size less then 50 mature

individuals and restricted distribution. These are described below:

1. Acer cappadocicum Gled.

Seventeen mature individuals of Acer cappadocicum were recorded in six

stands Guchai, Ledai, Charoona, Chailkambar, Gabrai kandao and Lekoni on

steep slopes between elevations of 2262-2912m. The reduction in its

population size was due to loss of habitat, sliding, fuel wood and leaf fodder

collection.

2. Betula utilis D. Don

Fifteen mature individuals of Betula utilis were recorded in six stands of

alpine and subalpine zones including Belmaz, Lekoni, Shaheedgali, Kar-

Ganja, Alishera and Malkaisar between elevations of 2908-3780m on steep

slopes. There was no regeneration and reduction in population size was due

to sliding, leaf fodder and bark collection for spiritual means (Plate 9).

166

3. Cedrus deodara (Roxb. ex D. Don) G. Don

In the study area 13 mature individuals of Cedrus deodara in wild state were

recorded in Anora and Gada between elevations of 1254-1766m on moderate

steep slopes. The reduction in population size was due to its collection for fuel

wood, timber and medicinal uses.

4. Opuntia dilleni Haw.

It was recorded in the subtropical zone of Nandiar Khuwar catchment with 47

mature individuals restricted to graveyards and waste places. The reduction

in its population size was loss of habitat, attack of pathogen and effect of

exotic taxa.

5. Podophyllum emodi Wall .ex Hook.f

Forty-one mature individuals of Podophyllum emodi was recorded in Jaro,

Trapa, Chailkambar, Gabraikandao and Belmaz between elevations of 2222-

2908m. The reduction in its population size was due to its medicinal

collection, over-grazing and loss of habitat.

6. Populus ciliata Wall. ex Royle

Thirteen mature individuals of Populus ciliata were recorded in Chapra hill at

1781m above mean sea level. The reduction in its population size was due to

its fuel wood collection and timber.

7. Psilotum nudum L.

Psilotum nudum was recorded in single locality of subtropical zone of Nandiar

Khuwar catchment area restricted in rock crevices in very small patches with

a total of 38 individuals. The reduction in its population size was due to loss

of habitat, effect of exotic species and overgrazing (Plate 1).

167

8. Taxus contorta Griff.

Thirty-two mature individuals of Taxus contorta were recorded in nine stands

in the sub alpine zones between elevations of 2339-2997m. The regeneration

capacity was low and 13 plants were recorded in bushy form. The reduction

in population size was due its collection for fuel wood and furniture.

9. Ulmus wallichiana Planch.

A total of 20 mature individuals of Ulmus wallichiana were recorded singly

and in group of two restricted in graveyards, paddy fields and nullahs beds of

Nandiar Khuwar catchment. The reduction in its population size was due to

its collection for fuel wood, making wood utensils and loss of habitat (Plate 6).

10. Viscum album L.

Viscum album the parasitic epiphyte of Ulmus wallichiana in the study area is

also Critically Endangered and was recorded on 9 plants. The loss of the host

plants has also reduced the population size of Viscum album (Plate 6).

4.12.2 Endangered Species.

There are 12 plant species identified as endangered under criteria D of

endangered species having a population size less than 250 mature

individuals. The endangered species are described below.

1. Aesculus indica (Wall.ex. Cambess) Hook.f.

A total of 198 mature individuals of Aesculus indica were recorded in eleven

stands between altitudinal range of 1781-2899m in moist temperate and sub

alpine zones of Nandiar Khuwar catchment area. The reduction in its

population size was due to fuel wood collection, timber, house and

agricultural tools and medicinal uses.

168

2. Bauhinia variegata L.

Bauhinia variegata was recorded in three stands of subtropical zone of Nandiar

Khuwar catchment between elevations of 539-759m. A total of 178 mature

individuals were recorded. Its population size was reduced due to fuel wood

collection and effect of exotic species.

3. Cornus macrophylla Wall.

It was recorded in subtropical and temperate zone of Nandiar Khuwar

catchment. A total of 198 mature individuals were recorded along nullah

beds. It is used for fuel wood and timber. The bark is collected for medicinal

uses.

4. Crataegus songarica G. Koch.

A total of 245 mature individuals of Crataegus songarica were recorded in the

subtropical zone of Nandiar Khuwar catchment in wild state. The reduction

in its population size was due to fuel wood, loss of habitat and attack of exotic

species.

5. Dioscorea deltoidea Wall. ex Griseb.

Ninety-nine mature individuals of Dioscorea deltoidea were recorded in four

stands between altitudinal range of 1488-2032m along nullah beds and moist

shady places. The plant is endangered under criteria D of Endangered

species. The reduction in population size was due to loss of habitat, medicinal

uses, fish poison and effect of exotic species.

6. Dioscorea melanophyma Prain & Burkill.

It was recorded in two stands Gada and Rajmira with 112 individuals

between elevations of 1254-1488m. The reduction in its population size was

unknown.

169

7. Ehretia serrata Roxb.

A sum of 240 mature individuals of Ehretia serrata was recorded between

altitudinal zones of 986-1500m along nullah beds. Its population size was

reduced due to introduction of exotic species, collected for fuel wood and leaf

fodder.

8. Filipendula vestita Maxim.

Ninety-nine individuals of Filipendula vestita were recorded in single locality

of Machaisar at altitude of 2812m. The reduction in its population size was

unknown.

9. Grewia optiva Drum.ex Burret.

It was recorded in three stands of subtropical zone of the study area between

altitudinal zones of 550-1210m. Presently 151 mature individuals were

recorded. The reduction in its population size was due to loss of habitat,

exotic species, fuel wood and leaf fodder collection.

10. Populus alba L.

One hundred and forty-four mature individuals Populus alba in its wild state

were recorded in two stands Sarmast and Lamai between elevations of 1814-

2036m along nullah beds and paddy fields. The reduction in its population

size was due to its leaf collection, mouth and foot disease of cattle, fuel wood

and thatching purposes.

11. Salix babylonica L.

The plant was recorded in the subtropical zone of the study area. In the wild

state 190 mature individuals were recorded. The reduction in its population

size was due to loss of habitat, attack of exotic species and medicinal

collection.

170

12. Trachelospermum lucidum (D. Don) Schum.

It was recorded in the temperate zone of Nandiar Khuwar catchment along

nullah beds and moist places. A total of 123 individuals were recorded in

different localities. The population size was reduced due to loss of habitat and

attack of exotic species.

4.13 Major threats to the plant resources

Due to increase of human population and constant over use of vascular plants

for medicine, timber, firewood, leaf fodder and for thatching purposes has

resulted in ill or unplanned collection of wild vascular plants particularly

medicinal plant species. This over collection has damaged the flora and even

threatens the extinction of many plant species. In the study area habitat loss

was observed in many localities especially in the subtropical and temperate

zone where the forest land has been converted into agriculture land and the

native plant species were removed for construction purposes (Plate 14). The

loss of habitat results reduction in population size of both flora and fauna of

study area. Deforestation due to cutting of trees and shrubs in bulk has also

threatened the extinction of many plant species. The alpine plant diversity has

been reduced due to overgrazing and lumbering process. Fire factor in the

study area has also reduced the population of native plant species ((Plate 7).

Soil erosion and land sliding in subalpine steep slopes has threatened many

plant species. In the study area it was observed that the population size of

native plant species has been reduced due to increase in the population size of

exotic plant species particularly in the vacant localities.

171

Table- 4.12.1 Critically Endangered Species of Nandiar Khuwar Catchment.

S. No.

Botanical Name Habit Mature individuals

Altitude (meter)

1 Acer cappadocicum Tree 17 2262 - 2912

2 Betula utilis Tree 15 2908 - 378

3 Cedrus deodara Tree 13 1254 - 1766

4 Opuntia dilleni Hedge plant 47 1092 - 1276

5 Podophyllum emodi Herb 41 2222 - 2908

6 Populus ciliata Tree 13 1780 - 1912

7 Psilotum nudum Herb 38 1205 - 1235

8 Taxus contorta Tree 32 2339 - 2997

9 Ulmus wallichiana Tree 20 1710 - 1943

10 Viscum album Epiphyte 33 1710 - 1943

Table- 4.12.2 Endangered Species of Nandiar Khuwar Catchment.

S. No.

Botanical Name Habit Mature individuals

Altitude (meter)

1 Aesculus indica Tree 198 1781-2899

2 Bauhinia variegata Tree 178 539-759

3 Cornus macrophylla Tree 198 1223 - 1587

4 Crataegus songarica Small tree 245 1212 - 1498

5 Dioscorea deltoidea Climber 99 1488-2032

6 Dioscorea melanophyma Climber 112 1254-1488

7 Ehretia serrata Tree 240 986-1500

8 Filipendula vestita Herb 99 2744 - 2812

9 Grewia optiva Tree 151 550-1210

10 Populus alba Tree 144 1814-2036

11 Salix babylonica Small tree 190 1197 - 1310

12 Trachelospermum lucidum Shrub 123 1254 - 1515

172

Chapter 5

DISCUSSION

Biodiversity is referring to organisms found within the living world and

consists of ecosystem diversity, genetic diversity and species diversity

(Ahmad, 2009; Murthy, 2007). Biodiversity varies greatly across the globe as

well as within regions and there is a latitudinal gradient in species diversity.

The tropical regions are rich in biodiversity as compared to polar regions.

Biodiversity is greatly affected by different factors such as temperature,

precipitation, altitude, soil, geography and the presence of other species

(Tandon, 2005).

In Nandiar Khuwar catchment area a total of 324 vascular plant species were

recorded. Maximum diversity index value was recorded at Rajmira (4.18)

followed by Jaro (4.13) in moist temperate zone while minimum diversity

index value was recorded at Kar-Ganja (2.64) in the alpine zone of the study

area. The maximum species richness value was recorded at Rajmira (0.94)

followed by Belandkot (0.77) on north-facing slopes indicating undisturbed

vegetation. The minimum species richness value was recorded at Basha Khan

(2.75) and Kiari (2.86) showed the disturbed vegetation due to overgrazing.

Similar results were also presented by Vujnovic et al. (2012) from central

Alberta, western Canada who reported that species diversity was low in

disturbed and lightly grazed plots.

Life form is the indicator of climate (micro and macroclimate) and can be used

in comparing geographically widely distributed plant communities

(Angelova and Tashev, 2005). In Nandiar Khuwar catchment area biological

spectrum was dominated by phanerophytes having 118 (36.41%) plant species

followed by therophytes with 82 species (25.30%). The dominance of

phanerophytic life form indicates that most of the stands are better preserved

while therophytic life form indicates that some stands of the study area are

173

under severe deforestation, overgrazing, soil erosion and loss of habitat.

Shimwell (1971) reported similar results from India that therophytes are the

indicators of desert climate. Our results are in agreement with Meher-Homji

(1981) who reported that phanerophytic life form indicates the most

preserved forests.

Phenology refers to the appearance of various plants at different seasons of

the year and depends on temperature, sunlight, rainfall, soil moisture and

atmospheric humidity (Pearson, 1979; Angelova and Tashev, 2005). The

flowering and fruiting stages of life-cycle of 324 vascular plant species were

recorded in spring, summer and autumn. The maximum flowering stages

were recorded from April-July (68.51%). The maximum fruiting stages were

recorded from May-August (77.53%). The appearance of the flowers of

different plant species started first at lower altitude. Suresh and Paulsamy

(2010) recorded similar phenological observation from Western Ghats and

concluded that maximum flowering stages were found from May-July.

Bijalwan et al. (2013) also observed similar results from Garhwal Himalayas

and reported that appearance of flowers of different plant species started first

at lower altitude while delayed at alpine zone due to snow cover.

Leaf size classes have been found to be very useful for plant associations

(Tareen and Qadir, 1993). There is consistent variation of leaf, leaf size and

texture between individual plant communities and in various climatic

conditions (Malik et al., 2007). In Nandiar Khuwar catchment area microphyll

was dominant with 137 (40.28%) species followed by mesophyll having 103

(31.79%) species. The dominance of microphyll and mesophyll indicates that a

large part of Nandiar Khuwar catchment receive a high amount of rain fall,

having moderate temperature and moist condition. Our results lie close to the

observations of Amjad (2012) who reported that leptophyll and nanophyll are

the indicators of subtropical and disturbed vegetation while microphyll and

174

mesophyll are the indicators of temperate zone with low temperature and

moist conditions from Azad Jammu and Kashmir.

Out of 324 vascular plant species 157 plant species were used for medicinal

purposes including 22 ethno veterinary important plants. Majority of the

recipes are prepared in the form of decoction from freshly collected plant

parts. Mostly a single species was used and mainly taken orally for the

treatment of asthma, cough, tonic, abdominal pain, expectorant, anthelmintic,

carminative, jaundice, diarrhea and dysentery etc. Haq et al. (2011) reported

156 medicinal plants from the same area and concluded that majority of the

recipes are prepared from freshly collected plant parts.

Phytosociology is concerned with plant communities, their relationships,

structure, composition, distribution, development and the short-term

processes modifying them (Poore, 1955). Phytosociological surveys helps in

planning, management and exploitation of natural resources (Haq et al.,

2015a). The presence or absence of vegetation is controlled by environmental

variables where soil is of high importance in plant growth. Topography

affects soil and climate, in addition to affecting temperature and evapo-

transpiration, makes deeper soil and higher content of organic matter. Certain

plants species perform well in a wide range of environmental conditions

while it is impossible for individual genotypes to perform well across the full

range of conditions (Hoveizeh, 1997; Leonard et al., 1984).

The phytosociological attributes were recorded in six vegetational zones of

Nandiar Khuwar catchment area. These include: subtropical zones, mixed

Pinus roxburghii and Pinus wallichiana zone, moist temperate pure Pinus

wallichiana zone, mixed coniferous zone, pure Abies pindrow and Picea

smithiana zone and alpine zone (Haq et al., 2010). From these six vegetational

zones 80 stands were selected on the basis of physiognomy for

phytosociological attributes. The IVI data obtained from these stands were

175

further analyzed for classification and ordination. Ahmed et al. (2006) also

divided the Himalayan forests of Pakistan into different climatic zone for

phytosociological investigations on the basis of indicator plant species.

Six plant communities were recognized by TWINSPAN classification in

subtropical zone of Nandiar Khuwar catchment area. The biological spectrum

dominated by phanerophytes and leaf size spectra dominated by microphyll.

Our results resemble to certain extent with Siddiqui et al. (2009) who worked

on subtropical forests of Lesser Himalayan and Hindu Khush range of

Pakistan and reported 13 plant communities among which 12 communities

were of pure Pinus roxburghii forests.

In mixed Pinus roxburghii and Pinus wallichiana zone four plant communities

were recognized by TWINSPAN classification. Biological spectrum

dominated by phanerophytes and leaf size spectra dominated by microphyll.

Ahmed et al. (2006) examined such type of vegetation in Himalayan forests

and declared it as subtropical moist temperate ecotonal zone.

Five plant communities were recognized in moist temperate pure Pinus

wallichiana zone of Nandiar Khuwar catchment. Life form dominated by

phanerophytes and leaf size spectrum dominated by microphyll. Ilyas et al.

(2012) also described eight plant communities in the temperate zone of

Qalagai hills Swat.

In western mixed coniferous forests of Nandiar Khuwar catchment four plant

communities were recognized. Biological spectrum dominated by

phanerophytes and leaf size spectrum dominated by microphyll. Saima et al.

(2009) explored same type of vegetation of mixed coniferous forests of Ayubia

National Park Abbottabad and reported five plant communities by cluster

analysis. They reported that the environmental factors has direct role on

species distribution

176

Three plant communities were recorded in pure Abies pindrow and Picea

smithiana forests. Life form dominated by phanerophytes and leaf size spectra

dominated by microphyll. Our results are in line with Akber et al. (2010) who

analyzed similar vegetation of Skardu forests and reported three plant

communities from six stands.

Two plant communities were identified in alpine zone. The biological

spectrum was dominated by hemicryptophytes and therophytes each

contributing 8 species. The leaf size spectra dominated by microphyll having

12 species. Our results resemble to certain extent those of Pharswan et al.

(2010) who explored the floristic composition and biological spectrum of

vegetation in alpine meadows of Garhwal Himalaya and reported low plant

diversity due to harsh climatic conditions.

Among different vegetational zones the maximum Bray-Curtis ordination

scores (0.921) and maximum gradient length (3.35) was recorded in

subtropical zone. The permutation test result was more significant in the

moist temperate pure Pinus wallichiana zone as compared to other

vegetational zones. In all vegetational zones the plant species were sensitive

to environmental variables including altitude, density altitude, temperature

and barometric pressure. Other environmental variables are positively

correlated in one zone may be negatively correlated in other zone. Similarly

the effects of environmental variables were also varied in different vegetation

zones. Similar observations were also reported by Khan et al. (2013) during

their study on phyto-climatic gradient of vegetation and habitat specificity in

the high elevation western Himalayas and Shaukat et al. (2014) in their

phytosociological investigations of the vegetation of Hub dam catchment

area, Pakistan.

The data obtained from 80 stands were collectively analyzed for classification

and ordination beside separate classification and ordination in each

177

vegetational zone (Brendenkamp et al., 1983). In division 1 of TWINSPAN

classification the eigenvalue were 0.63 and the primary indicator species were

Abies pindrow 1(+), Viola canescens 1(-) and Berberis lyceum 1(-). In this division

53 stands were placed in negative grouped (*0) while 27 stands were placed in

positive group (*1). In division 2 (53) indicator species was Sarcococca saligna

1(+) and in division 3 (27) the indicator species were Picea smithiana 1(-) and

Fragaria nubicola 1(-). At the end of TWINSPAN classification 13 major plant

communities were recognized from the vegetation of Nandiar Khuwar

catchment area. Peter and Erik (1992) obtained similar results from Senegal

and reported sixteen plant communities in 44 stands by TWINSPAN

classification. Jurisic et al. (2014) also reported similar results from Posavina’s

floodplain forests in Serbia. 114 samples were grouped into seven association

groups at the third TWINSPAN classification level.

The community 1 Acacia, Dodonaea, Dalbergia community was similar to the

first community of subtropical vegetational zone while community 13

Juniperus, Sibbaldia, Primula community was similar to the last community of

alpine zone. The remaining communities obtained from whole data set and

the communities of each vegetational zone are slightly different due to

similarity and borderline of some stands and species. Similar observations

were also recorded by Ahmed et al. (2006) who listed such type of

communities during exploring the vegetation of Himalayan forests.

Ordination is the ordering of objects along axes according to their

resemblances (Saima et al., 2009; Brendenkamp et al., 1983). Objects close in

the ordination space are generally more similar than objects distant in the

ordination space (Nezerkova and Hejcman, 2006). The response data are

compositional and have a gradient 6.4 SD units long so linear method is not

appropriate (Khan et al., 2013).

178

In Bray-Curtis Ordination the score was maximum for axis 1 (0.960). The

regression coefficient for axis 1 was -54.11; variance in distance from the first

end point was 2.53. Axis 1 extracted 21.95% of original distance matrix. In

DCA ordination maximum gradient length (6.36) was recorded for axis 1 with

eigenvalue 0.71. Total variance in the species data was 7.07, supplementary

variables account for 37.1%. The DCA ordination clearly indicates that the

whole data set was dominated by single dominant gradient. Our results

coincide with the observations of Khan et al. (2013) who indicated that the

DCA of whole data set in the western Himalayas is dominated by single

gradient length.

In DCA ordination space clusters of different species show their positive and

negative correlation. The subtropical species showed positive correlation at

one side of ordination space while the species of alpine and subalpine zone

clusters on other side. Between these two ends the species of other zones

showed correlation. DCA ordination showed that the subtropical vegetation

is negatively correlated with alpine vegetation due to environmental

conditions. Similar results were also indicated by Khafagi et al., (2013) during

exploring the vegetation composition and ecological gradients in Saint

Katherine Mountain, Egypt.

Canonical correspondence analysis (CCA) was used for the ordination of

samples and species constrained by their relationships to environmental

variables. In CCA ordination the maximum eigenvalue was recorded for axis

1 (0.699). The permutation test results for all axes were pseudo-F=2.2, P=0.002.

The correlation between sample score for an axis derived from the species

data and the sample scores that are linear combination of the environmental

variable. Our results are in line with the results of Khafagai et al. (2013) and

Khan et al. (2013) that also showed the correlation of species with

environmental variables.

179

Among the environmental variables the maximum positive strength was

recorded for barometric pressure (0.967) and temperature (0.960) while

maximum negative strength was recorded for altitude (-0.974) and density

altitude (-0.954). Barometric pressure and temperature were negatively

correlated with altitude and density altitude. Wind speed, phosphorus,

electrical conductivity and slope angle are negatively correlated with wet

bulb, dew point and organic matter. CCA ordination also indicates that

maximum stands clusters near average position. In CCA ordination different

plant species were sensitive to environmental variables. Khan et al. (2013) also

indicated similar results of environmental factors on species distribution in

the high elevation western Himalayas. Shaukat et al. (2014) also estimated

similar effects of environmental gradients on species composition in Hub-

Dam catchment area.

The community similarity and dissimilarity were used for the comparison of

all communities within the study area. Among vegetational zones the

maximum similarity index (37.83%) was recorded between mixed coniferous

forests and pure Abies pindrow and Picea smithiana forests. The maximum

dissimilarity index value (98.97%) was recorded between subtropical forests

and alpine scrub. The maximum index of similarity (35.7%) was recorded for

Wikstroemia, Viburnum, Androsace community and Juniperus, Sibbaldia, Primula

community. The index of dissimilarity of Juniperus, Sibbaldia, Primula

community were 100% with three different plant communities Acacia,

Dodonaea, Dalbergia community, Pinus, Cynodon, Rubus community, and

Quercus, Dodonaea, Myrsine community. Our results resemble to the results of

Nazir and Malik (2006) who examined the vegetation of Sarsawa hills, district

Kotli.

Microclimate is a local atmospheric zone where climate differs from the

surrounding area and it may be small as a few square feet or as large as many

square miles (Haq et al., 2015a). The contributing factor to microclimate is the

180

slope or aspect of an area. South-facing slopes in the northern hemisphere and

north-facing slopes in the southern hemisphere are exposed to more direct

sunlight than opposite slopes and are warmer. The microclimate has a great

impact on vegetation of the study area. The microclimate of Nandiar Khuwar

catchment varies from sub tropical to alpine zone (Haq et al., 2015b). The

altitude, latitude and longitude were responsible for change in microclimate.

Vegetation was denser on north facing slopes as compared to south facing

slopes (Haq et al., 2015a).

The altitudinal, latitudinal and longitudinal spatial variation of temperature

affect climate and distribution of biodiversity. In the Nandiar Khuwar

catchment area maximum temperature value represents the scrub forests

while the minimum temperature value represents the alpine scrub. Our

results are in agreement with Daubenmire (1943) who discussed the effect of

temperature on mountain vegetation. The average wind speed in different

stands ranges from 0.4 to 1.9m/s. and are negatively correlated with axis 1

and axis 2. The atmospheric humidity ranges from 18.1% to 62% and

negatively correlated with axis 1 and axis 2. Similar results were also

presented by Malik et al. (2007) who reported that humidity of certain zones

varies with altitude and is a significant factor in determining altitudinal

zonation.

Dew point is the temperature at which air must be cooled in order for

condensation to occur at constant barometric pressure resulting in the

formation of dew on solid surface. The dew point values range from 2.6 to

20.5. The dew point has not much great effect on the distribution of plant

community in the study area and it is almost parallel to axis 1 and 2 and its

value lies below 21. The wet bulb data range from 6.7 to 23.6. Lawrence (2005)

reported that wet-bulb temperature below 35°C is suitable while above this

value is likely to be fatal even to fit and healthy life.

181

Heat index is a practical measure of how hot the current combination of

relative humidity and temperature feel to human body. The heat index values

ranges from 10.2 to 38.6. The correlation of heat index with axis 1 was 0.499

and with axis 2 was -0.277. Barometric pressure in different stands of Nandiar

Khuwar catchment ranges from 640.8 at Malkaisar 950.3 at Thakot II and is

positively correlated with axis 1 and 2 and its value are significant. The

barometric pressure has direct effect on the distribution of species. At high

barometric pressure the subtropical scrub forests were recognized while at

low barometric pressure the alpine scrub were identified. Altitude of different

stands ranges from 530m in Thakot 1 to 3780m at Malkaisar. Altitude and

density altitude are significantly negatively correlated with axis 1. At low

altitude and density altitude the subtropical scrub forests were identified

while at higher altitude and density altitude the alpine scrub forests were

recognized. Similar results were also presented by Daubenmire (1943), Peter

and Rob (1991), Shipley and Keddy (1987), Angelova and Tashev (2005). They

reported that altitudinal zonation in mountainous regions describes the

natural layering of ecosystems that occurs at distinct altitudes due to varying

environmental conditions and produces discrete plant communities along an

elevation gradient.

Edaphic factors play an important role in the local difference of plant

communities in Nandiar Khuwar catchment area (Leonard et al., 1984). The

top soil is constantly being washed away by run off from higher slopes (Malik

et al., 2007). Gravels, stones, rocks and boulders are common. Soil under fir

and spruce is deep and quite rich in humus, whereas it is shallow and poor

under pines and scrub zones (Haq et al., 2012). The relative proportion of soil

particles indirectly affects plant communities by bringing variations in the soil

water and soil air (Hoveizeh, 1997). Gravels, coarse sand, fine sand silt and

clay particles were recognized in different plant communities (Ajaib et al.,

2008).

182

In the study area slope aspect and slope angle have direct impact on diversity

and species richness. 29 stands were recorded on north-facing slopes which

are rich in biodiversity as compared to south-facing slopes. In the study area

it was also noted that at gradual slope the grazing activity were more as

compared to steep slope. Similar results of slope aspect on diversity and

species richness were also presented by Hoveizeh (1997) and Ahmed (1988).

The soil saturation data ranges from 38% in Anora II and Anora III to 69% in

Riar and are negatively correlated with axis 1 was -0.550. Soil saturation is

influenced by soil texture and soil structure. The effect of soil saturation on

plant community was also recorded by Ajaib et al. (2008).

In study area sandy soils have low conductivity and clays soil have high

conductivity and their values ranges from 0.38dS/m in Deshara to 0.80 dS/m

in Kar Ganja, Chail and Mirani 1. Due to variation in electrical conductivity

the species composition of different stands was different. Johnson (1996) in his

study on subalpine treed fen in Colorado also reported that electrical

conductivity was significantly correlated with species distribution. Malik et al.

(2007) and Zuo et al. (2014) also reported similar results. Soil pH ranges from

5.15 in Batangi to 7.72 in Chapra and Anora II. The pH values were optimal

for most of the species of the study area. The optimum pH range for most

plant species is between 5.5 and 7.0 (Hanet al., 2014).

In Nandiar Khuwar catchment area the soil organic matter concentration

ranges from 0.73% in Hill and Jatial to 1.92% in Chapar. Out of 80 stands 23

stands have less then 1% of organic matter showing weak soil and 67 stands

have the organic matter more than 1% showing suitable for most of the

species. Senesi, et al. (2006) declared that organic soils contain 12-18% organic

matter while desert soil contains less than 1% organic matter.

The soil phosphorous (P) concentration ranges from 1.8 -19.50mg/kg and it is

significantly correlated with both axis 1 and 2. The soil of 28 stands was too

183

weak, 11 stands were slightly weak, 30 stands were suitable for most of the

plant species and soil of 10 stands indicating that some plant species will

grow actively while others cannot. The soil potassium (K) concentration

ranges from 98 – 400mg/kg. In 30 stands the potassium concentration was

less than 200mg/kg showed that the soil was suitable for most of the species.

In 50 stands the potassium concentration was more than 200mg/kg showing

that the specialized plant species will grow actively. Our results are in line

with the observations of Nezerkova and Hejcman (2006), Khafagai et al.

(2013), Khan et al. (2013) and Han et al. (2014).

Present study reveals that the inhabitants of the valley use 157 plants as

healing agents for the treatment of different diseases, 15 of them are sold in

the local market. Mostly, freshly collected plants are sold in the market. The

highest purchase price is that of Morchella species. Haq et al. (2011) also

described similar results from the market survey of the same area.

In present study the mature individuals of 22 plant species were recorded in

few numbers. According to criteria D of IUCN criteria (version 3.1) 10 plant

species are critically endangered having population size less then 50 mature

individuals and 12 plant species are endangered having population size less

then 250 mature individuals. The major threats to the flora of the study area

include urbanization, deforestation, overgrazing, habitat loss, medicinal,

timber and fuel wood collection and effect of exotic species. Our results are in

agreement with the observation of Haq et al. (2010) who explored the species

diversity of vascular plants of Nandiar Khuwar catchment, district Battagram.

The people of the study area mainly depend on the plant diversity for various

purposes and thus leading many plants to the verge of extinction. These

include; increase the demand of timber for construction purposes, fuel wood,

torchwood, fodder and medicinal uses. Damage to the plants are careless and

illicit cutting and smuggling of trees and shrubs, fire factor that damages the

184

seedling of many plants, overgrazing and browsing along with roots, bark

extracted as medicine and extensive storage of fuel wood for winter. Beside

that habitat loss, urbanization, converting the plan slopes in the forests for

cultivation also exerts enormous stress on vegetation and result in

environmental degradation. Some other causes, which threaten species

diversity, include ignorance, poverty, joblessness and lack of scientific

knowledge. Extensive grazing and deforestation should be minimized

because these factors may lead to further fragmentation and degradation of

the habitat. We concluded that Nandiar Khuwar Catchment has great

potential for conservation of the native plant species of Western Himalayan

Ecoregion. The conservation issues can easily be addressed through devising

strategies for protection, recovery and rehabilitation of the threatened species

within their respective stands.

185

RECOMMENDATIONS

1. Biodiversity conservation

The study area is rich in plant biodiversity and we need it for its invaluable

ecosystem to survive. However over consumption of the plant resources is the

main cause of biodiversity loss. We should consume less and be more mindful

about what we consume.

2. Control of major threats

The major threats to the biodiversity of the study area, like deforestation,

overgrazing, habitat loss, soil erosion, overexploitation of natural resources

and invasive species should be controlled.

3. Establishment of plant communities

Habitats should be recognized to establish a new plant community having

similar climatic and edaphic factors.

4. Cultivation of native plants

To protect biodiversity it is necessary to cultivate the native plants along

roadsides and waste places.

5. Medicinal plants cultivation

Medicinal plants should be cultivated and it will reduce pressure on natural

medicinal flora.

6. Documentation and conservation of indigenous knowledge

The local communities of the area have the knowledge of traditional uses of

most of the medicinal plants. But the future generation will not inherit the

precious indigenous knowledge of medicinal plants if it is not properly

documented and conserved.

186

7. Proper training of the community

The forests are continuously being depleted due to high human pressure and

lack of proper management. The local people are unaware of the proper

collection of timber, fuel wood and medicinal plants. Community training

and induction of the scene of the conservation for floral diversity will lead to

sustainable use of plants in the area.

8. Social organization

Social organization and community training on the sustainable use of plant

resources is only effective, if carried out through an organized community.

With local organization, the community can fully and efficiently achieves its

goals and objectives.

9. Rangeland management

Due to lack of management the rangelands are degraded, because of livestock

pressure. The potential of these rangelands should be restored through

control grazing.

10. Agricultural development

Agricultural can play an important role in the development of the area due to

the availability of fertile land and water resources adopting modern

agricultural tools and techniques can increase the production of existing

crops. The promotion of horticultural activity, fruits trees plantation and

vegetables growing is strongly needed through extension services.

11. Development of cottage industry

The selected area provides ideal potential for poultry forming. To train the

community with the modern techniques of apiculture and poultry forming

will create extra job opportunities and dependence of people on the natural

resources will be minimized. Improved apiculture will also improve the crop

yield through effective pollination.

187

12. Mass awareness

Governmental and community level campaign should be launched for mass

awareness about conservation and importance the flora.

13. Provision of civic facilities

Provision of civic facilities in the area like road, health, education, water

supply, electricity, natural gas and telephone will improve not only the living

standard of the people but will also lead to the sustainable use of the

resources and ecological development of the area.

188

REFERENCES

Abbas, F., T. Akhtar and A. Mian. 2009. Phytosociological analysis within the

range of grey goral in Pakistan and Azad Kashmir. Pak. J. Bot., 41(2): 667-

682.

Ahmad, H. 2009. Ecology and biodiversity of the Siren River catchment.

Cultural and Biological Resources of Pakistan. 1-7.

Ahmad, H. and A. A. Khan. 2004. Conservation and sustainable use of

medicinal and aromatic plants of Pakistan. Proceedings of the

international workshop held in Islamabad, December 2-4, 2003.

Ahmad, H., M. Alam and F. Haq. 2010. Species diversity and conservation

status of the diversity of vascular plants of Nandiar Khuwar District

Battagram, Pakistan. Workshop on international symposium on Biology

of rare and endemic plant species. Biorare Symposium May 26 - 29, 2010,

Fethiye-Mugla, Turkey.

Ahmed, M. 1976. Multivariate analysis of the vegetation around Skardu. Agri.

Pak., 26: 177-187.

Ahmed, M. 1986. Vegetation of some foothills of Himalayan range in

Pakistan. Pak. J. Bot., 18(2): 261-269.

Ahmed, M. 1988. Plant communities of some northern temperate forests of

Pakistan. Pak. J. For., 38: 33-40.

Ahmed, M., K. Nazim, M. F. Siddiqui, M. Wahab, N. Khan, M. U. Khan and S.

S. Hussain. 2010. Community description of deodar forests from

Himalayan range of Pakistan. Pak. J. Bot., 42(5): 3091-3102.

Ahmed, M., T. Hussain, A. H. Sheikh, S. S. Hussain, M. F. Siddiqui. 2006.

Phytosociology and structure of Himalayan forests from different

climatic zones of Pakistan. Pak. J. Bot., 38(2): 361-383.

Ajaib, M., Z. Khan, S. Muhammad, R. Mahmood. 2008. Biological spectra of

Saney Baney Hills district Kotli Azad Jammu and Kashmir. P. J. Sci. 60

(1-2): 53-58.

189

Akbar, M., M. Ahmed., M. Afzal., F. Choudhary., M. U. Zafar and A. Hussain.

2010. Phytosociology and structure of some forests of Skardu District of

Karakorum Range of Pakistan. American-Eurasian J. Agric. Environ. Sci.,

9(5): 576-583.

Alam, J. and S. I. Ali. 2009. Conservation status of Astragalus gilgitensis Ali

(Fabaceae): A Critically Endangered Species in the Gilgit District,

Pakistan. Phyton. 48: 211–223.

Ali, K., H Ahmad, N. Khan and S. Jury. 2014. Future of Abies pindrow in Swat

District, northern Pakistan. J. For. Res., 25: 211-214

Ali, S. I., M. Qaisar. 1992-2009. Flora of Pakistan. Nos. 194-208. Department of

Botany, University of Karachi.

Ali, S. I. and Y. J. Nasir. 1990-1992. Flora of Pakistan. No. 191-193. Department

of Botany, University of Karachi and National Herbarium, PARC,

Islamabad.

Amin, A. and R. M. Ashfaque. 1982. Phytosociological studies of Ayub

National Park, Rawalpindi. Pak. J. For., 32(4): 130-135.

Amjad, M. S. 2012. Life form and leaf size spectra of vegetation in Kotli Hills,

Azad Jammu and Kashmir (Pakistan). Greener J. Agr. Sci., 2 (7): 345-350.

Angelova, K. and A. Tashev. 2005. Complex analysis of the life forms of

flowering plants in Mount Chepan and their vertical ranges of spread in

altitude.Trakia J. Sci., 3(6): 32-35.

Backéus, I. 1992. Distribution and vegetation dynamics of humid savannas in

Africa and Asia. J. Veg. Sci., 3: 345–356.

Backeus, I. 1993. Ecotone Versus Ecocline: Vegetation zonation and dynamics

around a small reservoir in Tanzania. J. Biogeo., 20(2): 209-218.

Batanouny, K. H. and N. A. Baeshin. 1983. Plant communities along Medina-

Badr road across the Hejaz Mountains, Saudi Arabia. Vegetatio 53(1): 33–

43.

Batanouny, K. G. 1979. Vegetation along the Jeddah-Mecca road: pattern and

process as affected by human impact. J. Arid Environ., 2: 21–30.

190

Barik, K. I. and B. N. Misra. 1998 “Biological Spectrum of grassland of South

Orissa”, Ecoprint, 5(1): 73 – 77.

Beg, A. R. 1975. Wildlife habitats of Pakistan. Bull, no. 5. Pak. Forst. Inst.

Peshawar.

Beg, A. R. and M. H. Khan. 1984. Some more plant communities and the

future of dry oak forest zone in Swat valley. Pak. J. For., 34(1): 25-35.

Bhandari, B.S., J. P. Mehta and S. C. Tiwari. 1999. Floristic composition,

biological spectra and diversity of burnt and unburnt submontane

grazing lands of Garhwal Himalaya. J. Indian bot. Soc.,78: 107-110.

Bijalwan, R., M. Vats and S. P. Joshi. 2013. Plant phenological response to

microclimatic variations in an alpine zone of Garhwal Himalaya. J.

Applied and Nat. Sci., 5(1): 47-52.

Blažková D. 1993. Phytosociological study of grassland vegetation in North

Korea. Folia Geobot. Phytotax., 28(3): 247-260.

Brendenkamp, G. J., G. K. Theron and D. R. J. Van Vuuren, 1983. Ecological

interpretation of plant communities by classification and ordination of

quantitative soil characteristics. Bothalia, 14(3-4): 692–699.

Brown, C., R. Law, J. B. Illian, D. F. R. P. Burslem. 2011. Linking ecological

processes with spatial and non-spatial patterns in plant communities. J.

Eco., 99 (6): 1402–1414.

Brown, R. J., and J. J. Curtis. 1952. The upland conifer-hardwood communities

of southern Wisconsin. Ecol. Manog., 22: 217-234.

Buckland, S. T., D. L. Borchers, A. Johnston, P. A. Henrys, T. A. Marques.

2007. Line Transect Methods for Plant Surveys. Biometrics, 63 (4): 989–

998.

Cain, S. A. and G. M. D. Castro. 1959. “Manual of vegetation analysis”.

Harper and Brothers, Publication New York. pp: 355.

Champion, G. Harry and S. K. Seth. 1965. Forest types of Pakistan. Pakistan

Forest Institute, Peshawar. 233 pp.

191

Chaudhry, A. A., M. Hameed, R. Ahamd and A. Hussain. 2001.

Phytosociological studies in Chhumbi Surla Wildlife Sanctuary,

Chakwal, Pakistan II. Phytoecology. Int. J. Agr. Bio., 3(4): 369-374.

Chaudhri, I.I. 1960. The vegetation of Kaghan valley. Pak. J. For., 10(4): 285-

294.

Chaudhri, I.I. 1961. The Vegetation of Karachi. Vegetatio, 10(3-4): 229-246.

Daubenmire, R.F. 1943. "Vegetational zonation in the rocky mountains". Bot.

Rev., 9(6): 325–393.

El-Demerdash, M., A. Hegazy and A. Zilay. 1994. Distribution of the plant

communities in Tihamah coastal plains of Jazan region, Saudi Arabia.

Vegetatio, 112: 141-151.

Farooq, S., A. Z. Khan, M. Yousaf and F. Haq. 2010. Phytosociological study of

Push Ziarat area (Shawal) in the south Waziristan, Pakistan. Pak. J. Weed

Sci. Res. 16(1): 47-55.

Fonge, B. A., D. A. Focho, E. A. Egbe, A. S. Tening, A. N. Fongod, G. A. Neba

and Ze A. Mvondo. 2011. The effects of climate and edaphic factors on

plant colonisation of lava flows on Mount Cameroon. J. Eco. Nat. Env.,

3(8): 255-267.

Ghani, A.E. M. and W. Amer. 2003. Soil-vegetation relationships in a coastal

desert plain of southern Sinai, Egypt. J. Arid Env., 55(4): 607-628.

Hänninen, H., K. Tanino. 2011. Tree seasonality in a warming climate. Trends

Plant Sci., 16(8): 412-416.

Han, X., Z. Hu., G. Xin., X. Shi., D. Huang and G. Zhang. 2014. Studies on the

characteristics of vegetation and soil on Mount Sejila, Tibet. Pak. J. Bot.,

46(2): 457-464.

Haq, F., H. Ahmad., M. Alam. 2011. Traditional uses of medicinal plants of

Nandiar Khuwarr catchment (District Battagram), Pakistan. J. Med.

Plants Res., Vol. 5(1): 39-48.

Haq, F., H. Ahmad, M. Alam, I. Ahmad and R. Ullah. 2010. Species diversity

of vascular plants of Nandiar valley western Himalaya, Pakistan. Pak. J.

Bot., Special Issue (S.I. Ali Festschrift) 42: 213-229.

192

Haq, F., H. Ahmad, R. Ullah. Z. Iqbal. 2012. Species Diversity and ethno

botanical classes of the flora of Allai valley district Battagram Pakistan.

Int. J. Pl. Res., 2(4): 111-123.

Haq, F., H. Ahmed and Z. Iqbal. 2015a. Vegetation composition and ecological

gradients of subtropical-moist temperate ecotonal forests of Nandiar

Khuwar catchment, Pakistan. Bangladesh J. Bot. 44(2): 267-276.

Haq, F., H. Ahmed and Z. Iqbal. 2015b. Vegetation description and

phytoclimatic gradients of sub tropical forests of Nandiar Khuwar

catchment District Battagram. Pak. J. Bot. 47(4)1399-1405.

Haq, F., S. Rehman, H. Ahmad, Z. Iqbal, R. Ullah. 2012. Elemental analysis of

Paeonia emodi and Punica granatum by atomic absorption spectroscopy.

Amer. J. Bioch. 2(4): 47-50.

Hargreaves, P. 2008. Phytosocialogy in Brazil. American J. Plant Sci. biot., 2 (2):

12-20.

Hoveizeh, H., 1997. Study of the vegetation cover and ecological

characteristics in saline habitat of Hoor-e- Shagegan. J.Res. Const., 34(1):

27-31.

Huelber, K., M. Gottfried., H. Pauli., K. Reiter., M. Winkler., and G. Grabherr.

2006. Phenological response of snowbed species to snow removal dates

in the central Alps: Implication for climate warming. Arc. Antarct. Alp.

Res., 38(1): 99-103.

Hussain, A., M. A. Farooq, M. Ahmad, M. Akbar and M. U. Zafar. 2010.

Phytosociology and structure of central Karakoram national park of

northern areas of Pakistan. World Appl. Sci. J., 9 (12): 1443-1449.

Hussain, F., M. Ilyas, T. Suki. 1997. “Plant communities of Girbanr hills, Swat

district, north western Pakistan”, Ecol. Rev., 23(4): 247 – 60.

Hussain, F., A. Shah. 1989: Phytosociology of vanishing subtropical

vegetation of swat with special reference to Docut hills Pakistan i. Winter

aspect. Scientific Khyber, 2(1): 27-36.

193

Hussain, F., and A. Shah. 1991. Phytosociology of vanishing sub-tropical

vegetation of Swat with special reference to Docut Hills. II. Spring

Aspect. Sarhad J. Agri., 8:185-191

Hussain, F. and I. Illahi. 1991. Ecology and vegetation of Lesser Himalayan

Pakistan. Bot. Dept. Uni. of. Peshawar, pp. 187.

Ige, O. E., V. O. Olumekun and J. O. Olagbadegun. 2008. A Phytosociological

study of weed flora in three abandoned farmlands in Owo area, Ondo

state, Nigeria. Pak. J. Weed Sci. Res., 14(1-2): 81-89.

Ilyas, M., Z. K. Shinwari and R. Qureshi. 2012. Vegetation composition and

threats to the montane temperateforest ecosystem of Qalagai hills, Swat,

Khyber Pakhtunkhwa, Pakistan. Pak. J. Bot., 44: 113-122.

Iqbal, M. S., A. Ghafoor, H. Ahmad and Inamullah. 2013. Multivariate

analysis and selection to enquire genetic variation patterns in Nigella

sativa. Int. J. Agric. Biol., 15: 443-450.

Iqbal, M. Z., M. Shafiq. 1996. Plant communities on the sandy areas of

Karachi University campus. J. Isl. Acad. Sci., 9(3): 89-98.

Jabeen, T., and S. S. Ahmad. 2009. Multivariate analysis of environmental and

vegetation data of Ayub National Park Rawalpindi. Soil Environ., 28(2):

106-112.

Jensen, M., G. Simonson and M. Dosskey, 1990. Correlation between soils and

sagebrush-dominated plant communities of northeastern Nevada. Soil

Sci. Soc. Am. J., 54: 902-910.

Johnson, J. B. 1996. Phytosociology and gradient analysis of a sub alpine treed

fen in Rocky Mountain National Park, Colorado. Can. J. Bot., 74(8): 1203-

1213.

Johnson, J. B. and D. A. Steingraeber. 2003. The vegetation and ecological

gradients of calcareous mires in the South Park valley, Colorado. Can. J.

Bot., 81: 201–219.

Jurisic, B., B. Vidicki., N. C. Bojat and N. Puvača. 2014. Floristic diversity of

Posavina’s floodplain forests in Serbia and their wider geographical

context. Pak. J. Bot., 46(2): 447-456.

194

Kassas, M. A. and M. Imam. 1954. Habitat and plant communities in the

Egyptian desert. III. The wadi-bed ecosystem. J. Ecol., 42: 424–441.

Khafagi, O. M. A., A. E. A. Sharaf, E. B. E. Hatab and M. M. Moursy. 2013.

Vegetation composition and ecological gradients in Saint Katherine

Mountain, South Sinai, Egypt. American-Eurasian J. Agric. and Environ.

Sci., 13 (3): 402-414.

Khan, D. and S.S. Shaukat. 2005. Above ground standing phytomass of some

grass-dominated communities of Karachi: Winter aspect. Int. J.

Bio.Biotech., 2(1): 85-92.

Khan, N., M. Ahmed., M. Wahab., M. Ajaib. and S. S. Hussain. 2010. Studies

along an altitudinal gradient in Monotheca buxifolia (Falc.) A.D, forest,

district lower Dir, Pakistan. Pak. J. Bot., 42(5): 3029-3038.

Khan, N., M. Ahmed, M. Wahab, K. Nazim and M. Ajaib. 2010.

Phytosociology, structure and physiochemical analysis of soil in Quercus

baloot griff, forest District Chitral, Pakistan. Pak. J. Bot., 42(4): 2429-2441.

Khan, S. M., S. Page, H. Ahmad and D. Harper. 2014. Ethno-ecological

importance of plant biodiversity in mountain ecosystems with special

emphasis on indicator species of a Himalayan valley in the northern

Pakistan. Eco. Ind., 37: 175-185.

Khan, S. M., S. Page, H. Ahmad, H. Shaheen and D. Harper. 2012. Vegetation

dynamics in the Western Himalayas, diversity indices and climate

change. Sci., Tech. Dev., 31 (3): 232-243.12.

Khan, S. M., S. Page., H. Ahmad., Zahidullah., H. Shaheen., M. Ahamd and D.

Harper. 2013. Phyto-climatic gradient of vegetation and habitat

specificity in the high elevation western Himalayas. Pak. J. Bot., 45(SI):

223-230.

Korzeniak, J. 2013. Scope and data set of the phytosociological database

‘Grasslands in the Polish Carpathians’. Acta Soc. Bot. Pol., 82(3):237–242.

Laurance, W. F., A. A.Oliveira, S. G. Laurance, R. Condit, H. E. M.

Nascimento, A. C. S. Thorin, T. E. Lovejoy, A. Andrade, S. D'Angelo, J. E.

195

Ribeiro and C. W. Dick. 2004. Pervasive alteration of tree communities in

undisturbed Amazonian forests. Nature, 428: 171-175.

Lawrence, M. G. 2005. The relationship between relative humidity and the

dew point temperature in moist air: A simple conversion and

applications", Bull. Am. Meteorol. Soc., 86, 225–233.

Leonard, S.G., R. L. Miles, J. W. Burkhartdt. 1984. Comparision of soil

properties associated with basin wild rye and black greasewood in the

Great Basin region.

Leonard, S. G., R. L. Miles and P. T. Tueller. 1988. Vegetation science

applications for rangeland analysis and management. Hand book of

vegetation science, Springer Netherlands, 14: 225-252.

Lu Chang-yi., W. Yukshan., F. Y. T. Nora and B. Richard. 1998. Vegetation

analysis of a typical mangrove swamp—Lai Chi Wo coast of Hong Kong.

Chin. J. Oceanol. Limnol., 16(1): 72-77.

Malik, N. Z., M. Arshad and S. N. Mirza. 2007. Phytosociological Attributes of

different plant Communities of Pir Chinasi Hills of Azad Jammu and

Kashmir. Int. J. Agr. Bio., 09(4):569–574.

Malik, N. Z., F. Hussain and Z. H. Malik. 2007. Life form and leaf size spectra

of plant communities harbouring at Ganga Chotti and Bedorii hills. Int. J.

Agri. Bio., 15(6): 833-838.

Malik, Z. H. and N. Ahmed. 2006. Life form and index of similarity of plant

communities recorded at Sarsawa hills, Disrtict Kotli. J. Res. Sci., 17(1):

27-33.

Malik, Z. H. and F. Hussain. 1987. Phytosociological study of vegetation

around Muzaffarabad. (Eds.): Illahi and Hussain. Modern Trend of Plant

Science Research in Pakistan. Botany Dept. Peshawar University, 13-17.

Malik, N. Z. and Z. H. Malik. 2004. “Life form and index of similarity

recorded at Kotli Hills during Monsoon Pakistan. J. Life Soc. Sci., 2: 54-56.

Mashwani, Z. R., M. Arshad, M. Ahmad and M. A. Khan. 2011. Diversity and

distribution pattern of alpine vegetation along Lake Saif-ul-Mulook,

Western Himalaya, Pakistan. International Conference on

196

Environmental, Biomedical and Biotechnology IPCBEE vol.16 IACSIT

Press, Singapoore, 155-163.

McMahon, S. M., S. P. Harrison, W. S. Armbruster, P. J. Bartlein, C. M. Beale,

M. E. Edwards, J. Kattge, G. Midgley, X. Morin and I. C. Prentice. 2011.

Improving assessment and modeling of climate change impacts on

global terrestrial biodiversity. Trends Ecol. Evol., 26(5): 249-259.

Meher-Homji, V. M. 1981. Environmental implication of life form spectra from

India. J. Eco. Tax. Bot., 2:23-30.

Muhammad, S. 2003. Resource Management Plan Hillan-Battagram forests.

pp. 1–32.

Namgail, T., G. S. Rawat, C. Mishra, S. E. Van Vieren and H. H. T. Prins. 2012.

Biomass and diversity of dry alpine plant communities along altitudinal

gradients in the Himalayas. J. Plant Res., 125: 93-101.

Nasir, E., and S. I. Ali. 1970-1989. Flora of Pakistan. No. 1-190. National

Herbarium, PARC, Islamabad and Department of Botany, University of

Karachi, Pakistan.

Nazir, A. and Malik Z. H. 2006. Life form and index of similarity of plant

communities recorded at Sarsawa hills District Kotli. J. Res. Sci., 17(1): 27-

33.

Nezerkova, H. P. and M. Hejcman. 2006. A canonical correspondence analysis

CCA) of the vegetation-environment relationships in Sudanese

savannah, Senegal. S. Afr. J. Bot., 72: 256-262.

Nowak, A., S. Nowak., and M. Nobis. 2014. Diversity and distribution of rush

communities from the Phragmito-magno-caricetea class in Pamir

AlaiMountains (middle Asia: Tajikistan). Pak. J. Bot., 46(1): 27-64.

Pangtey, Y. P. S., R. S. Rawal., N. S. Bankoti and S. S. Samant. 1990. Phenology

of high-altitude plants of Kumaun in Central Himalaya, India. Int. J.

Biomet., 34(2):122-127.

Pearson, L. C. 1979. Effects of Temperature and Moisture on Phenology and

Productivity of Indian Ricegrass. J. Range Manag., 32(2): 127-134.

197

Peter, F. J. and G. S. Rob. 1991. An altitudinal zonation of tropical rain forests

using byrophytes. J. Biogeo., 18(6): 669–678.

Peter, F. and L. J. Erik. 1992. Vegetation types and patterns in Senegal based

on multivariate analysis of field and NOAA-AVHRR satellite data. J.

Veg. Sci., 3: 535-544.

Pharswan, K., J. P. Mehta, Subodh. 2010. Floristic composition and biological

spectrum of vegetation in alpine meadows of Kedarnath: Garhwal

Himalaya. Nat. Sci., 8(7): 109-115.

Poore, M. E. D. 1955. The use of phytosociological methods in ecological

investigations: I. TheBraun-Blanquet System. J. Ecol., 43 (1): 226-244.

Qadir, S. A., S. Z. Qureshi and M. A. Ahmed. 1966. A phytosociological

survey of Karachi University Campus. Vegetatio, 13(8): 339-362.

Qadri, M. Z. H. 1986. Phytosociological study on the vegetation of Kotli Hill,

Azad Kashmir, M. Phil. Thesis, Bot. Dep. Peshawar, University, 211 pp.

Ram, J. and P. Arya. 1991. “Plant forms and vegetation analysis of an alpine

meadow of Central Himalaya, India”, Proc. Indian Nat. Sci. Acad., 57(5):

311 – 317.

Rashid, A., F. Hussain and I. Illahi. 1987. Phytosociology of Attock-Nizampur

Hills. 1: Summer aspect. In: Modern Trends of Plant Science.Res.in

Pakistan. (Eds.): Illahi and Hussain. Bot. Dep. Peshawar University, pp.

47-52.

Saima, S., A. A. Dasti, Q. Abbas and F. Hussain. 2010. Floristic diversity

during monsoon in Ayubia national park, District Abbottabad, Pakistan.

Pak. J. Pl. Sci., 16 (1): 43-50.

Saima, S., A. A. Dasti., F. Hussain., S. M. Wazir and S. A. Malik. 2009. Floristic

compositions along an 18 – km long transect in Ayubia National Park

District Abbottabad, Pakistan. Pak. J. Bot., 41(5): 2115-2127.

Senesi, N., B. Xing and P.M. Huang. 2006. Biophysico-chemical processes

involving natural nonliving organic matter in environmental systems,

New York: IUPAC, 2006.

198

Shaheen, H., R. A. Qureshi and Z. K. Shinwari. 2011. Structural diversity,

vegetation dynamics and anthropogenic impact on lesser Himalayan

subtropical forests of Bagh District, Kashmir. Pak. J. Bot., 43(4): 1861-1866.

Shaukat, S.S., M.A. Khan., M. Mett and M. F. Siddiqui. 2014. Structure,

composition and diversity of the vegetation of Hub dam catchment area,

Pakistan. Pak. J. Bot., 46(1): 65-80.

Sher, Z. and Z. D. Khan. 2007. Floristic composition, life form and leaf spectra

of the vegetation of Chagharzai valley, District Buner. Pak. J. Pl. Sci., 13

(1): 57-66.

Shimwell, D. W. 1971. The description and classification of vegetation.

Sedgwick and Jackson, London. pp. 332.

Shipley, B. and P. A. Keddy. 1987. The individualistic and community-unit

concepts as falsifiable hypotheses. Vegetation, 69: 47–55.

Siddiqui, M. F., M. Ahmed, M. Wahab, N. Khan and M. Uzair. 2009.

Phytosociology of Pinus roxburghii Sargent. (chir pine) in lesser

Himalayan and Hindu kush range of Pakistan. Pak. J. Bot., 41(5): 2357-

2369.

Singh, E. and M. P. Singh. 2010. Biodiversity and phytosociological analysis of

plants around the municipal drains in Jaunpur. Int. J. Biol. Life Sci., 6:2.

Song, J. S. 1992. A comparative phytosociological study of the subalpine

coniferous forests in northeastern Asia. Vegetatio, 98 (2): 175-186.

Sundriyal, R. C., A. P. Joshi, R. Dhasmana. 1987. Phenology of high altitude

plants at Tungnath in the Garhwal Himalaya. Trop. Ecol.,28: 289- 299.

Suresh, D. and S. Paulsamy. 2010. Phenological observation and population

dynamics of six uncommon medicinal plants in the grasslands of

Nilgiris, Western Ghats, India. Maejo Int. J. Sci. Tech.,4(2): 185-192.

Suzuki, K. and P. Saenger. 1996. A phytosociological study of mangrove

vegetation in Australia with a latitudinal comparison of East Asia. Mang.

Sci., 1: 9-27.

199

Tareen, R. B. and S. A. Qadir. 1993. Life form and leaf size spectra of the plant

communities of diverse areas ranging from Harnai, Sinjawi to Duki

regions of Pakistan. Pak. J. Bot., 25(1): 83-92.

Thorpe, R. S., R. P. Brown, M. Day, A. Malhotra, D. P. McGregor and W.

Wuster. 1994. Testing ecological and phylogenetic hypotheses in

microevolutionnary studies. In: Eggleton, P and Vane-Wright, R. I. (eds)

phylogenetics and ecology. Academic Press, London, UK. PP. 189-206.

Velazquez, A. 1994. Multivariate analysis of the vegetation of the volcanoes

Tláloc and Pelado, Mexico. J. Veg. Sci., 5(2): 263-270.

Vogiatzakis, I. N., G. H. Griffiths and A. M. Mannion. 2003. Environmental

factors and vegetation composition, Lefka Ori Massif, Crete, S. Aegean.

Global Eco. Biogeo., 12(2):131-146.

Vujnovic, K., R. W. Wein., M. R.T. Dale. 2002. Predicting plant species

diversity in response to disturbance magnitude in grassland remnants of

central Alberta. Can. J. Bot., 80(5): 504-511.

Weber, H. E., J. Moravec and J. P. Theurillat. 2000. International code of

phytosocialogical nomenclature. J. Veg. Sci., 11 (5): 739–768.

Whittaker, R. H., S. A. Levin, R. B. Root. 1973. Niche, habitat and ecotype. Am.

Nat., 107(955): 321-338.

Wiens, J. J. and C. H. Graham. 2005. Niche conservatism: integrating

evolution, ecology and conservation Biology. Ann. Rev.Eco. Evo. Syst., 36:

519–539.

Yavari, A., S.M. Shahgolzari and M. Atri. 2010. Application of floristic marker

in eco phytosociology method for diagnosing existing intra specific

diversity in plants: a case study of Astragalus glaucops. Int. J. Agr. Bio., 12:

887–890.

Zuo, X. A., S. K. Wang., X. Y. Zhao., J. Lian. 2014. Scale dependence of plant

species richness and vegetation-environment relationship along a

gradient of dune stabilization in Horqin Sandy Land, Northern China. J.

Arid Land, 6(3): 334–342.

200

APPENDIX I

Stands Altitude Barom Press

Density altitude

Temp Wind speed

Hum Heat Index

Dew point

Wet bulb

Thakot 1 539 949.4 1301 33.9 1.3 26.6 31.3 11.2 19

Thakot 2 530 950.3 1363 33.6 1.5 24.6 32.3 9.6 18.4

Chorlangay 759 924.7 1700 30 0.5 23.8 30.5 10.5 18

Peshora 881 915.2 1751 31.1 0.9 18.1 33.7 8.7 18.1

Gagikot 986 899.3 1840 30.6 0.5 23.9 30.4 9.8 17.7

Shagai 1092 889 2015 30.2 0.8 26.1 34.1 8.6 18.5

Paimal 2 1145 882.9 2102 31.1 0.5 25.9 33.1 8.7 18.3

Naraza 1197 876.8 2189 28.4 0.5 48.5 38 20.5 23.6

Paimal 3 1264 869.8 2027 27 0.4 23.5 23.5 3.8 13.3

Paimal 4 1284 867.7 2075 29 0.5 39.5 28.5 13.7 18.6

Lamai 1814 813.9 2210 27.9 0.7 36.7 22.6 8.6 14.4

Khairabad 1328 862.8 2426 26.7 0.7 22.8 38.6 12.7 20.7

Gada 1254 870.8 1946 25.8 0.6 37.7 22.7 8.7 14.5

Nowshera 1212 875.3 1892 26.9 0.7 36.5 22.8 8.5 14.6

Nili Reen 1243 872.1 1923 27.4 0.5 23.5 23.5 5.8 13.6

Shabora 1 1265 869.7 2089 26.4 0.9 23.5 23.5 3.8 13.4

Paimal 5 1463 848.5 2240 25.8 0.4 37.9 22.9 8.9 14.6

Paimal 1 1229 873.4 1894 26.1 0.6 20.2 22.4 2.9 12

Deshara 1493 845.5 2195 26.2 0.6 20.1 23.6 2.7 11.9

Dabrai 1449 850.1 2148 27 1 19.2 23.7 5.6 8.4

Rajmira 1488 846.9 2381 25.6 0.5 37.9 22.9 8.8 14.7

Shabora 2 1456 849.3 1939 26.3 0.5 20 15.2 5 7.3

Lundai 1 1480 846.7 2377 26.1 0.7 32.8 28.6 13.7 20.7

Nil Sharkolai 1626 832.2 2090 26 1.6 48 15 6.2 10.4

Belandkot 1575 837.3 2520 25.7 0.6 32.9 28.7 13.6 20.6

Anora 3 1600 834.8 2607 25.5 0.9 50.1 16.4 8.8 12.5

Batangi 1624 832.3 2693 27 0.4 40 35.5 16.6 21.2

Nil Batangi 1792 815.3 2872 24.2 0.6 49.1 33.1 18.7 21.9

Lundai 2 1876 806.8 2961 24.1 1 49 16.4 7.7 12.4

Kiari 1918 802.6 3006 25.9 0.6 49.1 33.1 18.6 21.9

Bashakhan 1939 800.4 3028 25.8 0.7 49.2 33.2 18.7 21.8

Gat 1960 798.3 3050 25.6 0.8 49.3 33.3 18.8 21.7

Shinglai 1720 822.5 2760 25.5 0.5 41.2 34.5 18.4 22.5

201

Anora 2 1751 819.4 2683 25.4 1.1 49.1 16.4 7.8 12.5

Anora 1 1766 817.9 2644 25.2 0.9 49.2 16.6 7.9 12.6

Chapra 1781 816.3 2605 25.3 0.8 43.9 22.9 11.1 15.9

Jarotia 1858 808.6 2653 25.1 0.9 49.4 33.4 18.9 21.6

Habib ban 2 1934 800.8 2701 23.7 0.5 60.4 22.3 15.3 17.5

Habib ban 1 1840 810.3 2584 24.3 0.6 54.4 22.2 15.2 17.6

Bach maidan 2047 789.9 2764 21.2 0.6 46.3 19 8.1 12.9

Hill 1873 807 2572 24.4 0.5 61.2 20.9 13 15.6

Jatial 1889 805.3 2636 25.1 0.8 49.3 16.7 7.6 12.7

Chapar 1897 804.4 2668 25 0.4 59.3 22.3 14 16.5

Sandawali 1905 803.6 2699 24.9 1.2 35.5 22 7.8 14

Sharkolai 2016 792.8 2673 24.7 0.9 49.4 16.8 7.5 12.8

Riar 2042 790.4 2813 24.8 0.6 58 23.1 14 16.6

Doda 1 2032 791.3 2778 24.6 0.4 46.1 21.6 10.1 14.3

Sarmast 2036 791 2441 24.5 0.6 54.5 12.3 4.2 8.1

Mirani kandao 2105 784.3 3053 24 0.9 61.5 25.7 17.1 19.5

Sheed 2164 778.7 3128 23.5 0.9 51.5 23.7 15.3 18.1

Jaro 2222 773 3202 23.3 0.8 46.6 27.6 15.3 18.5

Guchai 2262 769.1 3145 22.8 0.7 50.6 22.6 13.3 17.5

Ledai 2282 767.2 3116 23 0.6 51.7 23.9 15.1 18

Terkana 2302 765.3 3087 22.5 0.6 48.3 21.6 10.3 14.2

Charoona 2365 759.1 3314 22.3 0.9 46.8 26.7 16.2 17.4

Manra 2336 762.1 3194 22.1 0.8 24.5 21.5 2.6 11.3

Bach upper 2241 771.3 3062 20.1 0.7 62 21 13.6 15.9

Trapa 2299 765.7 3122 22.2 0.9 46.9 26.8 16.1 17.7

Lunda Matra 2357 760.1 3182 21.7 0.7 44.6 20.3 8.6 13.4

Doba 2513 745.6 3356 21.5 0.8 56.7 21.6 13.4 17.3

Chail kambr 2668 731.1 3529 20.9 1.5 58.3 20.1 11.5 13.9

Gabrai 2664 731.6 3548 20.6 1.6 58.4 20.2 11.6 13.8

Mirani 1 2659 732 3567 21 0.9 56.3 21.5 13.3 16.3

Baleja 2697 728.4 3679 20.8 0.9 41.8 22.8 10.5 14.9

Chaprai 2677 730.2 3583 20.7 0.7 44.1 22.1 9.8 14.2

Birthmaidan 2760 722.7 3500 20.4 0.8 57.5 10.2 3.1 6.7

Harpal 2801 719 3458 19.7 0.7 54.2 20.5 11.9 15.2

Doda 2 2842 715.2 3416 19.6 0.8 57.4 10.4 3.5 6.8

Kachkol 2862 713.3 3626 19.5 0.9 55.2 15.2 7.8 10.4

202

Mirani 2 2882 711.5 3836 19.1 0.9 50.7 21.6 12.2 14.7

Machaisar 2899 710 3830 18.5 0.7 57.3 10.6 3.9 6.9

Belmaz 2908 709.2 3827 18.4 0.7 53.1 13.2 6.1 9.9

Lekoni 2912 708.9 3825 18.3 0.7 53.3 13.3 6.3 9.8

Karganja L 2916 708.5 3823 17.9 1.4 50.2 15.8 7.5 11.9

Chail 2997 701.1 4086 17.8 1.3 53.8 26.6 16.1 18.6

Magrai 2991 701.6 3974 17.7 1.4 53.6 26.4 16.3 18.4

Shaheed Gali 2985 702.1 3861 17.6 1.5 44.7 17.6 6.8 11.2

Kar Ganja H 3265 677.8 4363 17.5 1.6 53.5 20.5 11.7 14.5

Alishera 3608 657.7 4505 16.5 1.7 53.8 20.6 11.8 14.5

Malkaisar 3780 640.8 4712 15.5 1.9 54.5 20.7 11.9 14.5

203

APPENDIX II

Stands Slope angle

Slope aspect

E. C. pH Organic matter

P mg/ kg

K mg /kg

Saturation

Thakot 1 50 S 0.72 7.2 0.88 19.5 220 41

Thakot 2 50 N 0.67 7.3 0.98 12.7 200 50

Chorlangay 45 N 0.66 5.55 1.03 1.8 397 52

Peshora 30 SE 0.7 5.2 1.65 2.9 300 47

Gagikot 30 S 0.52 5.4 1.7 2.9 350 52

Shagai 30 N 0.58 5.24 1.67 2.8 400 49

Paimal 2 35 SW 0.59 5.3 1.66 2.7 300 48

Naraza 25 NE 0.59 5.9 1.2 2.9 250 44

Paimal 3 30 SW 0.64 5.6 1.1 2.8 205 41

Paimal 4 30 SW 0.59 5.34 1.69 2.8 380 49

Lamai 25 N 0.64 5.3 0.89 3.1 142 42

Khairabad 25 N 0.6 5.9 1.75 2.7 302 48

Gada 35 N 0.65 5.54 1.3 1.9 398 53

Nowshera 20 NW 0.66 5.29 0.88 2.1 140 43

Nili Reen 25 NW 0.62 5.55 1.08 5.1 160 45

Shabora 1 30 N 0.6 5.59 1.08 5.1 160 51

Paimal 5 40 S 0.65 5.64 1.3 1.9 398 54

Paimal 1 20 S 0.39 5.6 0.8 2.6 198 40

Deshara 25 N 0.38 5.65 0.94 3.2 180 40

Dabrai 30 S 0.6 5.9 0.9 2.6 270 44

Rajmira 30 NE 0.7 5.91 1.85 2.1 322 46

Shabora 2 30 N 0.51 6.7 1 4.1 123 47

Lundai 1 40 N 0.68 7.21 1.1 7.4 100 45

Nil Sharkolai 25 S 0.56 5.44 0.94 3.5 118 42

Belandkot 35 N 0.61 6 1.24 4.5 125 46

Anora 3 50 NW 0.45 7.72 0.98 7.8 128 38

Batangi 30 SE 0.62 5.15 1.72 3.2 320 41

Nil Batangi 40 N 0.56 5.48 0.94 3.5 118 42

Lundai 2 55 N 0.72 7.31 1.7 8.4 120 54

Kiari 40 N 0.41 6.8 1.04 4.5 121 42

Bashakhan 40 N 0.42 6.7 1.03 4.6 119 43

204

Gat 45 S 0.65 6 1.8 2.4 308 44

Shinglai 40 N 0.65 5.8 1.8 2.4 310 44

Anora 2 55 N 0.45 7.72 0.98 7.8 128 38

Anora 1 55 N 0.45 7.71 0.97 7.9 117 41

Chapra 40 NW 0.45 7.72 0.98 7.8 129 40

Jarotia 30 E 0.6 6.1 1.7 2.6 318 54

Habib ban 2 45 N 0.44 5.48 1.5 2.9 149 46

Habib ban 1 40 SE 0.45 5.46 1.3 2.8 139 41

Bach maidan 25 W 0.68 6.2 1.5 8.3 206 57

Hill 30 W 0.66 6.19 0.73 1.8 342 45

Jatial 25 S 0.66 6.19 0.73 1.8 340 45

Chapar 30 SW 0.55 5.4 1.92 13.3 230 68

Sandawali 30 W 0.45 7.62 0.9 7.9 131 50

Sharkolai 25 S 0.56 5.44 0.94 3.5 122 42

Riar 30 S 0.65 6.2 1.85 11.2 215 69

Doda 1 45 N 0.68 7.1 1.5 8.3 204 57

Sarmast 40 N 0.39 5.26 1.1 2.9 145 55

Mirani kandao

25 SW

0.5 6.5 1.1 2.8 150 61

Sheed 30 SE 0.65 6.4 1.8 4.6 305 64

Jaro 30 E 0.7 6.6 1.3 9.3 250 60

Guchai 50 W 0.7 6.25 1.4 9.4 248 59

Ledai 30 E 0.7 6.25 1.6 9.3 260 59

Terkana 30 W 0.66 6.5 1.73 10.8 316 65

Charoona 45 E 0.68 6.2 1.5 8.3 203 57

Manra 45 N 0.6 6.25 1.4 9.3 253 59

Bach upper 45 NW 0.7 6.25 1.4 9.3 251 59

Trapa 45 E 0.66 6.1 1.4 8.2 197 58

Lunda Matra 50 N 0.66 6.19 1.73 1.8 341 58

Doba 40 E 0.7 6.3 1.5 8 240 58

Chail kambr 50 E 0.68 6.2 1.5 8.3 199 57

Gabrai 45 SE 0.67 6.1 1.6 8.2 201 56

Mirani 1 35 SW 0.8 6.4 1.3 7.3 230 55

Baleja 40 N 0.65 5.7 1.68 15.4 178 53

Chaprai 40 N 0.64 5.6 1.69 15.3 179 54

205

Birthmaidan 35 W 0.68 6.9 1.6 8.7 235 67

Harpal 50 SE 0.6 6.5 1.1 7 210 56

Doda 2 45 N 0.6 6.7 1.5 8.5 219 60

Kachkol 40 E 0.68 6.2 1.5 8.3 202 57

Mirani 2 30 W 0.6 6.24 1.5 9.3 250 59

Machaisar 50 N 0.7 6.75 1.8 9.5 245 60

Belmaz 45 NW 0.7 6.5 0.98 10.3 208 55

Lekoni 45 NW 0.75 6.5 1 18.6 243 55

Karganja L 45 NW 0.8 6.2 0.8 17.3 216 50

Chail 35 S 0.8 6.26 1.4 9.4 255 58

Magrai 45 NW 0.65 5.7 1.68 15.4 182 53

Shaheed Gali 50 N 0.7 6.4 0.9 18.3 241 55

Kar Ganja H 55 N 0.75 6.5 0.9 18.6 239 55

Alishera 35 SW 0.73 6.6 0.88 18.6 239 53

Malkaisar 35 SW 0.74 6.6 0.89 18.6 238 55

206

A FEW FIELD ACTIVITIES DURING RESEARCH STUDY

Plate-1: Psilotum nudum in rock crevices Plate-2: Quercus glauca population in

Chorlangay

Plate-3: Rhododendron arboreum in Rajmira Plate-4: Ficus racemosa in Chorlangay

Pate-5: Caltha alba population in Baleja Plate-6: Ulmus wallichiana the host of Vescum

album in Shamlai

207

Plate-7: Overgrazing and fire destroyed vegetation in Lamai

Plate-8: The gorgeous location in Baleja maidan

Plate-9: Betula utilis population in

Shaheedgali

Plate-10: Primula denticulata population in

Karganja

Plate-11: Paeonia emodi and Viburnum

cotinifolium in Birth maidan

Plate-12: Gerardiana palmata and Pteridium

equilinum in Ledai

208

Plate-13: Wikstroemia canescens population

in Charoona

Plate-14: Pinus wallichiana forests showing

loss of habitat

Plate-15: A view of Nandiar Khuwar near

Chorlanga

Plate-16: A view of Pinus wallichiana stem

in Magrai

Plate-17: A view of regeneration of Quercus glauca

Plate-18: A view of field study near Banser

209

Plate-19: A view of field study near Banser Plate-20: A view of field study in Shamlai

Plate-21: Field study in Ayeen Plate-22: Field farming in Hill

Plate-23: A view of field study in Sandasaray

Plate-24: Leaf fodder collection of Quercus

semicarpifolia in Baleja