SPATIAL VARIABILITY IN SPECIES COMPOSITION IN NEOTROPICAL MONTANE TREE COMMUNITIES

BY

KARINA GARCIA CABRERA

A Thesis Submitted to the Graduate Faculty of

WAKE FOREST UNIVERSITY GRADUATE SCHOOL OF ARTS AND SCIENCES

in Partial Fulfillment of the Requirements

for the Degree of

MASTER OF SCIENCE

Biology

December 2011

Winston-Salem, North Carolina

Approved By:

Miles R. Silman, Ph.D., Advisor

Robert A. Browne, Ph.D., Chair

William K. Smith, Ph.D.

ii

ACKNOWLEDGEMENTS

I would like to sincerely thank my advisor, Miles R. Silman, for giving me the

opportunity to work and travel in the Peruvian Andes, especially for his patience, support,

and friendship. I thank my committee members, Dr. Robert Browne and Dr. William K.

Smith, for their time, helpful ideas and comments in this research.

A special gratitude to my companion William Farfan for all his help and support during

the field work and data processing. But especially for his friendship and love. Special

thanks to Norma Salinas, my undergraduate professor for introducing me to Botany in the

Peruvian Andes.

I thank my friends in Cusco for their help during the data collection but especially for

their friendship and support, to Natividad Raurau, Tatiana Boza, Yolvi Valdez., Vicky

Huaman, Judit Huaman. Without the Peruvian team I would not have been able to

complete this research, and my special thanks go to Luis Imunda, Alberto Gibaja, Flor

Zamora, Erickson Urquiaga, Percy Chambi, Israel Cuba, Jhoel Delgado, Kilmenia Luna,

Karina Cartagena, Luis Mansilla, Janet Mamani and Jesus Castaneda.

I also thank to my lab mates, past and present, Josh Rapp, Kenneth Feeley, Rachel

Hillyer, Noah Yavit, Becky Dickson and Sarah Maveety for their help and support.

Finally, I would like to thank all my family for their love and unconditional support,

especially to my mother Martha and my grandmother Rosa.

This study was funded by Andes Biodiversity and Ecosystem Research Group (ABERG),

Wake Forest University, the National Science Foundation grant DEB 0743666, the

Gordon and Betty Moore Foundation Andes to Amazon Program, and the Blue Moon

Foundation. I thank the Servicio Nacional de Areas Naturales Protegidas (SERNANP) of

the Manu National Park for all the research permits and help in the field.

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TABLE OF CONTENTS

ACKNOWLEDGEMENTS……………………………………………………….. ii

LIST OF TABLES………………………………………………………………… v

LIST OF FIGURES………………………………………………………………... vi

ABSTRACT……………………………………………………………………...... ix

INTRODUCTION…………………………………………………………………. 1

Factors that influence the composition and distribution of tree

communities………………………………………………………………... 1

Elevation…………………………………………………………… 1

Landscape variability not related to Elevation……………………. 3

Cloud regime……………………………………………………..... 3

Topographical factors……………………………………………... 4

Soils………………………………………………………………... 5

Solar radiation……………………………………………………. 5

Objectives………………………………………………………….. 6

METHODS………………………………………………………………………… 8

Study site…………………………………………………………………... 8

Geology and topography…………………………………………………... 8

Climatology………………………………………………………………... 8

Plot location………………………………………………………………. 9

Plot establishment………………………………………………………… 9

Community Analyses………………………………………………………. 10

iv

Ordination based on species composition and abundance………………... 10

Diversity index…………………………………………………………….. 11

Landscape variables …………...…………………………………………. 11

Aspect, Slope and Potential Solar Radiation (PSR)……………………….. 11

Soils………………………………………………………………………... 11

Disturbance………………………………………………………………... 12

ANOVA…………………………………………………………………….. 12

RESULTS………………………………………………………………………….. 13

Floristic composition……………………………………………………… 13

Diversity patterns………………………………………………………….. 14

Floristic similarity…………………………………………………………. 15

DCA ordination……………………………………………………………. 16

Environmental variables…………………………………………………... 17

ANOVA analysis…………………………………………………………… 18

DISCUSSION……………………………………………………………………... 19

Floristic composition……………………………………………………… 19

Floristic similarity......................................................................................... 21

Species composition trends………………………………………………... 22

Diversity patterns………………………………………………………….. 22

Interaction among variables…………………………………………......... 23

Combined analysis………………………………………………………… 24

CONCLUSIONS………………………………………………………………....... 25

LITERATURE CITED……………………………………………………………. 26

v

TABLES…………………………………………………………………………… 31

FIGURES………………………………………………………………………….. 41

CURRICULUM VITAE…………………………………………………………... 64

vi

LIST OF TABLES

Table I. Tree plot locations.

Table II. Number of plots per site.

Table III. Summary of stand structure and diversity.

Table IV. Environmental data for tree plots.

Table V. ANOVA tables for variation in diversity including all the plots.

vii

LIST OF FIGURES

Figure 1. Manu National Park (top) showing the locations of the tree plots. Inset (bottom)

shows a close ups of the plots

Figure 2. Landscape variation in aspect for the study area.

Figure 3. Landscape variation in slope for the study area

Figure 4. Seasonal variability in Potential Solar Radiation in the study area. Top,

February (Wet season). Bottom, July (Dry season)

Figure 5. Number of tree individuals per family for 0.1ha plots across the elevational

gradient. Values expressed as percentages.

Figure 6. Number of tree individuals per family for 0.1ha plots within the high elevation

plots near Andean treeline. Values expressed as percentages.

Figure 7. Floristic Similarity, Mantel test between Bray-Curtis index (abundance-based)

vs. geographic distance between pairs of tree plots. A. All plots included, B.

Andean treeline, across the elevational gradient.

Figure 8. Floristic Similarity, Mantel test between Sorensen’s index (presence-absence

based) and geographic distance between pairs of tree plots. A. All plots included,

B. Andean treeline, across the elevational gradient.

Figure 9. Detrended Correspondence Analysis (DCA) of the tree plots based on the

abundance at the genus level. A. Plots across the elevational gradient and B.

Plots in the Andean treeline. (above 3000 m).

Figure 10. Detrended Correspondence Analysis (DCA) of tree plots using tree species

abundance. A. All the plots across the elevational gradient included. B. Plots in

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Trocha Union at 2449m excluded. C. DCA based on presence-absence of tree

species.

Figure 11. Linear regression between Axis 1 (DCA of tree plots across the elevational

gradient) and elevation.

Figure 12. Detrended Correspondence Analysis (DCA) of plots within the high elevations

in the Andean treeline (above 3000m) A. Based on tree species abundance. B.

Based on presence-absence of tree species. Letters represent the different sites

followed by the elevation.

Figure 13. Linear regression of number of tree individuals per plot and elevation. A. All

plots included (36 plots), B. Plots across three different gradients: Trocha Union,

Callanga and San Pedro and C. Plots near the Andean treeline (above 3000 m).

Figure 14. Linear regression of species richness per plot and elevation. A. All plots

included (36 plots), B. Plots across three different elevational gradients: Trocha

Union, Callanga and San Pedro and C. Plots near the Andean treeline (above

3000 m).

Figure 15. Linear regression of diversity index (Fisher’s alpha) per plot and elevation. A.

All plots included (36 plots), B. Plots across three different elevational

gradients: Trocha Union, Callanga and San Pedro and C. Plots near the Andean

treeline (above 3000 m).

Figure 16. Correlation between variables: elevation, aspect, slope and potential solar

radiation for February (PSR_Feb) and July (PSR_Jul).

Figure 17. Linear regressions between aspect and A. Elevation, B. Species richness per

plot and C. Number of tree individuals per plot.

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Figure 18. Linear regressions between slope and A. Elevation, B. Species richness per

plot and C. Number of tree individuals per plot.

Figure 19. Linear regressions between the potential solar radiation for February (Wet

season) and A. Elevation, B. Species richness per plot, C. Number of tree

individuals per plot and between the potential solar radiation for July (Dry

Season) and D. Elevation, E. Species richness per plots and F. Number of tree

individuals per plot.

Figure 20. Linear regressions between the average C-stock (kg C/m2) in soils and A.

Number of tree individuals per plot and D. Species richness per plot; between

average N-stock (kg N/m2) in soils and B. Number of tree individuals per plot

and E. Species richness plot and between the C/N ratio in soils and C. Number

of tree individuals per plot and F. Species richness per plot.

x

ABSTRACT

A plot network was used to look at changes in stand-level characteristics, diversity, and

floristic composition across the elevational gradient and at tree line. Thirty-six 0.1-ha tree

plots were installed (1) along three different elevational transects in tropical montane

cloud forest (TMCF) between 1500 and 3600 m and (2) across a ~ 40 km landscape

transect near tree line above 3200 m in southeastern Peru. Stand variables were correlated

with explanatory variables such as geographic distance, environmental variables as aspect,

slope, potential solar radiation (PSR), and carbon-nitrogen soil content to examine the

variation explained by environmental variation in addition to elevation (temperature).

Results show a total of 435 species across the elevation gradient and 121 in the landscape

sample near Andean treeline. At mid elevation plots (1600 – 2900 m) Cyatheaceae and

Melastomataceae were the most abundant families. The plots near treeline (above 3200

m) were similar in their composition at family and genus level but distinct at the species

level, with Melastomataceae being the family with most individual trees and Asteraceae

the most species-rich family. In both the elevational transects and the landscape-level

within-elevation transect, geographic distance between plots had no correlation with

floristic similarity. Elevation was correlated with tree community composition and

diversity for all plots, but the strength of the trend changed between elevational transects,

indicating the importance of landscape heterogeneity. Correlations with environmental

variables (aspect and slope) showed no relationship with either species richness or

diversity. However there was a significant relationship with potential solar radiation

(PSR). In this study elevation was the main factor that influenced the floristic

xi

composition and diversity across the elevational gradients, even across small elevation

changes near tree line. Potential solar radiation had significant effects on species richness

in both the elevational transects and the landscape sample near Andean treeline. These

results indicate an important role of PSR. More empirical and experimental data are

needed to fully understand the effect of PSR on plant communities in these montane

forests. Future studies should incorporate additional explanatory variables such as

disturbance (both anthropogenic and natural), cloud regime and a broader array of soil

nutrients.

1

INTRODUCTION

The tropical Andes is the most diverse of the twenty five richest Biodiversity Hotspots in

the tropics (Tropical Andes Biodiversity Hotspot [TABH]; Myers et al., 2000) and

contains a disproportionate amount of the world's tropical montane cloud forest (TMCF)

diversity. Comprising 0.8 percent of earth’s surface area, it harbors 10 percent of all plant

species, with an estimated 50-60 percent of those being endemic.

In addition to containing high biodiversity and endemism, the Tropical Andes are also

highly threatened (Gentry, 1995), with land conversion and climate change being the

primary threats (Feeley & Silman, 2010b). Due to a nearly complete lack of information

about species distributions in the Andes and species distributions in response to

environmental gradients (Feeley & Silman, 2010a, c), conservation is occurring with little

to no information about community composition, or the niches of the organisms that

comprise them. A central need is to understand the environmental factors involved in the

species distributions in high elevation Andean forests, particularly how changes in

environmental factors such as climate and land use will affect high Andean tree

communities (e.g. Feeley and Silman, 2010a).

Factors that influence the composition and distribution of tree communities: Elevation

A major focus of tropical montane forest research has been descriptions of how diversity

changed with increasing elevation. Gentry (1988a) study tropical forests using data from

different continents, focusing on how the community diversity and floristic composition

2

change along altitudinal and latitudinal gradients. On an altitudinal gradient in the

tropical Andes composed with unreplicated 0.1 ha plot data gathered from Colombia to

Argentina diversity was found to decrease linearly from 1500 m to the upper limit of the

forest above 3000 m. Similar results to those found by Gentry (1988a) were found by

Sanchez-Gonzalez and Lopez-Mata (2005) in Sierra Nevada, Mexico (2800 m to 4000

m); Kessler (2000, 2001) in the Bolivian Andes, (220 m to 3950 m) and Luteyn (2009) in

Tropical Andes for a variety of plant growth forms.

Another major question has been whether community changes with elevation are

continuous, or whether there are ecotones in tropical montane forests (Grubb &

Whitmore, 1966). On an altitudinal gradient (600 to 3400 m) on Mount Kinabalu, Borneo,

Kitayama (1992) divided the species composition into four discrete altitudinal vegetation

zones. This contrasts with studies in the Neotropics in Volcan Barva, Costa Rica (30m to

2600m; (Lieberman et al., 1996) and in Sierra de Manantlan, Mexico (1500m to 2500m;

(Vazquez & Givnish, 1998) where the species composition varied continuously with

altitude with no evidence of discrete floristic zones.

Beyond elevation, there are other environmental gradients such as temperature and

precipitation that affect species richness (Gentry, 1988; Kitayama, 1992; Pyke et al.,

2001; Sánchez-González & López-Mata, 2005) and vary strongly with the elevation. In

tropical montane forest the temperature declines with elevation but the lapse rate varies

between sites, seasonally, and even day to day. A normal lapse rate for the eastern slopes

of the Andes in Ecuador is 0.65 – 0.68 ºC per 100 m which apply in the lower slopes up

3

to 1000 m, between 1500 – 2000 m, and again above 2500 m, but lapse rates are near

zero or temperatures rise between 1000 – 1500 m and 2000 – 2500 m (Richards, 1996).

For Mount Kinabalu, Borneo the air temperature decrease upslope with a lapse rate of

0.55 ± 0.01 ºC per 100 m. The daily temperature difference also decreases with

increasing the elevation (Kitayama, 1992). For the study area in the Peruvian Andes the

lapse rate for the canopy is 0.52 ºC per 100 m while for the understory is 0.53 ºC/100 m

(Rapp, 2010).

The TMCF occurs in a wide range of precipitation (500 – 10,000mm/year). In the

lowland Neotropics, plant species richness strongly correlates with the absolute

precipitation (Gentry, 1982, 1988a). In the Andes the correlation of the diversity with

precipitation is less significant than the relation with elevation, but the tests of this are

from sparse data in a single study (Gentry, 1995).

Landscape Variability Not Related to Elevation

Less well studied are the gradients and variability that arise from the topographical

complexity of tropical mountains. Within any elevation the rugged topography generates

a large degree of variation in cloud immersion, solar radiation, soils, and disturbance

(natural landslides and human linked landslides, fires and natural fire breaks). Soil depth

and nutrient cycling are related with the community composition and structure (Givnish,

1999; Sánchez-González & López-Mata, 2005; Whittaker, 1956; Young & Leon, 2001).

Cloud regime

4

Cloud formation in TMCF is typically between 1000 – 2500 m, depending on the

moisture content in the air (Foster, 2001), and determines the distribution of TMCFs

(Grubb & Whitmore, 1966; Richards, 1996). In montane forest, clouds are a constant

feature of the environment and are an important factor in these ecosystems, reducing the

incident solar radiation in 10 – 50% (Bruijnzeel & Veneklaas, 1998), which in turn

affects photosynthetic rates, evapotranspiration, and landscape and plant energy balance

(Richards, 1996). Clouds also increase the total precipitation through the water captured

by the vegetation, a phenomenon known as horizontal precipitation (Hamilton et al.,

1994). Horizontal precipitation will vary along the gradient depending of the cloud

movement (Stadtmüller, 1987). In an elfin forest in Venezuela ~66% of the water is

harvested from the cloud cover (Cavelier & Goldstein, 1989). The duration and

distribution cloudy periods through the day influenced the amount and quality of light

that reach the forest ground (Grubb & Whitmore, 1967). Alternate periods of cloud

presence can also affect other physiological processes such as photosynthesis, respiration

and also alter environmental conditions such as light, temperature and water regimes

(Grubb & Whitmore, 1966).

Topographical factors

Topographical factors, such as the slope of the terrain and the direction which it faces

(aspect) can have multiple effect on montane communities. Studies have demonstrated

that a combination of both slope and aspect with other variables such as solar radiation,

temperature and moisture could affect plant growth rates (Daniels & Veblen, 2003)

5

Near treeline the slope and aspect could be also important factors for seedling

establishment (Elliott & Kipfmueller, 2010) This suggests that complex topography in

small scale should be considered to assess the possible response of treeline to climate

change.

Soils

Soil composition and its variation across the landscape can also influence species

richness. A study in Mexico showed that the permanent wilting point and organic matter

had a positive and negative correlation respectively with species richness (Sánchez-

González & López-Mata, 2005). The chemical soils properties also showed a distinctive

elevational pattern. For example, organic carbon and nitrogen amounts are higher at mid

elevations and the higher C/N ratio at mid elevations indicate that the nitrogen

availability is limited in those soils (Kitayama, 1992). The pattern correlates with high

precipitation (Stadtmüller, 1987) and possibly reflects that decomposition rates are lower

at higher elevations (Zimmermann et al., 2010).

Solar Radiation

Another gradient that arises from topographical complexity in montane environments is

variation in solar radiation. This can affect the amount and timing (both daily and

seasonal) of light available to plants, and also have strong effects on energy balance, such

that leaf temperatures and microclimates may be highly variable, even within an area

with a homogeneous mean annual air temperature.

6

Solar radiation regime is expected to have large direct and indirect effects on both plant

physiological processes and environmental conditions, but has been relatively little

studied in montane systems. A comparison of the light intensity that reaches the forest

ground vegetation between a montane and a lowland forest suggests that montane forest

receive 40% more diffuse light in sunny conditions and about the same when clouds are

present (Grubb & Whitmore, 1967).

Objectives

In montane forest multiple studies on differing plant life forms have shown that many

factors--physical, biotic, and environmental--affect species composition and diversity. In

addition, mountains have been viewed in terms of cross-elevation studies, even though

landscape heterogeneity in montane forests can lead to important refugia for species

responses to climate change, greatly increasing potential ranges. In no study has there

been a comprehensive look at tree species composition at the landscape level with respect

to environmental factors, nor has there been a study of tree line with replicate plots within

elevation.

The aim of the current study is to (1) examine trends in tree community composition

along spatial and environmental gradients, and (2) evaluate the correlation of

environmental variables with community attributes such as community composition and

diversity. For the present study a large scale plot network of thirty-six 0.1 ha plots in

replicate elevation transects spanning 2500m and a within-elevation geographic transect

spanning ~40 km of geographic separation, were used to determine (1) how the tree

7

species composition and diversity change along an elevational gradient, and (2) the

variation in tree community composition and diversity within a single elevation band at

tree line. For each of these gradients we subsequently looked at the environmental

correlates of variation.

8

METHODS

Study site

The study was conducted in Manu National Park in the Kosñipata Valley, Department of

Cusco, southern Peru, elevations range from 1000 to 3700 m.

Geology and Topography

The terrain in the area is steep, with slopes up to 70%. Most of the transect is underlain

by Ordovician shales and slates (Salas A. et al., 1999), with granite at middle elevations

(1500-2000m). Soil carbon, nitrogen and phosphorus stocks in the top 50 cm are highest

in the 2000–3025 m band, where there is a thick layer of humic material (typically 20–30

cm) (C. A. J. Girardin, et al, unpublished results). A low phosphorus content and high

amount of potassium and aluminum create poor soils with low fertility and production.

(Pro-Manu, 2002)

Climatology

There are two seasonal periods, the wet season (October to April) and the dry season

(May to September), though along most of the transect no month at any elevation does

potential evapotranspiration exceed precipitation (Rapp, 2010). Annual precipitation

ranges from 7000 mm yr-1 at low elevations to 2400mm yr -1 at the highest elevation.

Precipitation varies through the year reaching a maximum in January and February and a

minimum in June and July. The transition between dry and wet season is mainly

determined by cloud formation which affects the insolation, temperature and humidity.

9

Mean annual air temperatures over the study area ranged from 18.1 °C (1500 m) to

6.5 °C (3600m). For every 1000m that the elevation increases along the gradient the

temperature decreases by 5.22 °C. Between the warmest month (wet season) and the

coolest month (dry season) the temperature varies in less than 2 °C (Rapp, 2010)

Plot Location

Two criteria were used to choose plot locations. First, locations were chosen to

encompass three different elevational gradients ranging from (1500 m to 3700 m). Plots

were installed in Trocha Union from 1800 m to 3450 m, Callanga 1750 m to 3500 m and

San Pedro transect 1500 m to 2300 m. To look at within-elevation landscape variation,

we chose high elevation forest within 200 m of Andean tree line, ranging from 3300 m to

3700 m, in order to determine within-elevation variation in tree composition (Figure 1)

Plot establishment

We established 36 plots of 50 x 20 m for a total of 3.6 ha in the montane forest and tree

line in Manu National Park (Table 1). Every tree, tree fern, shrub and vine with a

diameter (DBH) greater than 2.5 cm at 1.3 m above ground was measured for diameter

and height and collected for identification. Most individuals (90%) were identified to

genus level in the field, and some to species level. Unidentified individuals were

assigned a temporary name. Subsequently all the individuals were sent to major herbaria

(CUZ, UNSM, FMNH, NYBG, MO) and determined to species level or vouchered

morphospecies. Vouchers were compared across all plots and taxonomy was standardized.

10

,1

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bi

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nn

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Community Analyses

Two indices were used to summarize floristic similarity between two communities.

Sorensen’s index, which is a qualitative index based on presence or absence, with all

species equally weighted.

where na, nb are the number of species in samples a and b respectively, and c = the

number of shared species between samples a and b.

Bray Curtis index is a quantitative index computed from species abundances and weighs

common species more heavily than rare species.

S (A,B)

where k=number of species, and a

in = the abundance of ith species at plot a. For both

indices, 0 indicates no overlap and 1 a perfect overlap in either species composition.

Ordination based on species composition and abundance

Detrended Correspondence Analysis (DCA) was used to summarize patterns of floristic

composition across the elevational gradient and within elevation along the Andean

treeline. The analyses were perfomed in R version 2.12 (R Development Core Team

2011), function DECORANA in the R package “Vegan” (Oksanen et al. 2010).

,2ba nn

cs +=

11

Diversity Index

Fisher’s alpha was used to describe diversity in the plots (Condit et al., 1996). Fisher’s

alpha assumes that the abundance of species fits a log-series distribution, and uses this

assumption to normalize for sample size and area.

Landscape Variables

Aspect, Slope and Potential Solar Radiation (PSR) were tested as predictors of species

diversity and composition. The data for these three variables for each site were obtained

from a 90 m Digital Elevational Model of the area (DEM) using the Spatial Analysis

Tool from ArcView 9.2 (Table IV). To account for seasonal variation, two months were

selected to test PSR, one in the wet season (February) and one in the rainy season (July).

The value used for each month is the mean of twelve daily values distributed across the

month. A raster image for each variable (aspect, slope and potential solar radiation) was

generated to show the variability of these values across the landscape (Figures 2 – 4).

These explanatory variables were also tested for correlation inter se to make sure they

met the assumptions of independence.

Soils. The average values of carbon and nitrogen stock and the carbon and nitrogen ratio

were used for the analysis. The data were obtained from soil samples collected in each

plot. Five samples were collected, from the four corners and one in the middle of the plot.

The amount of sample in each of the five samples was based on the soil depth. The soil

analysis was made in each of the five samples per plot and for our analysis we used the

average from each plot. (Table IV).

12

Disturbance. Degree of disturbance was considered as a variable within the Andean

treeline elevations. Fire and cattle were considered factors for the degree of disturbance: a

value of 0 was assigned to plots with no visible or recorded effects of fire or cattle and a

value of 1 was assigned to those plots that had present effects of fire and/or cattle.

ANOVA. Analysis of Variance was used to evaluate the importance and significance of

the relationship between diversity and environmental variables. The variables were

selected and combined in a Two-way Factorial ANOVA analysis to determine any

significant interaction between them that could help to explain the variation in diversity

across the landscape. A F-test was performed for to test the equality of variances, and no

significant departures were found.

13

RESULTS

Floristic Composition

The 36 plots contained 9,718 individual trees from 540 species and 86 families. Across

the elevational gradient 20 plots, ranging from 1450 – 3000 m, were evaluated with 5,667

individuals, 435 species and 81 families. Within-elevation landscapes transect included

16 plots containing 4,051 individuals, 151 species and 26 families.

Across the entire elevational gradient the most common family is Cyatheaceae (tree fern

family). Individuals in the family Cyatheaceae are present in all 20 plots and they are the

most abundant arborescent stem in seven of the plots located at mid elevations (1601 –

2900 m). Melastomataceae is the second most common family, with individuals present

in all the plots along the gradient. Other important families are Rubiaceae,

Chloranthaceae and Lauraceae. An overview of floristic composition by family is given

in Figure 5. The results for the floristic composition shows variation among sites

(transects) where Cyatheaceae is the most common and abundant family in the Trocha

Union and San Pedro replicates, but not in Callanga where Chloranthaceae and

Cunoniaceae were much more abundant.

Within elevation in high Andean landscape, Melastomataceae is the most common family,

being present in all the plots. Melastomataceae is also the most abundant in terms of

number of individuals in eight of the 16 plots. Clusiaceae is second most common family,

being present in nine of the 16 plots and has the highest number of individuals in three

14

plots. A complete overview of family-level composition in high Andean landscape is

given in Figure 6. Other important families in high elevations include Cunoniaceae,

Myrsinaceae and Asteraceae, the latter mostly represented by lianas.

Diversity Patterns

Number of individuals

The average number of individuals per plot is 270 ± 37 (95% CI), with the highest value

of 742 at 3389 m in Trocha Union and the lowest value, 152 individuals, at 3476 m in

Refugio. Overall there is no relationship between number of individuals and elevation

(Pearson Correlation, r = 0.03, p = 0.33 Figure 13A). At the landscape-level, high Andean

treeline sample, the number of individual is not affected by the elevation (Pearson

Correlation, r = 0.007, p = 0.76) (Figure 13B, 13C).

Species richness

The average number of species per plot is 37.4 ± 5 (95% CI) with a maximum of 72

species at 1478 m in San Pedro, and a minimum of 13 species at 3407 m in Refugio. Plots

at lower elevations contain higher number of species and species richness is significantly

negatively correlated with the elevation (Pearson Correlation, r = 0.78, p < 0.001, Figure

14A). The degree of correlation varies between the three replicate transects (Figure 14B).

San Pedro transect shows the strongest relationship (Pearson Correlation, r = 0.95, p =

0.005), followed by Trocha Union transect (Pearson Correlation, r = 0.73, p = 0.01) while

Callanga shows a non significant relationship (Pearson Correlation, r = 0.24, p = 0.22).

High elevation plots near the Andean treeline are also significantly correlated with

15

elevation (Pearson Correlation, r = 0.44, p = 0.005), even though the elevation range

covers only ~340 m (Figure 14C).

Diversity

The diversity measure Fisher’s alpha is also significantly negatively correlated with

elevation (Pearson Correlation, r = 0.77, p = 0.0001). With the same trend is present

across the elevation gradient and also within the landscape network of high elevation

plots (Figure 15). A separate analysis for each site shows that the correlation changes

between sites with high values in San Pedro (Pearson Correlation, r = 0.86, p = 0.023) to

a non significant relationship in the Callanga transect (Pearson Correlation, r = 0.22, p =

0.25, Figure 15B).

Floristic Similarity

Patterns of floristic similarity showed little relationship to geographic distance and were

similar for Bray-Curtis (abundance-based) and for Sorensen’s (presence-absence based)

indices. When all the plots were included (Figure 7A, 8A) a Mantel test showed that the

relation between the Bray-Curtis values and the geographic distance were low (r = 0.07,

p<0.14) than for Sorensen’s index (r = 0.11 p <0.4). For the high elevation plots in the

Andean treeline (Figure 7B, 8B) the relationship was significant but held little

explanatory power, with values for Bray-Curtis (r = 0.05, p<0.9) and for Sorensen’s

index (r = 0.11, p = 0.9) and for the plots distributed along the elevation gradient the

trend was also significant but held little explanatory power (Figure 7B, 8B) with R values

16

for Bray-Curtis (r = 0.07, p<0.19) and for Sorensen’s index (r = 0.14, p<0.5) never

explaining more than 15% of variation.

DCA Ordination

At the genus level tree community composition along the gradient and across the

landscape at high elevation in the Andes (Figure 9) showed elevation to be the main

factor influencing the distribution. In both cases (along and across the elevation) location

or distance between plots is not influencing the composition.

At the species level tree community composition (based on tree species abundance)

across the elevational gradient showed also a strong relationship with elevation (Figure

10A) with the plots located at similar elevation closely related in compositional space,

even when geographically distance does not influence the composition. The exception to

this trend is the plot in Trocha Union at 2449 m that ordinated far from other plots. This

plot had large individuals of species that were not found in the rest of the plots as they are

normally understory species. A second DCA ordination was performed to evaluate if the

anomalous plot (Trocha Union – 2449 m) was influencing the previous results. When the

plot was excluded from the analysis (Figure 10B) the distribution of the plots emphasize

that elevation is not the only variable that influences the composition along the gradient.

The ordination based on presence – absence of species showed also that elevation is an

important factor. In this case with a better differentiation between elevational transects

(Figure 10C).

To test the significance of the relationship with elevation, a linear regression was

performed using the DCA1 values (Axis 1) and elevation (Figure 11). The results indicate

17

that elevation and species composition are tightly related (Pearson Correlation, r = 0.95, p

< 0.0001).

Separate DCA ordinations were used for the plots at high elevations based on species

abundance (Figure 12A) and presence – absence of species (Figure 12B). The DCA

based on the species abundance showed a disperse distribution of the plots, with no

influence of elevation or the location of the plots while the one based on presence –

absence appears to be more related with elevation.

Environmental Variables

The explanatory variables aspect, slope and potential solar radiation were analyzed

independently to determine their relationship with the response variables elevation,

number of individuals and species richness (Figure 16). Results show that there is not

relationship between variables, confirming the appropriateness of subsequent ANOVA

analysis.

There was also no significant correlation between aspect or slope with the number of

individuals or species richness per plot (Figures 17 and 18). Similar results were found

for PSR versus the number of tree individuals or species richness per plot, with the

exception that February PSR has a strong relationship with the elevation (Pearson

Correlation, r = 0.65, p<0.0001) and with species richness (Pearson Correlation, r = 0.62,

p<0.0001), but has no relation with number of tree individuals. PSR for July has a

slightly weaker but significant relation with elevation (Pearson Correlation, r = 0.22, p =

0.0036) and no relationship to species richness or number of tree individuals (Figure 19).

18

An analysis of the landscape variation in PSR in SE Andes shows that total direct

sunlight can vary by more than 100% at any elevation. This is due not only to changes in

aspect, but also the effects of other ridges and seasonal effects of changing solar angle.

Diffuse illumination is also affected by topography, with different areas having large

differences in the percent of visible sky. Combined, these would be expected to have

large effects on both physiological processes and environmental parameters, even at a

single elevation (Chueca & Julián, 2004; Fu & Rich, 2000; O'Brien et al., 2000)

ANOVA analysis

According to the F-test the data were found to meet the assumption of equality of

variances. With respect to diversity, the result of the ANOVA analysis indicate that the

elevation is a significant predictor of the diversity across the landscape and the

interaction between the potential solar radiation and slope has less influence over tree

diversity (Table V). The rest of the variables and their interactions are not significant.

19

DISCUSSION

This study provides the first landscape-level views of floristic composition, diversity,

species richness, stem abundance, and community-level changes in montane forests with

data obtained from replicate transects across elevational gradients. The research also

includes a large geographic sample within elevations near tree line provides a first

analysis of environmental variables that may account for patterns of diversity and

community composition.

The results suggest that the altitudinal trends in tree community composition and

diversity in TMCFs are difficult to generalize. Community composition is shown to

change gradually along the elevation transect and within the Andean treeline. Number of

tree individuals does not have any relationship with any variable used for the study.

Species richness and tree diversity decrease with elevation, although this trend is not

uniform in all the elevational transects and also has a strong relationship with PSR. From

the variables used for this study, elevation and PSR correlates with tree diversity and

composition. These results are discussed in detail below.

Floristic Composition

Tropical montane forests are composed of distinctive vegetation that varies greatly across

elevations, with complete turnovers in dominant taxa along the gradient. At mid-

elevations Cyatheaceae (tree ferns) and Melastomataceae are the most abundant families

and the most species-rich families are Melastomataceae, Lauraceae and Cyatheaceae.

20

And with Miconia and Cyathea are the most abundant and species-rich genera in the

1480 – 2500 m plots. These results are similar to those founds by Gentry (1995), with

minor differences e.g. Lauraceae as the most species-rich family between 1540 – 2550 m,

followed by Melastomataceae and Rubiaceae. Another difference is that he found

Moraceae as the fourth most speciose family between 1500 – 2000 m and the genus Ficus

as one of the largest genera in the Andes. In our study site the family Moraceae is the

tenth most speciose family, found in few of the plots along the gradient mostly in San

Pedro and Callanga represented by nine species, six of them belonging to the genus Ficus.

A possible explanation for these differences could be the degree of disturbance in each

site, if we consider that Ficus is a common genus in disturbed areas (Pennington et al,

2004).

In upper montane forest between 2500 – 3000 m, the floristic composition is similar to

mid-elevation forest. Melastomataceae and Lauraceae are the most species-rich families

and Chloranthaceae, that was poorly represented below this range of elevation, becomes

the third most species-rich family with the genus Hedyosmum the third most abundant at

these elevations. The most species-rich genera remain the same as mid-elevation,

Miconia and Cyathea. And, as in lower elevations, Cyatheaceae and Melastomaceae are

the most abundant.

At the highest elevations (above 3000 m) the tree species composition changes with

Melastomataceae becoming most abundant family and the genus Miconia the most

abundant and species-rich genus. Overall species composition also changes drastically.

21

Asteraceae is the most species-rich family. Araliaceae and Aquifoliaceae become the

third and fourth species-rich families. Genera as Ilex, Gynoxis, Weinmannia, Symplocos

and Clusia became the most species-rich and abundant after Miconia, similar results to

those found in studies from other areas (Gentry, 1995; Young & Leon, 2001; Young &

Leon, 2007).

Melastomataceae, the most abundant family across and within the elevation presents also

important differences. At higher elevations most of the representative species of the

family are small understory trees and shrubs, while at mid and lower elevations they are

canopy trees, showing the great variability in growth form between species in the same

family.

Floristic Similarity

This study found that abiotic effects, including the potential solar radiation receives at a

site and the local disturbance history, are central to explaining the landscape variation in

tree species composition, and that correlates of community change occur over relatively

fine (<2 km) spatial distances in the landscape (Figure 7, 8). Geographic distance was

found to have little effect in floristic similarity (as indicated by the results from Bray-

Curtis, Sorensen’s analyses) with the weakest relationship found on the landscape sample

near Andean tree line, even though the sample combined large geographic distances and

high dissimilarities. The same trend was found across the elevation gradient. The result

differs from that found in the adjacent lowlands, where geographic distance influences

the floristic similarity and also correlates with variation in the underlying environmental

variables (Masse, 2005). In Panama floristic similarity was found to decline as a function

22

of inter-plot distance at scales as small as 5km, suggesting a role for dispersal limitation

(Pyke et al., 2001). The Andean forests studied here responded to environmental

gradients over much finer spatial scales.

Species composition trends

The results from the plots along the elevational gradient show the elevation as the main

environmental gradient influencing the tree composition. The same trend was observed in

the correlation between elevation and DCA axis 1 (ordination based on species

abundance, Figure 11) demonstrating that there is a steady replacement of species with

elevation. This is in agreement with results from montane forest in Costa Rica

(Lieberman et al., 1996), from Mexico (Vazquez & Givnish, 1998) and Tanzania (Lovett,

1996; Lovett et al., 2006). There was no evidence of critical altitudes or discontinuities in

the composition such as those found by Kitayama (1992) in Borneo.

Analysis of tree species composition in the high Andean treeline (3284 – 3627 m) based

on species abundance shows that the vegetation varies considerably across the landscape

with no significant overlap between plots (Figure 12A). No influence was found by any

environmental predictor, unlike the plots distributed across the replicate elevational

gradients (1478 – 3054 m) where the main determinant of floristic differences is

elevation. This may indicates that the plots within the Andean treeline are heterogeneous

and other variables may be influencing the tree composition in this elevational range.

Diversity Patterns

23

Results from this study corroborates previous results showing that the species richness

decreases when increasing elevation (Gentry, 1995), as does diversity (Gentry, 1988;

Kitayama, 1992; Lovett, 1996; Luteyn, 2009). However, by looking at replicates across

the landscape we found that the relationship is heterogeneous across the landscape

(Figure 14, 15). In the Callanga transect the relationship was not significant, in contrast

with Trocha Union and San Pedro where the elevation is the main variable that influences

the diversity, though differently between the two transects. This difference may be

explained if we consider that Callanga transect is a more disturbed area that Trocha

Union and San Pedro. Other possible explanation is substrate differences.

Interaction between variables

The abrupt topography characteristic of the TMCFs provides a wide environmental

variability and numerous microhabitats that may influence the variation in tree

composition. This study tested the relationship between some of those environmental

variables (aspect, slope, potential solar radiation and carbon/nitrogen soil content) with

stand variables (number of individuals, species richness and diversity). No relationship

between the aspect, slope or carbon/nitrogen soil content was found with any of the stand

variables (Figure 17, 18, 20). Results are different from those found by Sánchez-

González and López-Mata (2005) in Sierra Nevada, Mexico, were both the species

richness and diversity positively correlated with the degree of slope. In the case of the

PSR, first we found a monthly variability and an even greater variability between seasons

(dry and wet season, Figure 4). Second the PSR for February (wet season) has a strong

relationship with the elevation and species richness, but the PSR for July only correlates

24

with elevation and not with species richness (Figure 19). This result may indicate that the

variation in potential solar radiation along the year and across the landscape has a

significant effect in the establishment of tree species.

Among soil properties, C/N ratio correlates with species richness (Figure 20F), where the

number of species is higher in the plots with low C/N ratio. C/N ratio correlates with

elevation (Kitayama, 1992), with high values of C/N ratio at higher elevations, and that

high values of C/N ratio indicates nitrogen limitation, which in turn can reflect that the

decomposition rates are lower at higher elevations. Therefore it can be assumed that the

species richness is greater in places where the decomposition rates are higher and where

more nitrogen is available for the plants.

Combined Analysis

Analyzing all explanatory variables together to evaluate any interaction between them

that could influence the tree composition and diversity, we found that within the high

Andean landscape sample, aspect, slope, and potential solar radiation do not have any

significant relationship with number of individuals, species or diversity. Therefore to

explain the heterogeneity in the tree species diversity and stand characteristics in the

Andean tree line it will be necessary to further investigate other variables such as soils

composition, cloud regime, water availability, and past disturbance/successional history.

Along the elevational gradient elevation appears to have a significant influence on

diversity, and also the potential solar radiation and slope could influence diversity

corroborating our previous result where the solar radiation influences the species richness.

25

CONCLUSIONS

Tree species composition varied with elevation, but these patterns also showed landscape

variability, with the dominant species and families changing among replicate transects. In

general, the most common and species rich family was Melastomataceae, present in all

plots for a total of 87 species. It had been demonstrated that elevation is the main variable

that correlates with tree community composition and taxa diversity, which decreased at

higher elevations.

Within a landscape-level sample of high Andean forests near treeline, floristic

composition and diversity also varied across the landscape. Between plots there are few

differences in the family-level composition, but many at the species level. Moreover, the

changes in species composition occurred at relatively fine spatial scales, with geographic

distance being a non-significant predictor over the ~30 km of distance the plots

encompassed. Elevation influenced the stand-level variables, but it was a weak predictor,

with most of the landscape-level variability remaining unexplained. There may be other

factors that could explain this variability such as soil nutrient, species interactions, and

age of the forest that should be considered in future studies.

The heterogeneity in the tree species composition found at Andean tree line can not be

explained by environmental variables (slope, aspect and potential solar radiation) or by

human or natural disturbance, at least as was measured in this study. However across the

elevation gradient solar radiation can be an important factor for tree species diversity.

26

LITERATURE CITED

Bruijnzeel, L.A. & Veneklaas, E.J. (1998) Climatic Conditions and Tropical Montane

Forest Productivity: The Fog Has Not Lifted Yet. Ecology, 79, 3-9.

Bubb, P., May, I., Miles, L., Sayer, J. (2004) Cloud Forest Agenda UNEP-WCMC,

Cambridge, UK.

Cavelier, J. & Goldstein, G. (1989) Mist and Fog Interception in Elfin Cloud Forests in

Colombia and Venezuela. Journal of Tropical Ecology, 5, 309-322.

Chueca, J. & Julián, A. (2004) Relationship between solar radiation and the development

and morphology of small cirque glaciers (Maladeta Mountain massif, Central

Pyrenees, Spain). Geografiska Annaler: Series A, Physical Geography, 86, 81-89.

Daniels, L.D. & Veblen, T.T. (2003) Regional and Local Effects of Disturbance and

Climate on Altitudinal Treelines in Northern Patagonia. Journal of Vegetation

Science, 14, 733-742.

Elliott, G.P. & Kipfmueller, K.F. (2010) Multi-scale Influences of Slope Aspect and

Spatial Pattern on Ecotonal Dynamics at Upper Treeline in the Southern Rocky

Mountains, U.S.A. Arctic, Antarctic, and Alpine Research, 42, 45-56.

Feeley, K.J. & Silman, M.R. (2010a) Biotic attrition from tropical forests correcting for

truncated temperature niches. Global Change Biology, 16, 1830-1836.

Feeley, K.J. & Silman, M.R. (2010b) Land-use and climate change effects on population

size and extinction risk of Andean plants. Global Change Biology, 16, 3215-3222.

27

Feeley, K.J. & Silman, M.R. (2010c) Modelling the responses of Andean and Amazonian

plant species to climate change: the effects of georeferencing errors and the

importance of data filtering. Journal of Biogeography, 37, 733-740.

Foster, P. (2001) The potential negative impacts of global climate change on tropical

montane cloud forests. Earth-Science Reviews, 55, 73-106.

Fu, P. & Rich, P.M. (2000) A geometric solar radiation model and its applications in

agriculture and forestry. In Second International Conference on Geospatial

Information in Agriculture and Forestry, Vol. I, pp. 357-364.

Gentry, A.H. (1988) Changes in Plant Community Diversity and Floristic Composition

on Environmental and Geographical Gradients. Annals of the Missouri Botanical

Garden, 75, 1-34.

Gentry, A.H. (1995). Patterns of Diversity and Floristic Composition in Neotropical

Montane Forests. In Biodiversity and Conservation of Neotropical Montane

Forests (ed B. S. Churchill, H., Forero, E., Luteyn, J.). The New York Botanical

Garden, New York.

Givnish, T.J. (1999) On the Causes of Gradients in Tropical Tree Diversity. Journal of

Ecology, 87, 193-210.

Grubb, P.J. & Whitmore, T.C. (1966) A Comparison of Montane and Lowland Rain

Forest in Ecuador: II. The Climate and its Effects on the Distribution and

Physiognomy of the Forests. Journal of Ecology, 54, 303-333.

Grubb, P.J. & Whitmore, T.C. (1967) A Comparison of Montane and Lowland Forest in

Ecuador: III. The Light Reaching the Ground Vegetation. Journal of Ecology, 55,

33-57.

28

Hamilton, L.S., Juvik, J.O., & Scatena, F., eds. (1994) Tropical Montane Cloud Forest.

Springer-Verlag, New York.

Kitayama, K. (1992) An Altitudinal Transect Study of the Vegetation on Mount Kinabalu,

Borneo. Vegetatio, 102, 149-171.

Lieberman, D., Milton, L., Peralta, R., & Hartshorn, G.S. (1996) Tropical Forest

Structure and Composition on a Large-Scale Altitudinal Gradient in Costa Rica.

Journal of Ecology, 84, 137-152.

Lovett, J.C. (1996) Elevational and latitudinal changes in tree associations and diversity

in the Eastern Arc mountains of Tanzania. Journal of Tropical Ecology, 12, 629-

650.

Lovett, J.C., Marshall, A.R., & Carr, J. (2006) Changes in tropical forest vegetation along

an altitudinal gradient in the Udzungwa Mountains National Park, Tanzania.

African Journal of Ecology, 44, 478-490.

Luteyn, J.L. (2009) Diversity, Adaptation, and Endemism in Neotropical Ericaceae:

Biogeographical Patterns in the Vaccinieae. The Botanical Review, 68, 55-87.

Masse, D. (2005) The Effects of Distance and Geomorphology on the Floristic

Composition of Lowland Tropical Tree Communities. M. S. thesis, Wake Forest

University, Winston Salem.

Myers, N., Mittermeier, R.A., Mittermeier, C.G., da Fonseca, G.A.B., & Kent, J. (2000)

Biodiversity hotspots for conservation priorities. Nature, 403, 853-858.

O'Brien, E.M., Field, R., & Whittaker, R.J. (2000) Climatic Gradients in Woody Plant

(Tree and Shrub) Diversity: Water-Energy Dynamics, Residual Variation, and

Topography. Oikos, 89, 588-600.

29

Pro-Manu (2002) Plan Maestro del Parque Nacional del Manu.

Pyke, C.R., Condit, R., Aguilar, S., & Lao, S. (2001) Floristic Composition across a

Climatic Gradient in a Neotropical Lowland Forest. Journal of Vegetation Science,

12, 553-566.

Rapp, J.M. (2010) Climate Control on Plant Performance across an Andean Altitudinal

Gradient, Wake Forest University, Winston Salem.

Richards, P.W. (1996) The Tropical Rain Forest: an ecological study, Second edition edn.

Cambridge University Press.

Salas A., G., Chávez V., A., & Cuadros P., J. (1999) Geología de los cuadrángulos de

Quebrada Honda y Parobamba INGEMMET Instituto Geológico Minero y

Metalúrgico, Lima.

Sánchez-González, A. & López-Mata, L. (2005) Plant species richness and diversity

along an altitudinal gradient in the Sierra Nevada, Mexico. Diversity and

Distributions, 11, 567-575.

Stadtmüller, T., ed. (1987) Cloud Forests in the Humid Tropics. A Bibliographic Review,

pp 82. The United Nations University, Turrialba, Costa Rica.

Vazquez, J.A., G & Givnish, T.J. (1998) Altitudinal Gradients in Tropical Forest

Composition, Structure, and Diversity in the Sierra de Manantlan. Journal of

Ecology, 86, 999-1020.

Whittaker, R.H. (1956) Vegetation of the Great Smoky Mountains. Ecological

Monographs, 26, 2-80.

Young, K.R. & Leon, B. (2001). Perú. In Bosques nublados del Neotropico (ed A.D.

Kappelle M., Brown ). Instituto Nacional de Biodiversidad, INBio, Costa Rica.

30

Young, K.R. & Leon, B. (2007) Tree-line changes along the Andes: implications of

spatial patterns and dynamics. Philosophical Transactions of the Royal Society B:

Biological Sciences, 362, 263-272.

Zimmermann, M., Meir, P., Silman, M., Fedders, A., Gibbon, A., Malhi, Y., Urrego, D.,

Bush, M., Feeley, K., Garcia, K., Dargie, G., Farfan, W., Goetz, B., Johnson, W.,

Kline, K., Modi, A., Raurau, N., Staudt, B., & Zamora, F. (2010) No Differences

in Soil Carbon Stocks Across the Tree Line in the Peruvian Andes. Ecosystems,

13, 62-74.

Table I. Tree plot locations. Coordinates in Universal Transverse Mercator (UTM), elevations in

meters (m).

Site Plot Code UTM.E UTM.N Elevation (m)

Acjanaco AC_I 215461 8540843 3473

Acjanaco AC_II 214195 8544328 3522

Acjanaco AC_III 214721 8545381 3531

Tres Cruces TC_I 216189 8547789 3625

Apu AK_I 214879 8550582 3627

Qurqurpampa QU_ I 198035 8569332 3385

Qurqurpampa QU_ II 196502 8568602 3521

Qurqurpampa QU_III 196642 8568982 3402

Pitama PIT_I 201786 8565038 3528

Refugio RE_I 207677 8550351 3337

Refugio RE_II 209984 8547705 3476

Refugio RE_III 206833 8550411 3407

Callanga CA_I 197313 8571977 3376

Callanga CA_II 197689 8572457 3284

Callanga CA_III 196653 8575091 3030

Callanga CA_IV 196038 8575866 2750

Callanga CA_V 195780 8576251 2676

Callanga CA_VI 195995 8576916 2548

Callanga CA_VII 196113 8576849 2500

Callanga CA_VIII 196220 8578219 2245

Callanga CA_IX 196364 8579065 2110

Callanga CA_X 196127 8579925 1983

San Pedro SP_I 222204 8557515 2286

San Pedro SP_II 223436 8556129 2024

San Pedro SP_III 224361 8556651 1750

31

San Pedro SP_IV 224609 8556264 1601

San Pedro SP_V 224948 8555973 1478

Trocha Union TU_I 217315 8548840 3389

Trocha Union TU_II 217451 8549028 3350

Trocha Union TU_III 217902 8549321 3054

Trocha Union TU_IV 218577 8549545 2900

Trocha Union TU_V 219092 8549570 2862

Trocha Union TU_VI 220092 8550314 2623

Trocha Union TU_VII 221108 8551845 2449

Trocha Union TU_VIII 222217 8552917 2096

Trocha Union TU_IX 222533 8553449 1970

32

Tabl

e II

. Num

ber o

f plo

ts p

er si

te.

“Acr

oss e

leva

tion”

plo

ts sp

an th

e el

evat

ion

grad

ient

, whi

le “

with

in e

leva

tion”

plo

ts sa

mpl

e

land

scap

e he

tero

gene

ity a

t a si

ngle

pos

ition

on

the

elev

atio

n gr

adie

nt.

Tro

cha

Uni

on

Cal

lang

a

San

Pedr

o A

cjan

aco

Tre

s

Cru

ces

Apu

R

efug

ioPi

tam

a Q

urqu

rpam

pa

Tot

al

Acr

oss

Ele

vatio

n 7

85

- -

- -

- -

20

With

in

Ele

vatio

n 2

2-

31

13

13

16

Tot

al

9 10

53

11

31

336

33

Table III. Summary of stand structure and diversity.

Composition Diversity

Plots

Elevation

(m) Number Species Number Individuals Fisher's Alpha

AC_I 3473 33 194 11.42

AC_II 3522 19 278 4.62

AC_III 3531 22 254 5.78

TC_IV 3625 15 168 3.98

AK_I 3627 19 261 4.71

QU_I 3385 26 167 8.63

QU_II 3521 19 199 5.17

QU_III 3402 30 184 10.17

PIT_I 3528 19 307 4.48

RE_I 3337 25 222 7.23

RE_II 3476 23 152 7.53

RE_III 3407 14 321 2.99

CA_I 3357 27 153 9.51

CA_II 3278 36 186 13.3

CA_III 3296 33 215 10.88

CA_IV 2791 53 309 18.42

CA_V 2665 29 323 7.72

CA_VI 2548 48 336 15.32

CA_VII 2500 49 370 15.14

CA_VIII 2245 50 252 18.71

CA_IX 2110 43 260 14.68

CA_X 1983 51 346 16.51

SP_I 2270 38 198 13.97

SP_II 2024 41 203 15.49

SP_III 1750 60 278 23.52

SP_IV 1650 64 310 24.48

SP_V 1500 72 241 34.77

TU_I 3389 41 742 9.35

TU_II 3350 33 263 9.97

34

TU_III 3081 38 240 12.71

TU_IV 2900 27 221 13.95

TU_V 2863 37 184 8.07

TU_VI 2622 38 229 12.99

TU_VII 2449 55 482 16

TU_VIII 2097 51 338 16.68

TU_IX 1970 69 332 26.48

35

Tabl

a IV

. Env

ironm

enta

l dat

a fo

r tre

e pl

ots.

L

ands

cape

So

ils

Plot

s E

leva

tion

(m)

Asp

ect

Slop

e SR

_Feb

SR

_Jul

AV

G C

Stoc

k (k

g

C/ m

2)

STD

C

stoc

k

AV

G N

stoc

k (k

g

N/ m

2)

STD

N

stoc

k

AV

G

dept

h

(cm

)

C/N

ratio

AC

_I

3473

11

6.1

24.4

2548

94.0

0714

1889

.57

- -

- -

- -

AC

_II

3522

17

8.2

15.6

2732

07.7

2815

0273

.77

- -

- -

- -

AC

_III

35

31

93.1

29.8

2364

88.6

9914

5348

.25

- -

- -

- -

TC_I

V

3625

15

9.2

30.1

2717

14.9

8315

1163

.38

- -

- -

- -

AK

_I

3627

34

8.7

17.8

2539

99.0

4218

3211

.89

- -

- -

- -

QU

_I

3385

99

.642

.324

8821

.106

1486

92.9

3-

- -

- -

-

QU

_II

3521

70

.139

.323

3473

.579

1563

98.7

1-

- -

- -

-

QU

_III

34

02

322.

530

.523

2522

.312

1662

16.7

2-

- -

- -

-

PIT_

I 35

28

322.

520

.827

0013

.504

1738

60.3

6-

- -

- -

-

RE_

I 33

37

31.1

15.8

2301

38.5

118

4666

.75

- -

- -

- -

RE_

II

3476

13

730

.226

7172

.084

1445

45.4

6-

- -

- -

-

RE_

III

3407

22

5.7

6.2

2690

49.5

6815

4535

.29

- -

- -

- -

36

CA

_I

3357

32

8.9

12.8

2530

92.8

1617

3881

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38

Table V. ANOVA table for variation in diversity including all the plots.

Analysis of Variance Source DF Sum of Squares Mean Square F Ratio

Model 10 6997.7357 699.774 13.0846

Error 25 1337.0143 53.481 Prob > F

C. Total 35 8334.7500 <.0001*

Parameter Estimates Term Estimate Std Error t Ratio Prob>|t|

Intercept 123.73468 24.20344 5.11 <.0001*

Elevation (m) -0.014888 0.004335 -3.43 0.0021*

Asp trans -2.451792 1.899339 -1.29 0.2086

Slope 0.1274695 0.1625 0.78 0.4402

SR_Feb -0.000266 0.000186 -1.43 0.1644

(Elevation (m)-2838)*(Asp trans+0.22669) -0.004684 0.005259 -0.89 0.3816

(Elevation (m)-2838)*(Slope-25.4346) -0.000608 0.000434 -1.40 0.1736

(Elevation (m)-2838)*(SR_Feb-176480) 1.899e-8 1.729e-7 0.11 0.9134

(Asp trans+0.22669)*(Slope-25.4346) 0.2658483 0.26937 0.99 0.3331

(Asp trans+0.22669)*(SR_Feb-176480) 0.0001629 0.000252 0.65 0.5244

(Slope-25.4346)*(SR_Feb-176480) 3.2045e-5 1.834e-5 1.75 0.0929

39

Sort

ed P

aram

eter

Est

imat

es

T

erm

E

stim

ate

Std

Err

ort R

atio

t Rat

io

Prob

>|t|

Elev

atio

n (m

) -0

.014

888

0.00

4335

-3.4

3

0.00

21*

(Slo

pe-2

5.43

46)*

(SR

_Feb

-176

480)

3.

2045

e-5

1.83

4e-5

1.75

0.

0929

SR_F

eb

-0.0

0026

60.

0001

86-1

.43

0.

1644

(Ele

vatio

n (m

)-28

38)*

(Slo

pe-2

5.43

46)

-0.0

0060

80.

0004

34-1

.40

0.

1736

Asp

tran

s -2

.451

792

1.89

9339

-1.2

9

0.20

86

(Asp

tran

s+0.

2266

9)*(

Slop

e-25

.434

6)

0.26

5848

30.

2693

70.

99

0.33

31

(Ele

vatio

n (m

)-28

38)*

(Asp

trans

+0.2

2669

)

-0.0

0468

40.

0052

59-0

.89

0.

3816

Slop

e 0.

1274

695

0.16

250.

78

0.44

02

(Asp

tran

s+0.

2266

9)*(

SR_F

eb-1

7648

0)

0.00

0162

90.

0002

520.

65

0.52

44

(Ele

vatio

n (m

)-28

38)*

(SR

_Feb

-176

480)

1.

899e

-81.

729e

-70.

11

0.91

34

40

FIGURE LEGENDS

Figure 1. Manu National Park (top) showing the locations of the tree plots. Inset (bottom)

shows a close up of the plots

Figure 2. Landscape distribution of aspect for the study area.

Figure 3. Landscape distribution of slope for the study area

Figure 4. Seasonal variability in Potential Solar Radiation in the study area. Top,

February (Wet season). Bottom, July (Dry season)

Figure 5. Number of tree individuals per family for 0.1ha plots across the elevational

gradient. Values expressed as percent.

Figure 6. Number of tree individuals per family for 0.1ha plots within high elevation

plots in the Andean treeline. Values expressed as percentages.

Figure 7. Floristic Similarity, Mantel test between Bray-Curtis index (abundance-based)

vs. geographic distance between pairs of tree plots. A. All plots included; r =

0.07, p < 0.14. B. Andean treeline, solid line, r = 0.04, p < 0.9; across the

elevational gradient, dashed line, r = 0.07, p < 0.19

Figure 8. Floristic Similarity, Mantel test between Sorensen’s index (presence and

absence based) vs. geographic distance between pairs of tree plots. A. All plots

included; r = 0.11, p < 0.4. B. Andean treeline, solid line, r = 0.11, p < 0.9;

across the elevational gradient, dashed line, r = 0.12, p < 0.5

Figure 9. Detrended Correspondence Analysis (DCA) of the tree plots based on the

abundance at the genus level. A. Plots across the elevational gradient and B.

Plots in the Andean treeline. (above 3000 m). Letters represent the three

41

different transect T – Trocha Union, C – Callanga and S – San Pedro followed

by the elevation respectively.

Figure 10. Detrended Correspondence Analysis (DCA) of tree plots using tree species

abundance. A. All the plots across the elevational gradient included. B. Plots in

Trocha Union at 2449m excluded. C. DCA based on presence-absence of tree

species.

Figure 11. Linear regression between DCA 1 (DCA of tree plots across the elevational

gradient) and elevation.

Figure 12. Detrended Correspondence Analysis (DCA) of plots within the high elevations

in the Andean treeline (above 3000m) A. Based on tree species abundance. B.

Based on presence-absence of tree species. Letters represent the different sites

followed by the elevation.

Figure13. Linear regression between number of individuals per plot and elevation. A. All

the plots included (36 plots). B. Plots across three different elevational

gradients: Trocha Union (dotted line) r = 0.52 p = 0.17, Callanga (solid line) r =

0.06 p = 0.55 and San Pedro (dashed line) r = 0.36 p = 0.16. C. Plots near the

Andean treeline (above 3000m) r = 0.007 p = 0.78

Figure 14. Linear regression between species richness per plot and elevation. A. All plots

included (36 plots). B. Plots across three different elevational gradients: Trocha

Union (dotted line) r = 0.7 p = 0.01, Callanga (solid line) r = 0.24 p = 0.22 and

San Pedro (dashed line) r = 0.95 p = 0.005. C. Plots near the Andean treeline

(above 3000 m) r = 0.44 p = 0.003

42

Figure 15. Linear regression between diversity index (Fisher’s alpha) per plot and

elevation. A. All plots included (36 plots). B. Plots across three different

elevational gradients: Trocha Union (dotted line) r = 0.65 p = 0.03, Callanga

(solid line) r = 0.22 p = 0.25 and San Pedro (dashed line) r = 0.86 p = 0.02. C.

Plots near the Andean treeline (above 3000 m) r = 0.49 p < 0.003

Figure 16. Correlation between landscape variables: elevation, aspect, slope and potential

solar sadiation for February (PSR_Feb) and July (PSR_Jul).

Figure 17. Linear regressions between aspect and A. Elevation (r = 0.15 p = 0.02). B.

Species richness per plot (r = 0.02 p = 0.39) and C. Number of tree individuals

per plot (r = 0.01 p = 0.69)

Figure 18. Linear regressions between slope and A. Elevation (r = 0.05 p = 0.3). B.

Species richness per plot (r = 0.04 p = 0.69) and C. Number of tree individuals

per plot (r = 0.003 p = 0.74)

Figure 19. Linear regressions between the potential solar radiation for February (Wet

season) and A. Elevation (r = 0.65 p < 0.0001), B. Species richness per plot (r =

0.62 p < 0.0001), C. Number of tree individuals per plot (r = 0.001 p = 0.83)

and between the potential solar radiation for July (Dry Season) and D. Elevation

(r = 0.22 p = 0.004), E. Species richness per plot (r = 0.09 p = 0.07) and F.

Number of tree individuals per plot (r = 0.05 p = 0.18).

Figure 20. Linear regressions between the average C-stock (kg C/m2) in soils and A.

Number of tree individuals per plot (r = 0.13 p = 0.12) and D. Species richness

per plot (r = 0.006 p = 0.72); between Average N-stock (kg N/m2) in soils and

B. Number of tree individuals per plot (r = 0.18 p = 0.04) and E. Species

43

richness per plot (r = 0.05 p = 0.32); and between the C/N ratio in soils and C.

Number of tree individuals per plot (r = 0.01 p = 0.68) and F. Species richness

per plot (r = 0.33 p = 0.004).

44

Figure 1.

Manu National Park

Cusco

Madre de

Callanga

Tree line San Pedro

Trocha Union Andes

Amazon

45

Figure 2.

Figure 3.

46

Figure 4.

A.

B.

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49

Figure 7.

A.

B.

Distance (Km)

0 10 20 30 40

Bray

-Cur

tis S

imila

rity

0.0

0.2

0.4

0.6

0.8

1.0

Distance (Km)

0 10 20 30 40

Bray

-Cur

tis S

imila

rity

0.0

0.2

0.4

0.6

0.8

1.0

50

Figure 8.

A.

B.

Distance (Km)

0 10 20 30 40

Sor

ense

n's

Sim

ilarit

y

0.0

0.2

0.4

0.6

0.8

1.0

Distance (Km)

0 10 20 30 40

Sor

ense

n's

Sim

ilarit

y

0.0

0.2

0.4

0.6

0.8

1.0

51

Figure 9.

A. B.

52

Figure 10.

A.

B.

53

C.

Figure 11.

Elevation (m)

1250 1500 1750 2000 2250 2500 2750 3000

Axis

1

-6

-4

-2

0

2

4

6

54

Figure 12.

A. B.

55

Figure 13.

A.

B. C.

Elevation (m)

1000 1500 2000 2500 3000 3500 4000

Num

ber o

f ind

ivid

uals

(0.1

ha-1

)

100

200

300

400

500

600

700

800

Trocha Union Callanga San Pedro Andes

Elevation (m)

1500 2000 2500 3000

Num

ber o

f ind

ivid

uals

(0.1

ha-1

)

150

200

250

300

350

400

450

500Trocha UnionCallangaSan Pedro

Elevation (m)

3300 3400 3500 3600

Num

ber o

f ind

ivid

uals

(0.1

ha-1

)

100

200

300

400

500

600

700

800

56

Figure 14.

A.

B. C.

Elevation (m)

3300 3400 3500 3600

Spe

cies

rich

ness

10

15

20

25

30

35

40

45

Elevation (m)

1500 2000 2500 3000

Spe

cies

rich

ness

20

30

40

50

60

70

80

Trocha Union Callanga San Pedro

Elevation (m)

1000 1500 2000 2500 3000 3500 4000

Spec

ies

richn

ess

10

20

30

40

50

60

70

80Trocha Union Callanga San Pedro Andes

57

Figure 15.

A.

B. C.

Elevation (m)

3300 3400 3500 3600

Fish

er's

alp

ha in

dex

2

4

6

8

10

12

14

Elevation (m)

1500 2000 2500 3000

Fish

er's

alp

ha in

dex

5

10

15

20

25

30

35

40

Trocha UnionCallanga San Pedro

Elevation (m)

1000 1500 2000 2500 3000 3500 4000

Fish

er's

Alp

ha In

dex

0

5

10

15

20

25

30

35

40

Trocha Union Callanga San Pedro Andes

58

Figure 16.

59

Figure 17.

Aspect

0 100 200 300 400

Num

ber o

f ind

ivid

uals

(0.1

ha-1

)

100

150

200

250

300

350

400

450

500

Aspect0 100 200 300 400

Spec

ies

richn

ess

10

20

30

40

50

60

70

80

Elevation (m)1000 1500 2000 2500 3000 3500 4000

Aspe

ct

0

100

200

300

400

60

Figure 18.

Slope

0 10 20 30 40 50

Num

ber o

f ind

ivid

uals

(0.1

ha-1

)

100

150

200

250

300

350

400

450

500

Slope0 10 20 30 40 50

Spec

ies

richn

ess

10

20

30

40

50

60

70

80

Elevation (m)1000 1500 2000 2500 3000 3500 4000

Slop

e

0

10

20

30

40

50

61

Figu

re 1

9.

Ele

vatio

n (m

)

1000

1500

2000

2500

3000

3500

4000

Pot. Solar radiation Feb (Wh/m2)

1600

0

1700

0

1800

0

1900

0

2000

0

2100

0

2200

0

2300

0

Ele

vatio

n (m

)

1000

1500

2000

2500

3000

3500

4000

Pot. Solar radiation Jul (Wh/m2)

8000

1000

0

1200

0

1400

0

1600

0

1800

0

2000

0

Pot

. Sol

ar ra

diat

ion

Feb

(Wh/

m2 )

1600

018

000

2000

022

000

Species richness

1020304050607080

Pot

. Sol

ar ra

diat

ion

Jul (

Wh/

m2 )

8000

1000

012

000

1400

016

000

1800

020

000

Species richness

1020304050607080

Pot

. Sol

ar ra

diat

ion

Feb

(Wh/

m2 )

1600

018

000

2000

022

000

Number of individuals (0.1ha-1

)

100

200

300

400

500

600

700

800

Pot.

Sola

r rad

iatio

n Ju

l (W

h/m

2 )

8000

1000

012

000

1400

016

000

1800

0

Number of individuals (0.1ha-1

)

100

200

300

400

500

600

700

800

62

Figu

re 2

0.

C/N

ratio

1416

1820

2224

2628

Number of individuals (0.1ha)

100

200

300

400

500

600

700

800

AVG

N-S

tock

(kg

N/m

2 )

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Number of individuals (0.1ha) 100

200

300

400

500

600

700

800

AVG

C-S

tock

(kg

C/m

2 )

46

810

1214

16

Number of indviduals (0.1ha) 100

200

300

400

500

600

700

800

C/N

ratio

14

1618

2022

2426

28

Species richness (0.1ha)

20304050607080

Avg

N-St

ock

(kg

N/m

2 )

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Species richness (0.1ha) 20304050607080

AVG

C-S

tock

(kg

C/m

2 )

46

810

1214

16

Species richness 20304050607080

63

CURRICULUM VITAE

Karina Garcia Cabrera Degrees Universidad San Antonio Abad del Cusco, Peru. BS in Biology. 2003 Regional, National, International meeting • Tree community composition in the tropical montane cloud forest in southern Peru.

Andes Biodiversity and Ecosystem Research Group. Florida Keys. 24 – 26 February 2010.

• Latin American Botanical Congress. La Serena. Chile. 4 – 10 October 2010. Awards received • Organization for Tropical Studies. December 2008. • Vecellio Grant. May 2009. • Red Latinoamericana de Botanica. October 2010. Publications Published Kenneth Feeley , Miles Silman , Mark Bush , William Farfan , Karina Garcia, Yadvinder Malhi , Patrick Meir , Norma Salinas R. , M. Natividad Raurau Q. , Sassan Saatchi. 2010. “Migration of Andean trees in response to increasing temperatures” (online) Journal of Biogeography. Adam Gibbon, Miles R. Silman, Yadvinder Malhi, Joshua B. Fisher, Patrick Meir, Michael Zimmermann, Greta C. Dargie, William Farfan R., Karina Garcia C. 2009 “Ecosystem carbon storage across the 1 grassland-forest 2 transition in the high Andes of Manu National Park, Peru” Ecosystems 13:1097-1111.. Michael Zimmermann, Patrick Meir, Miles R. Silman, Anna Fedders, Adam Gibbon, Yadvinder Malhi, Dunia H. Urrego, Mark B. Bush, Kenneth J. Feeley, Karina Garcia, Greta C. Dargie, Wiliam R. Farfan, Bradley P. Goetz, Wesley T. Johnson, Krystle M. Kline, Andrew T. Modi, M Natividad. Raurau Q., Brian T. Staudt, and Flor Zamora. 2010. “No differences in soil carbon stocks across the tree line in the Peruvian Andes” Ecosystems 13: 62-74 C.A.J. Girardin, W. Farfan, K. Garcia, Y. Malhi, T. Killeen, K. Feeley, M. Silman, C. Reinel, D. Niell, P. Jorgensen, M. Serrano, J. Caballero, M. A. de la Torre Cuadrada, M. Macía. 2009. “Expanding the Amazon Forest Inventory Network to the montane forests of the Andes” Technical Report, Conservation International. In review

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Daniel J. Gurdak; Luiz E. O. C. Aragão; Angela Rozas-Dávila; Walter Huaraca Huasco; William Farfan Rios; Karina Garcia Cabrera; Cecile A. J. Girardin; Miles Ross Silman; Daniel B. Metcalfe; Javier E. Silva Espejo; Norma Salinas Revilla; Yadvinder Malhi. 2009. “Tropical Necromass - Dynamics along an Elevational Gradient of Mature Forest in the Peruvian Andes” Jill Jankowski, Christopher Merkord, William Farfan Rios, Karina Garcia Cabrera, Norma Salinas Revilla, Miles Silman, 2010. “The role of floristics and vegetation structure in shaping diversity patterns in an Andean avifauna”

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