climate control on plant performance across an …
TRANSCRIPT
CLIMATE CONTROL ON PLANT PERFORMANCE ACROSS AN
ANDEAN ALTITUDINAL GRADIENT
BY
JOSHUA M. RAPP
A Dissertation 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
DOCTOR OF PHILOSOPHY
Biology
December 2010
Winston-Salem, North Carolina
Approved by:
Miles R. Silman, Ph.D., Advisor
James S. Clark, Ph.D., Chair
Kathleen A. Kron, Ph.D.
William K. Smith, Ph.D.
Clifford Zeyl, Ph.D.
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ACKNOWLEDGEMENTS
Science is a collaborative process, and I could not have completed this dissertation
without the support of many. Above all I'd like to thank my parents for their love and
encouragement along the way. It is their support that enabled me to pursue these many
years of education, and I am especially grateful for their care when fieldwork proved
perilous.
My advisor, Miles Silman, I thank both for his guidance throughout graduate school, and
for introducing me to the world of tropical forest ecology. I'm grateful for the opportunity
to conduct research on the eastern slope of the Andes, a spectacular ecological laboratory.
Miles took me to the Peruvian Andes less than a month after I started at Wake Forest, and
I was hooked. Miles also introduced me to the scientists of the Andes Biodiversity and
Ecosystem Research Group (ABERG), and the chance to work with this international
group of scientists added immensely to my education. There are too many members of
ABERG to mention them all, but I would specifically like to thank Cecile Girardin, Noah
Fierer, Jill Jankowski, Chris Merkord, Yadvinder Malhi, Norma Salinas, and Ken Feeley.
My Peruvian colleagues, who have provided so much insight into the cloud forest, and
tireless hours in the field deserve much more gratitude than I could ever give. William
Farfan, Karina Garcia, Marlene Mamani, Natividad Raurau, Tatiana Boza, and Luis
Imunda introduced me to the cloud forest and provided data and much help in the field.
Without Moyra Rojas, Richard Tito, and Darcy Galiano I would not have been able to
complete this dissertation. They formed the core of my field team, working long hours in
the pouring rain, braving landslides and biting insects. Darcy especially ran the show
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when I couldn't be in the Peru, and his observations and insight have added immensely to
my own research.
At Wake Forest University, I thank my committee, Kathy Kron, Bill Smith, Dave
Anderson, Clif Zeyl, and Jim Clark for their guidance and for challenging me to become
a better scientist. I'd also like to thank the members of the Silman Lab, and the Biology
Department graduate students for friendship, encouragement, and support. Richard
Amick taught me how to climb trees (safely!), which expanded my research horizons,
and added much enjoyment to being in the field. Wake Forest University undergraduates
Mitch Buder, Andrew Collins, Roger Kirkpatrick, and Oran Basel all provided help with
fieldwork.
Finally, Wake Forest University, the National Science Foundation, the Gordon and Betty
Moore Foundation, and the Blue Moon Foundation all provided generous support that
made this research possible. I thank the Amazon Conservation Association, Cock-of-the-
Rock Lodge, and Manu National Park for the opportunity to perform field research on
their properties and for logistical support.
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TABLE OF CONTENTS
ACKNOWLEDGEMENTS…………………………………………………………………….ii
LIST OF TABLES…………………………………………………………………………...vi
LIST OF FIGURES…………………………………………………………………………viii
ABSTRACT………………………………………………………………………………....x
CHAPTER I
Introduction…………………………………………………………………..……1
CHAPTER II
Climate across a 3900 meter altitudinal gradient in the humid tropics
ABSTRACT…………………………………………………………….….21
INTRODUCTION…………………………………………………………...23
METHODS………………………………………………………………...26
RESULTS………………………………………………………………….32
DISCUSSION………………………………………………………………37
LITERATURE CITED……………………………………………………….45
CHAPTER III
Epiphyte response to drought and experimental warming: Cloud forest resilience under climate change
Submitted to Journal of Biogeography
ABSTRACT……………………………………………………………..…80
INTRODUCTION…………………………………………………………...82
MATERIALS AND METHODS……….....…………………………………...86
RESULTS………………………………………………………………….92
DISCUSSION………………………………………………………………95
v
LITERATURE CITED……………………………………………………...101
CHAPTER IV
When do cloud forest trees grow?: Tree growth phenology across a 2500 meter altitudinal gradient in the Andes
ABSTRACT………………………………………………………………127
INTRODUCTION………………………………………………………….128
METHODS……………………………………………………………….131
RESULTS………………………………………………………………...134
DISCUSSION……………………………………………………………..136
LITERATURE CITED……………………………………………………...143
CHAPTER V
Intra- and inter-specific tree growth across a long altitudinal gradient in the Peruvian Andes
ABSTRACT………………………………………………………………160
INTRODUCTION………………………………………………………….162
METHODS……………………………………………………………….168
RESULTS………………………………………………………………...178
DISCUSSION……………………………………………………………..182
LITERATURE CITED……………………………………………………...189
APPENDIX.................................................................................................221
CHAPTER VI
Conclusion……………………………………………………………………...231
CURRICULUM VITAE……………………………………………………………………238
vi
LIST OF TABLES
Table II – 1. Weather stations and sensors in the Kosñipata Valley and adjacent areas...53
Table II – 2. Temperature in the Kosñipata Valley………………………………….......55
Table II – 3. Lapse rate in the Kosñipata Valley………………………………………...56
Table II – 4. Vapor pressure in the Kosñipata Valley………………………...………….57
Table II – 5. Vapor pressure deficit in the Kosñipata Valley…………………………....59
Table III – 1. Sample sizes and mean ramet survival and turnover for vascular
epiphytes..............................................................................................................109
Table III – 2. Analysis of Deviance for ramet survival for mats transplanted within
elevation...............................................................................................................112
Table III – 3. ANOVA table for ramet turn-over in mats transplanted within
elevation...............................................................................................................113
Table III – 4. Analysis of Deviance for ramet survival for mats transplanted across
elevation...............................................................................................................114
Table III – 5. ANOVA table for ramet turn-over in mats transplanted across
elevation…….......................................................................................................115
Table III – 6. Analysis of Deviance for ramet survival for mats transplanted from 1800
meters elevation……....……………………………………………………….. 116
Table III – 7. ANOVA table for ramet turn-over in mats transplanted from 1800 meters
elevation ……......................................................................................................117
Table III – 8. Permutational Analysis of Variance testing differences in composition in
mats across elevations and years..........................................................................118
Table IV – 1. Characteristics of one-hectare tree plots included in study.......................151
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Table IV – 2. Analysis of Deviance for tree growth phenology for model not including
taxonomy..............................................................................................................152
Table IV – 3. Analysis of Deviance for tree growth phenology for model including genus
as an explanatory variable....................................................................................153
Table V – 1. Characteristics of one-hectare tree plots included in study........................203
Table V – 2. Number of stems of Weinmannia species greater than 1 cm DBH present in
each plot...............................................................................................................204
Table V – 3. Number of stems of Weinmannia species fitted with dendrometers present
in each plot...........................................................................................................205
Table V – 4. Analysis of Deviance for tree growth of seven species of Weinmannia.....206
Table V – 5. Analysis of Deviance for tree growth of the three most common species of
Weinmannia for a model that included altitude within species...........................207
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LIST OF FIGURES
Figure II – 1. Map of the study area showing locations of weather stations and sensors..65
Figure II – 2. Temperature across the study area...............................................................66
Figure II – 3. Lapse rate for September 2007, January 2008, and June 2008....................67
Figure II – 4. Vapor pressure across the altitudinal gradient.............................................68
Figure II – 5. Time course of vapor pressure.....................................................................69
Figure II – 6. Vapor pressure deficit across the altitudinal gradient..................................70
Figure II – 7. Time course of vapor pressure deficit..........................................................71
Figure II – 8. Daily integrated photosynthetically active radiation (PAR) by month........72
Figure II – 9. Mean daily time course of photosynthetically active radiation (PAR)........73
Figure II – 10. Rain fall across the study area...................................................................74
Figure II – 11. Time course of mean hourly rain fall in the four wettest and driest
months....................................................................................................................75
Figure II – 12. Altitudinal gradient in rain fall..................................................................76
Figure II – 13. Wind direction and velocity for three days at four alttitudes....................77
Figure II – 14.Altitudinal trends in wind velocity.............................................................78
Figure II – 15. Maximum wind gust at four altitudes throughout the year........................79
Figure III – 1. Monthly rain fall in 2005 compared to mean monthly rainfall at the
Rocotal weather station........................................................................................121
Figure III – 2. Cloud base height in June and July for 2005 and 2006............................122
Figure III – 3. Ramet survival of four epiphyte species transplanted across elevation...123
Figure III – 4. Ramet turn-over for four epiphyte species transplanted across
elevation...............................................................................................................124
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Figure III – 5. Species composition of epiphyte mats before transplantation.................125
Figure III – 6. Species compositional change in epiphyte mats after transplantation.....126
Figure IV – 1. Tree growth phenology and climatic drivers............................................156
Figure IV – 2. Tree growth phenology for each plot along the altitudinal gradient........157
Figure IV – 3. Tree growth phenology for each genus....................................................158
Figure IV – 4. Tree growth phenology for six Weinmannia species...............................159
Figure V – 1. Two hypotheses relating growth within species to observed ecosystem
patterns in productivity across temperature gradients.........................................211
Figure V – 2. Mean annual diameter growth distribution determined from diameter
census data and dendrometer data........................................................................212
Figure V – 3. Modeled diameter and diameter growth for five example trees................213
Figure V – 4. Modeled diameter growth versus altitude for three species of Weinmannia
(GLM)..................................................................................................................214
Figure V – 5. Modeled mean diameter growth for each species plotted against the mean
altitude of the species (GLM)..............................................................................215
Figure V – 6. Parameter estimates for covariates included in Bayesian model...............216
Figure V – 7. Modeled diameter growth versus altitude for nine species of Weinmannia
(Bayesian model).................................................................................................217
Figure V – 8. Modeled mean diameter growth for each species plotted against the mean
altitude of the species (Bayesian model).............................................................218
Figure V – 9. Parameter estimates for year effect..........................................................219
Figure V – 10. Variance associated with parameters in Bayesian model........................220
x
ABSTRACT
Understanding the response of species and ecosystems to climate change is one of the
most pressing challenges of biology. The use of altitudinal gradients as 'natural
experiments' to test the influence of climate on species distributions and performance has
a long history in ecology and biogeography. This approach is a powerful way to gain
insight into how species and ecosystems respond to warming, since unlike latitudinal
gradients, temperature change across altitudinal gradients is not associated with changes
in sun angle or day length. The studies included here describe the microclimate along a
3900 meter altitudinal gradient in the eastern Andes of southern Peru, and examine the
demographic performance of vascular epiphyte and canopy tree species to this climatic
gradient. An expected step change in climatic variables near putative 'cloud base' was not
observed, contrasting with rapid changes in net primary productivity, soil properties, and
bryophytic epiphyte biomass observed between 1500 and 1800 meters altitude. Vascular
epiphyte species transplanted down slope across this gradient exhibited lower ramet
survival and negative turnover at lower altitude, but reciprocal transplants revealed no
clear trends in altered community composition. Lower altitude individuals appeared to be
more resistant to drought than those from higher altitudes, regardless of the altitude
where they were transplanted. While precipitation has the greatest seasonal variability of
any of the microclimate variables considered here, with a peak in January and February
and a minimum in June and July, seasonal patterns of cloudiness and the altitudinal
decrease of temperature (mean lapse rate: 5.2ºC/km) appear more important in
determining tree growth. Insolation, which is controlled largely by cloudiness in these
tropical latitudes, peaked from September to December, and was associated with a
xi
seasonal peak in tree diameter growth rate, suggesting that forests on the eastern slope of
the Andes may be light limited. Most species had similar growth phenology, suggesting
they responded to the same environmental forcing. Within Weinmannia, a genus of
canopy trees common in Andean cloud forests, species growing at lower altitude had
higher mean diameter growth rates than high altitude species. Within species, however,
diameter growth did not consistently decrease with altitude, and growth was significantly
positively correlated with altitude in some species. This suggests that the observed
positive relationship between temperature and productivity in the wet tropics is due to
species compositional turn-over rather than a direct physiological control of temperature
on growth. Together, these studies demonstrate the importance of both the temperature
gradient and the cloud regime in controlling species performance in the eastern tropical
Andes. Increasing temperature and a rising cloud deck are expected to drive changes in
species performance and distributions. Species range shifts are likely to be limited by
dispersal and because canopy trees are slow growing and long lived. This may constrain
the ecosystem response to climate change, since ecosystem productivity is determined in
part by the species composition of the community.
CHAPTER I
INTRODUCTION
With global temperatures on a trajectory to rise by 4 degrees Celsius by 2100 (Solomon
et al. 2007), understanding and predicting the biological response to climate change is
one of the most pressing challenges of biology. Species' response to climatic changes
range from transient dynamics to long-term trends, and different investigative approaches
are appropriate for understanding the species' response at different time scales. The three
principle approaches to studying species' response to climate change are species'
distribution modeling, controlled experiments, and observational studies of species
performance across environmental gradients. Species' distribution modeling is most
appropriate for understanding potential long term responses to climate change, while
controlled experiments are most appropriate for understanding transient dynamics.
Observational studies across climate gradients have the potential to provide information
on both long-term and transient dynamics.
Species distribution modeling (Guisan and Zimmermann 2000, Elith and Leathwick
2009) makes the assumption that species will track temperature and/or moisture changes,
migrating the necessary distance to remain in equilibrium with their climatic niche.
Paleoecological studies using pollen assemblages from lake sediments show that plant
communities have tracked past changes in climate (e.g. Bush et al. 2004), suggesting that
the species' distribution modeling may be useful for predicting the response of species'
distributions to current climatic changes. However, there are reasons why species may
1
not be able to track these modeled distributions. The pace of current climate change
outpaces that observed over the past one million years (Bush et al. 2004, Solomon et al.
2007), and it is unknown whether species will be able to migrate at rates fast enough to
keep pace with increasing temperatures. Dispersal limitation, biotic interactions, and
habitat fragmentation may slow species' migration (Thuiller et al. 2008). Indeed, studies
documenting the biological response to current climatic changes have consistently
reported migration rates that are slower than temperature changes (Chen et al. 2009,
Lenoir et al. 2009, Feeley et al. in press), and the pace of temperature change is expected
to accelerate (Christensen 2007).
While bioclimatic niche modeling has been useful for setting bounds on the long-term
response of species and ecosystems to climate change, they are unable to help us
understand the transient dynamics of ecosystems, particularly if migration rates do not
keep pace with climate change. Controlled experiments where one or more climatic
variables (typically temperature, CO2, or moisture) are manipulated and the biological
response measured give insight into these transient dynamics. FACE (Free Air CO2
Enrichment) experiments (Ainsworth and Long 2005, DeLucia et al. 2005), rain-
exclusion (drought) experiments (Nepstad et al. 2002, Fisher et al. 2007, Brando et al.
2008), warming experiments (Rustad et al. 2001, Walker et al. 2006, Luo 2007, Aronson
and McNulty 2009), and snowpack manipulation experiments (Hardy et al. 2001,
Maljanen et al. 2009, Wipf and Rixen 2010) benefit from tightly controlled experimental
treatments, but are limited by a small spatial footprint.
Studies of altitudinal gradients are sometimes considered 'natural' experiments, because
climate varies with altitude, and organisms respond to this climate gradient. Documenting
2
changes in climate and biological phenomena, can help ecologists understand the long-
term effect of climate on species and ecosystems, but can also give insight into transient
dynamics. Gradient studies have a long history in ecology, including the work of
Alexander von Humbolt, who described plant species distributions, climate, and
ecosystem properties across an altitudinal gradient in the Andes Mountains of South
America around the turn of the 19th century (von Humboldt and Bonplan 2009), and
provided inspiration for later naturalist explorers such as Charles Darwin (Jackson 2009).
Both observational and experimental studies on climate and plant species performance
across an altitudinal gradient in the Andes mountain range are presented in this
dissertation. These include observational studies relating tree growth to climate, and a
controlled experiment testing how climate affects epiphyte survival and turn-over. These
studies, in conjuction with other work I have participated in examining patterns of net
primary productivity (Girardin et al. 2010), wood decomposition (Meier et al. 2010), and
microbial biodiversity (Fierer et al. in press), help to elucidate patterns of species
distribution and performance, and ecosystem properties across a steep climatic gradient in
the Andes. These provide new understanding of an extremely biodiverse, threatened, and
insufficiently studied ecosystem – the Andean cloud forest – and contribute to
documenting and predicting the response of this system to ongoing climate change.
Altitudinal gradients as natural laboratories
Altitudinal gradients have long been exploited as natural ecological laboratories because
of the climatic gradients found along their slopes (Lomolino 2001). The most obvious
gradient is that of temperature, which declines at the environmental lapse rate of ~5.6
3
ºC/km (Terborgh 1971, Bush et al. 2004) in the Andes. Because altitudinal lapse rates are
similar to latitudinal gradients in mean annual temperature, altitudinal and latitudinal
gradients are often compared, although key differences exist that make direct comparison
problematic. One difference is that changes in temperature across latitudinal gradients are
associated with seasonal changes in sun angle and day length, while along altitudinal
gradients, temperature is decoupled from sun angle and day length since all altitudes
experience similar patterns of insolation throughout the year. This may be of particular
importance to plants, for which day length during the growing season is an important
determinant of total assimilation and growth (Kikuzawa and Lechowicz 2006). Because
of this, altitudinal gradients may be more appropriate than latitudinal gradients for
studying the effects of temperature on plant performance (growth, survival, and
fecundity). This is especially true if the results are to be used to predict the response of
plant species to climate warming, since ongoing warming at any particular spot on earth
is not associated with day length change. Likewise, because genetic distance between
populations increases with geographic distance (Pautasso 2009), altitudinal gradients may
be more appropriate for predicting species' response to climate change than latitudinal
gradients, because the relatively short distances across species altitudinal ranges may
preclude the high genetic diversity seen across latitudinal ranges. While understanding
the performance differences of genetically determined ecotypes across latitudinal
gradients is important for understanding the evolutionary response to climate change, for
long-lived organisms such as trees, the population response over the next century will
largely be the response of current individuals and their offspring. Understanding the
4
response of genetically similar individuals to variable climate conditions, can help us
predict the short-term response of current populations to ongoing climate change.
Cloud forest biodiversity, ecosystem properties, and threats from climate change
Cloud forests are found on mountains throughout the tropics, where the adiabatic cooling
of air results in the formation of persistent cloud cover. While cloud forests can vary in
many respects, it is the presence of persistent clouds that is their defining feature
(Bruijnzeel and Veneklaas 1998). These clouds, along with associated high air humidity,
both saturated and nutrient poor soils, and low radiation, have been implicated in the
unique features of cloud forests – short statured trees with large root systems, low
productivity, and high abundance and biomass of bryophytes and vascular epiphytes
(Leigh 1975, Grubb 1977, Foster 2001). While a given hectare of cloud forest contains
fewer species (alpha diversity) than a hectare of lowland rainforest, their position along
altitudinal gradients results in high species turnover (beta diversity). The tropical Andes
is the largest and most species rich 'biodiversity hotspot' (Myers et al. 2000), and much of
this diversity stems from the turn-over of species across more than 5000 meters of
altitude from lowland Amazonia to Andean glaciers. In the Kosñipata Valley and
adjacent lowlands in southern Peru, the study site of the Andes Biodiversity and
Ecosystem Research Group, ~1040 tree species have been identified (W. Farfan, personal
communication) in eighteen one-hectare permanent tree plots spanning altitudes from 900
to 3400 meters. Similarly, more than 200 mammal and more than 1000 bird species have
been identified in the region (Patterson et al. 1998, Patterson et al. 2006). Besides the
intrinsic value of this biodiversity, cloud forests provide important ecosystem services.
The headwaters of the Amazon lie in these Andean cloud forests, and cloud forests are
5
important in hydrological cycles that provide water to agriculture, industry, and
population centers throughout the tropics (Bruijnzeel 2004, Celleri and Feyen 2009).
Cloud forests also store significant carbon in biomass. Although trees there are short-
statured compared to lowland rainforests, there is greater below-ground carbon allocation
(Leuschner et al. 2007, Girardin et al. 2010), and because of low decomposition rates
(Meier et al. 2010), below-ground carbon stocks are high (Girardin et al. 2010).
Like other ecosystems, tropical montane cloud forests are susceptible to climate change
(Loope and Giambelluca 1998, Still et al. 1999, Foster 2001). Temperatures in the Andes
are predicted to rise more than 4ºC in the next century (Vuille et al. 2008). For species to
remain in equilibrium with their temperature niche, they will have to migrate at least 800
meters in altitude during this time period. Current observed migration rates are only half
what would be expected due to observed temperature change (Feeley et al. in press), and
even if dispersal could keep up with temperature change, long-lived organisms such as
trees would still experience a significant demographic lag; individuals would be exposed
to climates for which they were not adapted. Like temperature, changes in precipitation
are expected over the next century, but the direction of change is difficult to predict, with
models predicting increasing or decreasing precipitation in different areas of the Andes
(Neelin et al. 2006, Urrutia and Vuille 2009). Either outcome could be problematic for
cloud forests. Drought may negatively affect species adapted to high moisture levels
(Pounds et al. 1999), and enhance the release of below ground carbon if peat soils dry out
(Kluge et al. 2008). Increased precipitation, if it comes as heavy rains, could increase the
risk of landslides in the steep topography of the Andes. A lifting cloud base is an almost
universal prediction of climate models (Still et al. 1999, Cowling et al. 2008, Pinto et al.
6
2009), which could have negative impacts on cloud dependent species near the current
cloud base, especially epiphytes (Nadkarni and Solano 2002). How cloud forests respond
to these climate changes is important for the survival of cloud forest species, and also for
the Amazonian species expected to migrate upslope as temperatures warm.
Objectives
How will cloud forests respond to climate change? By observing survival and growth of
plant species across an altitudinal gradient, and relating the observed patterns to climate,
this dissertation provides insight into answering this question. Altitudinal gradients are
well recognized as temperature gradients, but other climatic factors vary across altitude,
as well. Chapter II describes the climate of the gradient in the context of how these
patterns are expected to influence plant performance. Particular attention is paid to
cloudiness, the defining feature of cloud forests, which is hypothesized to be particularly
important in shaping the patterns of species' distributions, and ecosystem structure and
productivity in these systems (Grubb 1977, Foster 2001).
The following three chapters examine the observed ecological response to this climatic
gradient. These focus on the two groups that make up the greatest proportion of cloud
forest plant biodiversity and biomass, epiphytes and canopy trees. Chapter III describes a
reciprocal transplant experiment in which vascular epiphytes were moved up and down
slope across the putative cloud base to test whether changes in atmospheric moisture
would affect ramet survival and turn-over. Vascular epiphytes can make up as much as
30 percent of cloud forest plant biodiversity (Kuper et al. 2004), are considered keystone
resources for animals (Nadkarni and Matelson 1989, Cruz-Angon and Greenberg 2005,
7
Yanoviak et al. 2007, Cruz-Angon et al. 2009), and are important in nutrient cycling
(Nadkarni et al. 2004). Since cloud base is expected to rise over the coming decades (Still
et al. 1999, Cowling et al. 2008, Pinto et al. 2009), understanding the response of
epiphytes to drought conditions is important for understanding the community's potential
response to climate change.
Chapters IV and V examine the response of tree growth to climate across the altitudinal
gradient. Canopy trees are diverse and account for most of the annual primary
productivity and carbon storage within the cloud forest (Girardin et al. 2010). Chapter IV
examines growth phenology of trees and asks: when do trees grow in the cloud forest?
This question has received little attention in cloud forests, even though growing season
timing and length are important determinants of ecosystem productivity in other systems
(Richardson et al. 2010). In the temperate zone, the growing season is largely determined
by temperature, while in the lowland tropics, tree growth is tightly correlated with
precipitation (Worbes 1999, Schongart et al. 2002, Borchert et al. 2005, Singh and
Kushwaha 2005). In the cloud forest, where moisture availability is high and temperature
is consistent throughout the year, it was not clear if there should be a distinct growing
season, and if there was, what the driving factors and timing of it should be.
Chapter V focuses on growth within and between species of one dominant cloud forest
tree genus, Weinmannia, to address the question: What is the relationship between tree
growth and temperature in the tropics? A positive relationship between productivity and
temperature across sites in the tropics is well supported (Raich et al. 2006), but a negative
within site relationship has been reported in Costa Rica (Clark et al. 2010). The nature of
this relationship is important because considerable debate exists over whether tropical
8
forests are increasing in biomass in response to ongoing climate change, and what the
future response will be (Phillips et al. 1998, Clark et al. 2003, Baker et al. 2004,
Chambers and Silver 2004, Feeley et al. 2007, Chave et al. 2008, Lloyd and Farquhar
2008, Clark et al. 2010). By examining tree growth within and between species across a
geographically compact climate gradient, I was able to disentangle the general effect of
temperature on tree growth from other factors that vary between geographically distant
sites (differences in species composition, day length, and genetic dissimilarity of
ecotypes). This represents a novel contribution to the debate on the fate of tropical forests
in the face climate change.
In summary, this dissertation examines how canopy trees and epiphytes respond to
climate variability across a 4000 meter altitudinal gradient in the eastern Andes of Peru,
through both observational studies and a controlled experiment. This work provides
important insight into factors limiting plant performance in cloud forest and how these
plants are likely to respond to climate change.
9
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20
CHAPTER II
CLIMATE ACROSS A 3900 METER ALTITUDINAL GRADIENT IN THE HUMID TROPICS
Abstract
Climate is often invoked as a driver of patterns of ecosystem productivity, biomass, and
biodiversity, but rarely described in detail across altitudinal gradients, especially in the
tropics. In this study we describe the diurnal and seasonal patterns in microclimate along
an altitudinal gradient on the eastern slope of the tropical Andes of southern Peru,
focusing on the Kosñipata Valley in Manu National Park and adjacent regions. We used
data from newly installed micrometeorological stations and sensors, as well as weather
station data in the public domain, to describe altitudinal patterns of temperature,
precipitation, air humidity, sunlight, and wind using data from across a 20,000 km2 area
with time-series at individual sites lasting from 2 to 17 years. The general diurnal weather
cycle was similar throughout the year, although most climate variables exhibited seasonal
cycles which were strongly associated with cloudiness caused by cycles of moisture flux
across the Amazon. Patterns of microclimate were also consistent across altitudes
throughout the year, with the exception that incident sunlight levels and vapor pressure
deficit reached a maximum in June at the highest altitudes and in September at lower
altitudes, likely because of seasonally shifting cloud dynamics. The present study
provides an important starting point for studies of microclimate effects on ecosystem
dynamics, biodiversity, and species’ distributions and demography across Andean
altitudinal gradients. Our data suggest that cloud dynamics are important in determining
21
diurnal, seasonal, and altitudinal trends in several microclimate variables. Thus, changes
to cloud frequency and altitudinal occurrence associated with global climate change
could have important impacts on ecosystem dynamics in addition to those of rising
temperatures.
22
Introduction
Altitudinal gradients have played a central role in the history of ecology and
biogeography (Lomolino 2001), and are often used to study temperature gradients
because of the consistent decrease in temperature with altitude. Altitudinal lapse rates in
temperature are often similar to latitudinal gradients in mean annual temperatures, and
are often considered analogous to temperature changes when comparing species diversity
and distributions (Stevens 1992). Beyond clear global patterns in temperature,
atmospheric pressure, and clear-sky radiation, altitudinal patterns in other micorclimate
variables are generally local in nature (Korner 2007). Microclimate variables such as
cloudiness and moisture can have important effects on plant growth, and basic questions
remain about how altitudinal patterns in these variables effect plants (i.e. are tropical
mountains dry or wet environments for plants?; (Leuschner 2000)). However, there are
surprisingly little published data on the climate of altitudinal gradients (Smith and Geller
1980), especially in the humid tropics. What data from the tropics that has been published
come from oceanic islands such as Hawaii (Kitayama and Mueller-Dombois 1994) and
Borneo (Aiba and Kitayama 2002). Here we present data from ground-based
micrometeorological stations describing the microclimate of an altitudinal gradient of
3900 meters extending from the lowlands of western Amazonia to above tree line in the
eastern Andes, the first study of its kind that we are aware.
The altitudinal gradient described contains one of the richest concentrations of
biodiversity on earth (Myers et al. 2000). Tree species richness in the tropical rainforests
of the western Amazon is the highest of any on earth (Gentry 1988, Ter Steege et al.
2000) and the adjacent cloud forests on the east Andean slopes are the most biologically
23
rich biodiversity hotspot (Myers et al. 2000). For example, over 1000 bird species can be
found along this gradient (Patterson et al. 1998, Walker et al. 2006), approximately 10%
of the earth’s bird species. While there are several meteorological stations in lowland
areas of the study area, data for the cloud forest is sparse, due to the inaccessible nature
of much of the terrain. Therefore, we focus on the climate of the cloud forest portion of
the altitudinal gradient, and compare this with some data from meteorological stations in
the adjacent lowland (below 1000 meters) and highland (greater than 3500 meters) areas.
Tropical montane cloud forests are a distinct ecosystem type found on mountains
throughout the tropics, with large areas of cloud forest found along the east side of the
tropical Andes of South America (Bubb et al. 2004). While cloud forests are variable
throughout the world, they are usually areas of high diversity and endemism because of
their often fragmented nature. Mountain slope or mountain top cloud forests are often
isolated from one another by warmer (and often drier) lowland areas that serve as barriers
to dispersal for cloud forest species. Many authors have commented on the unique
features of cloud forests as compared to adjacent lowland forests (decreased forest
stature, thick (often described as xeromorphic) leaves, low tree growth rates and forest
productivity, thick humic or peat soils, and heavy accumulations of epiphytes in the
forest canopy), and linked these features to the presence of clouds (Leigh 1975, Grubb
1977, Vitousek 1998, Foster 2001). While it seems obvious that these features are related
to the frequent occurrence of low cloud, proving the mechanism by which these features
have formed remains elusive (Bruijnzeel and Veneklaas 1998). Data are sparse describing
cloud forest climates, and where they exist are usually for isolated locations (Fleischbein
et al. 2006, Gomez-Peralta et al. 2008, Giambelluca et al. 2009, Holwerda et al. 2010)
24
rather than along altitudinal gradients. There is an urgent need for these data, as cloud
forests are especially vulnerable to climate change (Still et al. 1999, Foster 2001, Johnson
and Smith 2008, Reinhardt and Smith 2008).
Here we present climate data for an altitudinal gradient spanning 3900 meters from
lowland rain forest to above tree line in the Andes of southeastern Peru. We include data
from weather stations within tropical montane cloud forest, an understudied ecosystem.
We describe the diurnal and seasonal cycles and altitudinal trends in climate, which
provide context for other studies examining factors limiting epiphyte survival near cloud
base (Chapter III), tree growth phenology (Chapter IV), and interannual tree growth
(Chapter V) across the altitudinal gradient.
25
Methods
Study site
Microclimate data were available from climate sensors installed in or near 1-hectare
permanent tree plots established by the Andes Biodiversity and Ecosystem Research
Group in the Kosñipata Valley (-13º 6’ 18’’ latitude, -71º 35’ 21” longitude), in and
adjacent to Manu National Park in southeastern Peru, and from weather stations
maintained by the Peruvian government or biological field stations. See Table II-1 and
Figure II-1 for sensor locations and years of data collection, and below for a more
detailed description of the sensors and weather stations. For a full site description,
including additional plots in the ABERG network, see Silman (in prep). The ABERG
plot network was initiated in 2003, with plots approximately every 250 meters from 925
to 3400 meters altitude. The substrate throughout most of the montane part of the study
area is Ordivician shale and slate, with intrusions of Permian granite. The vegetation
ranges from moist tropical rainforest in the lowlands to tropical montane cloud forest
(TMCF) and puna (high altitude grassland) in the mountains.
Sensors
Climate sensors were installed across the study area from June 2007 to September 2009.
Due to infrequent access to the sensors (such that sensor memories became full before
data download) and sensor and battery failures, data were not continuous across the time
interval. However, data were available from most sensors from mid-2007 to mid-2008.
Two different sets of sensors were installed. The first type of sensors, HOBO Pro V2
Temperature/Relative Humidity Data Loggers (U23-001, Onset Computer Corporation),
26
were installed in the forest understory by attaching them to tree trunks at approximately 1
meter in height. This was typically done on the south or west side of the tree, or other
appropriate location where the sensor would be least likely to receive direct sun (most
direct sun in the Kosñipata Valley occurs in the morning). Data were logged every 10 or
15 minutes depending on the sensor location and time period. The other set of sensors
were Temperature/Relative Humidity Smart Sensors (S-THB-M002, Onset Computer),
0.2mm Rainfall Smart Sensors (S-RGB-M002, Onset Computer), Photosynthetic Light
(PAR) Smart Sensors (S-LIA-M003, Onset Computer), and Wind Speed and Direction
Smart Sensors (S-WCA-M003, Onset Computer) connected to HOBO Microstation Data
Loggers (H21-002, Onset Computer). The sensors were mounted on poles approximately
1 meter above the canopy, and the temperature/relative humidity sensors were fitted with
a sun shield. Microstations logged data every 10 minutes.
We also collated data from four other micrometeorological stations in the Kosñipata
Valley maintained by the Servicio Nacional de Meteorolgía e Hidrología del Perú (Table
II-1; SENAMHI 2010; www.senamhi.gob.pe) and micrometerorological stations at three
lowland sites, one cloud forest site, and one highland site from the Atrium Biodiversity
Information System (Table II-1; http://atrium.andesamazon.org).
Analysis
For datasets that had resolution of less than a day, we first censored the data for each
variable to include only days that had measurements for the entire day for that variable
(except for relative humidity, see below).
Temperature
27
For data from automatic micrometeorological stations, we first calculated minimum,
maximum, mean, mean daytime (06:00-18:00), and mean nighttime (18:00-06:00)
temperature values for each day using all values measured (temperature measured every
10 or 15 minutes, depending on sensor). We then calculated monthly and annual mean
and standard deviation for each variable. Mean values were calculated in two ways. The
first was using a one year period from July 2007 through June 2008. The second was to
average values for each day of the year (from multiple years) and then calculate the
metrics using this composite-year dataset. Means were very similar when calculated in
these two ways (usually a few tenths of a degree difference) while standard deviations
were smaller and minimum and maximum temperatures were more extreme for the
second method. Here we report the metrics for the first method (continuous year) because
the standard deviations more accurately represent the variability experienced by the
specific locations over the course of the year. However, we do also report the absolute
maximum and minimum temperature values recorded over the 2+ year measurement
period. We also calculated mean daily temperatures for manual weather stations by
averaging the maximum and minimum temperatures for each day. This likely
overestimates mean temperature by 0.5-1.0 °C as compared to the mean of temperatures
recorded every 10-30 minutes in the automatic weather station data. We calculated
monthly mean temperatures and 95% confidence intervals from 2000-2008 to compare
our one year data to the decadal trend.
Finally, we calculated the lapse rate with respect to altitude for both the understory and
canopy. Lapse rate was first calculated using mean temperature for each day, and then the
28
mean of the lapse rate for all days in each month, giving a monthly lapse rate to compare
changes throughout the year. We also calculated lapse rates for day and night separately.
Humidity
Relative humidity was recorded at the automatic micrometeorological stations and
understory sensors. Because relative humidity is temperature-dependent , it is not a good
measure of either the amount of atmospheric moisture or the moisture stress on plants
(Anderson 1936). We therefore calculated vapor pressure (VP) and vapor pressure deficit
(VPD). Relative humidity values greater than 100% were first removed from the dataset.
Values greater than 100% indicated the sensor was saturated, even though the
surrounding air might not be, such that measurements of environmental relative humidity
were not reliable. High humidity is common in the cloud forest understory, particularly in
the wet season, so there were many data points removed from the understory sensors.
Because this procedure likely removes a greater proportion of high relative humidity
values from the data set, the further analysis likely implies a drier environment than
actually exists. Using relative humidity and temperature data, we calculated actual vapor
pressure (VP) and vapor pressure deficit (VPD) for each ten minute measurement period
using equations 5,7,12, and 14 of Yoder et al. (2005). Finally, we calculated mean,
daytime mean, nighttime mean, maximum, and minimum vapor pressure deficit on daily
and monthly time scales.
Photosynthetically Active Radiation (PAR)
To calculate daily integrated PAR, we multiplied each measurement (which is the
average PAR for the previous ten minutes in micromoles/meter2/second) by 600
29
seconds/10 minutes and summed the values for each day. We then calculated mean daily
integrated PAR for each month. We also calculated mean PAR (in
micromoles/meter2/second) for five periods of each day (two hour periods from 07:00 to
17:00) and averaged these for each month to examine diurnal patterns in solar radiation.
Rain
Rainfall totals were calculated on hourly, daily, monthly, and yearly scales. Hourly mean
rainfall was calculated for each month, the four wettest and driest months, and for a
complete year for four automatic weather stations. Values were then fitted with a cubic
smoothing spline using the smooth.spline command in R version 2.9.2 (2009) to visualize
diurnal patterns in rainfall. Total rainfall was then found for each day and month. Total
monthly rainfall was calculated for months with more than 25 days with data, and
standardized to 30 days (monthly rain = (total measured rain/number of days with
data)*30). For stations with rainfall totals for multiple years we calculated 95 %
confidence intervals for both monthly mean precipitation and mean annual precipitation.
Potential evapotranspiration was calculated for four sites using the Turc (1961) and
Jensen & Haise (1963) equations as formulated in Fisher et al. (2009). These equations
were chosen because Fisher et al. (2009) showed that both performed well in tropical
forests, with the Turc equation slightly under estimating evapotranspiration and the
Jensen & Haise equation slightly over estimating evapotranspiration at 21 tropical forest
sites with eddy-flux towers.
Wind
30
We calculated daytime, nighttime, and overall mean wind speed on daily and monthly
time scales. Wind direction data was not reliable for long time periods because of rotation
of the weather stations, but individual day windrose plots show general patterns of daily
wind. We also present the maximum wind gust for each day.
31
Results
Overview
Diurnal, seasonal and altitudinal patterns were evident in the meteorological data. We
identified three seasonal periods, across which altitudinal patterns changed. The austral
summer wet season was the longest and most stable season running from November
through March. April was a short transition between the wet and dry seasons, with May
through July forming the second season, the austral winter dry season. In the austral
spring was the dry-wet transition, from August through October. Precipitation levels
separated the wet season from the rest of the year, although precipitation varied fairly
smoothly across the year reaching a maximum in January and February and a minimum
in June and July. The difference between the dry season and dry-wet transition, was in
patterns of insolation, temperature, and humidity determined by patterns of cloud
formation. The dry season was characterized by cloud bands primarily at lower altitudes,
leading to increased light levels and vapor pressure deficit with altitude, and a shallow
lapse rate. In the dry-wet transition, the situation was reversed, with cloud bands at higher
altitudes and more sun at lower altitudes leading to gradients of decreasing light and
vapor pressure deficit with altitude, and a steeper lapse rate. These patterns persisted into
the wet season, although in a more muted fashion.
Temperature
Temperatures were higher and more variable during the day than at night (Table II-2).
The daily range of temperatures experienced for a site was similar across altitude in the
canopy (~6°C), but was more variable in the understory (~3-6°C; Table II-2: difference
32
between daily average maximum and minimum temperatures). Variability in the
understory was likely dependent on microsite placement of the sensors (areas of higher or
lower canopy-understory air exchange or occasional sun exposure). Temperatures were
higher in the wet season than the dry season, although the difference in mean
temperatures between the warmest and coolest months was generally less than 2°C, with
greater variability at lower altitude. At all altitudes minimum temperatures were realized
in June, although there was an altitudinal gradient in the timing of maximum
temperatures. In 2007, September and October had the highest mean temperatures at low
altitudes, but multi-annual data from other stations suggest that this pattern is not
consistent for most years (Figure II-2). However, the pattern that temperatures peak later
for higher altitudes does seem robust (Figure II-2). These patterns were likely due to
changes in solar heating (see results on light measurements below), which switched from
greatest heating at high altitudes in the dry season to lower altitudes during the transition
from dry to wet, and were more stable during the wet season.
The overall lapse rate of the understory (mean=5.27°C/1000 meters, s.d.=0.67) was
similar to, but less variable than that of the canopy (mean=5.22°C/1000 meters,
s.d.=0.98). Daytime lapse rates were shallower and more variable than night time lapse
rates (Table II-3). The lapse rate varied throughout the year, with the dry season (June)
having a shallow lapse rate, while the wet-dry transition (September) had a much steeper
lapse rate, and the wet season had an intermediate and much less variable lapse rate
(Figure II-3; Table II-3). This seasonal change in lapse rate was caused by larger changes
in temperature at lower elevations (Figure II-3). Day and nighttime lapse rates were most
similar in the wet season as well.
33
Humidity
Vapor pressure decreased with altitude (Table II-4; Figure II-4), but was fairly consistent
throughout the year (Figure II-5) and between the canopy and understory (Figure II-4).
There were large diurnal changes (Figure II-4), with vapor pressure increasing during the
day. Vapor pressure deficit (VPD) was low across the altitudinal range studied (Table II-
5). There were seasonal patterns however; VPD was very low in the wet season, and the
altitudinal trend switched direction from the dry season to the dry-wet transition (Figure
II-6). In June, average VPD increased with altitude, while in September the reverse was
true (Figure II-6). This pattern was seen in both the canopy and the understory, although
VPD was lower in the understory (Figure II-6). There was also more variability in the
understory measurements, likely due to microsite placement of the sensors (Figure II-6,
Table II-5). Night and daytime average VPD was slightly higher in the canopy than in the
understory (Table II-5), and both followed the same yearly pattern as daily mean VPD
(Figure II-7), with higher VPD in the dry season than in the wet season. While this
pattern was evident in multiple years, in was stronger in the 2007 dry season than in 2008
or 2009.
Photosynthetically Active Radiation
For most of the year, photosynthetically active radiation (PAR; 400-700 nm) decreased
with altitude (Figure II-8). This pattern was reversed in the dry season (April-July), when
cloudiness was higher at lower altitudes. During the dry-wet transition (September-
October), when highest light levels were attained for all but the highest altitude, there was
the strongest gradient of decreasing PAR with altitude. Light levels remained high into
34
the early wet season (November and December), before decreasing (Figure II-8). There
was more solar radiation in the morning than in the afternoon at all elevations for most of
the year, most strongly during the dry season (Figure II-9).
Rain
For the most part, rainfall varied smoothly over the year, with highest totals in January
and February, and lowest totals in June and July (Figure II-10). This pattern was broken
in the dry-wet transition, with high interannual variability in rainfall from August to
October, and often very high rainfall totals in October (Figure II-10). Diurnal patterns in
rainfall were similar across the year, with two peak times of rainfall during the day,
afternoon and overnight (Figure II-11). These two peaks were most distinct at higher
elevations, but merged to form one broad peak at lower elevations (Figure II-11). Above
1000 meters, rainfall decreased monotonically with altitude at all times of the year, with
precipitation ranging from less than 1000 mm/y at 4130 meters to greater than 5000
mm/y at 890 meters (Figure II-12). Interannual variability was greatest at mid-altitude
(Figure II-12). Potential evapotranspiration (PET) was similar calculated from both Turc
(1961) and Jensen & Haise (1963) methods, and was always less than monthly rainfall at
all elevations (Figure II-10; only values from Turc equation shown).
Wind
There was very little seasonal variation in wind direction, with patterns largely
determined by local terrain and the upslope and downslope diurnal movement of air.
Nighttime downslope winds were from the west and generally stronger than daytime
northerly or easterly (depending on local terrain) upslope winds (Figure II-13). In
35
general, mean wind speeds were higher at lower altitude, particularly during the dry-wet
transition (Figure II-14). Strong wind gusts were rare, with the highest coming during the
wet season, and mostly affecting lower elevations (Figure II-15).
36
Discussion
Diurnal and seasonal patterns and altitudinal trends in climate across the eastern Andean
slope are determined by the interaction of a combination of factors. These include
seasonal variation in moisture flux and solar radiation, interactions between moist air
over the Amazon and dry air over the Andes, the adiabatic lapse rate, and the daily path
of the sun. The regional climate is driven by northeast trade winds bringing moisture
from the tropical Atlantic across the Amazon Basin, which then turn south along the
Andean front to form the South American Low-level Jet (SALLJ; Marengo et al. 2004).
Local topography then interacts with the SALLJ to produce dry and wet spots in the
eastern Andes (Killeen et al. 2007). The Kosñipata Valley is within the Tambopata-Manu
wet spot as described by Killeen et al. (2007).
Diurnal Patterns
Two air masses meet at the eastern Andean slope. To the east, warm, moist air fed by the
SALLJ lies over the Amazon, which is blocked from moving westward by the Andes.
This, in combination with persistent subsidence over coastal western South America, and
the development of the Bolivian high over the Altiplano in summer (Garreaud et al.
2009), keeps drier air over the Andes. These warm, moist and cold, dry air masses
interact along the east Andean front on a diurnal cycle. During the day, solar heating over
the Amazon causes the moist air there to expand, and push upslope. Along with higher
temperatures experienced during the day, this results in daytime upslope winds (Figure
II-13) and higher daytime vapor pressure along the Andean slope (Figure II-5). As the
moist air rises it condenses into clouds, such that light levels are lower in the afternoon
37
than in the morning (Figure II-9) and there is a rain fall maximum in the afternoon
(Figure II-11). After sunset, when solar heating stops, the flow of air reverses with cold,
dry air over the Andes sinking. Downslope winds prevail in the overnight hours (Figure
II-13) and vapor pressure is lower across the slope (Figure II-5). This sinking air
displaces near-surface moist air at lower elevations, causing convection (Bendix et al.
2009) and there is a second rainfall peak in the overnight hours (Figure II-11). This
second peak is most evident during the rainy season (Figure II-11). At lower elevations
these peaks run into each other, as the afternoon peak is shifted later, and the night peak
shifted earlier as compared to higher elevations (Figure II-11).
Seasonality
Seasonality in the tropical Andes is driven largely by changes in moisture fluxes,
epitomized by the strong seasonality in rainfall, although rainfall is always higher than
evapotranspiration on a monthly scale (Figure II-10). However, seasonality in other
variables may be at least as important as rainfall seasonality for the forest ecosystem (see
Climate Effects on Forest Ecosystem below). Seasonality in moisture flux, driven by the
strength of the SALLJ, plus seasonality in available solar radiation combine to shape
seasonal patterns in climate. Located at ~12 degrees south latitude, day length varies by
less than an hour and a half per day between the solstices, while solar angle varies
between 54.5º at the winter solstice and 90º in late October and mid-February. This
means more energy is available from solar radiation during the wet season than in the dry
season. However, because of greater moisture available and more clouds in the austral
summer, photosynthetically active radiation at the canopy surface reached highest levels
during September to December during the measurement period (Figure II-8), which was
38
correlated with the highest temperatures observed during our measurement period.
However, longer term data suggest that the high temperatures during September and
October 2007 may have been anomalous, with long term averages highest during the wet
season and lowest during the dry season. High temperatures and light levels were also
associated with high VPD values (Figure II-7), even though atmospheric vapor pressure
was fairly consistent throughout the year (Figure II-5). Winds were fairly consistent
throughout the year, with slightly higher wind speeds at lower altitudes. Highest
maximum wind speeds occurred from October through March, and were higher at lower
altitudes (Figure II-15). These gusts were likely associated with storms forming over the
lowlands, which rarely reach high altitudes.
Altitudinal Trends
In contrast to oceanic islands such as Hawaii (Kitayama and Mueller-Dombois 1994,
Leuschner 2000), the trade wind inversion does not seem to strongly affect climate
patterns across the eastern Andes, although an inversion does exist between 3500 and
4000 meters in the dry season, and may have some influence on the location of tree line
(Y. Mahli, personal communication). Instead of a step change in precipitation as in
Hawaii (Kitayama and Mueller-Dombois 1994), annual precipitation decreased linearly
with altitude above 1000 meters (Figure II-12). Likewise, vapor pressure (Figure II-5)
and temperature decreased consistently with altitude, although the lapse rate varied over
the course of the year and from night to day (Table II-3). Light, vapor pressure, and wind
speed also generally decreased with altitude, but less consistently and there were times of
the year when these patterns are reversed. Light levels and vapor pressure deficit were
higher at high altitudes during the middle of the dry season (Figures II-5, II-6, and II-7),
39
and for much of the year wind speeds were low and did not change much with altitude
(Figure II-13). This is in contrast to temperate mountains where wind speed generally
increases with altitude (McVicar et al. 2010).
Climate Effects on Forest Ecosystem
Relatively smooth changes in climate variables across the altitudinal gradient contrast
with sharper changes in ecosystem structure. Above 1500 meters there are sharp changes
in biomass and productivity of trees (Girardin et al. 2010) and epiphytic bryophytes
(Howrath, in prep). This is a common pattern in tropical montane forest ecosystems, and
is typically attributed to the location of cloud base (Leigh 1975, Grubb 1977, Foster
2001). Tree line is located at ~3500 meters, although scattered patches of trees are
present above this. Anthropogenic fire has been implicated in enforcing current tree line
in the Andes (Sarmiento and Frolich 2002, Bader et al. 2008, Coblentz and Keating
2008), although presumably fire is supported at least partially by climatic conditions
which do not seem to change dramatically across this threshold. Our data suggest that
large changes in ecosystem structure do not require large changes in climate, and that
gradual changes are sufficient if thresholds are reached.
While our data do not reveal a sharp change in either light levels or humidity above 1500
meters that we would expect to be associated with a consistent cloud base, a humidity
threshold may contribute to the change in biomass and productivity above 1500 meters.
Vapor pressure deficit (VPD) below 0.2 kPa can promote growth of fungal pathogens
(Grange and Hand 1987), which is a level often reached in the wet season in the cloud
forest. In fact, mean VPD is below 0.2 kPa during the peak of the wet season above 1500
40
meters (Figure II-6). This, along with reduced transpiration (Motzer et al. 2005) may
contribute to lower productivity above 1500 meters for trees, while moist conditions may
be favorable for bryophyte growth.
Tree Line
Tree line is an obvious ecotone on mountains worldwide, and climatic, edaphic, and
anthropogenic factors have been implicated in determining tree line location (Korner
1998, Holtmeier and Broll 2005, Smith et al. 2009). In the tropical Andes, fire is often
cited as a factor that has lowered tree line from its natural altitude (Sarmiento and Frolich
2002, Bader and Ruijten 2008, Coblentz and Keating 2008). One outstanding question is
why these high altitudes support fire when the cloud forest below is almost continuously
saturated. Our data give some indication of the factors allowing for fire at high altitudes.
During the dry season, the light gradient in the Kosñipata Valley switched direction from
what it is the rest of the year (Figure II-8). Light levels then increase with altitude,
indicating that cloud frequency decreased with altitude. Importantly, light levels reached
their maximum at this time of the year for only the highest altitude, although in extreme
fire years, fire has reached as low as 2400 meters (M.R. Silman, personal
communication). Concurrent with this, VPD values were also relatively high at high
altitudes (Figure II-7). The combination of high solar radiation and high VPD at high
altitude may allow desiccation of the vegetation, promoting fire. Occasional freezing
temperatures above 3400 meters (Table II-2) may also inhibit tree regeneration at tree
line.
41
Within the cloud forest, we can make predictions about tree growth in relation to climate.
In most lowland tropical forests with a dry season, tree growth rates are correlated with
rainfall (Worbes 1999, da Silva et al. 2002, Clark et al. 2010). However, because
evapotranspiration was always lower than rainfall when measured on monthly scales
between 1500 meters and 3400 meters, photosynthesis is unlikely to be limited by lack of
water at these elevations. The exception may be during the dry season at the highest
elevations, where evapotranspiration was close to monthly precipitation and there may be
temporary drought stress on very dry days. Photosynthesis in cloud forest trees is
typically not limited by moisture stress until VPD values exceed 1-1.2 kPa (Cunningham
2004, Motzer et al. 2005). In this cloud forest, these values are only exceeded for short
periods at mid-day at the driest time of the year, and then only at lower altitudes (Figure
II-7). Coupled with the fact that light levels are highest during the dry-wet transition
period for most of the gradient, we predict that this season may be most favorable for tree
growth (Chapter IV), since increased cloudiness associated with the rainy season may be
more limiting to photosynthesis and growth than dry season drought stress (Condit et al.
2004).
Climate Change Impacts
While drought stress within the cloud forest portion of this altitudinal gradient appears
rare under current climatic conditions, future climate scenarios predict drying trends in
much of the tropics (Neelin et al. 2006). In addition, the lifting condensation level (LCL),
which influences the height at which clouds form, is predicted to rise by 1000 meters or
more over the Amazon by 2100 (Cowling et al. 2008, Pinto et al. 2009). A rising cloud
base has been linked to the extinction of cloud forest species in Costa Rica (Pounds et al.
42
1999). Climate model projections for the Andes predict warmer temperatures and lower
precipitation in the dry season (Vuille et al. 2008), potentially exposing cloud forest
plants to drought stress. The severe regional drought in 2005 (Marengo et al. 2008, Zeng
et al. 2008) led to high tree mortality and biomass losses in the Amazon Basin (Phillips et
al. 2009). There have been no published accounts of the effects of the drought on cloud
forest trees, while epiphyte responses were species-specific (Chapter III). Predicting the
effects of decreased cloudiness and reduced precipitation on cloud forest plants is not
straightforward. On the one hand increasing solar radiation may enhance tree growth. On
the other hand, if low humidity thresholds are reached, mortality and reduced growth of
cloud forest trees may result.
In either event, climate change is likely to cause shifts in species distributions, as climate
is a key driver in determining species distributions, either directly through imposing
physiological limitations on species growth, survival, or reproduction, or indirectly
through its effects on biotic interactions that affect these processes. For trees (and plants
generally), climate can influence range limits in a number of ways. Temperature effects
on metabolic rates, moisture and light condition impacts on carbon gain (photosynthesis),
and damage from wind and extreme temperatures are direct effects of climate on plant
performance. Indirectly, climate can influence such animal-mediated processes such as
pollination and dispersal by modifying the phenology of the plant, its animal symbiont, or
both. Climate can also affect populations of natural enemies such as herbivores, seed
predators, or pathogens, or the ability of the plant to defend against these enemies. While
many of these factors are complex, especially the indirect mechanisms, understanding the
climate regime a particular species is exposed to is the first step towards understanding
43
the causes of species ranges. While not all species will respond in the same way to
climate change since different climate variables may be more important to different
species, correlations between range limits and climate variables are often used to predict
species and ecosystem response to climate change. A more detailed understanding of
present climate improves our understanding of the relationship between climate and
species’ distributions and our ability to predict species distribution change.
44
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52
Table II-1. Summary of weather stations and sensors in the Kosñipata Valley and
adjacent areas. Site names with codes are associated with tree plots; all others are at field
stations, communities, or park ranger stations. Altitude is in meters. Location refers to
whether the sensors are in open fields, or in the understory or above the canopy of
continuous forest. Sensor types include manual weather stations where temperature and
rainfall are recorded daily, automatic weather stations that record several climate
variables every half hour or less, and automatic temperature and relative humidity sensors
that record every ten or fifteen minutes. Data sources include sensors installed in
association with a tree plot network established by the Andes Biodiversity and Ecosystem
Research Group (ABERG), data available on the Atrium Biodiversity Information
System of the Botanical Research Institute of Texas data portal (Atrium;
atrium.andesamazon.org), and data from the Servicio Nacional de Meteorolgía e
Hidrología del Perú (SENAMHI; www.senamhi.gob.pe). Latitude and Longitude are in
decimal degrees. Years refer to the years when sensors were installed, although data is
not continuous for all years for most sensors.
53
Site Altitude Location Sensor Source Lat. Lon. Years
Tambopata 230 open Manual W.S. Atrium -13.137 -69.612 1995-2008
Los Amigos 280 open Manual W.S. Atrium -12.577 -70.069 2000-2009
Cocha Cashu 340 open Manual W.S. Atrium -11.888 -71.407 1984-2001
Chontachaca 982 open Manual W.S. SENAMHI -13.024 -71.468 2000-2008
SP_1500 1500 canopy Automatic W.S. ABERG -13.049 -71.537 2007-2009
SP_1500 1500 understory Temp/RH ABERG -13.049 -71.537 2007-2009
SP_1750 1750 understory Temp/RH ABERG -13.047 -71.542 2007-2009
TU_1800 1840 canopy Automatic W.S. ABERG -13.070 -71.556 2007-2009
TU_1800 1840 understory Temp/RH ABERG -13.070 -71.556 2007-2009
Rocotal 2010 open Manual W.S. SENAMHI -13.113 -71.571 2000-2008
TU_2000 2020 understory Temp/RH ABERG -13.074 -71.559 2007-2009
TU_2250 2250 understory Temp/RH ABERG -13.080 -71.566 2007-2009
TU_2500 2520 understory Temp/RH ABERG -13.094 -71.574 2007-2009
TU_2750 2720 canopy Automatic W.S. ABERG -13.105 -71.589 2007-2009
TU_2750 2720 understory Temp/RH ABERG -13.106 -71.589 2007-2009
WA_3000 3000 open Automatic W.S. ABERG -13.190 -71.587 2006-2009
TU_3000 3020 understory Temp/RH ABERG -13.109 -71.600 2007-2009
TU_3250 3200 understory Temp/RH ABERG -13.110 -71.604 2007-2009
TU_3450 3400 canopy Automatic W.S. ABERG -13.113 -71.608 2007-2009
TU_3450 3400 understory Temp/RH ABERG -13.114 -71.607 2007-2009
Acjanaco 3487 open Manual W.S. SENAMHI -13.196 -71.620 2001-2007
TC_3600 3600 understory Temp/RH ABERG -13.123 -71.618 2007-2009
Qero 4131 open Automatic W.S. Atrium -13.453 -71.179 2006-2008
54
Table II-2. Temperature in the Kosñipata Valley. Mean annual temperature (MAT), mean
daytime (06:00-18:00) and nighttime temperatures, mean daily maximum and minimum
temperatures, and absolute maximum and minimum temperatures are shown. Mean
temperatures are for the period July 2007 through June 2008, except for the sensors at
3000 meters (June 2006 – May 2007) and 4130 meters (January-December 2007).
Absolute maximum and minimum temperatures are from the entire period each sensor
was installed (see Table 1). Numbers in parentheses are standard deviations.
Altitude Location N.days Max.T Min.T
1500 canopy 347 18.1 (1.8) 19.0 (2.1) 17.2 (1.7) 21.9 (2.5) 15.8 (1.8) 28.6 7.5
1500 understory 329 17.4 (1.5) 18.1 (1.7) 16.8 (1.4) 19.7 (2.0) 15.6 (1.6) 25.2 8.9
1750 understory 360 15.8 (1.3) 16.3 (1.4) 15.4 (1.2) 17.4 (1.6) 14.5 (1.3) 23.6 9.2
1840 canopy 361 16.5 (1.6) 17.2 (1.9) 15.8 (1.4) 20.0 (2.3) 14.4 (1.6) 25.5 6.7
1840 understory 366 16.0 (1.3) 16.7 (1.5) 15.4 (1.2) 18.7 (2.0) 14.4 (1.3) 24.2 8.5
2020 understory 366 14.9 (1.1) 15.4 (1.2) 14.3 (1.1) 17.2 (1.7) 13.3 (1.2) 22.4 8.6
2250 understory 365 14.3 (1.2) 15.6 (1.6) 13.0 (1.0) 18.3 (2.2) 11.8 (1.2) 25.3 7.8
2520 understory 366 12.1 (1.0) 12.6 (1.0) 11.6 (1.0) 13.9 (1.3) 10.7 (1.1) 26.3 7.0
2720 canopy 365 11.8 (1.2) 12.7 (1.5) 10.9 (1.1) 16.2 (2.2) 9.5 (1.2) 22.1 4.4
2720 understory 343 11.1 (1.0) 11.6 (1.0) 10.6 (1.0) 12.7 (1.2) 9.7 (1.1) 16.8 6.2
3000 open 354 11.1 (1.2) 12.7 (1.6) 9.5 (1.1) 15.9 (2.2) 8.2 (1.3) 21.7 3.6
3020 understory 342 9.5 (1.0) 10.3 (1.2) 8.7 (1.0) 11.7 (1.6) 7.7 (1.1) 16.4 2.6
3200 understory 341 8.9 (1.0) 9.7 (1.2) 8.1 (1.0) 11.6 (2.1) 7.2 (1.0) 18.6 3.6
3400 canopy 313 8.4 (1.0) 9.3 (1.2) 7.6 (0.9) 11.8 (1.8) 6.3 (1.0) 18.0 2.4
3400 understory 341 7.7 (1.1) 8.7 (1.3) 6.6 (1.2) 10.7 (2.5) 5.6 (1.4) 24.6 1.1
3600 understory 289 6.5 (0.9) 7.3 (0.8) 5.6 (1.1) 8.6 (1.0) 4.5 (1.2) 16.2 -1.1
4130 open 331 4.8 (1.2) 5.8 (1.2) 3.8 (1.4) 8.0 (1.5) 2.1 (1.7) 11.6 -5.4
mean.Min.TMAT day.MAT night.MAT mean.Max.T
55
Table II - 3. Lapse rate in the Kosñipata Valley by month (July 2007 – June 2008). Lapse
rates calculated for each day using mean daily, daytime (06:00-18:00), and nighttime
temperatures, then averaged over each month. Numbers in parentheses are standard
deviations.
Month N
July 31 5.04 (0.86) 4.80 (1.00) 5.27 (0.76) 4.85 (1.15) 4.72 (1.39) 4.96 (0.97)
August 31 5.53 (0.69) 5.34 (0.79) 5.71 (0.70) 5.43 (1.24) 5.14 (1.41) 5.72 (1.14)
September 30 6.23 (0.58) 6.13 (0.63) 6.33 (0.58) 6.30 (0.63) 6.38 (0.69) 6.23 (0.62)
October 31 5.81 (0.63) 5.70 (0.62) 5.91 (0.66) 5.87 (0.62) 6.02 (0.75) 5.72 (0.60)
November 30 5.15 (0.44) 4.98 (0.56) 5.31 (0.36) 5.30 (0.72) 5.31 (0.90) 5.28 (0.61)
December 31 5.21 (0.32) 5.09 (0.41) 5.33 (0.33) 5.40 (0.51) 5.44 (0.66) 5.36 (0.44)
January 31 5.05 (0.17) 5.02 (0.18) 5.08 (0.20) 5.20 (0.32) 5.26 (0.44) 5.14 (0.24)
February 29 5.14 (0.34) 5.05 (0.47) 5.23 (0.30) 5.28 (0.57) 5.33 (0.80) 5.24 (0.39)
March 31 5.32 (0.40) 5.23 (0.44) 5.40 (0.43) 5.42 (0.43) 5.42 (0.52) 5.41 (0.40)
April 30 4.98 (0.60) 4.86 (0.72) 5.10 (0.50) 4.84 (0.96) 4.78 (1.21) 4.90 (0.77)
May 31 4.94 (0.79) 4.75 (0.89) 5.13 (0.75) 4.58 (1.26) 4.53 (1.33) 4.63 (1.26)
June 30 4.82 (0.41) 4.48 (0.53) 5.16 (0.40) 4.17 (0.88) 3.92 (1.00) 4.43 (0.84)
Year 366 5.27 (0.67) 5.12 (0.76) 5.42 (0.64) 5.22 (0.98) 5.19 (1.15) 5.25 (0.88)
Understory Canopy
Mean Day Night Mean Day Night
56
Table II - 4. Vapor pressure (VP) expressed in kilo Pascals (kPa), in the Kosñipata
Valley. Mean, mean daytime (06:00-18:00), and mean nighttime VP, mean daily
maximum and minimum VP, and absolute maximum and minimum VP shown for
understory and canopy locations across the altitudinal gradient. Mean VP values are for
the period July 2007 through June 2008, except for the sensors at 3000 meters (June 2006
– May 2007) and 4130 meters (January-December 2007). Absolute maximum and
minimum VP values are from the entire period each sensor was installed (see Table 1).
Numbers in parentheses are standard deviations associated with the preceding value.
57
Altitude Location N.days Max Min
1500 canopy 347 1.84 (0.18) 1.92 (0.19) 1.75 (0.17) 2.22 (0.24) 1.56 (0.20) 2.91 0.96
1500 understory 329 1.91 (0.17) 1.99 (0.18) 1.82 (0.17) 2.20 (0.20) 1.65 (0.20) 2.75 1.09
1750 understory 360 1.76 (0.14) 1.82 (0.14) 1.71 (0.14) 1.96 (0.17) 1.56 (0.16) 2.94 1.07
1840 canopy 361 1.67 (0.15) 1.75 (0.15) 1.59 (0.15) 2.00 (0.19) 1.41 (0.19) 2.57 0.74
1840 understory 366 1.74 (0.14) 1.81 (0.15) 1.67 (0.15) 2.04 (0.19) 1.50 (0.19) 2.66 0.86
2020 understory 366 1.70 (0.12) 1.75 (0.13) 1.64 (0.12) 1.96 (0.19) 1.52 (0.14) 2.47 0.70
2250 understory 365 1.56 (0.13) 1.67 (0.15) 1.44 (0.12) 1.91 (0.19) 1.30 (0.16) 2.49 0.62
2520 understory 366 1.43 (0.09) 1.48 (0.10) 1.38 (0.09) 1.60 (0.13) 1.29 (0.10) 2.29 0.91
2720 canopy 365 1.26 (0.12) 1.34 (0.12) 1.19 (0.13) 1.57 (0.15) 1.06 (0.15) 1.94 0.34
2720 understory 343 1.33 (0.10) 1.37 (0.10) 1.28 (0.11) 1.48 (0.11) 1.19 (0.13) 1.86 0.51
3000 open 354 1.20 (0.15) 1.28 (0.15) 1.11 (0.17) 1.43 (0.14) 0.97 (0.19) 1.85 0.12
3020 understory 342 1.18 (0.10) 1.25 (0.10) 1.12 (0.10) 1.37 (0.12) 1.03 (0.12) 1.72 0.38
3200 understory 341 1.13 (0.10) 1.19 (0.10) 1.08 (0.09) 1.33 (0.14) 1.00 (0.11) 1.88 0.36
3400 canopy 313 1.02 (0.11) 1.09 (0.11) 0.95 (0.12) 1.24 (0.11) 0.84 (0.16) 1.54 0.10
3400 understory 341 1.04 (0.10) 1.11 (0.10) 0.97 (0.11) 1.24 (0.15) 0.88 (0.12) 1.86 0.26
3600 understory 289 0.95 (0.09) 1.00 (0.08) 0.90 (0.09) 1.10 (0.08) 0.81 (0.11) 1.84 0.28
4130 open 331 0.74 (0.14) 0.79 (0.13) 0.69 (0.15) 0.95 (0.12) 0.56 (0.17) 1.25 0.07
Daily.minMean Day.mean Night.mean Daily.max
58
Table II - 5. Vapor pressure deficit (VPD) expressed in kilo Pascals (kPa), in the
Kosñipata Valley. Mean, mean daytime (06:00-18:00), and mean nighttime VPD, mean
daily maximum and minimum VPD, and absolute maximum and minimum VPD shown
for understory and canopy locations across the altitudinal gradient. Mean VPD values are
for the period July 2007 through June 2008, except for the sensors at 3000 meters (June
2006 – May 2007) and 4130 meters (January-December 2007). Absolute maximum and
minimum VPD values are from the entire period each sensor was installed (see Table 1).
Numbers in parentheses are standard deviations.
59
Altitude Location N.days Max Min
1500 canopy 347 0.29 (0.22) 0.32 (0.25) 0.25 (0.20) 0.62 (0.35) 0.08 (0.11) 1.96 0.00
1500 understory 329 0.12 (0.15) 0.12 (0.16) 0.12 (0.15) 0.27 (0.26) 0.04 (0.07) 1.22 0.00
1750 understory 360 0.07 (0.09) 0.06 (0.09) 0.07 (0.10) 0.15 (0.16) 0.03 (0.04) 0.84 0.00
1840 canopy 361 0.25 (0.18) 0.26 (0.20) 0.24 (0.17) 0.56 (0.29) 0.05 (0.07) 1.73 0.00
1840 understory 366 0.11 (0.12) 0.12 (0.13) 0.10 (0.12) 0.25 (0.22) 0.05 (0.05) 1.07 0.01
2020 understory 366 0.02 (0.04) 0.03 (0.05) 0.02 (0.03) 0.07 (0.09) 0.01 (0.02) 0.50 0.00
2250 understory 365 0.11 (0.11) 0.14 (0.15) 0.08 (0.08) 0.31 (0.29) 0.04 (0.03) 1.62 0.00
2520 understory 366 0.01 (0.02) 0.01 (0.02) 0.01 (0.01) 0.02 (0.04) 0.00 (0.01) 0.27 0.00
2720 canopy 365 0.15 (0.12) 0.16 (0.14) 0.13 (0.12) 0.43 (0.25) 0.04 (0.05) 1.56 0.01
2720 understory 343 0.01 (0.03) 0.01 (0.03) 0.02 (0.04) 0.04 (0.08) 0.01 (0.01) 0.59 0.00
2850 understory 242 0.03 (0.04) 0.03 (0.05) 0.02 (0.03) 0.07 (0.11) 0.02 (0.02) 0.82 0.00
3000 open 354 0.16 (0.14) 0.23 (0.18) 0.09 (0.12) 0.49 (0.31) 0.02 (0.06) 2.11 0.01
3020 understory 342 0.02 (0.05) 0.02 (0.06) 0.02 (0.05) 0.05 (0.10) 0.01 (0.03) 0.71 0.00
3200 understory 341 0.03 (0.05) 0.03 (0.06) 0.02 (0.04) 0.09 (0.17) 0.01 (0.02) 1.17 0.00
3400 canopy 313 0.10 (0.12) 0.10 (0.14) 0.10 (0.12) 0.32 (0.25) 0.02 (0.06) 1.49 0.00
3400 understory 341 0.03 (0.05) 0.04 (0.06) 0.03 (0.04) 0.12 (0.18) 0.01 (0.02) 1.58 0.00
3600 understory 289 0.03 (0.05) 0.04 (0.06) 0.03 (0.03) 0.07 (0.10) 0.02 (0.02) 0.69 0.00
4130 open 331 0.14 (0.11) 0.15 (0.13) 0.13 (0.10) 0.35 (0.21) 0.03 (0.04) 1.18 0.00
Mean.MinMean.VPD Day.VPD Night.VPD Mean.Max
60
Figure legends
Figure II - 1. Map of weather stations and sensors in the Kosñipata Valley and adjacent
areas. ABERG stations and sensors that are not labeled were installed in or near
permanent tree plots along an altitudinal gradient that increases in altitude from upper
right to lower left (inset). Location names are listed in Table 1. Map courtesy of Rebecca
Dickson.
Figure II - 2. Monthly temperatures in the Kosñipata Valley and an adjacent lowland site.
Bars show mean monthly temperatures during a one year period from July 2007 to June
2008 for above canopy weather stations at 1500, 1840, 2720, and 3400 meters. Lines with
error bars (95% confindece intervals) give mean monthly temperatures at the Cocha
Cashu (340 meters) Chontachaca (980 meters), Rocotal (2010 meters), and Acjanaco
(3490 meters) meteorlogical stations in the Kosñipata Valley and adjacent lowlands.
Mean daily temperatures for manual meteorological stations were taken as the mean of
the maximum and minimum temperatures, while the daily means from the other weather
stations were calculated as the mean of all measurements for each day (temperature
recorded every 10 minutes).
Figure II - 3. Mean monthly temperature versus altitude (lapse rate) for September 2007,
January 2008, and June 2008.
Figure II - 4. Mean vapor pressure (VP) in kilopascals (kPa) plotted against altitude for
three months in 2007 and 2008 in the Kosñipata Valley. Values for the canopy (dashed
lines) and understory (solid lines) are shown.
61
Figure II - 5. Time course of vapor pressure (VP) in kilopascals (kPa). Top panel shows
mean daily VP over the course of a year (July 2007 – June 2008). Three lower panels
show course of VP over 10 minute measurement intervals for 3 weeks during the dry-wet
transition (September-October 2007), wet season (January 2008), and dry season (June
2008). Lines show data for above-canopy weather stations at four altitudes (light gray to
black: 1500 meters, 1840 meters, 2720 meters, 3400 meters).
Figure II - 6. Mean vapor pressure deficit (VPD) in kilopascals (kPa) plotted against
altitude for three months in 2007 and 2008 in the Kosñipata Valley for both the canopy
(dashed lines) and understory (solid lines).
Figure II - 7. Time course of vapor pressure deficit (VPD) in kilopascals (kPa). Top panel
shows mean daily VPD over the course of a year (July 2007 – June 2008). Three lower
panels show course of VPD over 10 minute measurement intervals for 3 weeks during the
dry-wet transition (September-October 2007), wet season (January 2008), and dry season
(June 2008). Lines show data for above-canopy weather stations at four altitudes (light
gray to black: 1500 meters, 1840 meters, 2720 meters, 3400 meters).
Figure II - 8. Mean daily integrated photosynthetically active radiation (PAR, 400-700
nm wavelength light) by month in the Kosñipata Valley. Bars shading indicates the
altitude in meters where PAR was recorded.
Figure II - 9. Mean PAR in micromoles/m2/s by time of day for four elevations (1500,
1840, 2720, and 3400 meters) in the Kosñipata Valley. Each month from July 2007 to
June 2008 is plotted. Bar color represents time of day (dark to light gray: 7:00-9:00; 9:00-
11:00; 11:00-13:00; 13:00-15:00; 15:00-17:00).
62
Figure II - 10. Monthly rain in the Kosñipata Valley standardized to 30 days/month. Bars
represent rain totals for each month, while lines with error bars (95% confidence
intervals) represent mean monthly rain totals recorded at the Chontachaca (980 meters),
Rocotal (2010 meters), and Acjanaco (3490 meters) meteorlogical stations from 2000-
2008 (n=5-8 years of data for each month). Potential evapotranspiration (*) for each
month and altitude is also shown.
Figure II - 11. Diurnal pattern of rainfall during the wet (December to March) and dry
(May to August) seasons in the Kosñipata Valley. Lines show cubic spline fit to mean
hourly rainfall values.
Figure II - 12. Mean annual precipitation across the altitudinal gradient in the Kosñipata
Valley and nearby lowland and highland areas. Asterixes are weather stations with one
year of data. Points with error bars (95% confidence intervals) are stations with multiple
years of data.
Figure II - 13. Daily patterns in wind speed and direction for three days (rows) at four
different altitudes (columns). The radial axis represents percent of time the wind is
blowing from a particular direction. Shading represents wind speed (light gray is 0-2 m/s;
medium gray is 2-4 m/s; dark gray is 4-6 m/s (only small amount seen on 2 January 2008
at 1500 meters)). Westerly winds are nighttime downslope winds, while easterly and
northerly winds are daytime upslope winds.
Figure II - 14. Mean monthly wind speed versus altitude in the Kosñipata Valley for three
months.
63
Figure II - 15. Maximum wind gust for each day from July 2007 through June 2008 for
four micrometeorological stations in the Kosñipata Valley. The horizontal line at 14 m/s
is a near gale, the wind speed that will move large trees (Cullen 2002). Altitude in meters
of each station is labeled above each figure.
64
FIGURE II - 1
65
Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun
1500 m1840 m2720 m3400 m
Month (2007−2008)
Deg
rees
Cel
cius
0
5
10
15
20
25
30
●
● ●● ● ● ● ● ● ●
●●
● ●●
● ● ● ● ● ● ●●
●
● ●●
● ● ● ● ● ● ●●
●
●
●●
●● ● ● ● ● ●
●●
●
●
●
●
340 m980 m2010 m3490 m
FIGURE II - 2
66
●
●
●
●
1500 2000 2500 3000
810
1214
1618
20
Altitude (meters)
Deg
res
Cel
cius
●
●
●
●
●
●
●
●
●
●
●
SeptemberJanuaryJune
FIGURE II - 3
67
Altitude (meters)
VP
(kP
a)
1.0
1.5
2.0
1500 2500 3500
●●● ●
●●
●
●●
●
●
●
●
●
●
September 2007
1500 2500 3500
●
●●●
●
●●
●●
●●
●
●
●
●
●
January 2008
1500 2500 3500
●
●●●
●
●
●● ●
●
●●
●
●
●
●
June 2008
FIGURE II - 4
68
0.0
0.5
1.0
1.5
2.0
2.5
Month (2007−2008)
Mea
n V
P (
kPa)
Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul
0.0
0.5
1.0
1.5
2.0
2.5
VP
(kP
a)
Sep 29 Sep 30 Oct 1 Oct 2 Oct 3 Oct 4 Oct 5 Oct 6
0.0
0.5
1.0
1.5
2.0
2.5
VP
(kP
a)
Jan 14 Jan 15 Jan 16 Jan 17 Jan 18 Jan 19 Jan 20 Jan 21
0.0
0.5
1.0
1.5
2.0
2.5
VP
(kP
a)
Jun 2 Jun 3 Jun 4 Jun 5 Jun 6 Jun 7 Jun 8 Jun 9
FIGURE II - 5
69
Altitude (meters)
VP
D (
kPa)
0.0
0.2
0.4
0.6
1500 2500 3500
●
●
●
●
●
● ● ● ● ●●
●
●
●
●
September 2007
1500 2500 3500
● ●●●
●● ●● ● ● ● ●
●●
●●
January 2008
1500 2500 3500
●●
●●
●
●●
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June 2008
FIGURE II - 6
70
0.0
0.5
1.0
1.5
2.0
Month (2007−2008)
Mea
n V
PD
(kP
a)
Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul
1500184027203400
0.0
0.5
1.0
1.5
2.0
VP
D (
kPa)
Sep 29 Sep 30 Oct 1 Oct 2 Oct 3 Oct 4 Oct 5 Oct 6
0.0
0.5
1.0
1.5
2.0
VP
D (
kPa)
Jan 14 Jan 15 Jan 16 Jan 17 Jan 18 Jan 19 Jan 20 Jan 21
0.0
0.5
1.0
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2.0
VP
D (
kPa)
Jun 2 Jun 3 Jun 4 Jun 5 Jun 6 Jun 7 Jun 8 Jun 9
FIGURE II - 7
71
Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun
1500184027203400
Month (2007−2008)
Dai
ly In
tegr
ated
PA
R(m
oles
/m2)
0
5
10
15
20
25
30
35
FIGURE II - 8
72
7 8 9 10 11 12 1 2 3 4 5 6
3400m
icro
mol
es/m
2/s
040
080
0
7 8 9 10 11 12 1 2 3 4 5 6
2720
mic
rom
oles
/m2/
s
040
080
0
7 8 9 10 11 12 1 2 3 4 5 6
1840
mic
rom
oles
/m2/
s
040
080
0
7 8 9 10 11 12 1 2 3 4 5 6
1500
mic
rom
oles
/m2/
s
040
080
0
FIGURE II - 9
73
Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun
1500 m1840 m2720 m3400 m
Month (2007−2008)
Mon
thly
Pre
cipi
tatio
n (m
m)
0
200
400
600
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1000
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980 m2010 m3490 m
FIGURE II - 10
74
0 5 10 15 20
0.0
0.5
1.0
1.5
wet
Mea
n ho
urly
rai
nfal
l (m
m)
0 5 10 15 20
0.0
0.5
1.0
1.5
dry
Hour of Day
Mea
n ho
urly
rai
nfal
l (m
m)
1500 m1800 m2720 m3400 m
FIGURE II - 11
75
●
●
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0 1000 2000 3000 4000
0
1000
2000
3000
4000
5000
Altitude (meters)
MA
P (
mm
)
●
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●
FIGURE II - 12
76
1500 meters 1840 meters 2720 meters 3400 meters
4 September 2007
2 January 2008
15 June 2008
FIGURE II - 13
77
Altitude (meters)
Win
d S
peed
(m
/s)
0.0
0.5
1.0
1.5
2.0
2.5
1500 2000 2500 3000
●
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September 2007January 2008June 2008
FIGURE II - 14
78
05
1015
1500
Month (2007−2008)
Gus
t (m
/s)
Jul Nov Mar Jul
018
3654
05
1015
1840
Month (2007−2008)
Jul Nov Mar Jul
018
3654
(kilo
met
ers/
hour
)
05
1015
2720
Month (2007−2008)
Gus
t (m
/s)
Nov Jan Mar May Jul
018
3654
05
1015
3400
Month (2007−2008)
Jul Nov Mar Jul
018
3654
(kilo
met
ers/
hour
)
FIGURE II - 15
79
CHAPTER III
EPIPHYTE RESPONSE TO DROUGHT AND EXPERIMENTAL WARMING: CLOUD FOREST
RESILIENCE UNDER CLIMATE CHANGE
Abstract
Aims: To investigate the response of epiphyte communities in tropical montane cloud
forest to the predicted consequences of climate change, namely warmer temperatures,
higher cloud bases, and more frequent drought.
Location: Eastern slope of the tropical Andes of southern Peru, in the Kosñipata Valley
adjacent to Manu National Park.
Methods: We tested the hypothesis that warmer and drier conditions associated with
lifting cloud base would lead to increased mortality and/or decreased production of
epiphyte ramets, altering species composition in epiphyte mats. By using a reciprocal
transplant design, we were able to distinguish the effects of warmer and drier conditions
from the more general prediction of niche theory that transplanting epiphytes in any
direction away from their home elevation should result in reduced performance. In the
2005 dry season we surveyed and then transplanted epiphyte mats (N=60) between
Alzatea verticillata trees across an altitudinal gradient in cloud immersion (1500, 1650,
and 1800 meters elevation). Mats were resurveyed after one year.
Results: Ramet mortality was higher than production in transplanted, but not undisturbed
mats, and the best predictor of a ramet's survival was its species or functional type rather
than elevational treatment. Ramet mortality increased in mats transplanted down slope
80
from the highest elevation, into warmer and drier conditions, but there was no consistent
effect of elevation when all transplants were considered. Ramet production rates matched
mortality rates in all species such that there was no directional change in mat species
composition.
Main Conclusions: While epiphytes above cloud base were negatively affected by
drought and warmer temperatures, epiphytes of the same species below cloud base appear
to have greater resistance to drought, at least over the one year time scale of the
experiment, even in the face of a severe drought which affected the Amazon Basin and
adjacent Andes in 2005. This illustrates plasticity in epiphytes not revealed in previous
studies showing a strong influence of cloud immersion on epiphyte survival. If drier
conditions and higher cloud bases become a permanent feature in these forests as
predicted by climate projections, epiphyte communities could still experience biomass
loss and shifting species composition. Long-term experimental studies are needed to
understand the relationship between cloud immersion and epiphyte distributions in
tropical montane forests and to predict the response of these diverse communities to
climate change.
81
Introduction
Tropical montane forests, often referred to as cloud forests, harbor high species diversity,
provide water and protect water quality for numerous people in tropical countries, and are
under particular threat from climate change (Loope and Giambelluca 1998, Foster 2001).
Most of the main cloud forest regions of the world, including the tropical Andes, are
considered hotspots of biological diversity (Myers et al. 2000), and plant species
endemism often reaches high levels within cloud forest. Epiphytes, non-parasitic plants
that depend on other plants for support and are not in contact with terrestrial soil, are key
components of cloud forest biodiversity and play critical roles in the hydrological and
nutrient cycling of montane ecosystems. Not only can vascular epiphytes make up 30
percent or more of plant species diversity in tropical montane forests (Kuper et al. 2004),
they also provide keystone resources for birds, insects, and other animals (Nadkarni and
Matelson 1989, Cruz-Angon and Greenberg 2005, Yanoviak et al. 2007, Cruz-Angon et
al. 2009). Through cloud stripping, epiphytes increase total moisture captured by forest
canopies (Holscher et al. 2004, Gomez-Peralta et al. 2008), and are important in nutrient
cycling (Nadkarni et al. 2004). Cloud immersion is important for many epiphyte species
to maintain a positive water balance and avoid desiccation (Weathers 1999). Because of
their sensitivity to moisture levels, epiphytes are considered indicator species in cloud
forests for changing water balance conditions (Benzing 1998), particularly those in the
wet tropics (Zotz and Bader 2009).
On a typical tropical mountain, where temperature decreases with altitude, there is a
gradient of increasing cloud incidence with altitude. Cloud formation is dependent on
vapor content of the air and air temperature, both of which are predicted to change with
82
global warming (Foster 2001). Atmospheric moisture levels are much less easily
predicted than temperature in climate models, but a multimodel ensemble of climate
simulations has shown a trend towards drying in many tropical regions (Neelin et al.
2006). Climate model projections in the Andes include warmer temperatures and lower
precipitation in the dry season (Vuille et al. 2008, Urrutia and Vuille 2009), which could
place cloud forest plants under increased drought stress. Cloud base height is also
predicted to rise, due to a combination of higher temperature and lower atmospheric
moisture input by vegetation due to lowland deforestation and reduced transpiration
because of increased atmospheric CO2 (Still et al. 1999, Cowling et al. 2008, Pinto et al.
2009). In Costa Rica, an increase in the elevation of cloud base has been demonstrated
and has already lead to the extinction of cloud forest species (Pounds et al. 1999),
although this may have been associated with a severe El Niño event rather than a long
term drying trend (Anchukaitis and Evans 2010).
The sensitivity of vascular epiphytes to changes in cloud incidence was demonstrated
experimentally in Monteverde, Costa Rica, where vascular epiphytes transplanted below
cloud base had shorter lifespans and higher leaf mortality (Nadkarni and Solano 2002). It
is not clear, however, whether this result can be generalized to continental cloud forests
such as the eastern Andes. Cloud forests vary worldwide, with differences in cloud base
height and the proportion of moisture received by the vegetation via cloud stripping
versus rainfall (Stadtmuller 1987, Hamilton et al. 1994). Cloud forests near coastlines are
heavily influenced by ocean conditions (Bruijnzeel 2004), while cloud formation on
continental mountain ranges is dependent on moisture flux across continents driven by
synoptic weather patterns. Deforested areas in Costa Rica have fewer clouds than
83
adjacent forested areas (Lawton et al. 2001), but simulations suggest that sea-surface
temperature has a greater impact on lifting condensation level than deforestation (Pounds
et al. 2006). Cloud forests in continental mountains like the Andes are expected to be
more sensitive to conditions of the adjoining lowland ecosystems, particularly
deforestation (Bruijnzeel 2004, Pinto et al. 2009). Complex topography in the eastern
Andes and its interaction with prevailing winds leads to wet and dry areas within regions
broadly considered cloud forest (Killeen et al. 2007). Given the diversity of cloudiness
and precipitation regimes that epiphytes as a group are exposed to, it is reasonable to
expect that epiphytes may be adapted to local moisture regimes. For instance, epiphytes
in lowland dry or seasonal forest have significant desiccation tolerance (Andrade 2003,
Bader et al. 2009). Epiphytes in continental forests like the Andes which experience
variable cloudiness regimes may have more resistance to drought than those in locations
with more stable cloud bases. Likewise, epiphytes growing at lower elevations, below
cloud base, may have greater drought tolerance than epiphytes growing above cloud base.
In the 2005 austral winter (June and July), we conducted a reciprocal transplant
experiment across an elevational gradient in cloud formation in the eastern Andes of
southern Peru to test the effect that cloud immersion has on the performance of vascular
epiphytes. The reciprocal transplant design also allowed us to distinguish between the
effects of moving mats away from their home elevation versus moving plants into lower
moisture conditions. This is a key control, for if epiphytes are locally adapted, reduced
performance is expected if moved in any direction from their bioclimatic optimum. The
specific questions we addressed were: 1) Does demographic performance decline when
epiphytes are moved farther from their home elevation? 2) Is this effect greater when
84
transplanted down-slope into drier and warmer conditions as predicted by results from
Costa Rica (Nadkarni and Solano 2002)? 3) Do epiphyte species of different functional
types (e.g. strap-leaf ferns, orchids, ericaceous shrubs) respond similarly to moisture
gradients?
We focused on two aspects of demographic performance, ramet survival and ramet turn-
over (ramet production – ramet mortality). Ramet survival allows us to examine
treatment effects on existing individuals, while ramet turn-over gives insight into the
longer-term treatment effects on epiphyte populations. We expected that ramet survival
would decrease and turn-over become negative as mats were moved farther from their
home elevation. We also hypothesized that the decrease in ramet survival would be
greater for mats moved down-slope than for mats moved upslope if moisture level is the
dominant factor in determining epiphyte species distributions. We expected functional
types to have differing ramet mortality, but that these should be balanced by ramet
productions such that species composition would remain stable in undisturbed mats. A
priori, we expected different functional types to respond similarly to the altitudinal
gradient such that any transplant effect would be similar across groups.
85
Materials and Methods
Study Area
The Kosñipata Valley (13°03’S, 71°33’W) lies along the eastern slope of the Andes in
southern Peru. Elevations range from about 800 meters to over 4000 meters, and
vegetation changes from pre-montane rainforest at the lowest elevations to tropical
subalpine forest and puna (alpine grassland) at the highest elevations. The experiment
was installed along a single ridge, with three transplant sites at 1500, 1650, and 1800
meters. We chose these elevations because of a large increase in epiphytic bryophyte
biomass between 1500 and 2000 meters elevation in the Kosñipata Valley (A. Howrath,
unpublished data), and step changes in soil properties (Zimmermann et al. 2009) and
biomass carbon stocks (Girardin et al. 2010). Similar changes in biotic properties have
been associated with higher cloud incidence and lower temperatures on other tropical
mountains (Grubb 1974, Frahm and Gradstein 1991).
The climate of the Kosñipata Valley is described in detail in Chapter II. Temperature
decreases linearly with altitude and annual rainfall is high across the gradient, with a
distinct (but weak: monthly rainfall > monthly PET except during drought) dry season.
The dry season was stronger during the installation of the experiment in 2005, when a
severe drought struck the Amazon basin, resulting in the lowest water levels on the main
stem of the Amazon in the 30 years that accurate records have been kept, and some of the
lowest at any time over the past century (Marengo et al. 2008, Zeng et al. 2008). Since
precipitation in the Andes comes from moisture transported across the Amazon basin
(Marengo et al. 2004), and much of this moisture is recycled within the Amazon basin
86
(Eltahir and Bras 1996, Bosilovich and Chern 2006), the drought also affected the eastern
Andes. Rainfall totals at the Rocotal meteorological station at 2000 m in the Kosñipata
Valley for May – August in 2005 were the lowest for any year measured (mean May-
August precipitation for 2000-2008: 601 mm; 2005: 175 mm). There was no recorded
rainfall in July 2005, the only month during the nine-year measurement period with no
recorded precipitation (mean July precipitation: 112 mm). Rainfall remained low
compared to normal levels through December 2005 (Fig. 1).
Data Collection
Climate
Climate data were available for the decade bracketing our study from the Peruvian
SENAMHI meterological station at Rocotal (13°06’41”S, 71°34’14”), located at 2010
meters in the Kosñipata Valley, and approximately seven kilometers from the transplant
site. Monthly mean temperature, precipitation, and relative humidity for the years 2000-
2008 were available from this station (SENAMHI 2010). Further data comes from
micrometeorological stations (ONSET Computer Corporation, Bourne, Massachusetts)
maintained by the authors and installed on nearby ridges at 1500, 1840, 2720, and 3400
meters. Temperature, rainfall, and relative humidity were recorded at these stations every
10 minutes from June 2007 through September 2009 (Chapter II). We calculated vapor
pressure deficit (VPD) following equations 5, 7, 12, and 14 in Yoder et al. (2005). In
addition, the location of cloud base was evaluated in two ways: photographically and
with fog collectors. During both installation and the resurvey of the experiment, a
photograph was taken looking up valley from a vantage point at 1400 meters each day at
87
4:00pm during June and July (n = 38 days in 2005 and 19 days in 2006) . Fog collectors
made of shade cloth on a 1 m^2 frame were installed for four weeks in the canopy at
1500, 1750, and 1900 meters in the study area during July 2005, and water collected was
weighed weekly.
Transplant experiment
In the 2005 austral winter (dry season: June and July), we selected five Alzatea verticilata
trees at each of three elevations, 1500 meters, 1650 meters, and 1800 meters elevation.
Alzatea was chosen as the host tree because: 1) it was common at all three elevations; 2)
it attains large size and has strong wood suitable for supporting climbers working in the
trees; and 3) its unique architecture resulted in many large horizontal branches that
supported sizeable epiphyte mats. Trees were climbed using roped arborist techniques
(Dial and Tobin 1994). In each tree, four sections of epiphyte mat were chosen that were
at least 25 cm wide and 30-40 centimeters long. Within each of the 60 mats, an area of
25x25cm was marked with wire, and all ramets of vascular epiphytes within that area
were marked, identified to morpho-species, and length of shoot and number of leaves was
recorded. Nonvascular epiphytes were present, but not considered in this experiment.
Most mat dwelling epiphytes are clonal, with individual ramets connected by subsurface
stems, but capable of surviving without connection to other ramets. It was difficult to
determine individual genets without excavating the plants, so we identified and measured
individual ramets rather than genetically distinct plants. The condition of each ramet was
scored (fresh, mature, senescent, or dead) and percent of each ramet removed by
herbivory was estimated. On each tree, one of the mats served as an undisturbed control
and was left in place on the tree. The other three mats were cut from the tree, lowered to
88
the ground, and then one each transplanted in a random tree at each of the three
elevations. After all transplants were complete, each tree had one undisturbed mat, one
mat that had been removed and then replaced at the same elevation, and one mat each
from each of the other two elevations. Each mat was tied in place using wire, and then
watered with one liter of water after the transplant was completed to minimize any
desiccation effect handling may have had.
Mats were left undisturbed for one year, and resurveyed the following year (June and
July 2006). All marked ramets were searched for and new ramets of each morpho-species
were counted. Ramets obviously more than a year old (28 out of 1400+ original ramets)
were assumed to have lost their tag if previously marked ramets of the same species were
not found in the same mat. Eight ‘old’ ramets were still not accounted for, and these were
assumed to have been missed during the first census. Any other missing ramets were
assumed dead.
Statistical analysis
Two types of analyses were carried out which examined the response of individual
species and the response of communities, respectively. All analyses were performed
using the mat as the experimental unit. This was done to account for within-mat
correlations between ramets, i.e. to avoid pseudoreplication. For the species-level
analyses, four species were selected for analysis which had at least 70 ramets and
occurred in at least 15 (25%) of the epiphyte mats during the initial survey (Table 1).
These common morpho-species were identified to genus, by which we will refer to them.
These included a strap-leaf fern (Elaphoglossum Schott ex J. Sm., 706 ramets in 57
89
mats), two orchid species (Maxillaria Ruiz & Pav., 230 ramets in 34 mats; Scaphyglottis
Poepp. & Endl, 114 ramets in 18 mats), and an ericaceous shrub (Cavendishia Lindl., 77
ramets in 23 mats). Collectively, these species accounted for 78% (1127/1452) of the
ramets surveyed in the initial 2005 survey. Other species which did not occur in high
enough abundance to be included in the analyses of ramet survival and turn-over were
included in community-level analyses (see below). All analyses were done in R (Version
2.11.0; R Development Core Team 2010).
In analyzing the survival of initial ramets, our approach was to first test for altitudinal
differences and transplant effects using data for control mats and mats transplanted within
elevation. Then using data from just the transplanted mats, we tested for effects of source
and transplant elevation on the response variable. We took this two-tiered approach
because a full model including transplant and both elevations was unbalanced (e.g. there
could not be a control mat that moved between elevations) and statistical models
accounting for this would not converge computationally. To model ramet survival,
generalized linear models (GLMs) were used, with source elevation, transplant elevation,
treatment (transplant versus undisturbed), and species modeled as fixed effects. A
binomial distribution with a logit link function was used to account for the binary nature
of the response (alive, dead). Analysis of Deviance was used to assess statistical
significance. We also modeled ramet turn-over (new ramets – dead ramets in 2006) using
the same two-tiered approach as with survival, except using ANOVA to evaluate
statistical significance as ramet turn-over was normally distributed.
Both Detrended Correspondance Analysis (DCA) and Nonmetric multidimensional
scaling (NMDS) ordination on Bray-Curtis distance were used to investigate patterns in
90
mat species composition. Results from both approaches were qualitatively similar and
since our compositional data collected across a directional gradient matched the
assumptions of DCA, we present the DCA results here. We first investigated the changes
in composition versus elevation using the pre-transplantation composition of all mats.
Composition of all mats during both 2005 before transplantation and during 2006 one
year after transplantation were then ordinated together to investigate compositional
change due to experimental treatments. Permutation Multivariate Analysis of Variance
using distance matrices (function adonis in the vegan R package; Oksanen et al. 2010)
was used to test whether there were compositional changes with altitude and across years
due to the transplantation.
91
Results
Climate
Daily photographs during installation of the experiment in June and July 2005 and July
2006, showed that the cloud base during these times was often above the experimental
elevations (above 1800 meters; Fig. 2). The exception was during austral cold fronts—
friajes (Marengo et al. 1997, Garreaud 1999, Vera and Vigliarolo 2000, Doan 2004) —
when all altitudes studied experienced cloud immersion. Fog collectors set up in the study
area at 1500, 1750, and 1900 meters elevation during June and July 2005 collected no
water except during friajes, and at these times more water was collected at the lowest
elevation (Four week total weight of water collected: 1500 m, 1109 g m^-2; 1750 m, 35 g
m^-2; 1900 m, 72 g m^-2). Vapor pressure deficit (VPD) decreased with altitude across
the east Andean slope between 1500 and 3400 meters for all months except June for the
period June 2007 to September 2009 (Chapter II) during more normal moisture
conditions than prevailed during the 2005 Amazonian drought. While differences in
VPD between 1500 meters (lowest experimental elevation) and 1800 meters (highest
experimental elevation) were typically small, excursions to higher VPD (greater
desiccation) were more severe at 1500 meters (Chapter II).
Transplant effect: transplants within altitude
First, we tested for an effect of transplantation independent of altitudinal distance moved
by asking whether epiphytes in mats transplanted to the same elevation had different
ramet survival or turn-over than those in intact mats. Each model included species,
elevation, treatment (undisturbed or transplanted) and all interactions. Species, treatment,
92
and the species by source elevation interaction had significant effects on ramet survival,
while the species by treatment effect was marginally significant (Table 2).
Elaphoglossum had lower ramet survival than the other species, and had consistently
poorer ramet survival in transplanted mats, while ramet survival in transplanted versus
undisturbed mats was more variable in other species. Survival of Elaphoglossum ramets
decreased with altitude, while in Maxillaria, ramet survival increased with altitude. In
contrast to ramet survival, species did not have a significant effect on ramet turn-over,
illustrating compensatory ramet production in species with high ramet mortality. In fact,
none of the variables included in the models significantly affected ramet turn-over (Table
3), while overall mean ramet turn-over did not differ from zero (mean turn-over = -0.79;
two-sided t-test, p = 0.09).
Transplantation across altitude
For transplanted epiphytes, species, source elevation, transplant elevation, and species by
source elevation interaction had significant effects on ramet survival, but all other
interactions did not (Table 4). Elaphoglossum and Cavendishia ramets transplanted from
the lowest elevation to any elevation had higher survival, while Maxillaria ramets from
1500 meters had lower survival regardless of where they were transplanted (Fig. 3).
Ramets transplanted to 1650 meters often had higher survival (Fig. 3), perhaps due to
individual tree effects. For transplanted mats, more ramets died than were recruited
(mean change number of ramets per mat between years = -1.38; two-sided t-test, p =
0.01), but in the full model there were no significant effects of species, source elevation,
transplant elevation, or any of their interactions (Fig. 4; Table 5). However, if only mats
transplanted from 1800 meters are considered, ramet survival decreased and turn-over
93
became more negative at lower elevations (Figs 3 and 4). Both species and transplant
elevation significantly affected ramet survival (Table 6), while only transplant elevation
significantly affected ramet turn-over (Table 7), again showing compensatory ramet
production in species with high annual ramet mortality.
Community composition
Epiphyte community composition showed significant differences across the altitudinal
gradient (Table 8), with the most compositional separation of mats at 1500 meters from
the other two elevations (Fig. 5). Comparison of pre- and post-treatment species
compositions in mats revealed no directional shift in community composition due to
transplantation (Table 8). A few mats did show large changes (Fig. 6), often because of
large changes in abundance in Elaphoglossum, either through high ramet mortality or
production.
94
Discussion
Similarly to a previous study at another tropical montane site (Nadkarni and Solano
2002) which found fewer leaves and shorter life-spans for epiphytes moved down slope
for all species considered, epiphytes moved down slope from our highest elevation had
lower ramet survival and a net loss in ramets when transplanted to the lowest elevation in
three common species of epiphytes (Figs. 3 and 4). However, reciprocal transplants
between all elevations revealed unexpected dynamics. Species identity explained the
greatest amount of variation in ramet survival, while ramet turn-over was not affected by
species identity, suggesting that while ramet mortality and ramet production occurs at
different rates in different species, these are balanced and there was no differential
response to the experimental treatments. This interpretation is reinforced by the analysis
of species composition, which revealed no significant directional shift in composition due
to any of the transplant treatments (Fig. 6), although composition did change across the
altitudinal gradient as expected (Fig. 5). Source elevation significantly affected ramet
survival, with differences between species, but the direction of the effect depended on
species (Fig. 4). It appears that only epiphytes at the highest elevation showed a response
to water stress, indicating that epiphytes growing at lower elevations are adapted to
withstand greater drought stress, and are neither benefited by or suffer from increased
water availability, as occurs when they are transplanted to higher elevations.
2005 Amazonian Drought
The drought that affected the Amazonian basin during the 2005 austral winter (Marengo
et al. 2008, Zeng et al. 2008), lead to decreased precipitation (Fig. 1) and may have
95
contributed to a higher cloud base than is typical for the study region in the eastern Andes
(Fig. 2). Actual cloud water interception based on fog collectors in place during the
experiment did not show a gradient in moisture across the experimental elevations during
the 2005 dry season. All elevations were very dry, and desiccation was evident in
bryophytes and non-succulent vascular epiphytes in the study area. It appears that these
abnormally dry conditions effectively removed the normal moisture gradient across the
experimental elevations, making all elevations dry, during at least part of the experiment.
This could have influenced the results of our experiment by causing high ramet mortality
and/or reduced production at all elevations. However, ramet mortality in undisturbed
control mats was not significantly greater than production for any species at any
elevation. In addition, mat species composition did not change significantly between
years (Fig. 6). Thus, epiphytes between 1500 and 1800 meters in this Andean cloud forest
appeared resistant to drought over the one year time scale of our experiment.
Cloud bases in cloud forest
Another factor likely influencing our results is the relative importance of cloud
immersion and specifically the location of cloud base for the distribution of epiphytes in
this system. A consistent cloud base is a significant feature of many tropical montane
forests (Loope and Giambelluca 1998, Ritter et al. 2009), and this regular low cloud is
assumed to help maintain the diversity and abundance of cloud forest epiphytes.
However, vascular epiphyte diversity is often high in lowland rainforest below cloud base
(Gentry and Dodson 1987), and some studies have reported that vascular epiphyte
biomass does not change significantly with altitude (Freiberg and Freiberg 2000),
contrasting with a mid-elevation peak in richness (Kroemer et al. 2005). Bryophyte
96
biomass is consistently reported to be higher in cloud forest (Wolf 1995, Zotz 1999), and
indeed, it was observations of the step-change in abundance of bryophytes above 1500
meters that led us to choose the elevations we did for this experiment. However,
photographic observations of cloud base during the dry seasons of both a drought year
and a more ‘normal’ year did not reveal a consistent dry-season cloud base (Fig. 2),
although during the rest of the year it is entirely possible that cloud base is lower, as
suggested by patterns in bryophyte abundance (A. Howrath, unpublished data).
High rainfall in this part of the Andes may mean that epiphytes here may be less
dependent on cloud immersion to maintain their water balance than their counterparts in
other cloud forests. Precipitation in the study region averages greater than five meters per
year at 1000 meters elevation and decreases linearly with altitude (Chapter II). Even in
2005 under drought conditions, total precipitation for the year was 3273 mm. In this
pluvial system, cloud base may be less important in determining epiphyte distributions
than in other systems. It is noteworthy that the Nadkarni and Solano (2002) experiment
was carried out on the leeward Pacific slope of Monteverde, which is drier than the
Caribbean slope (Guswa et al. 2007). This leads to a steeper moisture gradient between
cloud forest and lower elevations, and likely a greater dependence of epiphytes on cloud
immersion. If this previous study had been out on the Caribbean slope the results may
have been similar to our study. On leeward slopes rainfall compensation may occur, in
which epiphyte survival is enhanced by high rainfall even when there is less frequent
cloud immersion.
However, rainfall at our study site is both variable and seasonal. Annual totals ranged
between 3 and 6 meters per year in a five year period not including the 2005 Amazonian
97
drought. Precipitation is lowest in June and July (Chapter II), when temporary drought is
possible. So, while the 2005 drought was exceptional (Marengo et al. 2008, Zeng et al.
2008), it did not seem to have had a particularly large effect on epiphyte dynamics.
Epiphyte species that live in this part of the Andes may possess adaptations for surviving
drought, similar to those in lowland dry or seasonal forests (Stancato et al. 2001, Andrade
2003, Bader et al. 2009). Many epiphyte species have adaptations for surviving drought –
CAM metabolism, desiccation tolerance, pseudobulbs, succulent leaves and other water-
storing organs. Epiphyte drought tolerance is higher in areas where drought occurs more
frequently (Zotz and Bader 2009), presumably the result of both adaptation and
ecological sorting.
Even though the moisture regime in the eastern Andes is variable, the forest in our study
system does show similarities with other cloud forest systems. There is a step-change
increase in both bryophytic and vascular epiphyte biomass above 1500 meters (A.
Howrath, unpublished data). Forest structure also changes, with tree height, above
ground biomass, and forest productivity decreasing (Girardin et al. 2010) and soil organic
matter increasing above 1500 meters (Zimmermann et al. 2009). Above 1500 meters is
also where tree diversity begins to decrease in the study region (M. Silman, unpublished
data), which is a general pattern in the Andes (Gentry 1995). These clear changes in
forest structure, diversity and productivity contrast with smoother changes in climatic
conditions. Mean temperature, precipitation, and vapor pressure deficit (VPD), a measure
of moisture stress on plants, all decrease linearly with elevation above 1000 meters in the
study area (Chapter II).
Epiphyte community dynamics
98
In all cases except epiphyte mats transplanted from 1800 meters, ramet turn-over was not
affected by any of the variables considered. Since ramet survival was dependent on
species and altitudinal treatment, ramet production must have increased in mats with
lower ramet survival. One reason for this may be that in the clonal epiphytes considered
in this study, ramet senescence triggers the production of a new ramet in that clone.
Another possibility is that ramet production is limited by competition for space, and that
when existing ramets die, space becomes available for recruitment of new ramets, of
whichever clone may be nearby. That mat species composition did not change
significantly over time (Fig. 6, Table 7), supports the first possibility, but in either case,
ramet production is controlled by existing individual epiphytes, suggesting a strong role
for competition in structuring epiphyte communities. The effect of faster ramet turn-over
on epiphyte communities is unknown. Will faster ramet turn-over lead to higher mortality
for the entire clone? Long-term studies that track genetic individuals rather than ramets
would be needed to answer this question, and unfortunately, this was not possible with
the present study.
The inconsistent results between epiphyte mats transplanted from 1800 meters and those
transplanted from lower elevations suggests that local adaptation to drought stress is
possible even within some epiphyte species. Epiphytes transplanted down slope from
1800 meters experienced elevated ramet mortality and mats had fewer ramets after one
year. Mats from lower elevations showed no similar pattern, even in a year that included
a severe drought. Since the same species were found at all sites, this suggests that even
within species, acclimation to drought is possible for some vascular epiphytes.
99
While elevated ramet mortality from mats transplanted down slope from 1800 meters is
consistent with results of a similar study in a different system (Nadkarni and Solano
2002), epiphytes growing below cloud base showed some resistance to drought, which
was not predicted by the previous study. Fundamental differences in the climate and
biogeographical contexts of the two systems may account for this difference. At least
some vascular epiphyte species appear to have the capacity to adapt to drought stress.
Long-term experimental studies in tropical montane systems are needed to understand the
drivers of patterns of epiphyte abundance, particularly why there is a change in
abundance at putative ‘cloudbase’ (which is correlated with changes throughout the
ecosystem), and how these diverse communities will respond to climate change. While
our experiment suggests that epiphytes in our study system show some resistance to
climate change, climate models predict more severe droughts in parts of the Andes
(Neelin et al. 2006, Urrutia and Vuille 2009), and the long-term (greater than one year)
response to these events is unknown. Given the keystone position of epiphytes in cloud
forest ecosystems, drought-induced mortality could have cascading effects throughout the
cloud forest ecosystem.
100
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Table III - 1. Sample sizes and mean ramet survival and turn-over organized by Species,
Treatment, Source Altitude, and Transplant Altitude. Sample sizes (N) are expressed as
both the number of ramets (r) and number of mats (m). Sample sizes can differ for ramet
survival (s) and turn-over (t.o.) because the values for ramet survival only include ramets
surveyed before transplanting mats, while values for turn-over include ramets both before
and after transplantation.
109
Species Treatment Source Trans. Survival N(s,r) N(s,m) Turn-over N(t.o.,r) N(t.o.,m)
Cavendishia control 1500 1500 NA NA NA NA NA NA
Cavendishia transplant 1500 1500 1.00 2 2 0.50 3 2
Cavendishia transplant 1500 1650 0.83 6 2 1.50 10 2
Cavendishia transplant 1500 1800 1.00 3 1 0.00 3 1
Cavendishia transplant 1650 1500 0.50 10 2 -1.50 12 2
Cavendishia control 1650 1650 0.70 10 2 2.00 17 2
Cavendishia transplant 1650 1650 0.57 14 4 -1.00 16 4
Cavendishia transplant 1650 1800 0.58 12 3 -1.33 13 3
Cavendishia transplant 1800 1500 NA NA NA NA NA NA
Cavendishia transplant 1800 1650 1.00 3 2 0.50 4 2
Cavendishia control 1800 1800 0.50 2 3 1.00 6 3
Cavendishia transplant 1800 1800 0.73 15 4 -0.75 16 4
Elaphoglossum control 1500 1500 0.56 84 5 -1.20 115 5
Elaphoglossum transplant 1500 1500 0.46 89 5 -6.20 106 5
Elaphoglossum transplant 1500 1650 0.46 89 5 -3.60 119 5
Elaphoglossum transplant 1500 1800 0.39 84 4 -2.25 126 4
Elaphoglossum transplant 1650 1500 0.33 36 4 5.25 81 4
Elaphoglossum control 1650 1650 0.51 45 5 0.40 69 5
Elaphoglossum transplant 1650 1650 0.34 50 5 -2.00 73 5
Elaphoglossum transplant 1650 1800 0.16 32 5 -2.40 47 5
Elaphoglossum transplant 1800 1500 0.14 57 5 -5.80 77 5
Elaphoglossum transplant 1800 1650 0.37 41 5 -2.00 57 5
Elaphoglossum control 1800 1800 0.42 57 5 -2.20 79 5
Elaphoglossum transplant 1800 1800 0.26 42 4 1.50 79 4
Maxillaria control 1500 1500 0.48 0 0 2.00 2 1
Maxillaria transplant 1500 1500 0.46 13 3 -2.00 14 3
Maxillaria transplant 1500 1650 0.67 3 1 0.00 4 1
110
Maxillaria transplant 1500 1800 NA NA NA NA NA NA
Maxillaria transplant 1650 1500 0.75 32 4 -2.00 32 4
Maxillaria control 1650 1650 0.67 24 4 -0.25 31 4
Maxillaria transplant 1650 1650 0.87 23 4 -0.25 25 4
Maxillaria transplant 1650 1800 0.82 39 5 -0.80 42 5
Maxillaria transplant 1800 1500 0.79 28 3 -2.00 28 3
Maxillaria transplant 1800 1650 0.86 29 4 -0.25 32 4
Maxillaria control 1800 1800 0.91 23 3 -0.33 24 3
Maxillaria transplant 1800 1800 0.88 16 4 -0.50 16 4
Schaphyglottis control 1500 1500 NA NA NA NA NA NA
Schaphyglottis transplant 1500 1500 NA 0 0 1.00 1 1
Schaphyglottis transplant 1500 1650 NA NA NA NA NA NA
Schaphyglottis transplant 1500 1800 NA NA NA NA NA NA
Schaphyglottis transplant 1650 1500 1.00 2 1 2.00 4 1
Schaphyglottis control 1650 1650 0.85 26 4 -0.50 28 4
Schaphyglottis transplant 1650 1650 1.00 4 2 0.00 4 2
Schaphyglottis transplant 1650 1800 1.00 1 1 0.00 1 1
Schaphyglottis transplant 1800 1500 0.51 37 3 -5.33 39 3
Schaphyglottis transplant 1800 1650 0.82 22 3 -0.33 25 3
Schaphyglottis control 1800 1800 0.73 11 2 -0.50 13 2
Schaphyglottis transplant 1800 1800 0.91 11 2 0.50 13 2
111
Table III - 2. Analysis of Deviance for ramet survival for mats transplanted within
elevation.
df Deviance P(>|Chi|)
NULL 68 210.38
Species 3 62.19 <0.0001
Source Elevation 2 2.40 0.3009
Treatment 1 4.10 0.0428
Species x Source Elevation 5 14.96 0.0105
Species x Treatment 3 7.53 0.0569
Source Elevation x Treatment 2 0.64 0.7262
Species x Source Elevation x Treatment 3 2.52 0.4715
Residuals 49 116.04
112
Table III - 3. ANOVA table for ramet turn-over in mats transplanted within elevation.
None of the variables were significant predictors at the 0.05 level.
Source of Variation df SS MS F____
Species 3 43.59 14.53 0.79
Source Elevation 2 32.19 16.10 0.88
Treatment 1 13.73 13.73 0.75
Species x Source Elevation 6 38.40 6.40 0.35
Species x Treatment 3 13.39 4.46 0.24
Source Elevation x Treatment 2 77.35 38.67 2.11
Species x Source Elevation x Treatment 4 26.29 6.57 0.36
Residuals 52 955.02 18.37
113
Table III - 4. Analysis of Deviance for ramet survival for mats transplanted across
elevations.
df Deviance P(>|Chi|)
NULL 99 353.89
Species 3 134.13 < 0.0001
Source Elevation 2 12.84 0.0016
Transplant Elevation 2 9.51 0.0086
Species x Source Elevation 5 22.78 0.0004
Species x Transplant Elevation 6 9.00 0.1735
Source Elevation x Transplant Elevation 4 5.71 0.2215
Species x Source Elevation x Transplant Elevation 8 5.49 0.7042
Residuals 69 154.42
114
Table III - 5. ANOVA table for ramet turn-over for mats transplanted across elevations.
None of the variables were significant predictors at the 0.05 level.
Source of Variation df SS MS F
Species 3 50.81 16.94 0.53
Source Elevation 2 41.80 20.90 0.65
Transplant Elevation 2 24.50 12.25 0.38
Species x Source Elevation 6 117.53 19.59 0.61
Species x Transplant Elevation 6 29.80 4.97 0.15
Source Elevation x Transplant Elevation 4 175.76 43.94 1.37
Species x Source Elevation x Transplant Elevation 8 154.98 19.37 0.60
Residuals 71 2283.05 32.16
115
Table III - 6. Analysis of Deviance for ramet survival for mats transplanted from 1800
meters.
df Deviance P(>|Chi|)
NULL 38 157.45
Species 3 88.70 < 0.0001
Transplant Elevation 2 14.82 0.0006
Species x Transplant Elevation 5 4.31 0.5058
Residuals 28 49.62
116
Table III - 7. ANOVA table for ramet turn-over for mats transplanted from 1800 meters.
Source of Variation df SS MS F
Species 3 25.51 8.503 0.50
Transplant Elevation 2 135.81 67.907 3.99 *
Species x Transplant Elevation 5 45.74 9.147 0.54
Residuals 28 476.63 17.023
Significance Levels: * p<0.05
117
Table III - 8. ANOVA table from permutational multivariate Analysis of Variance to test
differences in composition between mats at different elevations across years and
treatments.
Source of Variation df SS MS F R2
Elevation 1 0.75 0.75 3.38 0.0283**
Treatment 1 0.25 0.25 1.14 0.0095
Year 1 0.071 0.07 0.32 0.0027
Elevation x Treatment 1 0.17 0.17 0.74 0.0062
Elevation x Year 1 0.31 0.31 1.40 0.0117
Treatment x Year 1 0.04 0.04 0.20 0.0016
Elevation x Treatment x Year 1 0.10 0.10 0.44 0.0037
Residuals 112 24.92 0.22 0.9363
Total 119 26.61 1
Significance levels: **p < 0.01
118
Figure Legends
Figure III - 1. Monthly precipitation in millimeters recorded at the Rocotal
meteorological station at 2010 meters in the Kosñipata Valley. Solid line depicts monthly
mean for 2000-2008 with 95% confidence intervals. Dashed line shows precipitation in
2005.
Figure III - 2. Observed dry season cloud base height during June and July in 2005 (n =
38 days) and 2006 (n = 19 days). Cloud base was photographed from a vantage point at
1400 meters looking up valley toward the study area each day at 16:00.
Figure III - 3. Survival of ramets of four abundant epiphyte species from a reciprocal
transplant experiment across three elevations. Mats transplanted from the same elevation
(Source Elevation) are grouped together, while shading of bars indicates the transplant
elevation. Error bars depict the 95% confidence interval for mean survival.
Figure III - 4. Mean ramet turn-over per mat (new ramets – dead ramets in second
census) for each treatment of epiphyte mats transplanted in a reciprocal design across
three elevations. Mats transplanted from the same elevation (Source Elevation) are
grouped together, while different shading of bars indicates the transplant elevation (dark
gray – 1500 meters; medium gray – 1650 meters; light gray – 1800 meters). Error bars
depict the 95 % confidence interval for mean turn-over.
Figure III - 5. First two axes of a Detrended Correspondance Analysis (DCA) the
composition of epiphyte mats before mats were transplanted. Hulls are drawn around
mats from the same elevation. Color of points indicates elevation of mat (white – 1500
119
meters; gray – 1650 meters; black – 1800 meters). Points of control mats are larger than
transplanted mats.
Figure III - 6. First two axis of a Detrended Correspondance Analysis (DCA) the
composition of epiphyte mats both before and after mats were transplanted. Lines
connect the same mats before (circles) and after transplantation (squares) with arrow
pointing to mat after transplantation. Color of points indicates elevation of mat (white –
1500 meters; gray – 1650 meters; black – 1800 meters). Points of control mats are larger
than transplanted mats.
120
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FIGURE III - 2
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FIGURE III - 3
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FIGURE III - 4
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FIGURE III - 5
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FIGURE III - 6
126
CHAPTER IV
WHEN DO CLOUD FOREST TREES GROW?: TREE GROWTH PHENOLOGY ACROSS A 2500
METER ALTITUDINAL GRADIENT IN THE ANDES
Abstract
Growth phenology, the timing of the growing season in relation to climatic conditions,
has been little studied in cloud forest. This may be because the presence of persistent low
cloud is assumed to buffer cloud forest plants from dry season drought stress, which is
the major determinant of growth seasonality in lowland tropical forests. However, some
cloud forest trees have been observed to have growth rings, and annual cycles of
flowering, fruiting, and leaf flush suggest seasonal patterns in tree growth as well. We
recorded growth three times a year over two years on more than 1000 trees fitted with
band dendrometers across a nearly 2500 meter altitudinal gradient spanning premontane
to upper montane cloud forest on the east slope of the Andes in southern Peru. Growth
rates were highest in most species during the early wet season, a time of the year when
insolation is greatest in these forests. This pattern contrasts with most lowland tropical
forests where growth is generally correlated with precipitation, and suggests that growth
in cloud forest trees is light limited. Moisture flux over the Amazon is changing due to
global climate change and land-use change, with implications for cloudiness in east
Andean cloud forests. If cloudiness regimes change in the Andes, the timing of tree
growth may be altered.
127
Introduction
Phenology, the timing of annual events in the life-cycles of organisms such as leaf-flush,
flowering, and fruiting in plants, flight periods in insects, and spring arrival and nesting
dates in birds, influences nearly all areas of ecology (Forrest and Miller-Rushing 2010).
The timing of phenological events is often genetically controlled, and influenced by a
number of proximate factors, the most common of which are related to seasonal climate
patterns – photoperiod, temperature, and precipitation (Forrest and Miller-Rushing 2010).
Growth phenology (i.e. the growing season) is often determined by temperature and
moisture, both of which are changing in many areas of the world due to ongoing
anthropogenic climate change. In tropical cloud forests, where temperature changes little
and soil moisture remains high throughout the year, it was not clear if trees should have a
distinct growing season, or if so, when it should be. In this study we describe patterns of
tree growth phenology in an Andean cloud forest, and correlate these patterns with
climate across a 2500 meter altitudinal gradient
Phenological patterns in the tropics
Plant phenology studies in the tropics have been mostly phenomonenological, focusing
on flowering, fruiting, and leaf-flush (e.g. Medway 1972, Borchert 1983, WilliamsLinera
1997, Justiniano and Fredericksen 2000, Morellato et al. 2000, Numata et al. 2003,
Anderson et al. 2005, Brearley et al. 2007). While growth seasonality in the tropics has
been less well studied, a growing body of evidence shows that many trees from forests
with a distinct dry season do produce annual growth rings (Stahle et al. 1999, Enquist and
Leffler 2001, Brienen and Zuidema 2005, Lisi et al. 2008). Annual rings have even been
128
identified in wet lowland forests without a strong dry season such as La Selva, Costa
Rica, although deciduous trees in this forest have more distinct rings (Fichtler et al.
2003). At La Selva, tree growth is positively correlated with precipitation, such that even
a weak, two-month dry season produces a decline in growth (Breitsprecher and Bethel
1990, Clark et al. 2010). This correlation between precipitation and tree growth is
common in more seasonal forests (Worbes 1999, Pelissier and Pascal 2000, Enquist and
Leffler 2001, Brienen and Zuidema 2005), and present even where trees do not produce
distinct rings (Poussart et al. 2004).
Compared to lowland rainforest, the phenology of tropical montane forests is poorly
known, although there have been a number of studies examining flowering, fruiting, and
leaf-flush phenology of cloud forest trees (Sun et al. 1996, Camacho and Orozco 1998,
Berlin et al. 2000, Williams-Linera 2000, Gunter et al. 2008). In addition, trees produce
annual rings in sub-tropical cloud forests (Villalba et al. 1998, Grau et al. 2003), and
growth rings have been observed in some cloud forest trees in southern Peru (personal
observation). The presence of cyclical patterns of flowering, fruiting, and leafing, and
growth rings in some cloud forest trees suggests that cloud forest trees may exhibit
defined seasonality in growth. However, the timing of the growing season and the
climatic drivers of seasonality have not been previously studied in Andean cloud forest.
We investigated tree growth phenology and its climatic correlates in southern Peru,
across a ~2500 meter altitudinal gradient on the eastern slope of the Andes. Rainfall
seasonality across the altitudinal transect is strong, although there is almost always
>100mm of rain in any given month and potential evapotranspiration is nearly always
lower than rainfall (Chapter II) Also, like many cloud forests, soil moisture remains high
129
throughout the year (Zimmermann et al. 2009). Since it seems that there is usually
sufficient available moisture to support tree growth, it was not clear that growth should
be higher during one season over another. After determining the patterns of cloud forest
growth phenology, we then related those patterns to climate seasonality (described in
Chapter II). Finally we asked whether growth phenology varied with altitude or taxa
across the altitudinal gradient. We used data from dendrometers installed on more than
1000 trees across an altitudinal gradient spanning nearly 2500 meters (925-3400 meters
elevation) and measured three times a year over two years to illustrate patterns of
seasonal growth. We compared daily mean growth rates for three seasonal periods: dry
season (~June-October), early wet season (~October-January), and late wet season
(~January-June).
130
Methods
Study site
Data on tree growth were collected along a ~2500 meter altitudinal gradient in the
Kosñipata Valley (-13º 6’ 18’’ latitude, -71º 35’ 21” longitude), in and adjacent to Manu
National Park, on the eastern slope of the Andes in southern Peru. Data came from eleven
one-hectare permanent tree plots located approximately every 250 meters in elevation
from 925 meters to 3400 meters, established by the Andes Biodiversity and Ecosystem
Research Group (ABERG). For a basic description of the plots see Table IV - 1, and for a
a full site description, including additional plots in the ABERG network, see Silman (in
prep). The plots used in this study were established in 2003, with the exception of the
plots at 925 and 1500 meters, which were established in 2004 and 2006 respectively.
Eight of the plots are on a single ridge, with the other three plots nearby. The substrate of
most plots is Ordivician shale and slate, while all or parts of two plots (at 1840 and 2020
meters) are on Permian granite. The vegetation is tropical montane cloud forest (TMCF)
and the climate across the gradient is wet, with annual precipitation at the base of the
gradient averaging greater than 5000 mm, decreasing to about 2600 mm at the highest
elevation plot. Temperature decreases with altitude with a lapse rate of 5.2ºC/km, and
mean annual temperatures range from 7.7 ºC at the top of the transect to ~20 ºC at the
bottom. For a full description of the climate of the Kosñipata Valley, see Chapter II.
Data collection and analysis
Band dendrometers were installed on 100 random stems (of all species) greater than 10
cm DBH in each existing plot in October 2003. Dendrometers were installed on
131
additional stems in January 2007, for a total of 1781 individual stems. After each
installation, bands were allowed to settle on the stem, and the first measurement was
made 5-8 months after installation. This first measurement was taken as the baseline, and
later measurements were made relative to it. Measurements of dendrometers were made
in June 2004, July 2006 and then three times a year between June 2007 and August 2009.
In this study we focus on a subset of 1014 stems (which included 216 species) for which
there were measurements taken during all seven censuses between June 2007 and August
2009 (Table IV - 1). At each census, the change in circumference, (C) was measured. We
then calculated the mean daily diameter growth rate (dt) for each period as, dt =
C/π*ndays,t, where ndays,t is the number of days in a census interval. We then found the
relative growth rate for each stem in each period as, drel,t = dt/d1-6, where d1-6 is mean
daily growth rate for the entire period of the study. Standardizing growth rate in this way
ensures that faster growing trees do not have a disproportionate effect in the analysis. If
we did not standardize the data, faster growing trees would have a larger absolute
response to growing season, even though a slower growing tree might have a
proportionately strong response.
Relative growth rate was a gamma distributed variable, so we used generalized liner
models (GLMs) with an Inverse link function to compare daily mean relative growth
rates for three seasonal periods: dry season (June-October), early wet season (October-
January), and late wet season (January-June) over these two years. We tested for effects
on growth of season, year, altitude, plot, and taxa (at three levels: family, genus, and
species). We also explored effects of diameter, height, and canopy status (a measure of
light availability), but these had no significant effect on growth seasonality. The use of
132
relative growth rate precluded there being a main effect of any of the covariates other
than season and year, so the interpretation of the results focuses on the interaction of the
other covariates with season and year. Analysis of Deviance, with F tests to assess
significance, was used to determine the relative importance of each factor included in the
model. All analyses were performed in R (version 2.11; R Development Core Team
2010).
Finally, we compared the modeled seasonal growth with the mean seasonal trends in
rainfall and light availability (which was correlated with temperature and vapor pressure
deficit) as described in Chapter II.
133
Results
Across an altitudinal gradient of nearly 2500 meters in the eastern Andes, diameter
growth was greater in the first half of the wet season (October – January) than in the
second half of the wet season or during the dry season (Fig IV - 1). In addition to an
effect of season on relative growth, year, altitude, and plot had significant effects,
although these covariates explained much less of the deviance than season (Table IV - 2).
Overall, relative growth was lower in the second year of the study (Fig IV - 1). The main
pattern was consistent across the altitudinal gradient, although lower altitudes had a lower
growth response in the second year, with a slightly earlier peak growth period in some
plots, while the seasonality of growth was more consistent in the high altitude plots
across years (Fig IV - 2). A plot effect was included in the model because there was
significant variation between plots not accounted for by altitude. Both the altitude and
plot effects became less important when taxonomic identity was included in the model
(Table IV - 3). The analysis was consistent when using family, genus, or species as the
taxonomic level of interest. In general the finer the taxonomic level, the greater the share
of deviance was explained by taxa, although with the trade-off that fewer stems could be
included in the analysis at finer taxonomic levels. Here we present only the results at the
genus level for brevity (Table IV - 3). Most genera followed the same pattern of high
growth in the early wet season, although the magnitude of variation from mean growth
differed (Fig IV - 3). Exceptions include species of Clethra, which had peak growth
during the dry season, and several lower altitude species which had peak growth in early
wet season in the first year, and during the dry season in the second year. Some species
differed in the magnitude of relative growth deviation between years, the most striking
134
example being that of Axinea pennellii (Gleason), which had much higher growth in the
first year than the second year (Fig IV - 3), when it experienced a masting event
(personal observation).
Weinmannia showed similar patterns to other species, with high altitude species having
consistent patterns of growth across years, while lower altitude species such as W. ovata
and W. multijuga showing a shift in peak growth between years (Fig IV - 4). The
exception is W. reticulata, which had very little variation in growth across the two year
time period, the result of relatively high growth during the 2008 dry season (Fig IV - 4).
Climate data (Chapter II) allowed comparison of periods of high growth to the four
month periods of highest rainfall (December through March), lowest rainfall (May
through August), and highest light, temperature, and vapor pressure deficit (September
through December). High relative growth was most closely related to periods of high
light, temperature, and vapor pressure deficit (Fig IV - 1).
135
Discussion
Cloud forest trees show distinct growth phenologies, which are remarkably consistent
across taxa and altitude across an altitudinal gradient in the eastern Andes of southern
Peru. Growth was lowest during the late wet season (January – June) and greatest in the
early part of the wet season (October – January), although there is reason to believe that
growth may actually increase in late August or September, and the intermediate 'dry
season' growth may be due to averaging over two distinct growth periods. We suspect
this because the last 'late wet season' period includes most of the dry season (January –
August), and growth stays low for the entire period (Figure IV - 1). If the high growth
season begins in August or September, this correlates even more strongly with the period
of high solar radiation (September – December). Intermediate growth during the 'dry
season' being the result of averaging over a low and a high growth period may also help
explain why, for some species, the period of high growth switched from the 'early wet
season' to the 'dry season' between the two years of the study. In this case, small changes
in the timing of increased growth, or the timing of the census periods could result in the
highest growth period being incorporated into either of these census periods.
The novel observation of this study is that growth phenology in cloud forest trees in the
eastern Andes appears to be controlled by insolation seasonality, rather than moisture as
has been almost universally observed in lowland tropical forests (e.g. Breitsprecher and
Bethel 1990, Worbes 1999, Pelissier and Pascal 2000, Enquist and Leffler 2001, Poussart
et al. 2004, Brienen and Zuidema 2005, Clark et al. 2010). That solar radiation should
have an impact on phenology is not surprising. Solar radiation is often limiting for tree
growth, even for canopy trees in the tropics (Graham et al. 2003), and higher growth
136
during dry periods has been suggested to be due to higher insolation (e.g. Laurance et al.
2009). Insolation may control growth directly by limiting photosynthesis, or changing
levels of solar radiation throughout the year may used as a signal to synchronize growth
activity.
Three aspects of solar radiation potentially signal phenological change: photoperiod (day-
length), intensity, and their product, insolation (total light received). Peak solar radiation
intensity has been linked to initiation of flowering in the tropics (Anderson et al. 2005,
Yeang 2007). Flowering and leaf-flush are highest in wet tropical forests during annual
periods of greatest insolation (van Schaik et al. 1993, Wright and van Schaik 1994).
While tropical forests show a wide range of leaf flushing patterns, from annual to
multiple distinct periods per year to continual flushing (Lowman 1992), this variation
may be the result of different species responding differentially to a common signal. While
photoperiod has been proposed as the signal tropical plants use to initiate flowering and
leafing (Gunter et al. 2008, Stevenson et al. 2008), day length does not change at the
equator, while annual periodicity in flowering and leafing still occurs, making
photoperiod by itself an unlikely driver of phenology (Borchert et al. 2005, Luttge and
Hertel 2009). However, insolation changes in concert with photoperiod away from the
equator and independently at the equator, as sun angle changes. The timing of the
initiation of flowering and leafing in tropical plants relative to both increases and
decreases in insolation are consistent with insolation as the signaling mechanism for these
phenological events (Calle et al. 2010). Many species in our study area exhibit increased
leaf production in August and September (personal observation) when insolation is
increasing, although this trend is not well documented. Leaf photosynthetic capacity
137
declines with age (Kitajima et al. 1997, Mediavilla and Escudero 2003), so highest
photosynthate levels for growth are expected soon after leaf flush, especially when leaf
flush coincides with maximum insolation.
Perhaps the better question to ask is: why is tree growth not correlated with insolation
maxima in lowland forests? One reason may be that growth seasonality has been mainly
observed in seasonally dry forests where a strong dry season overwhelms any positive
growth response from increased light. A second reason may be that insolation is not
actually always greater in the dry season, as was seen in this study (Chapter II). In weakly
seasonal forest in Cote d'Ivore, Anderson et al. (2005) noted that dry season skies often
had diffuse high clouds, while wet season storms were often preceded by bright sunshine.
Finally, light levels are often lower in cloud forest than in lowland forest (Korner et al.
1983), and this greater light limitation may lead to a stronger correlation between
insolation and tree growth in these systems. Cloud forest tree growth phenology has been
neglected topic of study. More studies are needed to determine whether the pattern in tree
growth seasonality seen in this study is common in cloud forests.
Implications for forest measurement.
Data from forest diameter censuses are used in a wide range of ecological studies. The
timing of diameter censuses can lead to spurious results when comparing tree growth in
different plots, so efforts are often made to carry out censuses at a consistent time of the
year, and to measure trees in the same order in successive censuses to minimize bias
(Clark and Clark 1992). The results of our study suggest that by timing censuses in the
late wet season or dry season researchers can further reduce bias by measuring trees when
138
relatively little growth is taking place. We suggest that the period of August through
December be avoided when planning census work in this area, since small changes in the
timing of successive censuses could result in relatively large observed variability in year
to year growth, which would in fact be due to census timing. This effect would be
especially important in annual censuses, but less important for multiannual census
intervals as the variation would be 'averaged away'.
Phenological controls on species and ecosystems
Phenology plays a key role in determining species distributions (Chuine 2010), and
determines the length of the growing season, with implications for ecosystem
productivity. In the North American trees, northern range limits are often limited by
failure of fruits to ripen, while southern range limits are often limited by the inability to
flower because of insufficient chilling (Morin et al. 2007). Earlier bud burst has positive
effects of photosynthesis (Richardson et al. 2009), while later leaf fall increases
respiration (Piao et al. 2008). The effect of changes in spring and autumn phenology vary
with ecosystem (Ibanez et al. 2010), but in at least one study a longer growing season has
led to increased net ecosystem productivity (Richardson et al. 2010). Increasing growing
season length has also been implicated in increasing tree growth in the latter half of the
twentieth century in eastern North America (McMahon et al. 2010). In addition to its
effects on the ecology of species and ecosystems, phenological shifts have been among
the most widely observed ecological responses to global climate change (Hughes 2000,
Walther et al. 2002, Parmesan 2006, Cleland et al. 2007).
139
These examples of phenological control on ecological processes all come from the
temperate zone, where temperature is the major determinant of phenology. It is unknown
whether phenology can similarly enforce range limits and determine ecosystem
productivity in the tropical montane cloud forest. Variability between years in the timing
and magnitude of maximum growth for some species and plots shows that trees do
respond on annual time-scales to climate variability, as seen for tree growth response to
interannual temperature variability (Clark et al. 2010). Changes in climatic variables,
particularly solar radiation, could result in changes in the growth phenology of cloud
forest trees. Higher light levels might result in a longer growing season, and greater
overall growth. Species composition likely affected growth phenology at the plot level,
since both altitude and plot effects decreased in importance when taxonomic identity was
included in the model. If species respond differently to changes in solar radiation, shifts
in growth phenology could cause shifts in species composition, which may be
contributing to the observed upslope migration of tree communities in this system (Feeley
et al. in press).
One issue not addressed in this study is whether the observed growth phenology patterns
could be the result of changes in the allocation of photosynthate between growth and
reproduction throughout the year. Trade-offs between growth and reproduction are
commonly observed in plants (Obeso 2002, Banuelos and Obeso 2004, Matsuyama and
Sakimoto 2008), and it is possible that the seasonality of growth we observed is the result
of carbon allocation shifting between growth and reproduction at different times of the
year. The striking differences in growth between years in Axinea pennellii, suggests that
there is a trade-off in this masting species between growth and reproduction (Stevenson
140
and Shackel 1998), although negative correlations between growth and reproduction do
not always indicate a trade-off (Knops et al. 2007). Trade-offs between growth and
reproduction within years could affect the seasonal timing of growth in species that
flower annually. However, while flowering and fruiting in Weinmannia, which flower
and fruit annually, is seasonal, the timing differs between species (unpublished data),
while the timing of growth is much more consistent between species (Fig IV – 4). In
addition, if growth phenology was driven by a trade-off with reproduction, we would not
expect to see a distinct growth season in mast species such as Axinea in years when it was
not reproducing. However, the timing of maximum growth was consistent across years in
this species. This suggests that growth phenology is independent of reproductive
phenology in cloud forest trees, but more studies on this topic are needed.
Conclusion
Our observation that tree growth is highest during the time of greatest insolation, and not
correlated with precipitation in a cloud forest in southern Peru suggests that this forest is
light limited. While the suggestion that cloud forests are light limited in the broad sense is
not new (Bruijnzeel and Veneklaas 1998), the pattern of insolation driving growth
phenology has not, to our knowledge, been previously observed. Cloudiness determines
annual insolation patterns in our study area, and variation in cloudiness may contribute
significantly to year-to-year variation in tree growth. This means that any changes in
cloudiness regime due to climate change and/or anthropogenic land-use change (Still et
al. 1999, Cowling et al. 2008, Pinto et al. 2009), may have significant effects on tree
growth in the Andes. More studies are needed examining tree growth phenology in cloud
141
forests to understand the proximate causes of seasonality in growth and to predict how
these forests will respond to ongoing climate change.
142
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Table IV - 1. Characteristics of plots in the study area included in the analysis, including
location of each plot, number of total stems and number of stems with dendrometers in
each plot, and the substrate and mean annual temperature and precipitation where
available.
Site Altitude Lat. Long. Stems Dendr. Substrate MAT MAP
Tono 925 -12.95 -71.55 484 73 shale - -
San Pedro 1500 -13.05 -71.54 886 75 shale 17.4 5000
Trocha Union 1840 -13.07 -71.56 1311 103 granite 16.0 3600
Trocha Union 2020 -13.07 -71.56 1029 113 granite 14.9 -
Trocha Union 2250 -13.08 -71.57 1105 49 shale 14.3 -
Trocha Union 2520 -13.09 -71.57 1122 56 shale 12.1 -
Trocha Union 2720 -13.11 -71.59 950 151 shale 11.1 3100
Trocha Union 3020 -13.11 -71.60 619 123 shale 9.5 -
Wayqecha 3025 -13.19 -71.59 1196 121 shale - -
Trocha Union 3200 -13.11 -71.60 810 84 shale 8.9 -
Trocha Union 3400 -13.11 -71.61 675 66 shale 7.7 2600
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Table IV - 2. Analysis of Deviance table for model not including taxa.
Df Deviance F Pr(>F)
NULL 6083 3112.7
Altitude 1 0.163 0.44 0.508
Plot 9 1.548 0.46 0.900
Year 1 13.491 36.3 <0.001 ***
Season 2 247.801 333 <0.001 ***
Altitude x Year 1 8.75 23.5 <0.001 ***
Plot x Year 9 25.706 7.68 <0.001 ***
Altitude x Season 2 9.679 13 <0.001 ***
Plot x Season 18 47.823 7.14 <0.001 ***
Year x Season 2 4.188 5.63 0.004 **
Altitude x Year x Season 2 10.222 13.7 <0.001 ***
Plot x Year x Season 18 32.297 4.82 <0.001 ***
Residuals 6018 2711
Significance codes: 0 ‘***’ 0.001 ‘**’ 0.01
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Table IV - 3. Analysis of Deviance table including genus as an explanatory variable.
Df Deviance F Pr(>F)
NULL 4919 2393.6
Genus 20 1.0 0.1 1.000
Altitude 1 0.1 0.4 0.538
Plot 9 1.2 0.4 0.940
Year 1 4.9 14.2 < 0.001 ***
Season 2 213.2 310.4 < 0.001 ***
Genus x Year 20 34.9 5.1 < 0.001 ***
Altitude x Year 1 1.8 5.1 0.024 *
Plot x Year 9 17.1 5.5 < 0.001 ***
Genus x Season 40 35.3 2.6 < 0.001 ***
Altitude x Season 2 6.4 9.3 < 0.001 ***
Plot x Season 18 33.3 5.4 < 0.001 ***
Year x Season 2 6.5 9.5 < 0.001 ***
Genus x Year x Season 40 30.8 2.2 < 0.001 ***
Altitude x Year x Season 2 2.0 2.9 0.057 .
Plot x Year x Season 18 25.6 4.1 < 0.001 ***
Residuals 4734 1979.4
Significance codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1
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Figure legends
Figure IV - 1. Mean relative seasonal growth for all stems across a ~2500 meter
altitudinal gradient in southern Peru. Points show mean relative growth; x-error bars
show length of the census interval; y-error bars are the 95% confidence interval on the
mean. Black and dark gray bars at the top show the four wettest and driest months,
respectively. Light gray rectangles depict the four months with the highest mean daily
insolation, which is correlated with higher temperature and vapor pressure deficit.
Figure IV - 2. Seasonal relative growth trends for each plot (light gray lines) in the study.
Highlighted are one low altitude (solid line) and one high altitude (dashed line) plot
which are representative of low and high altitude trends. Census intervals and the mean
trend in seasonal growth are depicted at the bottom of the figure (growth trend not to
scale).
Figure IV - 3. Seasonal growth trends for the 21 most common genera included in the
study (light gray lines). Clusia is representative of most species, particularly high altitude
ones which follow the general seasonal trend. Ilex is representative of some low altitude
species with an earlier growth peak in the second year. Clethra is the only genus with
species which have peak growth during the dry season in both years. Axinea is unusual in
the very different patterns in the magnitude of growth deviation between the two years.
Axinea had a mast flowering and fruiting event in 2008. Census intervals and the mean
trend in seasonal growth are depicted at the bottom of the figure (growth trend not to
scale).
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Figure IV - 4. Seasonal growth in six common Weinmannia species. Census intervals and
the mean trend in seasonal growth are depicted at the bottom of the figure (growth trend
not to scale). Species median altitude is listed along with the species binomial in the
legend.
155
●
●
●
●
●
●
Month
Rel
ativ
e G
row
th
●
●
●
●
●
●
●
●
●
●
●
●
Jun07 Sep07 Dec07 Mar08 Jun08 Sep08 Dec08 Mar09 Jun09 Sep09
0.8
1.0
1.2
1.4
Dry Dry DryWet Wet
High Light, High Light,Temp & VPD Temp & VPD
FIGURE IV - 1
156
Month
Rel
ativ
e G
row
th
2000 m3400 m
Jun07 Sep07 Dec07 Mar08 Jun08 Sep08 Dec08 Mar09 Jun09 Sep09
0.0
0.5
1.0
1.5
2.0
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●
●
●
FIGURE IV - 2
157
Month
Rel
ativ
e G
row
th
AxinaeaClethraClusiaIlex
Jun07 Sep07 Dec07 Mar08 Jun08 Sep08 Dec08 Mar09 Jun09 Sep09
01
23
4
●●
●●
●
●
FIGURE IV - 3
158
Month
Rel
ativ
e G
row
th
Weinmannia_bangii; 2720mWeinmannia_crassifolia; 3025mWeinmannia_microphylla; 3400mWeinmannia_multijuga; 2250mWeinmannia_ovata; 2020mWeinmannia_reticulata; 2720m
Jun07 Sep07 Dec07 Mar08 Jun08 Sep08 Dec08 Mar09 Jun09 Sep09
0.0
0.5
1.0
1.5
2.0
2.5
●
●
●
●
●
●
FIGURE IV - 4
159
CHAPTER V
INTRA- AND INTER-SPECIFIC TREE GROWTH ACROSS A LONG ALTITUDINAL GRADIENT IN THE
PERUVIAN ANDES
Abstract
Tropical forests play a significant part in the global carbon cycle, and their response to
rising temperatures has implications for the trajectory of carbon dioxide accumulation in
the atmosphere and subsequent warming. Some studies suggest that forest biomass is
increasing as temperatures rise, while others find a negative correlation between
temperature and growth. Across sites in the tropics, primary productivity increases with
temperature, but this trend is associated with a shift in species composition. In this study,
we investigated tree diameter growth across a 1650 meter altitudinal gradient within and
between species of Weinmannia, a dominant and widespread genus of cloud forest trees
in the Andes. By simultaneously studying growth in multiple species across their
altitudinal ranges we were able to separate the effect of species growth response to
temperature from the effect of species compositional turn-over across this temperature
gradient. We used data on over 2400 trees from two data sources: repeated diameter
censuses and dendrometer measurements over multiple years, and analyzed the data using
generalized linear models and a Bayesian state space modeling framework to examine
growth within and between species across the altitudinal gradient. Results were similar
for each modeling approach, and suggest that species turn-over is largely responsible for
the positive correlation between productivity and temperature in the tropics, while growth
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within species showed no consistent trend with altitude. For species that did have a
significant growth response to temperature, that response was negative, with higher
growth at higher altitude. If this result holds for other species, our results imply that any
positive response in ecosystem productivity to increasing temperatures in the Andes will
be dependent on the migration of species to higher altitudes. Given the rapid pace of
climate change, and the slow observed rates of community migration, we expect the
response to warming of ecosystem productivity to be slow, and may initially be negative
if, as observed within Weinmannia, a negative relationship between growth rate and
temperature is common for cloud forest tree species.
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Introduction
Tropical forests contain ~25% of carbon in the terrestrial biosphere, and account for
~33% of global terrestrial net primary productivity (Bonan 2008). Understanding the
response of tree diameter growth to temperature is important for predicting forest carbon
dynamics under climate change, but studies examining recent trends in tree growth in the
tropics have yielded conflicting results (Phillips et al. 1998, Clark et al. 2003, Baker et al.
2004, Feeley et al. 2007b, Chave et al. 2008, Lewis et al. 2009, Clark et al. 2010). The
observed positive relationship between ecosystem productivity and temperature (Raich et
al. 2006) could be due to a general physiological response to temperature, with increasing
growth in all species, or due to a change is species composition, with species with higher
growth rates found at warmer sites. This distinction is important because of implications
for forest response to climate change. If growth within individual species shows a
positive relationship to temperature, forests are likely to respond quickly, with an
increase in net primary productivity (NPP). If compositional change is driving the
positive relationship between temperature and NPP, forest productivity may respond
more slowly, since tree species will need to migrate. Model predictions suggest a lag
between climate change and species migrations (Iverson et al. 2004a, Morin et al. 2008),
and observations demonstrate that tree species migration rates are slower than needed to
keep pace with climate change (Feeley et al. in press). This is true even if long-distance
dispersal is common enough for high rates of propagule migration (Clark et al. 1999),
because most trees are slow growing, and most of the biomass and annual productivity in
closed canopy forests is accounted for by canopy trees (Clark et al. 2001).
Tree growth and environmental change
162
The carbon balance of forests depends on numerous factors, many of which are changing
as a result of anthropogenic activities. Rising atmospheric CO2, increasing temperatures,
changing precipitation and insolation patterns, nitrogen deposition and ozone pollution,
deforestation and forest fragmentation, invasive species, and widespread hunting all
affect forest functioning (Wright 2005, Lewis et al. 2006), and ultimately the carbon
balance of the forest biome. While tree regeneration and mortality, decomposition, and
soil carbon dynamics all influence the carbon balance of forests, tree growth is
particularly important. In lowland tropical forests above-ground carbon stocks are similar
to below-ground stocks, which contrasts with temperate and boreal forests where more
biomass is stored below-ground, (Dixon et al. 1994). The majority of carbon in living
biomass in forests is in tree boles, and allometric equations allow the estimation of
above-ground biomass from stem diameter measurements (Chave et al. 2005). In
Amazonian and Andean forests, and likely other tropical forests, annual carbon fluxes
(net primary productivity) are greatest in leaves and stem, while residences times are
much greater for carbon fixed in stems than in leaves (Aragao et al. 2009, Malhi et al.
2009, Girardin et al. 2010). The large carbon stocks and fluxes, and relatively long
residence times of carbon in tree boles, means that tree diameter growth is an important
component of the carbon cycle. Studies in temperate forests have shown increasing tree
growth and forest productivity in the last century for forests not limited by moisture
(Boisvenue and Running 2006, Bontemps et al. 2009, McMahon et al. 2010). Results
from tropical forests are less clear. Studies in the two largest areas of tropical forest, the
Amazon Basin and equatorial Africa, have shown evidence for increasing biomass stocks
(Phillips et al. 1998, Baker et al. 2004, Lewis et al. 2009), while studies in Central
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America and Asia have yielded mixed results with some forests increasing in biomass
and others decreasing (Clark et al. 2003, Feeley et al. 2007b, Chave et al. 2008, Clark et
al. 2010). There is also evidence of some tropical forests becoming more dynamic over
time, with recruitment, growth, and mortality rates all increasing (Lewis et al. 2004b,
Phillips et al. 2004). These changes have been variously attributed to increasing
atmospheric CO2, rising temperatures, higher insolation, changing disturbance regimes,
and sampling artifacts by different authors (Nemani et al. 2003, Chambers and Silver
2004, Clark 2004, Lewis et al. 2004a, Wright 2005, Feeley et al. 2007a, Lloyd and
Farquhar 2008, Phillips et al. 2008).
Forest response to temperature
While there are multiple interacting factors that determine tree growth and forest
productivity, in this paper we focus on the response of tree growth to temperature.
Temperature has been increasing at a rate of 0.26ºC/decade in the tropics since the 1960's
(Malhi and Wright 2004) and this warming is expected to accelerate (Christensen 2007).
Understanding the response of tree growth to rising temperatures will aid in predicting
whether tropical forests will be sources or sinks of carbon in the future.
Predictions for forest response to rising temperature come from studies of short-term
physiological responses to temperature change, growth responses over years to decades at
single sites, and comparative studies of tree growth across species ranges. At the
physiologic level, both photosynthesis and respiration are temperature dependent.
Maximum assimilation occurs at 30-40ºC for many plants (Farquhar et al. 1980).
Respiration increases exponentially with temperature, although there is some evidence
164
for acclimation of respiration to high temperatures (Atkin et al. 2005). Because leaf
temperatures are often higher than air temperatures, especially when water is limiting,
plants likely reach maximum carbon assimilation (needed for growth) at somewhat lower
air temperatures. However, scaling from leaf level physiology to stand-level carbon gain
(tree growth) is problematic (Chambers and Silver 2004), so that it is not clear whether
productivity of tropical forests is likely to become limited by rising temperatures
(Chambers and Silver 2004, Lloyd and Farquhar 2008). This is especially difficult to
predict when other, concurrently changing variables are considered. For example, CO2
and temperature can have interacting effects on growth. Under higher CO2
concentrations, temperature is less limiting to photosynthesis (Cannell and Thornley
1998, Lloyd and Farquhar 2008), while climate mediates the stimulating effect of higher
CO2 in FACE (Free-Air CO2 Enrichment) experiments (Moore et al. 2006). Even so, one
12-year study in undisturbed forest in Costa Rica showed that inter-annual temperature
variability had a stronger (negative) effect on tree growth than CO2 increase (Clark et al.
2010)
Studies of growth across species latitudinal ranges have generally shown a positive
relationship between temperature and growth, but these trends may be confounded by
other factors that change across large geographic gradients. Many temperate and boreal
tree species obtain maximum growth rates near their warm range boundaries (Coomes
and Allen 2007, Purves 2009), and often can grow successfully when planted in warmer
areas outside their native range (Bonan and Sirois 1992, Vetaas 2002). North American
trees exhibit a growth – cold tolerance trade-off where faster growing trees have lower
tolerance to freezing temperatures, so that species (and ecotypes within species) with
165
higher growth rates, and hence a competitive advantage, have more southerly
distributions, and species with higher cold-tolerance (and lower growth) have more
northerly distributions (Loehle 1998). Another mechanism for observed higher growth at
warm range margins is the longer growing season enjoyed by these locations. Increased
growing season length has also been proposed as a contributing mechanism for increasing
growth in northern high latitude forests during the 20th century (Myneni et al. 1997,
McMahon et al. 2010).
In the tropics, neither freezing tolerance nor growing season length is likely to be
important in mediating temperature effects on tree growth. Frost is rare in the tropics
except near tree-line on high mountains, and growing season is most commonly
determined by moisture (Borchert 1999, Worbes 1999, Schongart et al. 2002, Singh and
Kushwaha 2005) or radiation seasonality (Chapter IV) rather than temperature.
Nonetheless, there is a positive relationship between forest productivity and temperature
across sites in the tropics (Raich et al. 2006). Within sites, however, a negative
relationship has been found between average minimum temperature and growth rate
across years (Clark et al. 2003, Feeley et al. 2007b, Clark et al. 2010). This suggests that
the temperature-dependence of growth for individuals may be very different than the
ecosystem trend. Changes in ecosystem productivity with altitude could be due to general
climate control on plant productivity, or trade-offs within and among species could lead
to species at higher altitudes having lower growth rates even while the temperature-
growth relationship within species could differ from the general trend (Fig V – 1). If the
latter case dominates, species turnover may be largely responsible for the general
temperature-productivity relationship in the tropics. If so, this would highlight a key
166
difference between how temperate and tropical forests will respond to increasing
temperature, since in temperate areas both species turnover and increasing productivity
within species are observed with increasing temperature (Loehle 1998, Purves 2009).
In this study, we used a 1650 meter altitudinal gradient in the Peruvian Andes to study
the tree growth response to temperature within and between species in the cloud forest
tree genus Weinmannia, at a single locale. Our study system allowed us to test the effects
of temperature independent of those of precipitation and growing season length since
precipitation is high across the entire gradient (Chapter II), and growth phenology
(growing season) is consistent with altitude (Chapter IV). Previous studies, at this site
and others, have shown that both total and above-ground biomass, and productivity
decline with altitude (Kitayama and Mueller-Dombois 1994, Delaney et al. 1997,
Kitayama and Aiba 2002, Leuschner et al. 2007, Girardin et al. 2010). A key component
of net primary productivity (NPP) is the diameter growth of tree stems, and allometric
relationships mean that in closed canopy forests NPP is correlated with tree diameter
growth. Here we test whether the pattern of increasing NPP with temperature in the wet
tropics is due to a general physiological response of species to temperature, or whether
the trend is largely due to a change is species composition. By comparing growth of
species within a single genus that differ in altitudinal range, we maximize the likelihood
that differences in growth rates between the species are due to different temperature
regimes rather than other phylogenetically determined traits. This is the first study we are
aware of in which the relationship between temperature and tree growth has been
examined both within and across species in a single study in the tropics, and increases our
understanding of the potential effects of warming on forest productivity in the tropics.
167
Methods
Study site
Data on tree growth were collected along a ~1700 meter altitudinal gradient in the
Kosñipata Valley (-13º 6’ 18’’ latitude, -71º 35’ 21” longitude), in and adjacent to Manu
National Park, on the eastern slope of the Andes in southern Peru. Data came from ten
one-hectare permanent tree plots located approximately every 250 meters in elevation
from 1750 meters to 3400 meters, established by the Andes Biodiversity and Ecosystem
Research Group (ABERG). For a full site description, including additional plots in the
ABERG network, see Silman (in prep). Table V - 1 shows the center altitude, location,
substrate, years of data, and mean annual temperature and precipitation of the plots. The
plots used in this study were established in 2003, with the exception of the plot at 1750
meters, which was established in 2005. Eight of the plots are on a single ridge that forms
the northern margin of the Kosñipata Valley, with the other plot on a nearby ridge, also
on the northern margin of the Kosñipata Valley. The substrate of most plots is Ordivician
shale and slate, while all or parts of two plots (at 1840 and 2020 meters) are on Permian
granite. The vegetation is tropical montane cloud forest (TMCF) and the climate across
the gradient is wet, with annual precipitation at the base of the gradient averaging greater
than 4000 mm, decreasing to about 2600 mm at the highest elevation plot. Temperature
decreases with altitude with a lapse rate of 5.2ºC/km, and mean annual temperatures
range from 7.7 ºC at the top of the transect to 15.8 ºC at the bottom. For a full description
of the climate of the Kosnipata Valley, see Chapter II. Both aboveground and
belowground net primary productivity decrease approximately 4-fold between lowland
(200 m) and high elevation (3000 m) sites (Girardin et al. 2010), while decomposition
168
rates decrease with altitude (Meier et al. 2010). This, along with a shift in carbon
allocation in trees from above- to below-ground leads to decreasing above-ground
biomass and increasing below-ground carbon stocks with altitude (Zimmermann et al.
2009, Girardin et al. 2010)
Study species
Tree species differ in many traits that can have an impact on growth, but only some of
those traits will be related to the growth response to temperature. By focusing our study
on sympatric congeners with ranges distributed across an altitudinal gradient we improve
the likelihood that differences in growth rate will be related to species altitudinal niche
rather than other phylogenetically controlled traits (Harvey and Pagel 1991). For this
study we chose to focus on the genus Weinmannia, an abundant and diverse genus of
cloud forest trees. The genus Weinmannia contains about 150 species of cloud forest trees
and shrubs, widespread throughout the tropics (Bradford 1998). Its center of diversity is
the tropical Andes, with over 40 species in Peru (Brako and Zarucchi 1993). In general,
the environmental niche is conserved across neotropical members of the genus, which
form a monophyletic group (Bradford 1998, 2002). Most Weinmannia are cloud forest
trees, with ranges between 2000 and 3500 meters in altitude (Brako and Zarucchi 1993).
In our study area, we have observed Weinmannia as both shrubs and trees, with species
found as low as 950 meters and as high as 3800 meters elevation. They form a dominant
part of the tree community between 2000 and 3700 meters. In our study area, we have
identified 17 species of Weinmannia, 13 of which occur in the plot network (Table V - 2).
Data collection
169
Tree diameter growth data for this study were collected from two sources: repeated
diameter measurements on all trees greater than 1 cm diameter at breast height (DBH) in
one hectare permanent tree plots, and yearly diameter increments derived from
dendrometer measurements on a subset of the same trees. Individuals greater than 10 cm
DBH were first censused in 2003 for all plots, except the plot at 1750 meters (first
censused in 2005). Individuals of Weinmannia 1-10 cm DBH were first censused in 2006.
All individuals were censused in 2007, 2008, and 2009, with a total of 2478 stems
included in this analysis (Table V - 2). At each census, DBH in centimeters was
measured (point of measurement marked by paint and/or located a fixed distance below a
tag nailed to the tree), height (in meters) was estimated, and canopy status was scored.
Canopy status was originally scored on a scale of 1-5 following Clark and Clark (1992),
but this scale was later collapsed to a three level scale (1 = understory; 2 = mid-canopy
with some direct light on crown; 3 = canopy or emergent tree with greater than 90%
direct sunlight on top of crown) for analysis. Where there were multiple estimates (range:
1 - 7 per stem) for height and canopy status for a single tree, the median value was used
in the analysis. In the 2009 census we also noted whether a tree was damaged and if trunk
sprouting had occurred.
Band dendrometers were installed on 100 random stems (of all species) greater than 10
cm DBH in each existing plot in October 2003. Dendrometers were installed on a larger
group of Weinmannia in January 2007, for a total of 441 individual Weinmannia stems
included in this analysis (Table V - 3). After each installation, bands were allowed to
settle on the stem, and the first measurement was made 5-8 months after installation. This
first measurement was taken as the baseline, and later measurements were made relative
170
to it. Measurements of dendrometers were made in June 2004, July 2006 and then three
times a year between June 2007 and August 2009.
To calculate annual diameter increment from dendrometer measurements (circumference
increment since first measurement), we first assigned each day of a measurement period
the average daily growth rate for that period (di = Ci/(π*Ndays,i), where di is the mean daily
diameter increment for the interval, Ci is the measured circumference for the interval, and
Ndays,i is the number of days in the interval). Daily diameter increments for each year were
then summed to get the diameter increment for that year. This approach was taken
because there were usually multiple census intervals (of varying length) within each year.
A year for this study was defined as beginning on July 15th, and running to July 14th of
the next year. We define it this way for three reasons: to correspond to the diameter
censuses, which were typically done June-August of each year; to make the greatest use
of the dendrometer data; and because the dry season (June-August) is a period of
relatively low tree growth (Chapter IV), so that defining a year in this way is comparable
to north temperate zone studies where the growing season is within one calendar year.
Growth years are labeled as the calendar year at the start of the time period. Hence, the
2004 growth year runs from July 15th 2004 to July 14th 2005. Also note that, because
dendrometers first measured in 2004 were next measured in 2006, the diameter increment
for the 2004 and 2005 growth years was the mean value for that two-year measurement
period (i.e. the diameter increment for 2004 and 2005 is the same).
Analysis
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Our sampling scheme provided us with two types of data typically used in analysis of tree
growth; diameter data from repeated censuses using measuring tapes, and diameter
increments based on high-precision dendrometers, each of which have strengths and
weaknesses. The diameter censuses provided good coverage of individuals since we
measured every stem greater than 1 cm DBH in multiple years. However, this did not
always provide yearly growth resolution if trees were not measured each year. Also, since
cloud forest trees grow slowly and often have irregular trunks, measurement errors are
relatively large compared with diameter growth rates. Dendrometers provide much more
accurate measurements and can be measured more frequently to provide sub-yearly
measurements on growth, even on slow growing trees (Chapter IV). With dendrometers,
however, accurate measurements are only obtained on trees with fairly round, damage-
free stems, are not effective on small (<10 cm DBH) stems, and are expensive in terms of
materials and installation effort.
Analyzing growth rates from DBH and dendrometer data
Dendrometer and DBH-tape measurements are difficult to compare directly, largely
because of different error structures and biases. This can be seen in the distribution of
mean annual growth rates calculated from census and dendrometer data. Stem mean
annual growth rate was calculated from the census data as the slope of the regression line
through the diameter measurements of all censuses for a given stem. Stem mean annual
growth was calculated from the dendrometer data as the mean of all yearly increments
available for each stem. Figure V - 2 shows that mean annual growth calculated from
census data indicates large numbers of individuals with negative diameter growth, while
this is not true of mean annual growth calculated from dendrometer data.
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While trees must add wood to their stems (through the process of cambium renewal) to
stay alive, there are two possible reasons for the observed negative growth. The first is
that stem diameter does actually fluctuate due to stem moisture content (Karling 1934,
Sheil 2003). Stem shrinkage due to water loss can be caused by daily and seasonal
changes in soil moisture availability and transpiration demand in healthy trees, or through
damage or death of trees. Diameter censuses were all undertaken at approximately the
same time of year, which should control for seasonal change in stem water content. We
also expect seasonal variation in stem moisture content to be low in the cloud forest since
soil water content varies little throughout the year (Zimmermann et al., 2009). Diameter
measurements and dendrometer readings of dead trees were removed from the datasets,
but trees can take multiple years to die, and lose water before completely expiring,
leading to some negative growth measurements. However, mortality rates would have to
be implausibly high to account for the number of trees with apparent negative growth in
the diameter census dataset, and we don’t see this pattern in the dendrometer data set.
The second reason for apparent negative growth is measurement error, which can occur
when there is an undocumented point of measurement change, the diameter tape is not
held exactly perpendicular to the stem during measurement, or the tape is not held
directly against the stem. The possibility of these error sources is increased when tree
boles are irregular (i.e. bumps, indentations, buttresses, damage, sprouting, or concave
stems) or bryophytes and/or lianas cover the stem, both of which are common for cloud
forest trees. While effort was made to remove bryophytes and lianas, and avoid stem
irregularities when choosing the point of measurement, these factors may have
contributed to apparent negative growth.
173
Negative growth is commonly observed in tree census data (e.g Clark and Clark 1999,
Holder 2008), and this is usually dealt with by either removing trees with negative
growth from the dataset, or transforming the data by adding a constant to make all values
positive (Clark et al. 2007). Neither of these approaches is entirely satisfactory, since in
the first data is lost when trees are removed from the data set, while adding a constant
results in a distribution of growth rates that has a mode well away from zero. The true
distribution of growth rates is likely to be similar to the distribution of growth rates in the
dendrometer dataset, with a mode near zero, and a long tail of higher values (Fig V - 2b).
To make use of both datasets while accounting for the unrealistic negative growth evident
in the census data, we took a dual approach. We first analyzed the dendrometer data
alone using a generalized linear model (GLM) framework, and then used a hierarchical
Bayes modeling technique that both combines DBH and dendrometer measurements, and
constrains growth rates to be positive using informative priors. We initially performed the
GLM analysis as a way of exploring the data before running the Bayesian model, and
include it here as it may be a more familiar approach for many against which to compare
the Bayesian model. The Bayesian model, since it combines data from two sources, has a
much larger sample size and was run on more species than included in the GLM analysis.
We were also able to examine year to year differences in growth with the Bayesian
model, something we did not do in the GLM analysis. The results of the two approaches
were consistent, giving us greater confidence in our results. All of the analyses were
performed in R (Version 2.11; R Development Core Team 2010).
Generalized Linear Model analysis
174
Using the dendrometer data, we fit generalized linear models with a Gamma distribution
and a log link using the glm function in R. We started by testing the effect of single
explanatory variables (species, canopy status, diameter, stem altitude, and species mean
altitude) on mean annual diameter increment, and then built more complex models. This
approach allowed us to explore the importance of each variable individually and the
relationship between variables, which was more important for this exploratory phase of
the analysis than building a model that would explain the most variation. Because this
modeling exercise uses only data from dendrometers, sample sizes were relatively small
(Table V - 3) and only the three most common species (W. microphylla, W. bangii, and
W. reticulata) were included in the most complex model, which included a species by
stem altitude interaction.
To ground our interpretation of the effects of the various explanatory variables on growth
in ecological reality, we examined 3 scenarios for tree size and light environment
(understory sapling – DBH = 10cm, canopy status = 1; sapling in gap – DBH = 10cm,
canopy status = 3; canopy tree – DBH = 30 cm, canopy status = 3) and plotted growth
versus elevation both within and between species.
Hierarchical Bayes analysis
The Bayesian state space model we used is based on that used by Clark et al. (2007), with
the addition of covariates for altitude, canopy status, and diameter. For a complete
description of the model see Clark et al. (2007). Here we give a description of only the
most relevant features.
175
Different information was available for each tree, because censuses began in different
years for different plots and sets of trees (i.e. > 10 cm DBH stems vs. 1-10 cm DBH
stems), and dendrometers were installed at different times on different trees. Because of
this, we needed to both be able to combine different data sources and make a probability
statement about growth in years for which data was missing. The model estimated growth
for each individual tree and year (tree-year) with confidence envelopes reflecting
information about how the different sources of variation affected each tree-year. The
model partitioned measurement error of each data source (diameter census and
dendrometer data), and included a term for “process” error: variability that could not be
attributed to measurement error or variability associated with covariates (canopy status,
diameter, and stem altitude), and fixed (year) and random (individual) effects.
The model was structured to emphasize the blending of data, and for “borrowing
strength” across the full dataset since data were sparse (Clark et al. 2007). For those tree-
years where both diameter measurements and dendrometer increments were available and
in agreement as to the estimate of diameter growth, confidence envelopes are tight around
the estimate. For tree-years with missing data, or where the data didn’t agree, confidence
envelopes are wider. In general, confidence envelopes are smaller around diameter
increments than around diameter measurements, so there are smaller confidence
envelopes around tree-years for which there were dendrometer increment data and these
data dominated over census data when available (Fig V - 3). For tree-years where data
were lacking, the population mean growth rate dominated, modified by the effects of
covariates.
176
All of the significant interaction terms in the GLM modeling involved species identity, so
we modeled each species separately in the Bayesian analysis. We analyzed growth for the
nine most common species, which were distributed across the gradient. Gibbs sampling
(Gelfand and Smith 1990) was used to implement the model in R. Finally, we compared
modeled species mean growth rates versus species mean altitude to examine growth
trends across species and altitude.
177
Results
GLM Analysis
When the five explanatory variables (species, diameter, canopy status, stem altitude,
species mean altitude) were each modeled individually with mean annual diameter
increment using the dendrometer dataset including the seven most common species, only
canopy status (F = 9.24, dfnum = 2, dfden = 421, p = 0.0001) and species (F = 7.58, dfnum =
6, dfden = 417, p < 0.0001) were significant predictors. However, once canopy status was
accounted for, both diameter (F = 4.62, dfnum = 1, dfden = 420, p = 0.032) and species
mean altitude (F = 7.12, dfnum = 1, dfden = 420, p = 0.008) became significant predictors.
Canopy status generally increased with diameter, but the correlation was imperfect (r =
0.48) since small trees in canopy gaps can have high light exposure. When canopy status
was accounted for, the relationship between diameter and growth was negative, such that
saplings in gaps had the highest growth rates (e.g. Fig V - 4). Once species was
accounted for, stem altitude became a significant predictor (F = 21.06, dfnum = 1, dfden =
416, p < 0.0001). In addition, there was an interaction between species and canopy status
(F = 1.93, dfnum = 12, dfden = 403, p = 0.029), suggesting a maximum-growth shade-
tolerance trade-off (Kobe et al. 1995), with species differing in how strongly growth
responds to high light (or is suppressed by low light). When both canopy status and
species mean altitude were accounted for, stem altitude became a significant predictor (F
= 17.02, dfnum = 1, dfden = 402, p< 0.0001), with an effect in the opposite direction as
species mean altitude.
178
Finally, we developed two models that included all of the above factors. The first was run
with the same dataset as above and included (terms added sequentially) canopy status,
diameter, species mean altitude, species identity, stem altitude, and a species by canopy
status interaction. We added terms in this order because the first three terms differ
between species (i.e. can be thought of as constrained by species identity since species
differ in maximum size and altitudinal range), so by adding species identity after these,
species identity only accounts for variation in the model that cannot be ascribed to the
other variables. Stem altitude is added after species because it is only important within
species. All terms were significant (Table V - 4). That both species mean altitude and
species identity were significant predictors indicates that while temperature niche was
important in determining species growth rate (with lower altitude/warmer niche species
having higher growth rates; Fig V - 3), other, unmeasured, species specific traits were
also important.
We then ran the same model with a term added for the species identity by stem altitude
interaction on a subset of the data that included only the three most abundant species.
(This model would not converge with the seven species dataset because some species did
not have sufficient individuals at multiple altitudes.) In this model, all terms were
significant except for species (Table V - 5). The species effect in the reduced model is
likely due to the inclusion of W. ovata, which had a much lower mean growth rate than
predicted by its mean altitude (2024 m). The species by stem altitude interaction showed
that the relationship between altitude and growth differs between species, but the
direction of the effect was generally not consistent with the effect of species mean
altitude (significantly positive for one common species (W. bangii, F = 24.5, dfnum = 1,
179
dfden = 118, p <0.0001), with no significant trend for two others, while the trend in
species mean altitude is negative; Fig V - 4), which is why both species mean altitude and
stem altitude have explanatory power.
The GLM analysis of dendrometer data illustrated several interesting trends, but did not
make full use of available information and inference is limited by small sample sizes in
most species. We now turn to the hierarchical Bayesian analysis which estimated
parameters for more species and individuals.
Bayesian Analysis
The Bayesian analysis included more species, but was consistent with the GLM analysis
in general growth trends. Species differed in their response to the covariates canopy
status, DBH, and altitude, with growth in only about half the species having a significant
response to any of the covariates (Fig V - 6, Appendix). As in the GLM analysis, an
increase in canopy status (increasing light availability) had a positive effect on growth,
while increasing diameter had a negative effect, although both factors were only
significant for four of nine species. The altitude effect was only significant for two
species, W. bangii and W. ovata, and for these species the effect was positive (Figs V - 6
and V - 7, Appendix). The addition of more species in the Bayesian analysis confirms the
negative trend with altitude for growth between species; lower altitude species had higher
mean growth rates than high altitude species (Fig V - 8). Very little year-to-year variation
in growth rates was detected, with no single year effect being significantly different from
zero (Fig V - 9, Appendix). There was a trend of lower growth for the first three years of
the study. This may have been related to a severe drought that affected the western
180
Amazon basin in 2005 (Marengo et al. 2008), although there is also less data for this
early period than for later years, which could have contributed to the trend. For most
stems with increment data the time series starts in 2007, and the stems less than 10 cm
DBH were first censused in 2006. In either case, year effects were small, suggesting that
we did not lose much information by averaging growth across years in the GLM analysis.
Variability associated with measurement and process error, and individual effects was
distributed in a similar way for all species, with diameter measurement error and
individual effects being larger than “process” and increment measurement error (Fig V -
10).
181
Discussion
Temperature, tree growth, and the niche
There was a trade-off between altitude and diameter growth rate at the genus level in
Weinmannia (Figs V - 5 and V - 8); species growing at high altitude grew more slowly
than species growing at lower altitude. Since temperature changes at a steady lapse rate
of 5.2 ºC/km (Chapter II), this altitudinal trend in growth rate is closely correlated with
temperature. This trend is consistent with studies across latitudinal gradients at high
latitudes that compared ecotypes within species, where lower latitude ecotypes had higher
growth rates than higher latitude ecotypes (Loehle 1998, Purves 2009). If this latitudinal
trend was due to a general effect of temperature on tree growth, we would expect to see
the same pattern within species across altitude, with decreasing growth within species for
individuals growing at higher altitude. However, we did not see decreasing growth with
altitude within species in our study. Of the four species (W. microphylla, W. bangii, W.
reticulata, and W. lechleriana) with large numbers of individuals at multiple elevations,
two showed a trend of decreasing growth with altitude, and two showed an increasing
trend (Fig V - 7). For only one species (W. bangii), was a trend significant, and this trend
was for higher growth at higher altitude (lower temperature). There are three possible
explanations for the inconsistency seen between studies of tree growth within species
across temperate latitudinal gradients and this tropical altitudinal gradient: 1) the
temperature effect on tree growth seen at high latitudes is really an effect of a longer
growing season, which is absent in the tropics; 2) the temperature effect on growth is
mediated by genetically determined traits, and the short distances between populations in
our study has not allowed for the ecotypic differentiation possible across latitudinal
182
gradients; and/or 3) biotic factors hypothesized to limit warmer species range limits
(MacArthur 1972) are stronger in the tropics, reversing the effect of temperature on
growth.
Latitudinal studies (and altitudinal studies at high latitudes) are confounded by the fact
that it is generally impossible to separate the direct effect of temperature on growth from
the effect of a longer growing season, particularly when temperature determines growing
season length. Where moisture is not limiting a longer growing season should lead to
greater tree growth (Myneni et al. 1997, McMahon et al. 2010), even though a
lengthening growing season may lead to greater drought, increased mortality, and lower
carbon sequestration in dry sites (van Mantgem et al. 2009, Hu et al. 2010). The
constraint of a temperature-dependent growing season is generally not present in the
tropics, where growing season length is usually determined by precipitation patterns
(Worbes 1999, da Silva et al. 2002, Clark et al. 2010) or solar radiation (Chapter IV). At
our study site, the growing season is correlated with times of higher mean solar radiation
driven by seasonal cloudiness patterns, and these patterns are consistent across most of
the gradient (Chapters II and IV), suggesting that growing season length does not change
much with altitude.
Differences in mean growth rate between species (Figs V - 5 and V - 8) suggest genetic
control of traits determining growth. Genetic differences between ecotypes within species
may help account for patterns in tree growth observed across latitudinal gradients since
genetic variation across species ranges are commonly observed, and this variation is
associated with climatic variation (Eckert et al. 2010, Sork et al. 2010). The steep
altitudinal gradient of our study site means that the entire altitudinal ranges of most
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species are contained within a distance of a few kilometers on the ground. This, coupled
with good seed dispersal capabilities in Weinmannia through small (~0.0001 g) wind-
dispersed seeds, may mean that our study species have relatively little genetic variation
within the study area. Low intraspecific genetic variation may be why the trade-off
between altitude and diameter growth seen at the genus level is not present within
species. It is unlikely, however, that genetic variation is the only reason for the positive
tree growth-temperature relationship seen in temperate areas. A study of mountain beech
across a 780 meter altitudinal gradient revealed a monotonic decrease in growth with
altitude within that one species in a 9000 ha study area (Coomes and Allen 2007). It is
doubtful that genetic variation could have been large enough across this gradient to fully
account for the sizeable variation in growth.
Trade-offs and range limits
Species range limits are fundamentally demographic phenomena (Angert 2006). Growth
is an important vital rate, as it both correlates with mortality risk (Wyckoff and Clark
2002), and determines when a tree reaches reproductive maturity since reproductive
effort is generally dependent on size in plants (Samson and Werk 1986, Weiner et al.
2009). In this section we evaluate the hypothesis that biotic interactions should be
stronger at warmer range margins and discuss evidence for life-history trade-offs
involving growth in light of our results.
MacArthur (1972) proposed that species ranges should be limited by abiotic factors at
their higher latitudinal and altitudinal limits, and by biotic factors such as competition
and natural enemies at their warmer, lower latitudinal and altitudinal range limits. A
184
related hypothesis is that abundance should be greatest at species range centers and that
performance (growth, fecundity, survival) should also be highest there, although in
general there has been little evidence for this (Sagarin and Gaines 2002, Murphy et al.
2006, Sagarin et al. 2006, Samis and Eckert 2007). While growth has been shown to have
a unimodal distribution with respect to altitude in some studies (Gworek et al. 2007),
decreased growth at warmer range limits is often due to drought, especially in semi-arid
systems (e.g. Jump et al. 2006). We saw no evidence for a unimodal relationship of
growth to altitude in our data, and flat or monotonic trends in growth with altitude are
common in other systems (e.g. Berg et al. 2007, Coomes and Allen 2007). In some
species growth was positively correlated with altitude, suggesting that it is possible that
biotic factors may have a role in determining patterns of tree growth.
Biotic interactions generally increase in importance towards the tropics, including
herbivory and disease (Schemske et al. 2009), and while this trend is also likely to hold
across altitudinal gradients, the topic has received little attention in our study area.
Competition may be important for sorting species at the genus level, and help explain the
pattern of decreasing growth across species with altitude. Tree height decreases with
altitude in the study area (unpublished data), which is expected to increase competition
for light at lower altitudes. For species sorting to occur by this mechanism, there should
be a trade-off involving performance across altitude, such that species that grow faster
have higher population growth at lower altitude, with the reverse true for high altitude
species. This trade-off has not yet been identified, and suggests a productive direction for
future research.
185
We did see evidence for the commonly cited trade-off between high light growth and low
light survival (Kobe et al. 1995, Valladares and Niinemets 2008), with species differing
in how strongly they responded to increases in canopy status. Species with large
differences in growth between low and high canopy status are predicted to be early
successional species that have poor survival in the forest understory, while species that
can regenerate in the understory are unable to increase their growth proportionately in
high light conditions. An example of this is seen in Fig V - 4, where W. bangii has much
higher growth in high light than in low light, whereas W. reticulata has more similar
sapling growth rates under the different light regimes. The low growth rates seen in W.
ovata (Figs V – 5 and V – 8) may also be related to life-history trade-offs. W. ovata is
only occasionally observed as a single stemmed canopy tree in our study area. It often
occurs with the main stem lying horizontal or at an angle, with many vertical sprouts.
This scandent strategy may improve survival in this species but preclude a high diameter
growth rate.
Species composition and ecosystem productivity
If the divergent patterns seen here for growth within and between Weinmannia species
across a temperature gradient hold for other tropical tree taxa, this would suggest that the
pattern of increasing NPP with temperature in the wet tropics is largely due to species
compositional change. This has implications for both the interpretation of ecosystem
productivity data, and for predictions of how ecosystems respond to climate change.
In this study we saw evidence for differences in species temperature niche, as well as a
light mediated growth-survival trade-off (successional niche). If the light environment
186
(successional stage) of a forest is altered, species compositional shifts can lead to changes
in ecosystem productivity, independently of temperature changes. Indeed, the observed
increase in forest biomass in much of the tropics over recent decades has been attributed
to succession by some authors (Wright 2005, Feeley et al. 2007a). The interpretation of
patterns of ecosystem productivity with respect to climate should take species
composition into account, especially since there are many reasons why species
composition may be out of equilibrium with climate.
Understanding the effects of species composition when projecting future patterns of
ecosystem productivity based on projected climate changes may be particularly
important. Both observations and theory suggest there will be a lag between climate
changes and species migrations, and that the lag will vary between species (Iverson et al.
2004b, Lenoir et al. 2008, Morin et al. 2008, Feeley et al. in press). Given this lag, our
results suggest that initial ecosystem-level productivity responses to climate change are
likely to be small, with flat or possibly decreasing productivity as temperature increases.
Only later, once species migrations have ‘caught up’ with climate, will increases in
ecosystem productivity be seen. Temperatures are predicted to continue to rise for the
forseeable future given current projections of anthropogenic green house gas emissions
(Meehl 2007), so ecosystems are likely to be out of equilibrium with climate for decades
or longer. In the meantime, the rate of species migration will determine the rate of
ecosystem response, and since species migrate at different rates, no-analog communities
are likely to be the norm in many areas (Williams and Jackson 2007, le Roux and
McGeoch 2008). Given the dependence of ecosystem productivity on species
187
composition, predicting the ecosystem properties of these no-analog communities will be
difficult.
Conclusions
Our study suggests that, for a dominant genus of cloud forest trees, trade-offs between
growth and temperature niche between species lead to the observed distribution of
species across an altitudinal gradient. This is in contrast to growth rate within species
across the altitudinal gradient, which is not positively correlated with temperature, as
would be predicted if there were a general physiologic relationship between temperature
and growth. Nor is there a hump-shaped relationship with altitude as would be predicted
if species have an optimum temperature for growth. These results highlight the
importance of considering community species composition when interpreting studies on
ecosystem productivity across temperature gradients in the tropics, especially when
considering the response of ecosystems to climate change. However, this study also
leaves many questions unanswered. How well do these results generalize to other
species? How different are the drivers of tree growth in the tropics and temperate zone?
Should we expect a fundamentally different response to climate change between the
tropics and temperate areas? What temperature-driven trade-off(s) exists in the tropics to
set species growth rates? How fast will species migrate? These questions provide fodder
for future studies on tree growth across environmental gradients in the tropics.
188
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Table V - 1. Characteristics of plots included in this study.
Site Altitude Latitude Longitude Years Substrate MAT MAP
San Pedro 1750 -13.04733 -71.54245 2005-2009 shale 15.8 -
Trocha Union 1840 -13.06990 -71.55582 2003-2009 granite 16.0 3600
Trocha Union 2020 -13.07373 -71.55935 2003-2009 granite 14.9 -
Trocha Union 2250 -13.07958 -71.56592 2003-2009 shale 14.3 -
Trocha Union 2520 -13.09380 -71.57445 2003-2009 shale 12.1 -
Trocha Union 2720 -13.10517 -71.58937 2003-2009 shale 11.1 3100
Trocha Union 3020 -13.10905 -71.59998 2003-2009 shale 9.5 -
Wayqecha 3025 -13.1903 -71.5869 2003-2009 shale - -
Trocha Union 3200 -13.11023 -71.60442 2003-2009 shale 8.9 -
Trocha Union 3400 -13.11375 -71.60747 2003-2009 shale 7.7 2600
203
Table V - 2. Number of stems of Weinmannia greater than 1 cm DBH present in each
plot. Species marked with an asterix was not included in the Bayesian analysis.
Plot elevation (meters)
Species 1750 1840 2020 2250 2520 2720 3020 3025 3200 3400
W. lechleriana 22 35 52 - - - - - - -
W. pinnata 33 1 2 - - - - - - -
W. sorbifolia* - 1 - - - - - - - -
W. ovata - 1 204 4 - - - - - -
W. sp. 1* - - - 2 - - - - - -
W. multijuga - - - 95 - 1 - - - -
W. reticulata - - - - 40 251 130 69 - -
W. bangii - - - - 70 56 6 101 194 -
W. mariquitae - - - - - 25 - - - -
W. sp. 2* - - - - - - 1 - - -
W. crassifolia - - - - - - 17 794 - -
W. auriculata* - - - - - - 2 3 6 -
W. microphylla - - - - - - - - 87 173
204
Table V - 3. Number of stems of Weinmannia fitted with dendrometers of each species in
each plot. Species marked with an asterix were not included in the GLM analysis.
Plot elevation (meters)
Species 1750 1840 2020 2250 2520 2720 3020 3025 3200 3400
W. lechleriana 4 2 7 - - - - - - -
W. pinnata* 4 - 1 - - - - - - -
W. sorbifolia* - - - - - - - - - -
W. ovata - 1 32 1 - - - - - -
W. sp. 1* - - - - - - - - - -
W. multijuga - - - 28 - - - - - -
W. reticulata - - - - 20 44 33 14 - -
W. bangii - - - - 38 38 3 4 40 -
W. sp. 2* - - - - - - - - - -
W. mariquitae* - - - - - 11 - - - -
W. crassifolia - - - - - - 12 43 - -
W. auriculata* - - - - - - - - 1 -
W. microphylla - - - - - - - - 26 34
205
Table V - 4. Analysis of Deviance table of full model used with full dendrometer dataset
(seven species).
Df Deviance F Pr(>F)
NULL 423 556.98
Canopy Status 2 18.22 9.67 <0.001 ***
DBH 1 4.45 4.72 0.030 *
Species mean altitude 1 5.51 5.85 0.016 *
Species 5 33.01 7.01 <0.001 ***
Individual altitude 1 14.43 15.33 <0.001 ***
Canopy Status x Species 12 25.63 2.27 0.009 **
Residuals 401 455.74
Significance codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05
206
Table V - 5. Analysis of Deviance table of full model used with three most common
species in dendrometer dataset.
Df Deviance F Pr(>F)
NULL 293 325.05
Canopy Status 2 11.89 8.45 <0.001 ***
DBH 1 5.57 7.92 0.005 **
Species mean altitude 1 5.81 8.27 0.004 **
Species 1 0.70 1.00 0.318
Individual altitude 1 15.33 21.81 <0.001 ***
Canopy Status x Species 4 13.92 4.95 0.001 ***
Species x Individual altitude 2 4.78 3.40 0.035 *
Residuals 281 267.04
Significance codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05
207
Figure legends
Figure V – 1. Two hypotheses for the observed increase in ecosystem productivity with
temperature in the wet tropics: a) growth increases within species with temperature, and
b) growth rate is fixed within species, but a trade-off between temperature and growth
leads to faster growing species to have warmer temperature niches.
Figure V - 2. Distribution of measured mean annual diameter increment values from (a)
census and (b) dendrometer datasets.
Figure V - 3. Diameter and growth rates (diameter increment) for five example trees with
different amounts and combinations of data. Shown are observations (circles), predictive
median (solid lines), and 95% predictive intervals (dashed lines). Each tree is labeled
with the altitude of the plot it came from and a unique identification number.
Figure V - 4. Modeled mean diameter increment for three common Weinmannia species
using the dendrometer data. For each species, diameter increment is modeled for three
different scenarios: saplings in the understory (dbh = 10 cm, canopy status = 1); saplings
in gaps (dbh = 10 cm, canopy status =3); and canopy trees (dbh = 30 cm, canopy status =
3). Thick lines show modeled trend, while thin lines show the 95% confidence intervals.
Figure V - 5. Modeled mean diameter increment versus altitude for canopy trees (dbh =
80th percentile of dbh distribution for each species in centimeters, canopy status = 3)
using the dendrometer data. Each point represents a species modeled at its mean altitude.
Mean diameter increment for each species is shown with the 95% confidence interval on
the mean. The trend line shows the mean species altitudinal trend with 95% confidence
interval for canopy trees. The altitudinal pattern is similar for saplings (dbh = 10 cm) in
208
the understory (canopy status = 1) and in gaps (canopy status = 3); except that modeled
increment is shifted lower and higher, respectively.
Figure V - 6. Parameter values of the three covariates included in the Bayesian model.
Points represent the mean posterior parameter value, while lines depict the 95 % credible
interval of the parameter. Parameter estimates greater (less) than zero indicate that
parameter had a positive (negative) effect on growth. Note that W. mariquitae and W.
pinnata had relatively small sample sizes (see Table 2), and the credible intervals for
these two species are truncated, as is the credible interval for W. crassifolia for altitude
effect since it was only found at one altitude.
Figure V - 7. Mean diameter increment versus stem altitude for each of the nine species
included in the Bayesian analysis. Points are modeled mean diameter increment for each
tree. The trend line is a linear regression through the points with a 95% confidence
interval. Species decrease in altitudinal range from upper left to lower right. The x-axis
spans the same altitudinal distance (700 meters) for each species to aid comparisons.
Figure V - 8. Modeled diameter increment for a mature tree exposed to high light plotted
against mean species altitude for each species included in the Bayesian analysis. Each
point represents a species modeled at its mean altitude. The regression line shown (r2 =
0.30, p = 0.07) was fit through the points of all species. The fit of the regression line is
much better if W. ovata (mean species altitude = 2024 m) is excluded (r2 = 0.71, p =
0.005).
209
Figure V - 9. Year effects from the Bayesian model. Points represent the mean posterior
parameter value, while lines depict the 95 % credible interval of the year effect. Numbers
above each line indicate the number of stems from each year included in the analysis.
Figure V - 10. Magnitude and distribution of four sources of variability included in the
Bayesian model (increment and diameter measurement error, process error, and
individual variability) for W. bangii. Patterns were similar for each species (see Appendix
for parameter values for all species).
210
Temperature
Gro
wth
(a)
Temperature
Gro
wth
(b)
FIGURE V - 1
211
Fre
quen
cy
−0.
5
0.0
0.5
1.0
1.5
2.0
0200400600800
10001200
(a)
Mean annual diameter increment (cm)
Fre
quen
cy
−0.
5
0.0
0.5
1.0
1.5
2.0
050
100150200250300350
(b)
FIGURE V - 2
212
FIGURE V - 3
213
2500
2700
2900
3100
01234
W.
bang
ii
Alti
tude
(m
)
Diameter growth (mm)
3200
3300
3400
01234
W.
mic
roph
ylla
Alti
tude
(m
)
Diameter growth (mm)
2500
2700
2900
01234
W.
retic
ulat
a
Alti
tude
(m
)
Diameter growth (mm)
sapl
ing
(und
erst
ory)
sapl
ing
(gap
)ca
nopy
adu
lt
FIGURE V - 4
214
2000 2500 3000
01
23
4
Altitude (meters)
Mea
n an
nual
dia
met
er in
crem
ent (
mm
)
●
●
●
●
●
●
●
215
pinnata
lechleriana
ovata
multijuga
mariquitae
reticulata
bangii
crassifolia
microphylla
●
●
●
●
●
●
●
●
●
−0.6 0.2
Altitude
parameter value
●
●
●
●
●
●
●
●
●
−0.10 0.10
Canopy status
parameter value
●
●
●
●
●
●
●
●
●
−0.10 0.10
DBH
parameter value
FIGURE V - 6
216
FIGURE V - 7
217
2000 2500 3000
0.0
0.1
0.2
0.3
0.4
0.5
Altitude (meters)
Mea
n di
amet
er in
crem
ent (
cm)
●
●
●
●
●
●
●
●
●
FIGURE V - 8
218
● ● ● ● ● ●
−0.
100.
000.
10
microphylla
Par
amet
er V
alue
2003
2004
2005
2006
2007
2008
87 94 94 146 227 221
● ● ●● ● ●
−0.
100.
000.
10
crassifolia
Par
amet
er V
alue
2003
2004
2005
2006
2007
2008
311 311 311 666 731 714
● ● ●● ● ●
−0.
100.
000.
10
bangii
Par
amet
er V
alue
2003
2004
2005
2006
2007
2008
236 242 242 328 370 364
● ● ● ● ● ●
−0.
100.
000.
10
reticulata
Par
amet
er V
alue
2003
2004
2005
2006
2007
2008
169 170 170 307 450 434
● ● ●●
● ●
−0.
100.
000.
10
mariquitae
Par
amet
er V
alue
2003
2004
2005
2006
2007
2008
13 13 13 17 18 18
● ● ●● ●
●
−0.
100.
000.
10
multijuga
Par
amet
er V
alue
2003
2004
2005
2006
2007
2008
33 33 33 49 85 82
● ● ● ● ● ●
−0.
100.
000.
10
ovata
Par
amet
er V
alue
2003
2004
2005
2006
2007
2008
44 44 44 84 208 202
● ● ●● ●
●
−0.
100.
000.
10
lechleriana
Par
amet
er V
alue
2003
2004
2005
2006
2007
2008
10 10 10 48 106 104
● ● ● ● ●
●
−0.
100.
000.
10
pinnata
Par
amet
er V
alue
2003
2004
2005
2006
2007
2008
1 1 1 10 35 35
FIGURE V - 9
219
FIGURE V - 10
220
Appendix I
Tables of priors and posterior parameter estimates for each species analyzed with the Bayesian state space tree growth model.
221
Table 1. Prior and Posterior parameter estimates for Weinmannia microphylla.
Credible interval
Prior Parameter Values
Parameter
No. Obs.
Posterior mean
Bayesian SE 2.5% 97.5% par1 par2
Prior mean
Intercept 869 0.335 0.196 -0.0451 0.727 0 10 0
Random effect Individual 260 0.0107 0.00124 0.00847 0.0133 2 0.4 0.4 Error sources
Diameter 776 0.0669 0.00252 0.0623 0.0721 1550 62 0.04 Increment 157 0.00051 0.0000465 0.000427 0.000608 157 0.0624 0.0004 Process 869 0.00308 0.000352 0.00242 0.0038 10 0.09 0.01
Fixed year effects
2003 87 -0.00505 0.0122 -0.029 0.0187 NA NA NA 2004 94 -0.00387 0.0118 -0.0271 0.0194 NA NA NA 2005 94 -0.00501 0.0117 -0.0278 0.0178 NA NA NA 2006 146 0.00366 0.0099 -0.0156 0.0231 NA NA NA 2007 227 0.00556 0.00866 -0.0114 0.0226 NA NA NA 2008 221 0.00472 0.00868 -0.0122 0.0217 NA NA NA
Covariates Canopy Status 869 0.00279 0.0089 -0.0148 0.0204 0 10 0 DBH 869 -0.00378 0.00519 -0.0139 0.00634 0 10 0 Altitude 869 -0.0578 0.0591 -0.176 0.0571 0 10 0
222
Table 2. Prior and Posterior parameter estimates for Weinmannia crassifolia.
Credible interval
Prior Parameter Values
Parameter
No. Obs.
Posterior mean
Bayesian SE 2.5% 97.5% par1 par2
Prior mean
Intercept 3040 0.888 3.66 -6.3 8.05 0 10 0 Random effect Individual 811 0.00445 0.000314 0.00386 0.00509 2 0.4 0.4 Error sources
Diameter 2510 0.0518 0.00114 0.0496 0.0541 5020 201 0.04 Increment 119 0.000469 4.68E-05 0.000386 0.000569 119 0.0472 4.00E-04 Process 3040 0.000834 6.05E-05 0.000724 0.000961 10 0.09 0.01
Fixed year effects
2003 311 -0.005 0.00398 -0.0129 0.00273 NA NA NA 2004 311 -0.00511 0.00398 -0.013 0.00263 NA NA NA 2005 311 -0.00504 0.00398 -0.0128 0.00275 NA NA NA 2006 666 0.00517 0.00302 -0.00078 0.0111 NA NA NA 2007 731 0.00529 0.00294 -0.00045 0.0111 NA NA NA 2008 714 0.00469 0.00298 -0.00112 0.0105 NA NA NA
Covariates Canopy Status 3040 0.0179 0.0034 0.0112 0.0245 0 10 0 DBH 3040 -0.028 0.004 -0.0358 -0.0199 0 10 0 Altitude 3040 -0.256 1.21 -2.62 2.12 0 10 0
223
Table 3. Prior and Posterior parameter estimates for Weinmannia bangii.
Credible interval
Prior Parameter Values
Parameter
No. Obs.
Posterior mean
Bayesian SE 2.5% 97.5% par1 par2
Prior mean
Intercept 1780 -0.275 0.0443 -0.362 -0.188 0 10 0
Random effect Individual 427 0.0114 0.00104 0.00959 0.0137 2 0.4 0.4 Error sources
Diameter 1410 0.0517 0.00139 0.0491 0.0546 2820 113 0.04 Increment 310 0.000458 0.0000284 0.000405 0.000517 310 0.124 0.0004 Process 1780 0.00162 0.000137 0.00136 0.0019 10 0.09 0.01
Fixed year effects
2003 236 -0.00151 0.00713 -0.0154 0.0124 NA NA NA 2004 242 -0.00461 0.00698 -0.0183 0.00907 NA NA NA 2005 242 -0.00513 0.00705 -0.019 0.0087 NA NA NA 2006 328 0.00522 0.00625 -0.00703 0.0175 NA NA NA 2007 370 0.00461 0.00599 -0.00718 0.0164 NA NA NA 2008 364 0.00142 0.00607 -0.0106 0.0134 NA NA NA
Covariates Canopy Status 1780 0.0402 0.00617 0.028 0.0522 0 10 0 DBH 1780 -0.0125 0.0042 -0.0207 -0.00422 0 10 0 Altitude 1780 0.124 0.0138 0.0974 0.151 0 10 0
224
Table 4. Prior and Posterior parameter estimates for Weinmannia reticulata.
Credible interval
Prior Parameter Values
Parameter
No. Obs.
Posterior mean
Bayesian SE 2.5% 97.5% par1 par2
Prior mean
Intercept 1700 0.0256 0.0532 -0.0793 0.131 0 10 0
Random effect Individual 490 0.00892 0.000758 0.00753 0.0105 2 0.4 0.4 Error sources
Diameter 1600 0.0491 0.00129 0.0466 0.0516 3200 128 0.04 Increment 277 0.000428 0.0000275 0.000378 0.000485 277 0.11 0.0004 Process 1700 0.00146 0.000126 0.00123 0.00172 10 0.09 0.01
Fixed year effects
2003 169 -0.00315 0.00747 -0.0178 0.0115 NA NA NA 2004 170 -0.00491 0.00741 -0.0196 0.00957 NA NA NA 2005 170 -0.00468 0.00745 -0.0192 0.00984 NA NA NA 2006 307 0.00176 0.006 -0.0101 0.0136 NA NA NA 2007 450 0.00506 0.00529 -0.00526 0.0154 NA NA NA 2008 434 0.00593 0.00526 -0.00442 0.0163 NA NA NA
Covariates Canopy Status 1700 0.0253 0.00607 0.0134 0.0372 0 10 0 DBH 1700 -0.0177 0.00419 -0.0259 -0.00953 0 10 0 Altitude 1700 0.0373 0.0192 -0.00033 0.0752 0 10 0
225
Table 5. Prior and Posterior parameter estimates for Weinmannia mariquitae.
Credible interval
Prior Parameter Values
Parameter
No. Obs.
Posterior mean
Bayesian SE 2.5% 97.5% par1 par2
Prior mean
Intercept 92 0.0183 4.18 -8.14 8.3 0 10 0
Random effect Individual 25 0.0455 0.0152 0.0244 0.0824 2 0.4 0.4 Error sources
Diameter 76 0.0633 0.00841 0.0483 0.0813 152 6.04 0.04 Increment 23 0.000394 0.0000834 0.000262 0.00059 23 0.0088 0.0004 Process 92 0.00397 0.000762 0.00273 0.0057 10 0.09 0.01
Fixed year effects
2003 13 -0.000293 0.0571 -0.112 0.113 NA NA NA 2004 13 -0.00453 0.0571 -0.118 0.108 NA NA NA 2005 13 -0.00362 0.0572 -0.117 0.109 NA NA NA 2006 17 0.017 0.0524 -0.0854 0.121 NA NA NA 2007 18 -0.00858 0.0507 -0.109 0.0917 NA NA NA 2008 18 -2.59E-05 0.0502 -0.0991 0.0984 NA NA NA
Covariates Canopy Status 92 0.0526 0.0398 -0.0258 0.131 0 10 0 DBH 92 -0.0569 0.0395 -0.136 0.0196 0 10 0 Altitude 92 0.0362 1.54 -3.01 3.03 0 10 0
226
Table 6. Prior and Posterior parameter estimates for Weinmannia multijuga.
Credible interval
Prior Parameter Values
Parameter
No. Obs.
Posterior mean
Bayesian SE 2.5% 97.5% par1 par2
Prior mean
Intercept 315 0.352 0.441 -0.504 1.22 0 10 0
Random effect Individual 96 0.0287 0.00526 0.0201 0.0405 2 0.4 0.4 Error sources
Diameter 304 0.061 0.00376 0.054 0.0687 608 24.3 0.04 Increment 63 0.00047 0.0000664 0.000356 0.000616 63 0.0248 0.0004 Process 315 0.00631 0.00112 0.00443 0.00882 10 0.09 0.01
Fixed year effects
2003 33 -0.00458 0.0315 -0.0663 0.057 NA NA NA 2004 33 -0.00709 0.0313 -0.0685 0.0544 NA NA NA 2005 33 -0.00581 0.0314 -0.0674 0.0553 NA NA NA 2006 49 0.00911 0.0273 -0.0445 0.0626 NA NA NA 2007 85 0.0145 0.0222 -0.029 0.0582 NA NA NA 2008 82 -0.00617 0.0225 -0.05 0.0379 NA NA NA
Covariates Canopy Status 315 0.0425 0.0211 0.000886 0.0836 0 10 0 DBH 315 -0.0192 0.0128 -0.0445 0.00567 0 10 0 Altitude 315 -0.0721 0.194 -0.452 0.304 0 10 0
227
Table 7. Prior and Posterior parameter estimates for Weinmannia ovata.
Credible interval
Prior Parameter Values
Parameter
No. Obs.
Posterior mean
Bayesian SE 2.5% 97.5% par1 par2
Prior mean
Intercept 626 -0.568 0.292 -1.15 -0.0106 0 10 0
Random effect Individual 209 0.00912 0.00113 0.00715 0.0116 2 0.4 0.4 Error sources
Diameter 685 0.0433 0.00126 0.0409 0.0458 1370 54.8 0.04 Increment 58 0.000436 0.000061 0.000332 0.000571 58 0.0228 0.0004 Process 626 0.00199 0.000253 0.00156 0.00256 10 0.09 0.01
Fixed year effects
2003 44 -0.00303 0.0152 -0.0328 0.0268 NA NA NA 2004 44 -0.00328 0.0152 -0.0329 0.0265 NA NA NA 2005 44 -0.00265 0.0151 -0.0324 0.0271 NA NA NA 2006 84 0.00284 0.0121 -0.0207 0.0266 NA NA NA 2007 208 0.00497 0.00922 -0.0132 0.0231 NA NA NA 2008 202 0.00114 0.00924 -0.0168 0.0192 NA NA NA
Covariates Canopy Status 626 0.0101 0.00919 -0.00789 0.0281 0 10 0 DBH 626 -0.0636 0.0118 -0.0873 -0.0408 0 10 0 Altitude 626 0.363 0.145 0.0862 0.652 0 10 0
228
Table 8. Prior and Posterior parameter estimates for Weinmannia lechleriana.
Credible interval
Prior Parameter Values
Parameter
No. Obs.
Posterior mean
Bayesian SE 2.5% 97.5% par1 par2
Prior mean
Intercept 288 0.591 0.235 0.139 1.06 0 10 0
Random effect Individual 109 0.0182 0.00314 0.013 0.0251 2 0.4 0.4 Error sources
Diameter 351 0.0478 0.00238 0.0434 0.0527 702 28 0.04 Increment 24 0.000416 0.0000908 0.000275 0.000624 24 0.0092 0.0004 Process 288 0.00562 0.000969 0.004 0.00777 10 0.09 0.01
Fixed year effects
2003 10 -0.000688 0.0466 -0.0919 0.0911 NA NA NA 2004 10 -0.00903 0.0466 -0.101 0.0824 NA NA NA 2005 10 -0.00823 0.0468 -0.102 0.0829 NA NA NA 2006 48 0.0106 0.0273 -0.0431 0.0644 NA NA NA 2007 106 0.0115 0.023 -0.0336 0.0571 NA NA NA 2008 104 -0.00407 0.0227 -0.0487 0.0405 NA NA NA
Covariates Canopy Status 288 -0.00293 0.0238 -0.0479 0.0452 0 10 0 DBH 288 -0.00482 0.0248 -0.0536 0.0437 0 10 0 Altitude 288 -0.193 0.122 -0.434 0.0434 0 10 0
229
Table 9. Prior and Posterior parameter estimates for Weinmannia pinnata.
Credible interval
Prior Parameter Values
Parameter
No. Obs.
Posterior mean
Bayesian SE 2.5% 97.5% par1 par2
Prior mean
Intercept 83 0.911 0.636 -0.345 2.16 0 10 0
Random effect Individual 36 0.038 0.0106 0.0224 0.0636 2 0.4 0.4 Error sources
Diameter 103 0.0511 0.00627 0.0409 0.065 206 8.2 0.04 Increment 6 0.000416 0.000206 0.000177 0.000945 6 0.002 0.0004 Process 83 0.00693 0.00188 0.00418 0.0114 10 0.09 0.01
Fixed year effects
2003 1 0.00255 0.195 -0.379 0.389 NA NA NA 2004 1 -0.000936 0.194 -0.382 0.383 NA NA NA 2005 1 0.00207 0.195 -0.382 0.386 NA NA NA 2006 10 0.0024 0.0945 -0.183 0.189 NA NA NA 2007 35 0.00626 0.0827 -0.155 0.169 NA NA NA 2008 35 -0.0123 0.082 -0.173 0.149 NA NA NA
Covariates Canopy Status 83 0.0745 0.0771 -0.0605 0.248 0 10 0 DBH 83 -0.0493 0.0378 -0.122 0.0254 0 10 0 Altitude 83 -0.412 0.333 -1.07 0.241 0 10 0
230
CHAPTER VI
CONCLUSION
High biodiversity in the Amazon and tropical Andes is threatened by rising temperatures,
and forest dynamics are an important feedback in the global carbon cycle. Understanding
the response of tropical plants to climate variability is therefore important for
understanding the implications and trajectory of climate change. In these studies, I
examined the response of cloud forest plants to climate along an altitudinal gradient in
the eastern Andes of southern Peru, with an aim of understanding climate controls on
plant demographic performance (survival and growth). Together, these studies provide
insight into the functioning of a tropical montane cloud forest and the short-term (years to
decades) response of plant species to ongoing climate change in this globally important
ecosystem.
The microclimate of the Kosñipata Valley (Chapter II), Peru, is controlled by three
factors: 1) the 4000 meter altitudinal gradient, 2) the site's position within the tropics,
with associated relative constancy of day length throughout the year, and 3) the
interaction of two air masses, moist air over the Amazon basin to the east and dry air over
the high Andes to the west. During this study, distinct diurnal cycles remained fairly
stable throughout the year. Mornings were the sunniest part of the day and daytime winds
blew upslope, bringing warm, moist air from the Amazon basin into the mountains. The
rising air lead to the formation of clouds, and rain was more common in the afternoon
and overnight hours. Colder, drier air over the Andes flowed with gravity at night,
231
creating downslope winds and clearing skies by morning. Seasonal cycles were driven
largely by changes in moisture flux across the Amazon, resulting in large differences in
rainfall between the wettest and driest months and smaller, although perhaps more
important, changes in cloudiness throughout the year. Cloudiness influenced solar
radiation, temperature, and vapor pressure deficit, which were important for driving
patterns in tree growth (Chapter IV). Altitudinal trends were strong across the gradient,
with an overall lapse rate of 5.2ºC/km and decreasing rain fall with altitude, although all
altitudes were consistently wet throughout the year. Other climatic variables also varied
smoothly across the altitudinal gradient. Seasonality was consistent across the altitudinal
gradient, except at the highest the highest altitude, where solar radiation peaked earlier in
the year than at lower altitudes, presumably because of differences in cloudiness
seasonality.
While the view of microclimate smoothly varying with altitude presented here is
dissimilar to sharper climatic discontinuities seen on other well studied tropical
mountains (e.g. Kitayama and Mueller-Dombois 1994), biotic changes at putative 'cloud
base' are similar to those observed in most tropical montane forests (Leigh 1975, Grubb
1977). Biomass carbon stocks (Girardin et al. 2010), soil properties (Zimmermann et al.
2009), and epiphytic bryophyte abundance ( A. Howrath, unpublished data) all exhibit a
step change at 1500-1800 meters. An experiment where epiphyte mats were reciprocally
transplanted between three altitudes (1500, 1650, and 1800 meters) was undertaken to
test whether a gradient in cloud immersion hypothesized to exist across this altitudinal
range controls vascular epiphyte survival and distributions (Chapter III). Epiphytes
transplanted downslope from the highest altitude had reduced survival and recruitment,
232
consistent with a previous study in another neotropical cloud forest (Nadkarni and Solano
2002). However, mats transplanted from other altitudes showed a diversity of responses,
and there were no consistent directional shifts in community composition due to any of
the experimental treatments. This may indicate that vascular epiphytes at lower altitudes
are adapted to survive drought on short (one year or less) time scales, or that the high (but
variable) rainfall in this system means that cloud immersion is less important for epiphyte
survival than in other cloud forests.
Cloud forest trees, like vascular epiphytes, are expected to respond to climate change.
Growth is a useful metric of plant performance because it a measure of the plant's net
uptake of resources, and is correlated with both long-term total fecundity and mortality
risk. Two studies in this dissertation examined the tree growth response to climate
variability. The first (Chapter IV) showed that growth phenology in trees, from 900
meters at the base of the Andean slope to 3400 meters near tree line, is correlated with
periods of high insolation, and not with rainfall as is typically seen in the lowland tropics
(Clark et al. 2010). This suggests that trees in these pluvial forests are light limited, and
that seasonal patterns of cloudiness exert control on tree growth. Since climate change is
expected to change regional moisture flux, altered cloud dynamics could change growth
phenology in the Andes. This in turn could affect species distributions and ecosystem
carbon dynamics.
While growth phenology was consistent across the altitudinal gradient, annual growth
rate did change with altitude (Chapter V). Primary productivity has been observed to
increase with temperature in the wet tropics (Raich et al. 2006), so a negative relationship
between tree growth and altitude was expected in the Kosñipata Valley. When comparing
233
species of Weinmannia, the expected trend was observed, with species growing at higher
altitude having lower growth rates than those that grow at lower altitude. Within species,
however, growth rate did not decrease with altitude, and for some species growth actually
increased significantly with altitude. These results suggest that a trade-off between
growth rate and the ability of a species to complete its life cycle at lower temperatures
may contribute to determining a species altitudinal niche. It also suggests that a positive
response of ecosystem productivity to increasing temperature will be dependent on the
migration of new, warmer niche species into the community. Since most tree species are
slow growing and long-lived, there may be a considerable lag in the ecosystem response
to increasing temperatures, even under a scenario of perfect migration.
Together these studies show that the demographic performance of plant species in the
eastern Andes is constrained by climate variability, but that the relationship is not straight
forward. Ecosystem response to climate change is likely to be slow, both because
ecosystem response is dependent on species migration, and species are adapted to the
inherent climatic variability of the system. While communities have tracked changes in
climate variability in the past (Bush et al. 2004), current slow rates of community
migration (Feeley et al. in press), and these current results, suggest a lag in the response
of cloud forest ecosystems to climate change. Ecosystem properties depend on the
species composition of communities, and species respond to climate variability in ways
that are often idiosyncratic. Species ranges, and shifts in them due to environmental
changes, are population phenomena and as such depend on the summed demographic
rates across the life-cycles of individuals. Further studies linking growth, survival, and
234
fecundity across species altitudinal ranges are required to predict species response to
climate change.
235
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Nadkarni, N. M. and R. Solano. 2002. Potential effects of climate change on canopy
communities in a tropical cloud forest: an experimental approach. Oecologia
131:580-586.
Raich, J. W., A. E. Russell, K. Kitayama, W. J. Parton, and P. M. Vitousek. 2006.
Temperature influences carbon accumulation in moist tropical forests. Ecology
87:76-87.
Zimmermann, M., P. Meir, M. I. Bird, Y. Malhi, and A. J. Q. Ccahuana. 2009. Climate
dependence of heterotrophic soil respiration from a soil-translocation experiment
along a 3000 m tropical forest altitudinal gradient. European Journal of Soil
Science 60:895-906.
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Joshua M. Rapp
Doctoral Candidate in Biology T: 336-758-3967; E: [email protected]; W: http://www.wfu.edu/~rappjm4/
Department of Biology Wake Forest University 1834 Wake Forest Road
Winston-Salem, North Carolina 27106
RESEARCH INTERESTS
Plant Ecology, Global Change Ecology, Plant-Animal Interactions, Community Ecology, Conservation Biology, Forest Dynamics, Ecosystem Ecology
EDUCATION
Ph.D. Candidate Biology, Wake Forest University (2010 – expected)
* Advisor: Miles R. Silman * Climate control on plant performance across an Andean altitudinal gradient
M.S. Forestry, University of Vermont (2003) * Advisor: Deane Wang * Ecological community mapping using the National Vegetation Classification System at
the Lake Umbagog National Wildlife Refuge: an evaluation of methodology B.S. Geology, Duke University (1997)
* Cum Laude * Thomas V. Laska Award for outstanding graduating Geology major
Study Abroad, School for International Training: India, Nepal, Bhutan (1996)
* Tibetan Studies Geology Field Camp, Indiana University: Montana (1996) PROFESSIONAL EXPERIENCE
Research Assistant: University of Montana (2004): Snowshoe hare ecology Intern (Botany): Ashoka Trust for Research in Ecology and the Environment (ATREE) (2004):
Epiphyte diversity Botanist (GS-5): USDA Forest Service, Northeastern Research Station (2004): Vascular plant
diversity
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Ecologist: LIA Consultants (2002): Ecological community mapping and vascular plant diversity Research Assistant: University of California, Berkley (2001): Marbled Murrelet ecology Research Assistant: University of Massachusetts, Amherst (2000-2001): Darwin's fox ecology Geologist (GS-5): United States Geological Survey (NAGT_USGS Cooperative Summer Field
Training Program) (1997): Paleolimnology TEACHING and MENTORING EXPERIENCE
Teaching Assistant: Wake Forest University, Department of Biology (2005-2010)
* Biology & the Human Condition * Biological Principles (3x) * Ecology and Evolutionary Biology (3x) * Community Ecology
Teaching Assistant: University of Vermont, School of Natural Resources (2001-2003)
* Natural History and Field Ecology (2x) * Introduction to Environmental Science * Biostatistics
Teaching Assistant: University of Vermont, Biology Department (2003)
* Insect Biodiversity Guest Lecturer: Wake Forest University, Department of Biology (2009)
* Tropical Ecology Mentor for Undergraduate Research (2005-2010)
* Richard Amick, Salem College, USA * Mitchel Buder, Andrew Collins, Oran Basel, Roger Kirkpatrick, Wake Forest University,
USA * Darcy Galiano, Richard Tito, Universidad San Antonio Abad, Perú * Students in Computer Science 331: Object-oriented software engineering (iPhone app for
tree surveys), Wake Forest University, USA Teacher, 3rd Grade: Mayatan School, Copan Ruinas, Honduras (1998-1999) AmeriCorps member: Student Conservation Association New Hampshire Parks AmeriCorp program (1997-1998)
* Environmental educator in Concord and Manchester, NH schools and after-school programs
GRANTS, AWARDS, and OTHER ACTIVITIES
OTS-PASI, Expanding the Frontier in Tropical Ecology Through Embedded Sensors, La Selva Costa Rica (2010) Alumni Student Travel Award, Wake Forest University, Graduate School (2008, 2009) Elton C. Cocke Travel Award, Wake Forest University, Biology Department (2008, 2009)
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Vecellio Grant for Graduate Research, Wake Forest University (2005, 2009) Monetary Award for contribution to the Western Lakes Project, United States Geological Survey (1997) SERVICE
Manuscipt reviews: Journal of the Torrey Botanical Society, Journal of Environmental
Management, American Journal of Botany, Ecospheres Organizer: Ecolunch, Department of Biology Wake Forest University (2009) Member of Ecological Society of America (2004-present)
PUBLICATIONS Rapp, J.M. and M.R. Silman. Epiphyte response to transplantation across altitude and drought: Cloud forest resilience under climate change. Journal of Biogeography. In review. Fierer, N., C.M. McCain, P. Meir, M. Zimmerman, J.M. Rapp, M.R. Silman, R. Knight. Microbes do not follow the elevational diversity patterns of plants and animals. Ecology. In review. Girardin, C.A.J., Y. Malhi, L.E.O.C. Aragão, M. Mamani, W. Huaraca Huasco, L. Durand, K. J. Feeley, J. Rapp, J.E. Silva-Espejo, M. Silman, N. Salinas, R. J. Whittaker. 2010. Net primary productivity allocation and cycling of carbon along a tropical forest elevational transect in the Peruvian Andes. Global Change Biology. 16: 3176-3192.
Meier, C.L., J. Rapp, R. Bowers, M.R. Silman, and N. Fierer. 2010. Fungal growth on a common wood substrate across a tropical elevation gradient: temperature sensitivity, community composition, and potential for above-ground decomposition. Soil Biology & Biochemistry. 42: 1083-1090. Rapp, J., D. Wang, D. Capen, E. Thompson, and T. Lautzenheiser. 2005. Evaluating error in using the National Vegetation Classification System for ecological community mapping in northern New England. Natural Areas Journal 25: 46-54. Reynolds, R. L., J. G. Rosenbaum, J. Rapp, M. W. Kerwin, J. P. Bradbury, S. M. Colman and David Adam. 2004. Record of late Pleistocene glaciation and deglaciation in the southern Cascade Range; I, Petrological evidence from lacustrine sediment in Upper Klamath Lake, southern Oregon. Journal of Paleolimnology 31: 217-233. POPULAR ARTICLES and TECHNICAL REPORTS (non-peer reviewed)
Rapp, J. 2003. Ecological Communities of the Lake Umbagog National Wildlife Refuge: Classification and Mapping with the National Vegetation Classification System (report submitted
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to the Trust for Public Land and the U.S. Fish and Wildlife Service.) School of Natural Resources, University of Vermont, Burlington, Vermont, U.S.A. Rapp, J. 2003. A Place in Mind. Northern Woodlands magazine, Corinth, Vermont, U.S.A. Rapp, J. 2003. Costa Rica: Crossroads in the Plant World. Ecolog, Newsletter from the Ecological Planning students, School of Natural Resources, University of Vermont, Burlington, Vermont, U.S.A. http://www.uvm.edu/envnr/ep/ecolog/ecolog2003.pdf Rapp, J. 2002. Kinglets in the Cold: Small Survivors. Ecolog, Newsletter from the Ecological Planning students, School of Natural Resources, University of Vermont, Burlington, Vermont, U.S.A. http://www.uvm.edu/envnr/ep/ecolog/ecolog2002.pdf Barton, J., C. Dacey, J. Hilke, J. Kart, E. Faison, K. Lange, V. Levesque, J. Rapp, and A. Wheeler. 2002. Wheeler and Farm Bureau Tracts: Assessment and Recommendations for the Vermont Youth Conservation Corps. Botany Department, University of Vermont, Burlington, Vermont, U.S.A. PRESENTATIONS Conference Presentations Rapp, J.M. 2010 Growth phenology and climate seasonality in an Andean cloud forest Andes Biodiversity and Ecosystem Research Group Fourth Annual Conference, Marathon, Florida Rapp, J.M. and M.R. Silman. 2009 Diameter growth across an altitudinal gradient in the cloud forest tree genus Weinmannia Ecological Society of America 94th Annual Meeting, Albuquerque, New Mexico Rapp, J.M. and M.R. Silman. 2008. Does adult performance control distributions of Weinmannia? Andes Biodiversity and Ecosystem Research Group Third Annual Conference, Bettmeralp, Switzerland Rapp, J.M. 2002 Ecological Community Mapping of the Lake Umbagog National Wildlife Refuge. Graduate Research Symposium, School of Natural Resources, University of Vermont University Seminars Rapp, J.M. 2010. Tree growth and temperature in the tropics: Analysis across an Andean altitudinal gradient. Harvard Forest. Rapp, J.M. 2010. Demographic performance of cloud forest trees across an altitudinal gradient. Wake Forest University. Departmental Seminar. Rapp, J.M. 2003. Evaluation of ecological community mapping using the National Vegetation Classification System at the Lake Umbagog National Wildlife Refuge. University of Vermont, Master’s project defense
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Conservation and governmental organization meetings Rapp, J.M. 2003. Ecological Communities of the Lake Umbagog National Wildlife Refuge: Classification and Mapping with the National Vegetation Classification System. Lake Umbagog National Wildlife Refuge staff meeting Rapp, J.M. 2003. Natural History of the LaPlatte River. LaPlatte River Watershed Group Meeting Barton, J., Dacey, C., Hilke, J., Kart, J., Faison, E., Lange, K., Levesque, V., Rapp, J., and Wheeler, A. 2002. Landscape Inventory and Assessment of the Wheeler and Farm Bureau Tracts, Richmond, Vermont. Public meeting sponsored by the Richmond Land Trust and Vermont Youth Conservation Corps. Posters Rapp, J.M. and M.R. Silman. 2009. Epiphyte survival across an altitudinal gradient during drought. 5th International Canopy Conference, Bangalore, India. Rapp, J.M., K.J. Feeley, and M.R. Silman. 2009. Using demography to understand distribution limits in the Cloud Forest Tree Genus Weinmannia. Association for Tropical Biology and Conservation, Marburg, Germany Rapp, J.M. and M.R. Silman. 2009. Do natural history collections and plot data predict the same species distributions? Southeastern Ecology and Evolution conference. Gainesville, Florida LANGUAGES
English (native), Spanish (fluent)
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