Functional Diversity and Abundances of the Lemur Community at Ranomafana National Park, Madagascar

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2017 Functional Diversity and Abundances of the Lemur Community at Ranomafana National Park, Madagascar

Houston, Brianna Elyse

Houston, B. E. (2017). Functional Diversity and Abundances of the Lemur Community at Ranomafana National Park, Madagascar (Unpublished master's thesis). University of Calgary, Calgary, AB. doi:10.11575/PRISM/26242 http://hdl.handle.net/11023/4165 master thesis

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Functional Diversity and Abundances of the Lemur Community at Ranomafana National Park,

Madagascar

Brianna Elyse Houston

A THESIS

SUBMITTED TO THE FACULTY OF GRADUATE STUDIES

IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE

DEGREE OF MASTER OF ARTS

GRADUATE PROGRAM IN ANTHROPOLOGY

CALGARY, ALBERTA

SEPTEMBER 2017

ABSTRACT

Lemurs in Madagascar have been facing losses to population and diversity across the country, potentially resulting in a loss of functional and ecosystem diversity. While species diversity has long been studied, functional diversity allows us to more closely examine how abundances and traits of species are distributed in the community. I use lemur surveys conducted in 2004 at eight sites within Ranomafana National Park (RNP) to test the effects of habitat characteristics and anthropogenic disturbance on lemur functional diversity. In addition, I examine whether functional redundancy is present in the lemur community of RNP - that is, do multiple species fill similar functional roles. Niche separation should affect the traits present in the lemur community such that I expected functional redundancy to be low in most locations. Disturbance, elevation, and vegetation characteristics were all important factors in explaining functional diversity metrics. I found that most communities have low functional redundancy across all measures. I also resampled the site Valohoaka in 2015 to examine lemur abundances over time.

The results suggest abundances have remained generally stable, however, Microcebus rufus abundances appear to be rising. Used in conjunction with individual species studies, the information presented here can be useful in understanding what is shaping lemur community composition and the sensitivity of these communities to environmental change. It is important to continue monitoring for long-term population trends and responses to both natural and anthropogenic change.

ACKNOWLEDGEMENTS

I would like to thank Drs. Steig Johnson, Pascale Sicotte, and Peter Dawson for sitting on my thesis defense committee and Dr. Diane Lyons for acting as neutral chair. I would especially like to thank my supervisor, Dr. Steig Johnson, for his guidance and support in pursuing this degree.

I would also like to extend my thanks to Madagascar National Parks for allowing me to conduct research at Ranomafana National Park. As well, I would like to thank Centre ValBio

(CVB) and MICET for logistical support. At CVB, I am especially grateful to Julia

Rasoarimamonjy, Pascal Rabeson, and Prisca Andriambinintsoa, who answered numerous inquiries on logistics. Funding for this project was provided by the University of Calgary,

Government of Alberta, Conservation International, and Steig Johnson’s Natural Sciences and

Engineering Research Council Discovery Grant.

A special thanks goes to Drs. Patricia Wright and Steig Johnson, who allowed me to analyse both lemur and botanical data collected from 2004 in both chapters two and three of this thesis. Thanks also to Drs. Onja Razafindratsima and Kerry Brown who contributed their expertise on functional diversity and data analysis to the second chapter of this thesis. I would also like to thank Tracy Wyman for providing the maps for this thesis. I am also grateful to my fellow University of Calgary graduate students for advice and editing of this thesis as well as grant applications and my research proposal.

I am grateful to my Canadian research assistant, Rebecca Ollenberger, for her hard work during field research. My field season would not have been the same without her dedication to

iii research and her friendship, she was always able to find the humour in any situation. I was lucky to have skilled field technicians, including Justin Solo who identified lemurs, and occasionally bird and amphibians to satisfy my personal curiosity, as well as Pela Auguste who identified trees. I am also indebted to my Malagasy assistants, especially Chantal Hanitriniaina and

Modeste, as well as to the porters who helped move all the equipment and food to Valohoaka.

Finally, my family and friends, I am so grateful for their love and support. My friends, who have provided encouragement and been there to bounce ideas off of. To Nicolas

Rasolonjatovo and Alain Rasolo, for the advice on all things Madagascar, misaotra betsaka.

Karolis Jakaitis, thank you for all of your support right from the beginning, your guidance in data analysis, and hearing more about lemurs than you ever imagined you wanted to. To my brothers, who continue to inspire me, thank you. To my parents, thank you for always being my biggest supporters and encouraging me to dream big.

TABLE OF CONTENTS

ABSTRACT ...... ii ACKNOWLEDGEMENTS ...... iii TABLE OF CONTENTS ...... v LIST OF TABLES ...... viii LIST OF FIGURES AND ILLUSTRATIONS ...... ix Chapter 1 : GENERAL INTRODUCTION ...... 1 1.1 Introduction ...... 1 1.2 Functional Diversity ...... 3 1.2.1 Overview ...... 3 1.2.2 Functional Diversity in the Literature ...... 4 1.3 Study Site ...... 7 1.3.1 Ranomafana National Park ...... 7 1.3.2 Anthropogenic Use ...... 8 1.4 Study Species ...... 10 1.4.1 Species Present ...... 10 1.4.2 Cheirogaleidae ...... 11 1.4.3 Daubentoniidae ...... 12 1.4.4 Indriidae ...... 13 1.4.5 Lemuridae ...... 13 1.4.6 Lepilemuridae ...... 15 1.5 Data Collection ...... 15 1.6 Overview ...... 17 Chapter 2 : TOPOGRAPHY AND VEGETATION CHARACTERISTICS AFFECT FUNCTIONAL DIVERSITY OF A LEMUR COMMUNITY IN SOUTHEAST MADAGASCAR ...... 18 2.1 Introduction ...... 18 2.1.1 Functional Diversity ...... 18 2.1.2 Functional Redundancy ...... 19 2.1.3 Niche Space ...... 20 2.1.4 Anthropogenic Disturbance ...... 20

2.1.5 Vegetation and Topographical Features ...... 22 2.1.6 Objectives ...... 24 2.1.7 Hypotheses and Predictions ...... 24 2.2 Methods ...... 26 2.2.1 Study Site ...... 26 2.2.2 Lemur Surveys ...... 27 2.2.3 Measuring Topographical Features and Vegetation...... 29 2.2.4 Functional Diversity Indices ...... 29 2.2.5 Functional Redundancy Analysis ...... 31 2.2.6 Functional Diversity Indices Analysis ...... 32 2.3 Results ...... 33 2.3.1 Variation in Functional Diversity and Redundancy Across Communities ...... 33 2.3.2 Functional Diversity Indices ...... 35 2.3.3 Functional Diversity ...... 36 2.3.4 Functional Richness ...... 39 2.3.5 Functional Evenness ...... 42 2.3.6 Functional Divergence ...... 45 2.4 Discussion ...... 48 2.4.1 Summary Patterns...... 48 2.4.2 Low Functional Redundancy in Ranomafana ...... 49 2.4.3 Forest Edge Reduces Lemur Functional Diversity ...... 51 2.4.4 High Elevation Increases Multiple Measures of Lemur Functional Diversity ...... 52 2.4.5 Tree Size, Diversity, and Treefalls Affect Lemur Functional Diversity...... 53 2.4.6 Caveats ...... 55 2.4.7 Conclusions ...... 56 Chapter 3 : LONGITUDINAL STUDY OF LEMUR COMMUNITY ABUNDANCES AT VALOHOAKA, RANOMAFANA NATIONAL PARK, MADAGASCAR ...... 57 3.1 Introduction ...... 57 3.1.1 Primate Communities and Habitat ...... 57 3.1.2 Lemur Abundances and Vegetation Characteristics ...... 58 3.1.3 Hypotheses and Predictions ...... 59

3.2 Methods ...... 60 3.2.1 Study Site and Species ...... 60 3.2.2 Lemur Surveys ...... 64 3.2.3 Vegetation Surveys ...... 64 3.2.4 Data Analysis ...... 65 3.3 Results ...... 67 3.3.1 Lemur Diversity and Abundance ...... 67 3.3.2 Habitat Structure ...... 69 3.4 Discussion ...... 70 3.4.1 Stability in Lemur Populations ...... 70 3.4.2 Frugivore Abundance ...... 71 3.4.3 Caveats ...... 72 3.4.4 Conclusions ...... 73 Chapter 4 : GENERAL DISCUSSION ...... 74 4.1 Summary of Objectives and Results ...... 74 4.2 Significance and Applications ...... 75 4.3 Future Research Directions ...... 76 REFERENCES ...... 80 APPENDIX A: TRAITS, LEMUR ABUNDANCES AND FUNCTIONAL DIVERSITY .. 94 APPENDIX B: LINEAR MIXED MODELS AND VARIABLES ...... 102 APPENDIX C: VALOHOAKA LEMUR DENSITY AND ABUNDANCE ...... 106 APPENDIX D: VUONG TESTS ...... 109

vii

LIST OF TABLES

Table 1.1 Lemur species present at Ranomafana National Park, Madagascar ...... 11

Table 2.1 Survey effort (kilometres walked) at each site during diurnal and nocturnal lemur surveys ...... 29

Table 2.2 Functional diversity metrics of the lemur community at eight sites in Ranomafana National Park ...... 35

Table 2.3 Comparison of fit of models for functional diversity ...... 36

Table 2.4 Comparison of fit of models for functional richness ...... 39

Table 2.5 Comparison of fit of models for functional evenness ...... 42

Table 2.6 Comparison of fit of model for functional divergence ...... 45

Table 3.1 Lemurs present at Valohoaka in Ranomafana National Park, Madagascar ...... 63

Table 3.2 Generalized linear model with a Poisson distribution on the effect of year on lemur group counts ...... 68

Table 3.3 Habitat characteristics measured in vegetation surveys at Valohoaka, Ranomafana National Park ...... 70

viii

LIST OF FIGURES AND ILLUSTRATIONS

Figure 1.1 Glossary to distinguish between terms used in this thesis with examples ...... 6

Figure 1.2 Photographs of the forest at Ranomafana National Park, Madagascar ...... 9

Figure 1.3 Pictures of four of the 13 lemur species present at Ranomafana National Park, Madagascar ...... 16

Figure 2.1 Map of Madagascar with an inset of Ranomafana National Park and the eight sites used for surveys ...... 27

Figure 2.2 Scatterplots showing results from each functional diversity metric and the null model analyses...... 34

Figure 2.3 Relationship between location (edge vs. interior) and mean functional diversity (FD) ...... 37

Figure 2.4 Relationship between functional diversity and fallen tree density...... 38

Figure 2.5 Relationship between functional richness and fallen tree density...... 40

Figure 2.6 Relationship between functional richness and elevation...... 41

Figure 2.7 Relationship between functional evenness and tree diversity ...... 43

Figure 2.8 Relationship between functional evenness and elevation ...... 44

Figure 2.9 Relationship between functional divergence and tree diameter at 1.3 m ...... 46

Figure 2.10 Relationship between functional divergence and tree diversity...... 47

Figure 2.11 Relationship between functional divergence and elevation ...... 48

Figure 3.1 Map of Ranomafana National Park with an inset of Valohoaka transect sites ...... 61

Figure 3.2 Group encounter rates of lemurs at Valohoaka, Ranomafana National Park...... 69

Chapter 1 : GENERAL INTRODUCTION

1.1 Introduction

Primates across the globe are facing risk of extinction, with declining abundance of individual species and overall diversity (Chapman et al. 2006; Danquah and Tetteh 2016; Estrada et al. 2017; Schwitzer et al. 2014). The primate communities of Madagascar are especially threatened in the face of deforestation and habitat degradation, hunting, and climate change

(Golden 2009; Razafimanahaka et al. 2012; Schwitzer et al. 2014). Illegal logging and clearing of the forest remain a large source of habitat loss and modification, with additional contributions from livestock grazing, bamboo harvesting, and mining (Schwitzer et al. 2014). While the national parks established in Madagascar help to protect habitat and species (Wright et al. 2012), they may still face anthropogenic threats. For example, gold mining, as well as conversion of forest into farmland, has occurred recently within the boundaries of Ranomafana National Park

(RNP; Sarrasin 2013; Vuola 2015).

The general trend among lemurs is decreasing abundance (IUCN 2017). Just as in other primate communities, lemurs have a complex relationship with environmental heterogeneity, including that caused by habitat disturbances. Some species appear to be tolerant of a low level of disturbance while others can only be found in pristine conditions (Balko and Underwood

2005; Ganzhorn et al. 1997; Lehman et al. 2006a). Primate diversity and abundances are shaped by both plant diversity and resource availability (Hanya and Chapman 2013; Rovero and

Struhsaker 2007; Stevenson 2001), as well as anthropogenic factors including hunting and habitat degradation (Remis and Jost Robinson 2012; Rovero et al. 2012). One of the most important factors in lemur abundance and diversity is resource availability (Herrera 2017). Low levels of habitat disturbance by logging, for instance, can increase lemur species richness and

1 density as the increased sunlight in forest gaps can lead to higher protein levels in leaves and increased fruit production (Ganzhorn 1995; Ganzhorn et al. 1997). Gumnivores may also benefit from low levels of logging as the damaged trees have increased sap and gum production

(Ganzhorn 1995). This is a level of logging that would probably mimic natural tree falls. At higher levels of disturbance, responses differ among lemur species, with frugivores in particular being less abundant (Herrera et al. 2011).

In addition to habitat disturbance, lemur abundances may be affected by specific natural features of their habitat such as tree size, density, plant diversity, or dead trees (Balko and

Underwood 2005; Ganzhorn and Schmid 1998; Lehman et al. 2006b). Rather than a generalized pattern, these relationships are species-specific and relate to resource requirements. For example,

Microcebus murinus abundances may be affected by availability of suitable sleep sites

(Ganzhorn and Schmid 1998). Varecia variegata editorum abundance has been related to the presence of food trees (Balko and Underwood 2005), while Cheirogaleus major density has been related to tree diameter, which may affect food production (Lehman et al. 2006b). These natural patterns of abundance are important to consider in addition to human-caused disturbances, as both can contribute to abundance and distribution changes in lemurs (Lehman et al. 2006c).

The risks that lemurs face make it important to understand how diversity and abundance of lemurs are impacted by variation in forest characteristics. In this study, I investigate how lemur functional diversity is influenced by vegetation and topographical characteristics, as well as how lemur abundances change over time. In the following sections, I will give an overview of functional diversity, give an overview of the study site and species, and finally, I will present a general outline of the thesis.

1.2 Functional Diversity

1.2.1 Overview

While many measures can be used to study diversity, it is most commonly estimated by using the species present in the area (i.e., species richness) or species diversity (i.e., richness weighted by abundance). Functional diversity uses the abundance and diversity of functional traits in the community to estimate biodiversity (Díaz and Cabido 2001; Vandewalle et al. 2010).

Approaching the ecosystem as being made up of functional roles that are filled by species puts an emphasis on conserving ecosystem goods, services, and processes that preserve biodiversity

(Díaz et al. 2006; Farias and Svensson 2014). The traits should reflect what is functionally important to the study question or there is a risk in artificially inflating functional diversity by using traits that are not relevant (Petchey and Gaston 2006). Traits chosen may relate to how a species lives, interacts, competes, or how a species contributes to ecosystem functions (Cadotte et al. 2011). Care needs to be taken in choosing functional traits so that they are appropriate for the study community and are measurable (Petchey and Gaston 2006).

Functional diversity may be separated into three main components: functional richness, functional evenness, and functional divergence (Mouchet et al. 2010; See Figure 1.1). Each of these measures a different component of functional diversity. Species richness is often used as a surrogate for functional richness, but it can both over- or underestimate functional richness (Díaz and Cabido 2001). Functional richness measures the amount of traits present, while evenness and divergence take into account the abundance of these traits (Villéger et al. 2008). Evenness and divergence differ in that evenness examines how evenly abundance is spread across the traits present while divergence measures where this abundance in traits is concentrated (i.e., are the most abundant traits near the centre of the trait range or not; Villéger et al. 2008). In this thesis, I

3 examine both a measure of overall functional diversity, as well as these three distinct components of functional diversity (Petchey and Gaston 2006; Villéger et al. 2008).

1.2.2 Functional Diversity in the Literature

By grouping species by functions, we can reveal patterns that would not be observed when using species alone (Vandewalle et al. 2010). For example, comparisons can be made between the biodiversity of different regions as it is not dependent on the biogeography and species but rather on the traits present (Farias and Svensson 2014). Functional diversity can also be applied to traits that are found in different trophic levels. Moretti and Legg (2009) used traits related to surviving disturbance in plants and invertebrates to examine the community response to disturbance.

Functional diversity has been explored in many different plant communities (Díaz and

Cabido 2001; Mayfield et al. 2005; Razafindratsima et al. 2017). More recently, functional diversity measures have been applied to vertebrate communities, including fish (Pool et al. 2010;

Villéger et al. 2010), amphibians (Strauß et al. 2010), birds (Barbaro et al. 2014), carnivores

(Farias and Svensson 2014), and primates (Herrera 2016, 2017; Razafindratsima et al. 2013).

Razafindratsima et al. (2017) examined tree communities in Madagascar by separating the functional diversity analysis into two categories, dispersal traits and productivity traits. They found lower diversity in productivity traits near the forest edge, but not for dispersal traits. This highlights the importance of trait selection and how it can affect the results. A study on tadpoles in streams in RNP found that functional diversity was correlated with species richness (Strauß et al. 2010). However, in a continent-wide study on carnivores in South America, researchers revealed that there was higher than expected functional richness and functional evenness in species-rich regions (Farias and Svensson 2014). Functional divergence was found to shift from

4 being higher than expected to lower than expected as species richness decreased (Farias and

Svensson 2014). Recently, Herrera (2016) examined phylogenetic diversity (a measure of evolutionary distance within a set of taxa) and functional diversity within Ranomafana National

Park and found that there was a positive relationship between the two. Herrera (2017) also investigated functional diversity for lemur communities in protected areas across Madagascar.

Functional diversity was not related to lemur species richness or environmental factors, and was highest in areas with the lowest species richness (Herrera 2017). As Herrera’s (2017) study looked at lemur communities on a large scale and the 2016 study used a single functional diversity measure and did not explicitly test the effect of habitat characteristics, my study will compare communities on a finer scale as well as use multiple indices of functional diversity to examine lemur communities.

Glossary of Terms

Functional diversity: A measure that takes into account the overall size of the trait

space. Functional diversity is similar to functional richness but uses a dendrogram to

estimate diversity instead of a convex hull. Example: High diversity when there is a large

amount of trait space taken up by the community.

Functional richness: The amount of functional space filled by the community. Example:

Higher richness where the community takes up a larger amount of trait space (diverse

traits).

Functional evenness: The distribution of abundance among traits in the community.

Example: Higher evenness when distinct traits are represented by a similar number of

individuals than if the majority of individuals have the same trait.

Functional divergence: Takes into account how abundance is spread through functional

trait space for the community. Example: Low divergence when the most abundant

species have traits that are close to the centre of the functional trait space; high

divergence when the most abundant species are at the edge of trait space (most extreme

trait, e.g., highest or lowest weight).

Functional redundancy: The amount of overlap of traits in a community. Example:

High redundancy where there is a lot of overlap in traits, e.g., similar body size, diet.

Figure 1.1 Glossary to distinguish between terms used in this thesis with examples (Diaz and Cabido 2001; Petchey and Gaston 2006; Villéger et al. 2008)

1.3 Study Site

1.3.1 Ranomafana National Park

The research took place at Ranomafana National Park in southeastern Madagascar. Many research projects have been conducted here, with the first taking place in 1986 (Meier et al.

1987). In 1991, RNP was created by designating 43,500 ha to be National Park (Wright et al.

2012). The park is a continuous forest and located in a mountainous region, with elevations ranging from 400-1500 m (Benstead et al. 2001). The habitat is heterogeneous and there are lowland rainforest, cloud forest, bamboo stands, swamps, and natural clearings (Figure 1.2;

Wright 1992). RNP receives an average annual rainfall of 2830 mm (Dunham et al. 2011). The rainy season lasts from December to March, with the driest months being from June to October

(Dunham et al. 2011). Temperatures in RNP range from lows in June to September (4-12 °C) to highs in December to February (36-40 °C).

Within RNP, there are four main study sites that have been used to conduct lemur research (Wright et al. 2012). One of these, Talatakely, is accessible from the main road and research station, Centre ValBio. The remaining three sites (Valohoaka, Mangevo, and

Vatoharanana) have permanent bush camp facilities. In addition to the permanent research sites, research has been conducted throughout the park for shorter term studies (Wright et al. 2012).

RNP is home to 330 species of trees and large shrubs (Razafindratsima and Dunham

2015). While many lemur studies have been conducted at RNP (Wright et al. 2012), research has also been conducted on plants (Brown et al. 2011, 2013; Brown and Gurevitch 2004;

Razafindratsima et al. 2017), invertebrates (Benstead et al. 2003), amphibians (Strauß et al.

2010), chameleons (Jenkins et al. 1999), small-mammals (Benstead et al. 2001), and carnivores

(Gerber et al. 2010, 2012).

1.3.2 Anthropogenic Use

When the park was designated, any villages that were inside the borders of the park were relocated to outside park boundaries (Vuola 2015). There are approximately 160 villages within five kilometers of RNP’s boundaries (Vuola 2015) and the recent relocation of villages follows on past relocations that moved villages off of mountaintops to the roadsides (Peters 1999). The forest has long been used by humans in this area. Within the park are memorial stones as well as tombs; these areas are sacred and disturbance is forbidden (Peters 1999). The forest has been used to extract resources, including: wood for fuel, buildings, and tool construction; plants for weaving, thatching, fermenting liquor, consuming, and medicinal use; and animals for food

(Herrera 2016; Peters 1999). The forest is also used for zebu cattle to graze (Peters 1999) and is cleared for cultivable land (Vuola and Pyhälä 2016). More recently, areas within the park boundaries have been illegally mined for gold by migrants that come to the area (Herrera 2016;

Vuola and Pyhälä 2016). The gold mining that occurs is artisanal and appears to be unorganized and driven by poverty (Vuola and Pyhälä 2016).

Figure 1.2 Photographs of the forest at Ranomafana National Park, Madagascar. Clockwise from top left: a) View of the landscape of the park. b) Eulemur rubriventer in a tree at Valohoaka c) Patch of forest with bamboo d) Forest thick with lianas and ground cover e) Setting up a transect at Valohoaka f) Transect surveying for lemur. 9

1.4 Study Species

1.4.1 Species Present

After their arrival in Madagascar (50-80 Mya; Yoder and Yang 2004), the lemur taxa flourished and diversified into over 100 species (Frasier et al. 2016; Hotaling et al. 2016; Lei et al. 2015; Mittermeier et al. 2010). In addition to these living lemur species, there are at least 16

Malagasy primate species that have gone extinct in the last 2,000 years (Godfrey and Jungers

2003; Karanth et al. 2005). These extinct species are larger bodied (9-200 kg) than the largest of the extant lemurs (i.e., Indri indri are 5.8-7.1 kg; Godfrey and Jungers 2003; Taylor and

Schwitzer 2011). As these species are recently extinct, it is important to note that present-day lemur communities are not intact. Currently, five families of lemur can be found on Madagascar.

These include: Daubentoniidae, Cheirogaleidae, Lemuridae, Lepilemuridae, and Indriidae.

Daubentoniidae was the first to diverge from other lemur families; Cheirogaleidae was likely the second family to diverge (Horvath and Willard 2007). The order of the remaining three families’ divergence is still being debated.

Ranomafana National Park is home to at least 13 species of lemur (Table 1.1; Wright et al. 2012). All five of the lemur families are represented in RNP. While some are easily spotted, some are difficult to detect or quite rare. Daubentonia madagascariensis is rarely sighted in forests, though its presence can be inferred based on distinctive holes in tree trunks made when it feeds on insects (Farris et al. 2011; Sefczek et al. 2012). Lepilemur microdon is also rarely sighted and patchily distributed (Porter 1998), as is Prolemur simus, which is known to have a small population within the park (Wright et al. 2008). The recently described Cheirogaleus sibreei is only found at high elevations and from one location within the park (Andriaholinirina et al. 2014; Blanco et al. 2009; Blanco and Godfrey 2013).

Table 1.1 Lemur species present at Ranomafana National Park, Madagascar

IUCN 2017 Red List Common Name Scientific Name Status Peyrieras’ woolly lemur Avahi peyrierasi Vulnerable Aye-aye Daubentonia madagascariensis Endangered Crossley’s dwarf lemur Cheirogaleus crossleyi Data Deficient Sibree’s dwarf lemur Cheirogaleus sibreei Critically Endangered Red-bellied lemur Eulemur rubriventer Vulnerable Red-fronted brown lemur Eulemur rufifrons Near Threatened Golden bamboo lemur Hapalemur aureus Critically Endangered Ranomafana grey bamboo lemur Hapalemur griseus ranomafanensis Data Deficient Small-toothed sportive lemur Lepilemur microdon Endangered Red mouse lemur Microcebus rufus Vulnerable Greater bamboo lemur Prolemur simus Critically Endangered Milne-Edward’s sifaka Propithecus edwardsi Endangered Southern black-and-white ruffed Varecia variegata editorum Critically Endangered lemur

1.4.2 Cheirogaleidae

The family Cheirogaleidae has undergone recent taxonomic changes as well as species discoveries (Lei et al. 2014, 2015, Rasoloarison et al. 2000, 2013). At RNP, there are three species of Cheirogaleidae: Cheirogaleus crossleyi, C. sibreei, and Microcebus rufus (Figure 1.3).

All three are nocturnal and small-bodied (i.e., M. rufus is 40 g, Cheirogaleus spp. are 270-330 g;

Lei et al. 2014; Radespiel et al. 2012). At high elevations (i.e., > 1400 m) all three species are found in sympatry, while at lower elevations C. crossleyi and M. rufus are sympatric (Herrera

2015).

All species of genus Cheirogaleus are obligatory hibernators (Blanco and Godfrey 2014).

During the hibernation period (March to September), both C. crossleyi and C. sibreei use underground hibernacula (Blanco and Godfrey 2014). In the active season, C. sibreei uses tree holes exclusively as sleep sites, while C. crossleyi uses both tree holes and nest-like structures

(Blanco and Godfrey 2014). Further research is required for detailed diets of the Cheirogaleus species found in RNP, but other species in the genus are frugivores that also consume flowers, nectar, seeds, leaves, and insects (Fietz 2003; Lahann 2007).

Microcebus rufus (Figure 1.3) undergo daily or seasonal torpor from June to September

(Schülke and Ostner 2007). M. rufus sleep sites are shared and include tree holes and piles of leaves on branches (Karanewsky and Wright 2015). They are solitary foragers and feed on fruits and insects, with the fruit of Bakerella (mistletoe, an ephiphytic semiparasite) being especially important to their diet (Atsalis 1999). Because of their small body size, M. rufus can use the lower levels of the canopy that includes small branches of shrubs and lianas (Kappeler and

Rasoloarison 2003).

1.4.3 Daubentoniidae

The family Daubentoniidae contains only one living species, Daubentonia madagascariensis, the aye-aye. Aye-ayes are nocturnal omnivores that have specializations including continuously growing incisors, large ears, and a long middle digit on their hands

(Sefczek et al. 2012). These adaptations allow aye-ayes to use 'percussive foraging' to locate larvae beneath tree bark (Farris et al. 2011). The aye-aye has a body size of 2.5-3 kg, which makes it the largest nocturnal primate species in the world (Sterling and McCreless 2006). Aye- ayes are the most widely distributed primate in Madagascar and are able to adapt to many habitat types, including degraded habitats and plantations (Ancrenaz et al. 1994; Farris et al. 2011). It

12 has been suggested that aye-aye population density is related to the distribution of one of its primary resources, Canarium seeds (Sefczek et al. 2012; Sterling 2003). Aye-ayes appear to specialize on structurally defended resources that other species may not be able to exploit

(Sterling 2003).

1.4.4 Indriidae

There are two species of Indriidae present at RNP: Propithecus edwardsi and Avahi peyrierasi. Species of Indriidae are known for their iconic style of locomotion, vertical clinging and leaping (Schmidt 2011). P. edwardsi is a diurnal lemur that occurs in both pairs as well as multimale/multifemale social groups (Figure 1.3; Pochron et al. 2004). At 5.7 kg, P. edwardsi is the largest lemur present in RNP (Rowe and Myers 2016). The diet of P. edwardsi includes both fruits and leaves, with habitat disturbance causing sifakas to shift towards a diet heavier in leaves

(Arrigo-Nelson 2006). P. edwardsi is primarily a seed predator, although some seeds may be dispersed (Dew and Wright 1998; King et al. 2005). A. peyrierasi is nocturnal and has a diet that consists entirely of leaves (Faulkner and Lehman 2006; Harcourt 1991). A. peyrierasi is approximately 1 kg and is pair-living (Harcourt 1991; Lei et al. 2008).

1.4.5 Lemuridae

The family Lemuridae are the best represented at RNP, with six species present. Two species of Eulemur are present, three species of bamboo lemur (genus Hapalemur and Prolemur simus), and one species of ruffed lemur (Varecia variegata editorum). Both E. rubriventer and E. rufifrons, as well as P. simus, are cathemeral (i.e., can be active diurnally or nocturnally), while the remaining Lemuridae species have diurnal activity patterns (Rowe and Myers 2016).

Eulemur rubriventer (Figure 1.3) and E. rufifrons are both frugivores that are medium bodied, though E. rufifrons is smaller (1.9 kg vs. 2.2 kg; Rowe and Myers 2016). Both species are sexually dichromatic, with E. rubriventer pair-living while E. rufifrons is found in multimale/multifemale groups (Rowe and Myers 2016). E. rubriventer and E. rufifrons have been suggested to be important seed dispersers in RNP (Dew and Wright 1998; Razafindratsima et al. 2014). In RNP the pattern of inverse density between the two Eulemur species has been observed, when one increases the other decreases (Wright et al. 2012).

All three species of bamboo lemur at RNP can be found in sympatry; however, Prolemur simus is patchily distributed (Tan 1999; Wright et al. 2008). At 0.85 kg, Hapalemur griseus ranomafanensis is the smallest of the bamboo lemurs at RNP (Rabarivola et al. 2007).

Hapalemur aureus (Figure 1.3) is larger at 1.5 kg, while P. simus is 2.6 kg (Frasier et al. 2015;

Rowe and Myers 2016). Bamboo lemurs are folivores that specialize on grasses, especially woody bamboo (family Poaceae). They exploit a resource that is inaccessible to other species as they are able to process the cyanide that is present in all parts of the giant bamboo plant

(Cathariostachys madagascariensis) that makes up a large part of their diet (e.g., 72-95% of diet;

Eppley et al. 2017; Tan 1999). The bamboo lemurs at RNP are able to survive in disturbed environments (Grassi 2006; Tan 1999), as disturbance may increase habitat that bamboo plants can exploit (Olson et al. 2013).

Varecia variegata editorum is the largest of the living Lemuridae at 3.7 kg (Baden et al.

2008). They are highly frugivorous and excellent seed dispersers; in RNP they are the only lemur that will disperse seeds further than 500 m from the parent tree (Dew and Wright 1998;

Razafindratsima et al. 2014). V. v. editorum is group living, however, they have a dynamic

14 fission-fusion organization and spend up to half of their time alone (Baden et al. 2016). They are not found in highly disturbed sites, even a decade after logging (Balko and Underwood 2005).

1.4.6 Lepilemuridae

A single species represents the family Lepilemuridae at RNP, Lepilemur microdon. Very little is known about this species as only a single study has been conducted (Kappeler and Porter

2016). The body size of L. microdon comes from a single adult male at Vohiparara, RNP, and was 0.97 kg (Porter 1998). It has been suggested that L. microdon may compete for food resources with Avahi peyrierasi as both are nocturnal folivores (Porter 1998). L. microdon may avoid competition by using disturbed habitats (Porter 1998). This species is rare in RNP and uses tree holes in large trees (diameter > 65 cm) as sleeping sites (Porter 1998).

1.5 Data Collection

In Chapter 2, all data collection was by Dr. Steig E. Johnson, Félix Ratelolahy, Ravalison and CVB field technicians, supervised by Dr. Patricia C. Wright, as part of the Modeling

Deforestation at Ranomafana (MODEF) project (e.g., Brooks et al. 2009; Johnson et al. 2005). In

Chapter 3, 2004 data were collected as part of the MODEF project by Dr. Steig E. Johnson, Félix

Ratelolahy, Ravalison and CVB field technicians, supervised by Dr. Patricia C. Wright. I collected the 2015 data in Chapter 3, supervised by Dr. Steig E. Johnson, as part of this thesis. I conducted all data analysis presented in this thesis.

Figure 1.3 Pictures of four of the 13 lemur species present at Ranomafana National Park, Madagascar. Propithecus edwardsi and Microcebus rufus photos were taken at Valohoaka while Hapalemur aureus and Eulemur rubriventer photos were taken at Talatakely.

1.6 Overview

The objective of this study is to have a better understanding of how environmental and topographic characteristics influence the functional diversity of lemur communities, as well as examine change over time in lemur community abundances. I outline the specific aims of each chapter below.

In Chapter 2, I use eight lemur communities in Ranomafana National Park to estimate functional diversity, richness, evenness, and divergence. I then test the effects of habitat characteristics and anthropogenic disturbance on lemur functional diversity. In addition, I examine whether functional redundancy is present in the lemur community - that is, whether multiple species fill similar functional roles.

In Chapter 3, I examine whether change over time has occurred in lemur populations between two time periods, 2004 and 2015, at a single site. I estimate lemur abundances and compare between the two surveys for lemurs overall, as well as for individual species and when grouped by diet (i.e., frugivores). Vegetation characteristics are also compared between surveys.

In the final chapter, a general discussion of the two data chapters is presented.

By examining what is affecting diversity and abundances, I hope to gain further insight into what is shaping lemur communities. Used in conjunction with individual species studies, the information presented here can be useful in understanding lemur community composition and the sensitivity of these communities to environmental change.

Chapter 2 : TOPOGRAPHY AND VEGETATION CHARACTERISTICS AFFECT

FUNCTIONAL DIVERSITY OF A LEMUR COMMUNITY IN SOUTHEAST

MADAGASCAR

2.1 Introduction

2.1.1 Functional Diversity

There are many measures that can be used to study biological diversity. Among the most common measurements have been species richness or richness weighted by relative abundance

(i.e., species diversity; Strauß et al. 2010; Vandewalle et al. 2010). However, these and other measurements of diversity ignore the fact that ecosystem function may not be equal in all species and individuals within species (Mouchet et al. 2010). Species perform functions in the environments that they occupy through ecosystem processes (e.g., nutrient cycling), with the varying roles of different species linked to their traits (Mouchet et al. 2010). Functional diversity is a relatively new measurement of diversity that takes into account these species differences in functional traits.

While there are many definitions, I will define functional diversity as a measure that takes into account the variation of traits present in a community based on the functions performed by organisms (Díaz and Cabido 2001). While it is still important to study the ecosystem as being composed of a set of individual species, creating functional groups that cluster species together based on a set of traits allows us to examine redundancy within a community and understand how different functional diversity metrics are influenced by forest characteristics. Two or more species with similar functions are considered redundant when the loss of one or more of these species does not affect ecosystem functioning (Díaz and Cabido

2001). The more species that belong to the same functional group, the greater the probability that

18 one of those species will survive and maintain ecosystem functioning (high functional redundancy). Species richness and diversity patterns do give us a starting place to make predictions but they are not equivalent to functional diversity and are not always positively correlated (Fergnani and Ruggiero 2015; Herrera 2017; Mayfield et al. 2010; Oliveira et al.

2016).

2.1.2 Functional Redundancy

As the landscape in Madagascar is rapidly changing due to anthropogenic effects

(Schwitzer et al. 2014), it may be particularly important to determine if functional redundancy is present in lemur communities – that is, whether or not multiple species fill the same functional role. In a community that has functional redundancy, functional traits of species overlap (Strauß et al. 2010). Thus, despite potential species loss associated with human-induced environmental change, overall ecosystem function may be retained. While functional redundancy is calculated using functional diversity metrics, it is important to include as a separate measure as it combines species richness and functional diversity to give an indication of potential resiliency (Brown et al. 2011).

Understanding redundancy in primates may be especially important as they play important roles in structuring the ecosystem (Chapman et al. 2013). Primates serve crucial functions such as seed dispersal (Ganzhorn et al. 1999; Godfrey et al. 2008; Nuñez-Iturri and

Howe 2007) and nutrient cycling (Feeley and Terborgh 2005; Neves et al. 2010). If there is low redundancy in the primate community, it is particularly concerning for conservation of the rainforest as a whole. In Madagascar, functional redundancy has been used to study tadpoles

(high redundancy; Strauß et al. 2010) and trees (low redundancy; Brown et al. 2011). Species- rich regions globally have been shown to be functionally redundant, potentially due to more

19 resources being present, not necessarily more niches (Oliveira et al. 2016). Herrera (2017) suggests that a high species richness is associated with functional redundancy in lemur communities across Madagascar. However, there may be limits, as there tends to be niche separation that reduces competition for resources in sympatric lemurs, which allows them to coexist (Dammhahn and Kappeler 2008a; Tan 1999; Vasey 2002).

2.1.3 Niche Space

In complex habitats, such as ones that offer microhabitats and diversity in food, there are a greater variety of ways in which to exploit resources – i.e., there are more ecological niches

(Ritchie and Olff 1999). A larger niche space should allow for a larger number of functional traits to exist in the ecosystem and higher species richness. If habitat complexity is reduced, then the number of niches may also decline, as will diversity. While niche overlap can occur in coexisting species (Agostini et al. 2010), species coexistence because of niche differentiation has been shown in many different communities (Beaudrot et al. 2013; Mason et al. 2008), including lemurs in Madagascar (Dammhahn and Kappeler 2014; Ganzhorn 1997; Tan 1999; Thalmann

2001). For example, Dammhahn and Kappeler (2014) showed that eight co-occurring lemur species in Kirindy Forest are separated into trophic niches. Heterogeneous environments (i.e., moderate amounts of anthropogenic disturbance and natural habitat complexity) allow microhabitats to exist within the rainforest and affect the diversity of species, including lemurs, living there.

2.1.4 Anthropogenic Disturbance

Anthropogenic habitat loss is one of the most important factors for the diversity of lemurs and other primates as forests are necessary for food resources, shelter, and other essential

20 resources (Brugiere et al. 2002; Chapman et al. 2010; Schwitzer et al. 2014). A meta-analysis among vascular plants, invertebrates, birds, and mammals, found that mammals were the most sensitive to degraded landscapes, for measures including abundance, diversity, and species richness (Sodhi et al. 2009). Logging has been shown to cause abundance changes in the primate communities of Malaysia (Johns 1992), which can cause diversity changes (i.e., diversity is weighted by abundance). A review study of rainforest primates by Johns and Skorupa (1987) found that in selectively logged forests (i.e., moderately disturbed), frugivorous and large-bodied primates had lower survival. Selective logging has also been found to have long-term effects on primate abundances and distribution (Chapman et al. 2000). Some primate species fail to recover abundances after logging while other species have higher densities in disturbed habitat

(Chapman et al. 2000).

Anthropogenic disturbances to the canopy can affect the availability of food to primate communities (Chapman et al. 2000; Ganzhorn 1995). In addition to habitat loss, deforestation also creates forest fragments which increase the edge effects faced by lemur species (Burke and

Lehman 2014; Harper et al. 2007; Lehman et al. 2006a). Edge effects can include a change in plant diversity and traits that affect productivity (i.e., food quality; Razafindratsima et al. 2017), as well as predation pressures on primates (Irwin et al. 2009). Even low levels of habitat disturbance from logging can lead to changes in species richness and density of lemurs as increased sunlight through forest gaps can lead to higher protein levels in leaves (Ganzhorn

1995, 1997). The gaps created by selective logging typically are larger than areas of natural tree fall (Felton et al. 2006).

While disturbance can lead to an increase in lemur species richness, at high levels of disturbance, there are mixed responses from species, with frugivores tending to be the most

21 adversely affected (Herrera et al. 2011; Irwin et al. 2010). This is because removing fruiting tree species reduces food availability for lemurs (Balko and Underwood 2005). In periods of food scarcity, frugivorous lemurs are able to survive on fallback foods, including leaves and flowers

(Irwin 2008; Wright et al. 2005). However, fruit is necessary for lemurs at key stages of reproduction, including weaning and lactation (Wright et al. 2005). In addition to food availability changes, disturbance can also increase parasite susceptibility (which can affect reproductive success and survival), and disrupt locomotion (Irwin et al. 2010; Junge et al. 2011;

Wright et al. 2009).

2.1.5 Vegetation and Topographical Features

Natural features of the topography and forest can affect diversity. As lemur species respond to specific features of the vegetation differently, this could affect the functional diversity of the lemur community. Higher density of large trees (i.e., diameter > 60 cm) has been shown to affect howler monkey (Alouatta palliata mexicana) distributions (Arroyo-Rodríguez et al. 2007).

Varecia variegata editorum has been found to prefer foraging in trees with large diameter at breast height (DBH) and voluminous crowns, and may be found in areas of forest that exhibit these characteristics (Balko and Underwood 2005). High tree species richness has also been associated with higher lemur diversity across communities in Madagascar (Ganzhorn et al.

1997). Another characteristic of the vegetation, shrub density, can be important to diversity as shrubs are food sources to smaller bodied lemurs such as Microcebus (Ganzhorn 1987).

Tree fall is a natural disturbance to the forest structure that can cause environmental changes as the understory is disturbed and exposed to more light (Arihafa and Mack 2013;

Muscolo et al. 2014). Some of these changes include variation in the microclimate (Marthews et al. 2008), increase in habitat diversity (Schnitzer and Carson 2001, 2010), and changes in animal

22 movement and survival (Beck et al. 2004). In two species of rodent in the Amazon rainforest, one (Oryzomys) had lower survival in forest gaps as ants would attack altricial young, while

Proechimys had higher survival in gaps than in the understory, possibly because of greater structural heterogeneity reducing predation rates (Beck et al. 2004). In primates, treefall gaps may be a preferred habitat for some species (e.g., Cercopithecus ascanius; Thomas 1991). A large amount of dead standing trees can be a sign that that site has community composition changes occurring in that location and dead trees will soon be replaced by pioneer species

(Laurance et al. 2006). This change in tree composition could affect species abundances. For example, bamboo lemurs (genus Hapalemur and Prolemur simus) may be found in bamboo microhabitats because of the importance of this food source in their diet (Grassi 2006; Olson et al. 2013; Tan 1999). As a community, lemur species composition has been explained by plant productivity, as it limits food resources (Herrera 2017).

Topographical characteristics such as elevation can influence the lemur community as each species has particular elevation limits. The highest diversity of small mammals has been found to be at intermediate elevations; in tropical areas, this is a few hundred metres below the persistent cloud cover at the mountaintop where intermediate climatic conditions are also found

(McCain 2005). Lemur species richness is highest at mid-elevations (i.e., 800-1200 m; Goodman and Ganzhorn 2004), which may affect functional diversity. Elevational gradients (topographic heterogeneity) have also been shown to be important in determining lemur diversity (Herrera

2017). In addition to elevation, moderate slopes that face the sun (aspect) may be associated with higher plant productivity and diversity (Gallardo-Cruz et al. 2009; Wondie et al. 2012). This higher food productivity may affect the lemur community composition (Herrera 2017).

2.1.6 Objectives

Because of the risk of extinction to many species of lemur (Schwitzer et al. 2014), it is important to understand how anthropogenic and natural factors predict the functional diversity of this taxon. Understanding both human-induced and natural influences can assist in conservation management. Functional redundancy can help to prioritize areas in conservation, as low redundancy can indicate that loss of a single species will mean a loss of ecosystem functions

(Chapman et al. 2013). Because functional diversity is not equal to species diversity, it can reveal patterns of trait diversity independent of traditional measures such as species richness and evenness. This may indicate which areas are more productive in terms of niche space, in that greater functional diversity is supported. By examining lemur communities in a protected, continuous forest, we will be able to see whether functional redundancy exists in these lemur communities and discern how functional diversity indices relate to changes in forest structure. I used lemur surveys conducted in 2004 at eight sites within Ranomafana National Park (RNP) to test the effects of habitat characteristics and anthropogenic disturbance on lemur functional diversity and test for functional redundancy.

2.1.7 Hypotheses and Predictions

I tested the hypothesis that functional redundancy is absent in the lemur community, with the prediction that redundancy will be lower in Ranomafana lemur communities than expected by chance. Niche separation has been shown in lemur communities (Dammhahn and Kappeler

2014; Ganzhorn 1997; Tan 1999) and should affect the traits present in the lemur community.

Niche differentiation is expected to allow a variety of functional traits to exist within the lemur community, while richness is not expected to exceed 10 species in any given site (Herrera 2016;

Johnson et al. 2005). As a result, functional redundancy is expected to be low.

I also tested two hypotheses in regards to how functional diversity metrics are affected by environmental gradients. First, anthropogenic disturbance influences lemur species distribution and assemblage, as well as the overall functional diversity of the lemur community. If anthropogenic disturbance decreases functional diversity, I will see lower functional diversity in communities with more cut trees and other signs of disturbance (trails, cattle dung, etc.), and in areas closer in proximity to forest edge. Anthropogenic disturbance is expected to affect diversity as disturbances to the forest canopy can affect food availability and quality (Balko and

Underwood 2005; Irwin et al. 2010). Second, topographical and vegetation characteristics influence lemur species distribution and assemblage, as well as the overall functional diversity of the lemur community. If forest stand maturity or vegetation complexity increases functional diversity, I will see higher lemur functional diversity in areas with more mature trees (larger diameter and height), likely indicating higher food availability (Arroyo-Rodríguez et al. 2007;

Balko and Underwood 2005). Higher functional diversity will also be found in areas with more vegetation complexity (higher tree species diversity, dead standing trees, fallen trees, tree density, and shrubs), as this indicates there are more potential niches available to exploit by lemur species (Ritchie and Olff 1999; Thomas 1991). If topographical features affect functional diversity, I will see differences in lemur functional diversity in areas with different topographical features (elevation, slope, and aspect), as this can affect productivity and diversity of plants and thus niches available (Gallardo-Cruz et al. 2009; Goodman and Ganzhorn 2004; Wondie et al.

2012).

2.2 Methods

2.2.1 Study Site

The research was conducted at RNP in southeastern Madagascar October 2003 –

November 2004. Data collection was supervised by Drs. Patricia C. Wright and Steig E. Johnson as part of the Modeling Deforestation at Ranomafana (MODEF) project (e.g., Brooks et al. 2009;

Johnson et al. 2005). RNP contains an elevational range of 400-1500 m (Benstead et al. 2001).

RNP contains continuous moist humid forest (montane rainforest) that receives annual rainfall in the range of 1,500 to 4,000 mm (Schwitzer et al. 2014). The rainy season lasts from December to

March, with the driest months being from June to October (Dunham et al. 2011). Temperatures in RNP range from lows from June to September (4-12 °C) to highs in December to February

(36-40 °C).

Eight sites in RNP were selected to represent the communities of lemurs and trees present. The sites are Ampozasaha and Tsinjorano (North), Vohiparara and Sahateza (West),

Valohoaka and Torotosy (Central), and Ambinanindranofotaka and Mangevo (South; Figure

2.1).

Figure 2.1 Map of Madagascar with an inset of Ranomafana National Park and the eight sites used for surveys. The transects indicate the complete 4 km trail, which was segmented into four 1 km transects in the present analysis. The basemap is from Conservation International’s 2005 forest layer. Source: Tracy Wyman

2.2.2 Lemur Surveys

Line-transect surveys (Thomas et al. 2010) were used to collect data on the density of diurnal and nocturnal lemur species in Ranomafana National Park. At each of the eight sites, a 4 km transect was established that ran from the edge of the forest towards the forest interior, roughly perpendicular to the edge. Transects were walked at a speed of 0.75 - 1 km/hr. The order and direction that each transect was surveyed was rotated daily. The amount of survey effort can

27 be found in Table 2.1. Each time a lemur was encountered, the species, method of detection, behaviour of the majority of the group, number of individuals, group spread, and location on the transect was recorded.

At each site, the 4 km transect was divided into three sections based on distance from the edge of the forest: 0-1000 m, 1001-2000 m, and 3001-4000 m (2001-3000 m section omitted due to the lack of corresponding vegetation quadrats; see below). Forest edge was considered to be up to 1000 m from the edge, while further into the forest was classified as interior forest. While distances to the edge of less than 1000 m have been considered interior forest in lemur studies

(e.g., 600 m; Burke and Lehman 2014), other studies have found that edge effects extend further into the rainforest (Briant et al. 2010; Brodie et al. 2015; Kinnaird et al. 2003). Values for interior forest were averaged between the 1001-2000 m and 3001-4000 m sections.

Lemur relative abundance (animals/km) was calculated for each of the transect sections.

While density estimates would be preferable, the sample size was too small for some species

(Buckland et al. 2010). While aye-ayes (Daubentonia madagascariensis) are present in RNP

(Sefczek et al. 2012), they are not included in this study. Aye-ayes are a cryptic species and current studies use secondary signs of aye-aye incisor marks on dead trees to indicate species presence (Sefczek et al. 2012). Because of this survey being designed to use transects, signs of aye-ayes would likely be missed even if they are present at the site, and abundance cannot be reliably estimated without animal sightings.

Table 2.1 Survey effort (kilometres walked) at each site during diurnal and nocturnal lemur surveys

Site Diurnal Effort (km) Nocturnal Effort (km) Ambinanindranofotaka 72 15 Ampozasaha 72 15 Mangevo 72 15 Sahateza 69 18 Torotosy 63 6 Tsinjorano 72 15 Valohoaka 69 12 Vohiparara 72 9

2.2.3 Measuring Topographical Features and Vegetation

Two types of plots for measuring vegetation data were used. Quadrats (40x40 m) were established at seven points along each transect (0, 100, 200, 400, 800, 1600, 3200 m). In the quadrats, for every tree with a diameter at 1.3 m (DBH) of > 10 cm, the species, DBH, and height were recorded. Additionally, the slope, aspect, and Index of Disturbance (number of cuts on trees, trails, evidence of fire, and cattle dung; See Appendix B) were recorded. The second type of plot established was circular plots. Every 200 m along the transect, a 5-metre radius plot was established; 25 metres from this centre plot at each of the four cardinal directions, an additional plot was established such that there were five circular plots to represent each location.

Data collected in the circular plots included: the number of dead trees (fallen and standing), number of cut trees, number of shrubs, elevation, slope, and aspect.

2.2.4 Functional Diversity Indices

Functional diversity was calculated for each lemur community based on abundance and on the three functional traits of body size, diet, and activity pattern. Traits were chosen to reflect characteristics that are important to niche partitioning (Dammhahn and Kappeler 2014;

Ganzhorn 1988). Trait measurements and sources can be found in Appendix A. Recently, there

29 has been a revision in dwarf lemur (genus Cheirogaleus) species designations. While in the past the species designation for Cheirogaleus present at RNP was C. major, recent genetic studies have indicated that it is C. crossleyi, at least near Valohoaka (i.e., sites Talatakely and

Vatoharana; Lei et al. 2014). Because of this, I used the species traits for C. crossleyi in this study. While C. sibreei is present in RNP, it is found at high-elevations (i.e., 1400 m; Blanco et al. 2009; Herrera 2016) and the sites surveyed do not reach elevations that high, thus it was not expected to be present.

Petchey and Gaston’s Functional Diversity Index (FD; Petchey and Gaston 2002) was used to obtain functional diversity. This measure uses a trait matrix that is then converted to a distance matrix and used to produce a dendrogram. Functional diversity is calculated using the total branch length of the dendrogram. Communities with species that have more similar traits are expected to have lower FD than communities with species that differ greatly. I used algorithms in R to calculate FD using the packages Picante and Ade4 (see Appendix A; Dray and

Dufour 2007; Kembel et al. 2010; R Core Team 2017). Continuous trait variables were standardized so that they had a mean of zero and a standard deviation of one, N (0, 1). Gower’s distance was used to calculate a distance matrix for the species.

Functional richness (FRic), evenness (FEve), and divergence (FDiv) were calculated using the same traits and lemur abundances as for the functional diversity calculations. The calculations were performed using the FD package in R (see Appendix A; Laliberté et al. 2014;

Laliberté and Legendre 2010; R Core Team 2017), which returns the three functional diversity indices developed by Villéger et al. (2008). To calculate these indices, at least three species need to be present at the site. Two locations, Mangevo 0-1000 m and Vohiparara 0-1000 m, did not have enough species present, so the three indices were not calculated. By connecting the most

30 extreme traits in the community, I generated a multidimensional convex hull, which also contains the less extreme traits. The volume of this convex hull can be used to calculate FRic

(Villéger et al. 2008). Communities with more extreme traits will have a larger volume and thus higher FRic. To measure FEve, a minimum spanning tree (MST) is created. MST produces a tree that links all the points contained in the T-dimensional space (T corresponding to the number of traits; in this case, three) with the minimum sum of branch lengths (Villéger et al., 2008). FDiv was measured by finding the centre of gravity of the species that formed the vertices of the convex hull. The deviation of distance for each species from the centre of gravity was calculated and used to create an index that ranges from 0 to 1 (Villéger et al., 2008). When highly abundant species are close to the centre of gravity relative to rare species, the index approaches 0.

2.2.5 Functional Redundancy Analysis

To assess how functional diversity measures in the communities compare to expected lemur functional diversity, I calculated the Index of Variance (IV) for each site and transect section (see Appendix A; Villéger et al. 2008). This index compares the observed functional diversity metric (Obs) to the expected functional diversity metric (Exp) and was calculated as follows:

IV has a range of -1 to 1, with values closer to 1 indicating that the observed diversity value is higher than those expected at random. A positive value indicates low functional redundancy, while a negative value indicates that trait diversity is lower than expected and high redundancy.

Expected functional diversity was estimated by creating 9999 expected communities from the lemur species pool of any species present at any of the sites in the survey. Samples were drawn

31 from the species pool without replacement and species richness was kept constant for each community. If observed and expected values are the same, then IV is equal to zero. I used the standard deviations calculated from the simulation of each IV value to perform t-tests to determine whether the mean IV differed from zero. Values that are significantly higher than zero have low redundancy as the functional diversity is higher than expected given the level of species richness. Values significantly lower than zero indicate functional redundancy, as given the species richness, functional diversity is lower than it could be given the species pool.

2.2.6 Functional Diversity Indices Analysis

To assess determinants of FD, FRic, FEve, and FDiv, I performed linear mixed effects modelling in R (R Core Team 2017). Variables of interest included: distance from edge, tree height, tree DBH, tree density, tree diversity, fallen tree density, cut tree density, shrub density,

Index of Disturbance, slope, aspect, and elevation. All continuous variables were standardized with a mean of 0 and a standard deviation of 1. I first measured for multicollinearity between the variables using the variance inflation factor (VIF). A VIF of 1 indicates there is no correlation, while a VIF of 5-10 indicates high correlation (Federman et al. 2017; Graham 2003). I used stepwise-deletion of variables until the remaining variables had a VIF of < 2 (car package in R;

Fox and Weisberg 2011; R Core Team 2017). Variables that remained in each category of interest were: disturbance (distance from edge and cut tree density), vegetation characteristics

(tree DBH, shrub density, tree density, fallen tree density, and tree diversity) and topographical characteristics (slope and elevation). Using these VIF selected variables, I then used the function dredge from the MuMIn package in R (Barton 2016) to generate models with the lowest Akaike

Information Criterion (AICc). Models with ΔAICc < 2, all candidates for being models to explain functional diversity measures (Baden et al. 2016; Burnham and Anderson 2004;

Symonds and Moussalli 2011), were cross-validated using leave-one-out technique and the model with the lowest mean squared error was chosen as the model with the best fit (Arnold

2010). Using the variables for the best fit model, I performed a linear mixed effect model (LMM) using the lme4 package in R (Bates et al. 2015; R Core Team 2017). Site was treated as a random effect. Variable values and descriptions can be found in Appendix B.

2.3 Results

2.3.1 Variation in Functional Diversity and Redundancy Across Communities

The null model was used to test if observed values of functional diversity measures differed significantly from randomly assembled species. Across all measures of functional diversity, IV was significantly different (p < 0.05) at most sites (Figure 2.2). There was also variation across sites in IV values at different species richness values. Both higher and lower than expected diversity for all measures occurred at sites across RNP (Figure 2.2).

Values of IV higher than zero indicate low redundancy (i.e., diversity is higher than expected given species richness) and an IV value lower than zero indicates redundancy (i.e., diversity lower than expected given species richness). For FD, 17 of the 24 sites had IV values that indicated low functional redundancy, while the remaining seven sites had functional redundancy. For FRic, FEve, and FDiv, only 22 sites had values to compare. Using FRic, 16 sites showed low redundancy, while five sites had redundancy. For FEve, eight sites showed redundancy. Finally, for FDiv, there were 17 sites with low redundancy and four with redundancy (Figure 2.2).

Figure 2.2 Scatterplots showing results from each functional diversity metric and the null model analyses. Index of Variance (IV) values for each of the functional diversity metrics versus species richness. Positive IV values indicate that the observed diversity metric was higher than the expected diversity, while holding species richness constant (i.e., functional redundancy). Open circles indicate plots where IV values are significantly greater or lower than the expected, p < 0.05; Triangle indicate no significance. N = 22 for functional richness, evenness, and divergence, and n = 24 for functional diversity.

2.3.2 Functional Diversity Indices

The highest FD values were found at the forest edge in Ampozasaha and the forest interior at Sahateza, while the lowest was found at the forest edge in Mangevo (Table 2.2). For

FRic, FEve, and FDiv, the forest edge at Ambinanindranofotaka had the lowest values, even though it did not have the lowest species richness. FRic was highest at the forest edge in

Ampozasaha, while FEve and FDiv were highest at the edge of the forest in Sahateza (Table

2.2).

Table 2.2 Functional diversity metrics of the lemur community at eight sites in Ranomafana National Park

Edge Interior 0-1000 m 1001-2000, 3001-4000 m Site FD1 FRic2 FEve3 FDiv4 SR5 FD FRic FEve FDiv SR Afotaka6 0.79 0.26 0.70 0.83 5 0.90 0.49 0.79 0.80 3-8

Ampozasaha 1.08 0.99 0.74 0.83 7 0.92 0.74 0.62 0.81 5-8

Mangevo 0.35 - - - 2 0.78 0.43 0.71 0.78 3-6

Sahateza 0.70 0.64 0.98 0.99 3 1.08 0.97 0.83 0.81 6

Torotosy 1.01 0.94 0.77 0.86 6 0.95 0.86 0.81 0.86 4-6

Tsinjorano 0.67 0.41 0.88 0.84 3 0.88 0.70 0.63 0.79 4-7

Valohoaka 1.01 0.59 0.78 0.85 6 0.89 0.54 0.81 0.75 4-6

Vohiparara 0.48 - - - 2 0.94 0.76 0.72 0.81 5-7

1 Gaston and Petchey’s functional diversity 2 Functional richness 3 Functional evenness 4 Functional divergence 5 Species richness 6 Ambinanindranofotaka

2.3.3 Functional Diversity

All of the top ten models had a ΔAICc of < 2. The model with the best fit based on cross validation and on log likelihood was the model with fallen tree density and location (edge vs. interior) as the variables used to explain FD (Table 2.3). FD increased in the interior location compared to the forest edge (Figure 2.3). Fallen tree density also positively influenced FD

(Figure 2.4). Fallen tree density and location was included in five and four, respectively, of the top ten models.

Table 2.3 Comparison of fit of models for functional diversity

Cross Validation AICc Log likeli- Standard Sq. Model Parameters df ΔAICc weight hood Error Fallen tree density + location 5 0.00 0.048 -30.04 0.98 Location 4 0.57 0.036 -31.94 1.08 DBH1 + elevation + fallen tree density 6 0.79 0.033 -28.63 1.02 None 3 0.88 0.031 -33.54 1.05 Elevation + fallen tree density 5 0.91 0.031 -30.67 1.02 Location + tree density 4 1.25 0.026 -32.28 1.00 Tree density 4 1.26 0.026 -32.29 0.99 Fallen tree density 6 1.28 0.026 -32.37 1.02 Elevation 6 1.43 0.024 -29.05 1.01 Fallen tree density + location + slope 5 1.62 0.022 -29.43 1.00 1 Tree diameter at 1.3 m height

Figure 2.3 Relationship between location (edge vs. interior) and mean functional diversity (FD). Error bars indicate standard deviation. The statistics reported are the results from the linear mixed model analysis (see Appendix B).

Figure 2.4 Relationship between functional diversity and fallen tree density. The statistics reported are from the linear mixed model analysis (see Appendix B), with a regression line and 95% confidence interval (grey shading).

2.3.4 Functional Richness

The model with fallen tree density and elevation as parameters was chosen as the model with the best fit based on cross validation and log likelihood. Even though the model that added shrub density had a higher log likelihood and lower cross validation standard square error, shrub density was not significant in the model; thus, it is potentially a spurious parameter and was not included in the final model (Table 2.4). Fallen tree density was included in five and elevation was included in six of the top ten models. Both fallen tree density and elevation was statistically significant in explaining FRic. The slopes of both of these parameters were positive, such that an increase in fallen tree density and increase in elevation increased the FRic (Figure

2.5, Figure 2.6).

Table 2.4 Comparison of fit of models for functional richness

Cross Log Validation Δ AICc like- Standard Sq. Model Parameters df AICc weight lihood Error Fallen tree density + elevation 5 0.00 0.11 4.60 0.089 Tree density + elevation + shrub density 6 0.93 0.068 6.06 0.10 Fallen tree density + elevation + shrub density 6 0.96 0.067 6.05 0.010 Tree density + elevation 5 1.77 0.045 3.72 0.097 Tree density 4 2.52 0.031 1.65 - Fallen tree density + elevation + tree diversity 6 2.75 0.028 5.16 - Fallen tree density 4 2.80 0.027 1.51 - Tree density + shrub density 5 2.96 0.025 3.12 - Fallen tree density + elevation + slope 6 3.28 0.021 4.89 - Fallen tree density + elevation + location 6 3.52 0.019 4.77 -

Figure 2.5 Relationship between functional richness and fallen tree density. The statistics reported are from the linear mixed model analysis (see Appendix B), with a regression line and 95% confidence interval (grey shading).

Figure 2.6 Relationship between functional richness and elevation. The statistics reported are from the linear mixed model analysis (see Appendix B), with a regression line and 95% confidence interval (grey shading).

2.3.5 Functional Evenness

All of the top ten models included tree diversity as a parameter, and seven included elevation. The model with the best fit included tree diversity and elevation as the variables.

Adding tree density to this model did increase the log likelihood and reduce the standard square error in cross validating, but tree density was not significant in the model so it was not included in the final model (Table 2.5). Tree diversity and elevation was statistically significant and had positive slopes with FEve (Figure 2.7, Figure 2.8).

Table 2.5 Comparison of fit of models for functional evenness

Cross Log Validation AICc like- Standard Model Parameters df ΔAICc weight lihood Sq. Error Tree diversity + elevation 5 0.00 0.115 -23.88 1.26 Tree diversity + elevation + tree density 6 0.66 0.083 -22.28 1.18 Tree diversity + tree density 5 1.78 0.047 -24.77 1.27 Tree diversity + elevation + shrub density 6 2.18 0.039 -23.04 - Tree diversity 4 2.24 0.038 -26.69 - Tree diversity + elevation + fallen tree density 6 2.30 0.036 -23.10 - Tree diversity + elevation + slope 6 2.86 0.028 -23.38 - Tree diversity + tree density + slope 6 3.20 0.023 -23.55 - Tree diversity + elevation + location 6 3.45 0.021 -23.67 - Tree diversity + elevation + DBH1 6 3.45 0.020 -23.68 - 1 Tree diameter at 1.3 m height

Figure 2.7 Relationship between functional evenness and tree diversity. The statistics reported are from the linear mixed model analysis (see Appendix B), with a regression line and 95% confidence interval (grey shading).

Figure 2.8 Relationship between functional evenness and elevation. The statistics reported are from the linear mixed model analysis (see Appendix B), with a regression line and 95% confidence interval (grey shading).

2.3.6 Functional Divergence

All of the top ten models included tree DBH, nine included tree diversity, and four included elevation. The model that included DBH, tree diversity, and elevation was chosen as the model with the best fit based on log likelihood and cross validation. Even though the model that excluded elevation had the same cross validation standard square error, it had a lower log likelihood, so elevation was kept in the model (Table 2.6). DBH, tree diversity, and elevation were statistically significant in explaining FDiv. Tree DBH had a negative slope with FDiv, as

DBH decreases FDiv increase (Figure 2.9). Both tree diversity and elevation had positive slopes, such that an increase in either of these parameters increased FDiv (Figure 2.10, Figure 2.11).

Table 2.6 Comparison of fit of model for functional divergence

Cross Log Validation Δ AICc likeli- Standard Model Parameters df AICc weight hood Sq. Error DBH1 + tree diversity + elevation 6 0.00 0.011 -19.38 1.41 DBH + tree diversity + slope 6 0.37 0.093 -19.55 1.53 DBH + tree diversity 5 0.43 0.090 -21.51 1.41 DBH + tree diversity + shrub density + elevation 7 1.89 0.044 -18.11 1.53 DBH + tree diversity + elevation + slope 7 2.13 0.039 -18.23 - DBH + tree diversity + shrub density 6 2.74 0.028 -20.74 - DBH + tree diversity + elevation + fallen tree density 7 3.10 0.024 -18.72 - DBH + tree diversity + fallen tree density 6 3.12 0.024 -20.93 - DBH + location 5 3.34 0.021 -22.96 - DBH + location + elevation 6 3.61 0.018 -21.17 - 1 Tree diameter at 1.3 m height

Figure 2.9 Relationship between functional divergence and tree diameter at 1.3 m. The statistics reported are from the linear mixed model analysis (see Appendix B), with a regression line and 95% confidence interval (grey shading).

Figure 2.10 Relationship between functional divergence and tree diversity. The statistics reported are from the linear mixed model analysis (see Appendix B), with a regression line and 95% confidence interval (grey shading).

Figure 2.11 Relationship between functional divergence and elevation. The statistics reported are from the linear mixed model analysis (see Appendix B), with a regression line and 95% confidence interval (grey shading).

2.4 Discussion

2.4.1 Summary Patterns

Functional diversity and functional redundancy differed across lemur communities at

Ranomafana. The majority of these communities had higher than expected functional diversity across all measures. This also indicated that there generally is low functional redundancy.

Accordingly, the hypothesis that functional redundancy would not be present in Ranomafana

National Park was mainly supported, as most locations had low redundancy. The hypotheses that

48 lemur functional diversity was affected by anthropogenic disturbance, topography, and vegetation characteristics were partially supported. Different variables had effects on the various measures of functional diversity, and no single variable had an effect on all of the functional diversity metrics. However, location (edge vs. interior), elevation, fallen tree density, and tree diversity were significant predictors in multiple top models.

2.4.2 Low Functional Redundancy in Ranomafana

Most lemur communities, across all four functional diversity metrics, were not functionally redundant. This implies that the lemur assemblages at Ranomafana may be at risk of losing functions provided by lemur species to the forest and other animal assemblages. A recent study on utilitarian (i.e., species used by people for a specific purpose) trees within Ranomafana also showed low functional redundancy (Brown et al. 2011). This low functional redundancy of lemurs suggests that there are few other lemur species that can replace the functions provided and suggests niches are filled by a single species. This finding is in contrast to Herrera (2017), who found that areas with high species richness had low functional diversity, which suggested that there was functional redundancy. The results may differ because while this study examined a single forest, Herrera (2017) studied multiple sites across all of Madagascar. While redundancy may be the general trend countrywide or potentially when examining a protected area as a single site, the results found in this study may be more typical when looking at a finer scale and examining multiple sites within a forest. For example, looking at RNP as a single site would indicate that 13 species are present; however, there is variance throughout the park in lemur community composition and the results of this study show that there were never more than eight species at a single site and as few as two.

It is important to note that current lemur communities are not intact as larger species of lemur have already gone extinct (Razafindratsima et al. 2013). A consequence of a community losing lemur species may be that plant species that rely on primates to disperse seeds are found in lower abundances (Ganzhorn et al. 1999; Godfrey et al. 2008). In addition to seed dispersal, primate herbivores may play an important role in nutrient cycling in the ecosystem. Primates may increase nutrients available in the soil and change the plant species composition (Feeley and

Terborgh 2005; see below).

As this rainforest shows low redundancy in lemur communities, there may be cascading effects from any lemur extirpations because of the distinct functions most species provide. In other ecosystems, there may be greater resiliency as some functions such as seed dispersal are alternatively provided by birds or bats (Galindo-González et al. 2000; Hodgkison and Balding

2003; Hutcheon 2003). However, in Ranomafana National Park, there do not appear to be alternative dispersers for most species of large-seeded plants that are dispersed by frugivorous lemurs (Razafindratsima et al. 2014). In addition, seeds that are dispersed by smaller lemurs (i.e.,

Cheirogaleus crossleyi and Microcebus rufus) potentially could be dispersed by bats, birds, or rodents, but because of dietary preferences for certain seeds, the niche may not be completely filled in the absence of these lemurs (Bollen et al. 2004). Invasive species also have potential to fill lemur niches. A study in Brazil indicated that feral pigs were able to disperse large seeds

(Donatti et al. 2007), and this role has also been suggested for Madagascar’s introduced wild pigs (Ganzhorn et al. 1999). Given these uncertainties, further research incorporating a larger selection of vertebrate taxa may better elucidate whether the Ranomafana rain forests are vulnerable to loss of ecosystem functions from extirpations or the fauna are sufficiently

50 redundant. Nonetheless, the data presented here suggest there is risk of loss of lemur-specific ecosystem roles.

2.4.3 Forest Edge Reduces Lemur Functional Diversity

The hypothesis that disturbance affects functional diversity was supported, as there was lower functional diversity closer to the edge of the forest. Not all species are tolerant of living near the edge of the forest as edge effects can affect the desirability of the habitat. Edge effects can include abiotic effects, such as sun and wind, as well as biotic effects, which include increased use of the forest by humans or changes to volume of food trees (Irwin et al. 2005;

Lehman et al. 2006a). For example, Microcebus rufus tends to be found closer to the edge than

Cheirogaleus major; this is thought to be because M. rufus relies more heavily on insects, which are found in greater numbers on forest edges (Lehman et al. 2006a). While this study looked only at lemur abundances, edge effects may be important to examine at a finer scale, such as in Burke and Lehman (2014), where edge effects demonstrated an effect on body size for one sex in

Microcebus.

It appears that lemurs in the forests of Ranomafana experience edge effects that reduce the amount of functional space the community uses when measuring functional diversity (FD).

However, edge effects do not necessarily influence how abundance in trait space is spread out

(i.e., functional evenness or divergence). Edge effects also did not predict functional richness – an interesting result because functional diversity and richness are similar measures. One possible explanation is that as species are added and increase the function trait space (FD), the overall abundance may not change with species additions. Abundance may also be even between the traits (FEve), with the most abundant species not changing across distance from edge (FDiv).

While natural edges to the forest can exist (e.g., cliff edge, wetlands, natural clearings;

Kull 2000), Ranomafana National Park has anthropogenically-induced forest edges, including those made for transportation, as well as clearings for agriculture and villages (Brooks et al.

2009; Peters 1999; Sussman et al. 1994). Edge effects are an indirect anthropogenic disturbance to the forest, caused by large-scale changes to the landscape. It would appear that lemur diversity in this forest is impacted only by large-scale forest loss and not by direct habitat degradation

(i.e., cut trees). Another possibility is that any habitat degradation occurring is an ongoing process and has not yet affected lemur diversity. However, habitat disturbance may cause changes to functional diversity and potentially extirpation of lemurs in the future (Cowlishaw

1999; Kuussaari et al. 2009). It is also possible that the study design did not capture direct anthropogenic disturbance well. A single transect was used at each site, which may have meant nearby disturbance was not recorded, but was within the home range of lemur species sighted from the transect.

2.4.4 High Elevation Increases Multiple Measures of Lemur Functional Diversity

Topographical characteristics appear to be one of the most important factors in explaining functional diversity metrics. Similar to other studies, elevation is important in explaining lemur community diversity (Herrera 2016, 2017). Higher values of FRic, FEve, and FDiv were found at higher elevations. This indicates that with increasing elevation, there was an increase in the functional trait space used, as well as rarer traits. The higher elevations on these transects do correspond to mid-elevations that have been found to have higher lemur richness (Goodman and

Ganzhorn 2004). Although the general trend is for transects to rise in elevation from the edge to the interior, there was enough variation in elevation at the forest edge between sites that it did not correlate with distance from the edge. As such, elevation and forest edge are distinct predictors

52 that are each important influences on lemur functional diversity metrics. Increasing elevation is also often associated with a decrease in anthropogenic disturbance (e.g., logging) so this may be an explanation for the increase in FRic at higher elevations (Olson et al. 2013; but see above).

FEve and FDiv both have a positive relationship with elevation. As both of these measures include abundance in their calculation, this result reflects abundance changes across the traits as elevation increases. At higher elevations, the abundance of lemurs starts to become more even across traits (FEve). In addition, higher elevations have more abundant lemurs with more extreme traits than at lower elevations (FDiv). There are possibly more specialized niches created at higher elevations (Herrera 2017; Wollenberg et al. 2008), such as a change in food resources or thermoregulatory demands, which is why the most abundant species have more extreme traits.

2.4.5 Tree Size, Diversity, and Treefalls Affect Lemur Functional Diversity

The hypothesis that forest stand maturity or vegetation complexity would affect diversity metrics was supported. The first prediction, that forest stand maturity would increase functional diversity because of increased food availability (Balko and Underwood 2005) was not supported.

However, the prediction that vegetation complexity would increase functional diversity as more niches would be available (Ritchie and Olff 1999; Thomas 1991) was supported.

Perhaps counter-intuitively, one measure of functional diversity was negatively correlated with average tree diameter. As tree size decreased, more extreme lemur traits (i.e., furthest from average) became more abundant (FDiv). In other words, forest stands with smaller trees may better support lemur species in the community that have more extreme traits. For example, trees with large diameter are difficult for many species of lemur to move on vertically (Ganzhorn

1987). This difficulty in travel may prevent some species from exploiting resources at multiple levels of the canopy in forests that have large diameter trees and act as a filter against species with extreme traits. Some species also appear to prefer undergrowth, such as Microcebus rufus

(Herrera et al. 2011), which has the “extreme” trait of small body size (Radespiel et al. 2012).

Areas with larger trees may be an indicator that there is less undergrowth, and species that exploit undergrowth will be present in less abundance. However, the exact mechanism behind this relationship is not understood and warrants further study.

Tree falls, indicated by fallen tree density in our vegetation sampling, are a natural type of disturbance that can create larger functional space (FD and FRic) for lemurs to occupy. Areas with canopy gaps can have higher bamboo density as some species (e.g., Cathariostachys madagascariensis, a food species for bamboo lemur) are fast growing and able to exploit this newly available space (Olson et al. 2013). As the gap from a tree fall is filled, the conditions will favour different tree species as the gap returns to pre-disturbance levels and create different microhabitats for other species to exploit (Muscolo et al. 2014). Fallen trees also can act as a source of water as cavities can collect rainwater for lemurs to drink from, with some species requiring water sources more than others (Overdorff 1993). In addition, fallen trees contribute to nutrient cycling as decaying vegetation (Chambers et al. 2000; Muscolo et al. 2014). A higher amount of nutrient cycling has been linked to increased biodiversity and potential for complexity in ecological niches available (Silver et al. 1996).

A higher tree diversity likely translates to higher FDiv because there are more species of trees as food resources, which may allow the abundance of a species with more extreme traits to persist in this area. Higher tree diversity also results in higher FEve, potentially because more niches can be exploited and distinct traits are represented by similar abundances between traits,

54 rather than the majority of abundance being on the same trait. The vegetation variables used in this study are not an exhaustive list. One that would be interesting to include in future research is bamboo, as it is an important species to the bamboo lemurs in the park (Tan 1999). Tree phenology – as a measure of food availability – may also be interesting to include as it may be important in primate distributions within the forest (Baden et al. 2016; Camaratta et al. 2017).

However, phenology studies would require year-round resampling of transects, which in a large landscape such as RNP, may be impractical.

2.4.6 Caveats

There are some important caveats to this research. When discussing redundancy and functions provided by the community, it is important to note that this study used only three traits in calculating functional diversity metrics. While there are many traits that are likely important in functional diversity of lemurs, the small number of species present at any one site limits the number of traits used (i.e., the number of traits must be lower than the number of species). The traits used here were also limited by availability of data across the literature. For example, the gape-size of most lemur species is unavailable in the current literature. As frugivorous lemurs are important seed-dispersers (Moses and Semple 2011; Razafindratsima and Dunham 2015), gape size would give us an indication of the seed-size that could be dispersed (Dew and Wright 1998;

Federman et al. 2016) and could represent an important functional trait.

While trait data preferentially were sourced from RNP, the trait measurements were averaged by species, even though variation among individuals within species is likely to exist in the trait (Cadotte et al. 2011). For instance, individual-based functional diversity metrics are relatively common in plant research (Albert et al. 2010; Hulshof and Swenson 2010; Lohbeck et al. 2012; Messier et al. 2010). While such measures might improve the validity of results in this

55 thesis, it would require long-term studies at each location for a representative sample of individuals from all species in the community each time the area was resampled; therefore, it is unlikely to be feasible for most primate (or vertebrate) communities. However, variation within functional traits for each species can be examined, and it may be important to consider intraspecific variation in trait values when examining communities across environmental gradients (Hulshof and Swenson 2010). In the future, more specific questions about lemur functional diversity can be answered when more comprehensive data on life history traits, diet, and body measurements exist across lemur species.

2.4.7 Conclusions

In general, while there was variation in functional diversity measures, there was low functional redundancy for most of the sites measured. While this work explored how variation in metrics of functional diversity is explained by anthropogenic disturbance, vegetation, and topographic characteristics, it is interesting to note that no one variable was important in all four of the metrics used. Taken as a whole, FD developed by Petchey and Gaston (2002) was sensitive to anthropogenic disturbance, while the three metrics developed by Villéger et al.

(2008) were more sensitive to elevation and to vegetation characteristics. Elevation appears to be the clearest predictor, as it was important to three of the measures. Though hypotheses were broad and applied to all metrics, it is perhaps not surprising that each metric – capturing distinct components of diversity – responded somewhat differently to various environmental predictors.

Future research may try to better understand the mechanisms behind why such measures respond differently to environmental pressures.

Chapter 3 : LONGITUDINAL STUDY OF LEMUR COMMUNITY ABUNDANCES AT

VALOHOAKA, RANOMAFANA NATIONAL PARK, MADAGASCAR

3.1 Introduction

3.1.1 Primate Communities and Habitat

Habitat loss, hunting, and habitat degradation are major threats to the conservation of primates in tropical rainforests (Chapman et al. 2006; Rovero et al. 2012). Rainforests are dynamic communities with complex species and habitat interactions. Even in areas where primates are protected from these anthropogenic disturbances, primates may face declines in abundance (Twinomugisha and Chapman 2007). A small alteration in the habitat can result in changes in the abundance and distribution of the primate community. One species declining or increasing can lead to a cascade of effects because of the significant role primates play in rainforest ecological processes. For example, many species of primates play important functions in seed dispersal, as well as nutrient cycling (Chapman et al. 2013; Feeley and Terborgh 2005;

Razafindratsima et al. 2014; Razafindratsima and Dunham 2015).

Habitat changes may not have immediately observable effects on primate populations as primates are generally long-living species. Therefore, longitudinal studies have been used to compare species or community abundances before and after extreme climatic events (e.g., cyclone) or anthropogenic disturbance (Chapman et al. 2000; Johnson et al. 2011; Pavelka et al.

2007). Johnson et al. (2011) compared density of Eulemur cinereiceps across a 12-year period

(i.e., 1995, 2007) that included a cyclone, and found that there were no significant differences in lemur density despite changes to the vegetation (i.e., tree size, stem density, basal area). In addition to primate studies, long-term studies on other taxa indicate a need for long-term

57 monitoring of lemur populations. A recent study on carnivores in Makira Natural Park,

Madagascar, indicated that over a 6-year period, exotic species were replacing native carnivores

(Farris et al. 2017). The shift in carnivore abundances towards exotic species is relevant as they prey on many species of lemurs (Brockman et al. 2008; Goodman 2003) and could cause a change to lemur abundances in a short time period (Irwin et al. 2009). Long-term studies have also been used to examine primate population dynamics in old-growth forest, as Lwanga et al.

(2011) studied in the forest of Ngogo, Kibale National Park, Uganda. Even though there were no major anthropogenic disturbances or climatic events, researchers measured a change in encounter rates of primate species at Ngogo and the primate community appears to be in a nonequilibrium state (Lwanga et al. 2011). This demonstrates a need to conduct longitudinal studies on primate communities even in well-protected areas.

3.1.2 Lemur Abundances and Vegetation Characteristics

The aim of this study is to investigate how lemur abundances and vegetation characteristics change over time at Valohoaka, Ranomafana National Park in southeastern

Madagascar. I build on a lemur survey conducted in 2004 by Johnson et al. (2005) with another survey conducted in 2015 using the same methods and survey routes. While Valohoaka does not have a history of being commercially logged (Wright et al. 2012), anthropogenic disturbance

(e.g., cut trees) could still affect lemur densities (Ganzhorn 1995). Lemur abundances have also been shown to be impacted by food resources (Herrera 2016), which may vary with disturbance or natural spatiotemporal heterogeneity. Tree density can indicate forest condition, as higher density of large trees is typically found in old growth forest (Ingram et al. 2005). Large trees

(e.g., height and diameter) are important to abundance as some primate species prefer larger trees for sleeping sites (Karanewsky and Wright 2015; Porter 1998). Large trees can also affect

58 abundances by increasing availability of food resources such as fruit (Balko and Underwood

2005). Dead standing trees can be a source of food for some insectivorous species (Sefczek et al.

2012), as well as sleep sites (Dammhahn and Kappeler 2008b). Natural tree falls can affect lemur abundances by contributing to nutrient cycling and creating light gaps for pioneer plants to establish, increasing small-scale habitat heterogeneity (Schnitzer and Carson 2001). While lemur abundances may be a response to changes in anthropogenic disturbance or in forest characteristics, other factors, including interspecific interactions (e.g., predation, disease) or natural, stochastic population fluctuations can contribute to changes over time (Lwanga et al.

2011).

3.1.3 Hypotheses and Predictions

Even in a protected forest, lemur abundances in Valohoaka could still be lower in 2015 for all species despite any changes in vegetation, as the general trend in lemurs is declining abundances (IUCN 2017; Schwitzer et al. 2014). Anthropogenic or natural disturbance may affect lemur abundances, though not uniformly across species. Frugivores may be more sensitive as canopy disturbances can affect fruit availability (Herrera et al. 2011). Bamboo lemurs (genus

Hapalemur) can be more abundant in areas of disturbance as food resources (i.e., bamboo) can exploit canopy openings (Grassi 2006; Herrera et al. 2011). Mouse lemurs (genus Microcebus) abundances are higher in areas of moderate disturbance as secondary vegetation is a preferred habitat and potentially increases resources for M. rufus (Ganzhorn 1995; Herrera et al. 2011). I am testing the null hypothesis of no change over time in lemur abundances for the entire community and predict no uniform changes for the lemur community at Valohoaka. If anthropogenic (e.g., cut trees) and natural (e.g., fallen trees, dead standing trees) disturbances are

59 higher in one time period, I expect frugivores to have lower abundances while species that do well in disturbed areas (i.e., bamboo lemurs, mouse lemurs) to have higher abundances.

3.2 Methods

3.2.1 Study Site and Species

This study was conducted in the rainforest of Ranomafana National Park (RNP) in southeast Madagascar (21°20′ S, 47°80′ E). Designated a park in 1991, this 41,000 ha park has served as a long-term ecological research site (Wright et al. 2012). RNP has highly variable monthly precipitation, with an average of 240 mm (Dunham et al. 2011). The peak wet season is from January to March when the average monthly rainfall is 508 mm, while the dry season runs from June to October when the average monthly precipitation is 143 mm (Dunham et al. 2011).

The study took place within the park at one of the main research sites, Valohoaka (Figure

3.1; Wright et al. 2012). Valohoaka is located in the southern part of RNP and has an elevation range from 900 to 1,300 m (Arrigo-Nelson and Wright 2004). Valohoaka is a primary forest which has been relatively undisturbed by logging activities that have occurred elsewhere in the park (Wright et al. 2009). There are three other main research sites in RNP (Vatoharanana,

Talatakely, and Mangevo). Valohoaka is more pristine than either Talatakely or Vatoharanana, which have a history of selective logging, and is at a higher elevation than all three main research sites (e.g., Mangevo is 660-1200 m; Baden et al. 2016; Grassi 2006). While commercial logging has not occurred at Valohoaka, logging by locals and cattle grazing may be anthropogenic sources of disturbance (Arrigo-Nelson and Wright 2004).

Figure 3.1 Map of Ranomafana National Park with an inset of Valohoaka transect sites. The three transects are 500 m. The basemap is from Conservation International’s 2005 forest layer. Source: Tracy Wyman

Ranomafana National Park is home to at least thirteen species of lemur (Wright et al.

2012), with ten of them expected to be found at Valohoaka based on past studies and elevational ranges of species in the park (Table 3.1; Arrigo-Nelson and Wright 2004; Balko and Underwood

2005; Farris et al. 2011; Razafindratsima et al. 2014; Wright et al. 2012). While there are two species of Cheirogaleus present in Ranomafana, it is very unlikely that C. sibreei is present at

Valohoaka as it has only been located at a higher elevation within RNP and elsewhere (i.e., 1400 m; Andriaholinirina et al. 2014; Blanco et al. 2009; Herrera 2016). I am following the latest

61 taxonomy for consistency with the current literature as there have been many recent taxonomic changes (IUCN 2017). The species designation for Cheirogaleus that is present at lower elevations is still unclear but it appears to be part of the Cheirogaleus crossleyi subgroup (Lei et al. 2014). Aye-ayes (Daubentonia madagascariensis) were not detected during these sample years; however, as a cryptic species, it can be difficult to detect in transect surveys (Farris et al.

2011). Aye-aye feeding sites have indicated that as of 2005 aye-aye were present at Valohoaka

(Farris et al. 2011).

Table 3.1 Lemurs present at Valohoaka in Ranomafana National Park, Madagascar

Activity Scientific name Diet1 IUCN 2017 Observed Pattern/ Red List status during this Common Name study Diurnal Red-bellied Eulemur Frugivore Vulnerable Yes lemur rubriventer Red-fronted Eulemur rufifrons Frugivore Near Yes brown lemur threatened Ranomafana Hapalemur Folivore Data deficient Yes grey bamboo griseus lemur ranomafanensis Milne-Edward’s Propithecus Frugivore/Folivore Endangered Yes sifaka edwardsi Southern black Varecia variegata Frugivore Critically Yes and white editorum endangered ruffed lemur Nocturnal Peyrieras’ Avahi peyrierasi Folivore Vulnerable Yes woolly lemur Crossley’s Cheirogaleus Frugivore Data deficient Yes dwarf lemur crossleyi Aye-aye Daubentonia Omnivore Endangered No madagascariensis Red mouse Microcebus rufus Frugivore/Insectivore Vulnerable Yes lemur Small-toothed Lepilemur Folivore Endangered No2 sportive lemur microdon

1 E. rubriventer and E. rufifrons (Mittermeier et al. 2010), H. g. ranomafanensis (Rabarivola et al. 2007), P. edwardsi (Rowe and Myers 2016), V. v. editorum (Baden et al. 2008), A. peyrierasi (Lei et al. 2008), C. crossleyi (Lei et al. 2014), D. madagascariensis (Sterling and McCreless 2006), M. rufus (Radespiel et al. 2012), L. microdon (Taylor and Schwitzer 2011) 2 A single L. microdon was detected off transect, 2850 m from the forest edge in 2004

3.2.2 Lemur Surveys

Lemur and vegetation surveys were conducted January-February 2004 and September-

October 2015. Data collection in 2004 was supervised by Drs. Patricia C. Wright and Steig E.

Johnson as part of the Modeling Deforestation at Ranomafana (MODEF) project (e.g., Brooks et al. 2009; Johnson et al. 2005). I collected the data in 2015, supervised by Dr. Steig E. Johnson as part of this thesis.

Three transects were established in interior forest at Valohoaka in 2004 that are approximately perpendicular to the edge of the forest (Figure 3.1). These transects were reused in

2015 by locating the flagging tape that indicated transect points and re-cutting the transect. Each transect was 500 m in length and separated from other transects by 250 m. They were located

1500-2000 m (Transect A), 2250-2750 m (Transect B), and 3000-3500 m (Transect C) from the forest edge. Line-transect surveys (Thomas et al. 2010) were used to collect data on diurnal and nocturnal lemur species in Valohoaka. Transects were walked at a speed of 0.75 - 1 km/hr, such that it took 40-60 minutes to walk a transect if no primates were sighted. The order and direction that each transect was walked was rotated daily. In 2004, the amount of survey effort for each transect was 11.5 km diurnally and 2 km nocturnally. Survey effort in 2015 was 5 km diurnally and 2 km nocturnally for each transect. Each time a lemur was encountered, the following information was recorded: species, method of detection, behaviour of majority of the group, number of individuals, group spread, and location on the transect.

3.2.3 Vegetation Surveys

In 2004, every 200 m along the transect, five circular plots (5 m in diameter) were set up and number of trees, fallen trees, cut trees, and dead standing trees were recorded. In addition to

64 these plots, there were two quadrat plots, one in Transect A (1600 m from the edge) and in

Transect C (3200 m from the edge), that were 40x40 m and tree species, height, and DBH

(diameter at 1.3 m height) was recorded for all trees > 10 cm DBH. Due to the original study design in 2004, Transect B was not included in this measurement type.

In 2015, at the beginning of each transect and then every 100 m, 5 m diameter circular plots were established – such that there were six vegetation plots for each transect. Within these plots, numbers of trees, fallen trees, cut trees, and dead standing trees were recorded. In addition to these plots, along the entire length of each transect, tree species, height, and DBH was recorded for each tree with a DBH > 10 cm that was within 1 m of either side of the transect line.

Tree heights were estimated after training first with a clinometer until estimated heights were close to observed. Because of tree density, it was not typically possible to use a clinometer in the forest.

3.2.4 Data Analysis

For each transect and transect survey repetition, the number of lemur groups sighted was used to examine change over time in Valohoaka. Due to the low number of sightings, density could not be calculated for all species (e.g., Buckland et al. 2010), however density estimates for species with adequate sample size are reported in Appendix C. Regression analysis was conducted using R Project Software version 1.0.143 for Windows (R Core Team 2017). Lemurs were analysed at the species level. Not all species were analyzed as there was not enough variation in number of sightings to fit to a model (i.e., > 1 sighting a year; Figure 3.2). As there were many zeros present in the count data, I used two different regression models to select the most informative model. With count of lemur groups as the response variable and year as the predictor variable, I used R package pscl (Jackman 2015; Zeileis et al. 2008) to model Poisson

65 regression (PRM) and zero-inflated Poisson regression (ZIP). A Poisson regression is used to predict the expected count value of the response variable (number of lemur groups) given the value or category of the predictor variable (year; Linder and Lawler 2012; Richards 2008). Zero- inflated models can be used when the Poisson regression is overdispersed, often because the observed counts of zero are greater than predicted under the Poisson distribution (Linder and

Lawler 2012). There are two classes of zeroes in data: false zeroes and true zeroes (Linder and

Lawler 2012). In this study, a false zero would suggest that no groups of lemurs were sighted even though they were present and not spotted. A true zero would indicate that no groups of lemurs were sighted and they were not present in the transect area at the time. Zero-inflated models deal with these two data types and model the probability that a zero is false or true zero, and in the case of ZIP, use a Poisson distribution to model true counts and true zeroes (Linder and Lawler 2012). As PRM and ZIP models are non-nested, the Vuong test was used for model selection (Appendix D; Wilson 2015). In addition, paired t-tests were used to compare between years for all species of lemur sighted. Paired t-tests were also used to compare between years for frugivorous species only (excludes Avahi peyrierasi and Hapalemur griseus ranomafanensis).

All vegetation characteristics (tree height, DBH, tree density, density fallen trees, density dead standing trees, density cut trees) were tested for normality using the Kolmogorov-Smirnov test. Any variables that were significantly different from normal were Box-Cox transformed so that statistical tests could be run. Two-way t-tests were used to compare vegetation variables between years. All tests were at set at = 0.05 level, but marginally significant tests (p < 0.10) were also reported.

3.3 Results

3.3.1 Lemur Diversity and Abundance

In 2004, there were seven species detected on transects; this increased to eight species with the addition of Avahi peyrierasi in 2015. Lemur species diversity was somewhat higher in

2015 (H = 1.86) than 2004 (H = 1.74). Overall, sightings were rare (i.e., n < 10) for any species in a year, suggesting that species are generally rare in this environment (Figure 3.2).

For all group count models for individual species, Vuong tests indicated that a Poisson model was a better fit to the data than a zero-inflated model (Appendix D). At the species level, year was weakly significant (p = 0.099) as a predictor for Microcebus rufus group encounters, with higher group encounters in 2015 than 2004 (Table 3.2, Figure 3.2). Year was not a significant predictor for group encounters for Propithecus edwardsi, Eulemur rubriventer or

Cheirogaleus crossleyi (Table 3.2). There were not enough sightings of Varecia variegata editorum, Eulemur rufifrons, Hapalemur griseus ranomafanensis, or Avahi peyrierasi to conduct a regression analysis (Figure 3.2). When compared as a group between years, lemur sightings were not different for all species (t = - 0.77, p = 0.47) or for frugivorous species (t = -0.20, p =

0.85).

Table 3.2 Generalized linear model with a Poisson distribution on the effect of year on lemur group counts

Lemur Species or Group Factors Coefficient SE z p

Propithecus edwardsi (Intercept) -2.85 0.50 -5.70 0.000000012

2015 0.55 0.76 0.71 0.48

Eulemur rubriventer (Intercept) -2.15 0.35 -6.09 0.00

2015 -0.15 0.68 -0.22 0.83

Cheirogaleus crossleyi (Intercept) -0.54 0.38 -1.43 0.15

2015 -0.85 0.69 -1.23 0.22

Microcebus rufus (Intercept) -1.39 0.58 -2.40 0.016

2015 1.10 0.67 1.65 0.099

1.4 n = 12

n = 10

1.2

1 n = 5 0.8

0.6

0.4 n = 11 n = 7 Encounter rate Encounter (groups/km) 0.2 n = 4 n = 3 n = 2 0

Species

2004 2015

Figure 3.2 Group encounter rates of lemurs at Valohoaka, Ranomafana National Park. Total number of encounters per species for both years indicated by n.

3.3.2 Habitat Structure

No significant differences were found in mean tree height (t = -0.93, p = 0.35), mean

DBH (t = 1.05, p = 0.30) or mean tree density (t = -0.62, p = 0.54) between 2004 and 2015

(Table 3.3). Mean fallen tree density was significantly higher in 2015 than 2004 (t = -3.44, p <

0.05), as was mean density of standing dead trees (t = -4.95, p < 0.001; Table 3.3). Mean density of cut trees was significantly lower in 2015 than 2004 (t = 2.08, p < 0.05; Table 3.3).

Table 3.3 Habitat characteristics measured in vegetation surveys at Valohoaka, Ranomafana National Park. Means ± SD are displayed.

Habitat Parameter 2004 2015 Area sampled (ha) 0.311 0.141 Mean tree height (m) 23.32 ± 16.80 26.93 ± 21.65 Mean DBH1 (cm) 94.99 ± 75.80 92.29 ± 75.86 Mean tree density (> 10 cm DBH/ha) 913.55 ± 544.70 919.56 ± 379.61 Mean density dead trees, fallen (ha)* 308.26 ± 199.97 724.15 ± 502.28 Mean density dead trees, standing (ha)* 113.92 ± 138.18 469.51 ± 300.29 Mean density cut trees (ha)* 13.06 ± 39.13 0 1DBH = Diameter at 1.3 m height * p ≤ 0.05 (habitat parameters with significant differences based on two-way t-tests)

3.4 Discussion

3.4.1 Stability in Lemur Populations

Overall, there was stability in lemur populations between 2004 and 2015, thus failing to reject the null hypothesis of no change over time. While the vegetation remained stable in tree size and density, there were changes to the amount of both natural and anthropogenic disturbance. The hypothesis that individual lemur species would respond differently to change over time was partially supported. There were no changes to overall frugivore abundance and there were not enough bamboo lemur (Hapalemur griseus ranomafanensis) sightings to determine if there was change over time. However, there were more encounters of Microcebus rufus in 2015 when indicators of natural disturbance were higher, supporting the predicted association of abundance and disturbance in this species.

Stability in lemur populations is an encouraging result in light of the general trend of population decline across their range (Schwitzer et al. 2014) and in disturbed locations (Wright

70 et al. 2012). The study site at Valohoaka was interior forest with relatively low disturbance, so results may vary in more disturbed or edge locations. In Ranomafana, edge habitat has been associated with lower lemur diversity (Chapter 2). Other sites across Madagascar have demonstrated an overall lower abundance of primates in disturbed sites (Ganzhorn et al. 1997;

Irwin et al. 2010). While there are no strong effects on populations at Valohoaka over 11 years, climate change has been identified as a threat to some species of lemurs and the effects may be seen over a longer time span (Brown and Yoder 2015; Dunham et al. 2011).

3.4.2 Frugivore Abundance

More disturbed locations in RNP have been associated with lower frugivore abundances

(Herrera et al. 2011). Disturbance did vary over time in this study, with decreasing anthropogenic disturbance and increasing natural disturbance. It is thus possible that these two forms of disturbance balanced each other out such that there was no net effect on frugivore abundances. While not significant, there were some shifts in frugivore abundances, which suggests that there may be different responses to disturbances or perhaps competition.

Propithecus edwardsi encounter rates increased over time in this study, which is in line with the results of other studies which indicate population density is increasing at Valohoaka (Wright et al. 2012). Varecia variegata editorum had stable encounter rates, but other studies have indicated that density is increasing at Valohoaka (Wright et al. 2012). It has been suggested that Eulemur rubriventer and E. rufifrons compete, such that when one species has high density the other has low density (Erhart and Overdorff 2008). The abundance pattern seen here may support this relationship, as in both years E. rubriventer was found in much higher abundances than E. rufifrons. However, both E. rubriventer and E. rufifrons were encountered less frequently in

2015 than in 2004. For nocturnal frugivores, Cheirogaleus crossleyi had a slight decline while

71 encounter rates of Microcebus rufus were higher in 2015.

Indeed, Microcebus rufus was the only species for which a marginally significant increase over time was found. In general, the slow life histories of primates may preclude seeing strong demographic changes over time periods such as the interval studied here (i.e., a decade).

However, species of Microcebus have faster life history than most primates. It can give birth to two surviving litters within a reproductive season (Blanco et al. 2015; Lahann et al. 2006) and is able to breed successfully at the age of 1 to 3 years (Wrogemann and Zimmermann 2001). For comparison, Propithecus edwardsi only reproduces every 1 to 3 years and is not fertile till age four (Pochron et al. 2004). Because of these differences in life history traits, M. rufus is probably more responsive to environmental changes over shorter time periods. The increase in density of dead standing trees is a potential reason for the increase in M. rufus. The tree holes in dead standing trees are used by Microcebus as sleeping sites (Karanewsky and Wright 2015; Schmid

1998). The tree holes may act to insulate Microcebus from outside temperatures and allow torpor to be maintained for longer, conserving energy (Schmid 1998). Ganzhorn and Schmid (1998) found that mouse lemurs in forests with fewer dead trees had lower body weights and year-to- year survival rates. Fallen trees may also act as a source of sleep sites to Microcebus (Ganzhorn and Schmid 1998). Other studies have found that M. rufus is more common in disturbed areas with secondary vegetation (Ganzhorn 1995; Herrera et al. 2011).

3.4.3 Caveats

The number of lemur observations in this study was very small. It is possible that the overall lack of change in lemur abundances shown in this study is due to low sample size. The only species that showed a change in abundance was Microcebus rufus, which also had the most sightings. Rather than increasing the number of walk repetitions, resampling the entire length of

72 the original 4 km transect rather than a subsection may yield different patterns in species abundance than those presented here. Because of the overall rarity of lemurs in the environment, it was not possible to test all species individually for change over time. Most notably, Avahi peyrierasi was not present at all in 2004, but in 2015 there were five sightings. While I was unable to use statistical tests to indicate if this is significant, it is intuitive that increasing from zero to five sightings may represent an increase in abundance.

3.4.4 Conclusions

In conclusion, there was overall stability in the abundances of lemurs observed at

Valohoaka over an 11-year period. I caution that the sample sizes for lemur observations were small, and it is challenging to draw broader conclusions from a single site. Ranomafana National

Park is a large area that has areas with different histories of anthropogenic disturbance (Wright et al. 2012) and distinct compositions of lemur species (Chapter 2). Examining temporal differences between all sites where data are available for lemur abundances (e.g., all eight sites in

Chapter 2) would improve our understanding of how variation present in vegetation may affect lemur communities. In addition, collecting more vegetation data may be useful in explaining lemur abundance and distribution changes over time. Bamboo density data would be especially valuable as it is an important food resource to bamboo lemurs (Arrigo-Nelson and Wright 2004;

Tan 1999). Furthermore, future studies of lemur communities should employ methods that would help to ascertain the presence and abundance of cryptic species such as aye-ayes (Daubentonia madagascariensis) and sportive lemurs (Lepilemur spp.). This may include increasing the number of transects, surveying areas off transect to find feeding sites, or increasing amount of nocturnal surveys (Farris et al. 2011). Camera traps may also be useful in determining species presence, especially if strategically placed at preferred food trees (Olson et al. 2012).

Chapter 4 : GENERAL DISCUSSION

4.1 Summary of Objectives and Results

The objectives of this thesis were to examine a lemur community in a protected forest and use functional diversity indices to determine if functional redundancy was present in sites across RNP. In addition, I aimed to understand how changes in forest structure and topography related to functional diversity values. Finally, at one location in RNP, I compared lemur abundances over time to see if changes had occurred to the community as a whole, as well as individual species.

The results of this study suggest that there is low functional redundancy across lemur communities in RNP. There was variation across sites in functional diversity measures. The various functional diversity indices were affected differently by suites of environmental predictors. Location (edge vs. interior), elevation, fallen tree density, and tree diversity were recurrent predictors of functional diversity. Overall, FD developed by Petchey and Gaston (2002) was sensitive to anthropogenic disturbance, while the three indices (i.e., FRic, FEve, FDiv) developed by Villéger et al. (2008) were more sensitive to elevation and to vegetation characteristics. FD was highest in interior forests while higher values of FRic, FEve, and FDiv were found as elevation increased. These results indicate that with increasing elevation, there was an increase in the functional trait space used, and greater abundance of extreme traits. Some vegetation predictors were difficult to interpret; for example, tree diameter had a negative relationship with FDiv. Positive relationships were found for tree diversity with FD and FRic, as well as fallen tree density with FDiv and FEve. The vegetation characteristics likely are predictors because of their relationship to food abundances and niche creation. However, as no

74 single variable was important in all four metrics used, further research is required to better understand why these measures respond differently to habitat pressures.

When examining a single location over time, lemur abundances were generally stable.

However, the abundance of Microcebus rufus appeared to be rising. A decline in anthropogenic disturbance (cut trees) and an increase in natural disturbance (standing dead trees and fallen trees) may be reasons for the observed abundance change in this species. Because of life history traits that allow for faster reproduction than other lemurs (Blanco et al. 2015), M. rufus may respond more quickly to changes in the environment. The overall stability of Valohoaka’s lemur community is a positive result in the face of widespread declines in lemur abundance (Schwitzer et al. 2014). However, sample size was small, so results should be interpreted with caution, and may not be representative of RNP as a whole as Valohoaka does not have strong anthropogenic disturbance.

4.2 Significance and Applications

The low functional redundancy found across sites in RNP suggests each lemur species fills an individual niche. Areas with low redundancy can be used to identify which sites should have priority in protecting ecosystem function. As a low redundancy indicates that there may be only a single primate species providing that function in an ecosystem, it may be particularly important to protect that lemur community if ecosystem processes are to be conserved

(Rosenfeld 2002). As discussed above, lemurs can provide important ecosystem functions such as seed dispersal (Dew and Wright 1998; Razafindratsima and Dunham 2015). Removal of frugivores can cause shifts in the functional traits of trees; e.g., fewer plants that are animal dispersed (Kurten et al. 2015).

By identifying what features of habitat predict the highest functional diversity, these features can be used to identify areas that are a priority for future surveys either within the park or outside its boundaries in the continuous forest. Elevation is particularly important to diversity and can be identified prior to setting up fieldwork (Goodman and Ganzhorn 2004; Herrera 2017).

In addition, sites with high diversity may be ideal locations to set up more permanent research camps, such as those at Mangevo and Valohoaka; such research may have both scientific and conservation benefits (Laurance 2013; Pusey et al. 2007). Ampozasaha has high functional diversity and is located in the north section of the park, where a permanent research site does not exist. Sahateza also maintains high functional diversity, with the additional benefit of being one of the few locations to study Lepilemur microdon.

While in this thesis functional diversity was applied to understand if redundancy is present and to identify characteristics of the habitat that predict functional diversity, there are other applications. For example, Razafindratsima et al. (2013) used present and Holocene lemur communities to show that following extinctions, there are changes to the trait structure of body size and niche space. Functional diversity has also been used in conjunction with species richness to look at vulnerability to extinction by modelling different extinction scenarios (Farias and Svensson 2014). Such approaches might be applied on a finer spatial scale within the

Ranomafana study system.

4.3 Future Research Directions

While I was only able to investigate temporal changes in lemur abundance in a single site, in the future it would interesting to study how functional diversity is changing over time across all eight sites or other locations for which there are data. Each location has a different history of disturbance (Brown and Gurevitch 2004), which may manifest in distinct patterns of

76 lemur diversity and ecosystem function. In addition to different disturbance histories, some sites within RNP are subject to ongoing anthropogenic disturbance. For example, at the time of this field research, there were reports of gold mining near Vohiparara and Torotosy. Farris et al.

(2011) reported that in 2005 Vohiparara was experiencing disturbance including forest fragmentation, poaching, and resource extraction. While these anthropogenic disturbances could have important effects on lemur communities, they are a challenge for fieldwork. In some areas, the relationship between local communities and the park authorities have broken down over gold mining within park boundaries and, as a result, the villages are considered unsafe to visit (Vuola

2015). In addition, miners that have migrated to the area to extract resources have attacked villages on the perimeter of the park, looting, destroying property, and causing villagers to abandon their homes until the conflict has passed (Vuola 2015).

Broadly, my goal in this thesis was to gain a better understanding of what features of the habitat account for variation in the functional diversity of the lemur community. Although no single variable was a predictor for all measures, my findings are a building block for future studies. This research was focused exclusively on primates. However, because functional diversity and redundancy relate to variation in trait space regardless of taxon, it is possible that including non-primates, such as small mammals and birds, may change these metrics (e.g., increase functional redundancy by filling similar ecological roles). Including species from multiple taxa may be essential to understanding ecosystem function and to developing appropriate conservation measures. Conversely, other studies on functional diversity have examined a sub-category of species; for example, Razafindratsima et al. (2017) examined dispersal traits and productivity traits of plants separately and Brown et al. (2011, 2013) looked only at species of plants that provided services to humans (e.g., firewood, timber). Similar

77 approaches might be applied to subsets of the lemur community – for example, the frugivore species highlighted in Chapter 3, as seed dispersal is one of the most important ecological functions of primates in tropical forests (Chapman and Russo 2011; Moses and Semple 2011;

Wrangham et al. 1994; Wright et al. 2011).

Studying change over time across the RNP sites would be limited by the data that were originally collected. However, in future studies, there are some additional environmental variables that may be important to examine in relation to lemur diversity and abundance.

Because of the importance of bamboo in the diet of bamboo lemurs (Hapalemur spp. and

Prolemur simus; Tan 1999), it would be useful to include bamboo density in vegetation surveys.

It may also be important to record liana and epiphyte density as both are important diet items for some lemur species (Atsalis 1999; Dröscher and Kappeler 2014; Razafindratsima et al. 2014), and epiphytes are used as sleep sites (Karanewsky and Wright 2015). For example, Bakerella, an epiphyte, was the predominant fruit in the diet of Microcebus rufus (Atsalis 1999); thus, because this lemur has some extreme traits (e.g., small body size), including epiphytes may improve models predicting overall trait diversity within the primate community.

This study used line-transects to estimate primate abundances. While line-transects do provide good estimates of lemur abundances (Meyler et al. 2012), they may perform poorly in the case of especially cryptic species. Aye-ayes (Daubentonia madagascariensis) are often missed in transect surveys (Herrera 2016) and their presence is typically determined by locating recent feeding sites (Farris et al. 2011). By complementing transect surveys with off-transect surveys in the area for signs of aye-aye, the accuracy of functional diversity metrics would be improved for indices that do not use abundance in their calculation (e.g., FD, FRic).

In addition, in the longitudinal study, only Microcebus rufus had marginally significant changes in abundance over the time period. Because of its life history traits, M. rufus appears to be the first primate to respond to habitat changes in a measurable way. It may be possible to use

Microcebus abundances to predict other lemur abundances or habitat disturbance. Microcebus are present across Madagascar (Louis and Lei 2016) and as such, may be useful as indicator species across lemur communities.

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APPENDIX A: TRAITS, LEMUR ABUNDANCES AND FUNCTIONAL DIVERSITY

Species abundances were calculated using the data collected from the transect surveys at eight sites. Typically diurnal or cathemeral species sighted in nocturnal surveys were discarded from abundance results.

Species traits were compiled by searching the recent literature, from 2010-current first.

Older sources were used if newer data were not available. Newer sources were preferred because of recent changes to the lemur taxonomy. If older sources were used, the original location of data collection was cross-referenced with the IUCN Red List (2017) current ranges for species or with newer literature describing the species and range. For morphological traits, data from females were used. If multiple sources of data were available for morphological traits, then the values were averaged. Data from Ranomafana National Park were preferred. When categorical traits were not available for a species, a category was assigned based on others in that genera, when data so far indicate that the genus only falls into a single category (e.g., Lepilemur spp. are nocturnal). Diet was assigned to a particular category if > 50% of such items were consumed as part of the diet. If species consumed items from multiple categories with no resources > 50% they were assigned to be omnivores if the diet contained insects and folivore-frugivores if the diet was near evenly split between these dietary categories. Split categories for diet were also assigned if the diet differed in wet/dry seasons and > 50% of such an item was consumed in each season.

Supplemental Table 1 Abundances (# animals/km) of diurnal lemur species at each site and by distance from edge in 2004.

Eulemur E. Hapalemur H. Propithecus Varecia Location2 Site1 rubriventer rufifrons aureus griseus edwardsi variegata Afotaka E 0.13 0.17 0.17 0 0 0.33 I-1 0.25 0 0.17 0.63 0 0.42 I-2 0.33 0 0 0 0 0 Ampo E 0.5 1.46 0 0.13 0.25 0 I-1 0.17 0.21 0 0 0.17 0 I-2 0.13 1.38 0 0.17 1.04 0 Mangevo E 0 0 0 0 0.13 0.38 I-1 0.17 0.58 0 0 1.38 3.21 I-2 0.083 0 0 0.42 0 0 Sahateza E 0 0 0 0.26 0.13 0 I-1 0.13 0 0 0.087 0.26 0 I-2 0.52 0 0 0.13 0.43 0 Torotosy E 0.14 0.43 0 0.76 0.19 0 I-1 0.71 0.38 0 0.24 0.67 0 I-2 0.19 0 0 0.52 0.14 0 Tsin E 0.17 0 0 0 0.21 0 I-1 0.58 0.58 0 0 0.17 0 I-2 0.083 0 0 0.33 0.13 0 Valo E 0.65 0.96 0 0.22 0 0.13 I-1 0.22 0 0 0 0.26 0.13 I-2 0.65 0.70 0 0 0.74 0.043 Vohi E 0 0 0 0 0.42 0 I-1 0.083 0.88 0 0.33 0.58 0 I-2 0.13 0.13 0 0.29 0.33 0 1 Afotaka = Ambinanindranofotaka; Ampo = Ampozasaha; Tsin = Tsinjorano; Valo = Valohoaka; Vohi = Vohiparara 2 E = Edge, 0-999 m; I-1 = Interior, 1000-2000 m; I-2, Interior 3000-4000 m

Supplemental Table 2 Abundances (# animals/km) nocturnal lemur species at each site and by distance from edge in 2004. Presence of Daubentonia madagascariensis feeding traces is also indicated.

1 Avahi Cheirogaleus Daubentonia Lepilemur Microcebus Site Location peyrierasi crossleyi madagascariensis2 microdon rufus Afotaka3 E 0 0.80 0 0 I-1 0.60 2.40 0.40 0.40 I-2 0 1.80 0 0.20 Ampozasaha E 0.60 1.00 0 1.00 I-1 0 2.60 Present 0.20 0 I-2 0.80 1.20 0.40 0.20 Mangevo E 0 0 0 0 I-1 0.60 0.80 0 0 I-2 0 0 0 0.40 Sahateza E 0 0 0 1.83 I-1 0.17 0.17 0 1.00 I-2 0.17 0.17 0 1.67 Torotosy E 0 2.00 0 1.00 I-1 0 0.50 0 0.50 I-2 0 0 0 1.50 Tsinjorano E 0 0 0 0.40 I-1 0 0 0 0.40 I-2 0.20 0.20 0.20 0.40 Valohoaka E 0 0.75 0 0.50 I-1 0 1.25 0 0 I-2 0 1.25 0 0.25 Vohiparara E 0 0 Present 0 0.33 I-1 0 0.67 Present 0 0 I-2 0 0.33 Present 0.67 0.67 1 E = Edge, 0-999 m; I-1 = Interior, 1000-2000 m; I-2, Interior 3000-4000 m 2 Daubentonia madagascariensis was only detected by feeding traces. As this was not a main sampling method, I have only indicated presence. 3 Ambinanindranofotaka; Daubentonia madagascariensis feeding trace detected off transect at 2950 m.

Supplemental Table 3 Traits for each species of lemur included in functional diversity analysis with reference sources.

Species Body Mass (kg) Diet Activity Pattern Avahi peyrierasi 1.041 Folivore2 Nocturnal3 Cheirogaleus crossleyi 0.334 Frugivore3,5 Nocturnal3 Eulemur rubriventer 2.006 Frugivore6 Cathemeral6 E. rufifrons 2.256 Frugivore6 Cathemeral6 Hapalemur aureus 1.506 Folivore3,7 Diurnal3 H. griseus ranomafanensis 0.858 Folivore6,7,9 Diurnal10 Lepilemur microdon 0.8711 Folivore6 Nocturnal3 Microcebus rufus 0.04112 Frugivore-Insectivore6,13 Nocturnal6 Propithecus edwardsi 5.703 Frugivore-Folivore3 Diurnal14 Varecia variegata editorum 3.8115 Frugivore6, 16 Diurnal3

1 Lei et al. (2008) 2 Faulkner & Lehman (2006) 3 Rowe & Myers (2016) 4 Lei et al. (2014) 5 Lahann (2007) 6 Mittermeier et al. (2010) 7 Tan (1999) 8 Rabarivola et al. (2007) 9 Grassi (2006) 10 Tecot et al. (2016) 11 Taylor & Schwitzer (2011) 12 Radespiel et al. (2012) 13 Atsalis (1999) 14 Pochron et al. (2003) 15 Baden et al. (2008) 16 Overdorff et al. (2005)

R Script for Calculating Functional Diversity and Index of Variation ## library needed library(picante) library(ade4) library(FD) # Data manipulations ## Remove all rows that have NAs trait <- na.omit(trait) ## Remove all species from the community matrix with no traits presab <- presab[,names(presab)[!is.na(match(names(presab),rownames(trait)))]] # scale continuous trait variables to have mean 0 and sd = 1 trait$Body.Mass..kg. <- scale(trait$Body.Mass..kg., scale = TRUE) #### "scale" spits out a matrix, so change the variable into numeric trait$Body.Mass..kg. <- as.numeric(trait$Body.Mass..kg.) ##Convert categorical traits to be numerical trait$Diet <- as.numeric(as.factor(trait$Diet)) trait$Activity.Pattern <-as.numeric(as.factor(trait$Activity.Pattern)) str(trait) ############################################################# # Measure of Petchey and Gaston's functional diversity FD ## create distance matrix for the traits distance <- vegdist(trait, "gower")

## Construct a dendrogram with the distance matrix (this would represent the similarity/dissimilarity among species according to their ecological traits) dendro <- hclust(distance, "average") ## Transform the dendrogram into a "phylo" file dendro.p <- as.phylo(dendro) ## calculate the functional diversity for each community diversity <- pd(presab, dendro.p) colnames(diversity) <- c("FD", "SR_FD")

###################################################### ## Measure the other Functional diversity metrics: FEve, FRic, FDiv diversity2 <- dbFD(trait, presab, w.abun = FALSE, stand.x = TRUE, corr = "cailliez", ord = c("podani","metric"), calc.FRic = TRUE, m = 3, stand.FRic = TRUE, calc.FGR = FALSE, calc.CWM = FALSE, calc.FDiv = TRUE) ###############################################################

# Null communities # shuffled species from a pool of all species in all communities # maintaining species richness n <- 9999 # number of randomizations xy.FD <- matrix(0,n,length(presab[,1])) xy.FRic <- matrix(0,n,length(presab[,1])) xy.FEve <- matrix(0,n,length(presab[,1])) xy.FDiv <- matrix(0,n,length(presab[,1])) new.mat <- as.matrix(presab) for(j in 1:n) { mm <- matrix(0,length(presab[,1]),length(presab[1,])) for(i in 1:length(presab[,1])) { set.seed(j) mm[i,] <- sample(new.mat[i,], length(presab[1,]), replace=F) }

#If j sample has all communities at 0 then do nothing and move on to next sample if(any(rowSums(mm == 0) == ncol(mm))) { next }

m.dat <- data.frame(mm) rownames(m.dat) <- dimnames(presab)[[1]] colnames(m.dat) <- dimnames(presab)[[2]]

div.null <- pd(as.matrix(m.dat), dendro.p) div.null.2 <- dbFD(trait, m.dat, w.abun = FALSE, stand.x = TRUE, corr = "cailliez", ord = c("podani","metric"), calc.FRic = TRUE, m = 3, stand.FRic = TRUE, calc.FGR = FALSE, calc.CWM = FALSE, calc.FDiv = TRUE)

xy.FD[j,] <- div.null$PD xy.FRic[j,] <- div.null.2$FRic xy.FEve[j,] <- div.null.2$FEve xy.FDiv[j,] <- div.null.2$FDiv } #### Index of Variance (IV) for each metric FD.obs <- as.data.frame(diversity$FD) FD.null <- apply(xy.FD,2,mean) sd.FD.null <- apply(xy.FD,2,sd) IV.FD <- as.data.frame((2 * (FD.obs / (FD.obs + FD.null)))-1)

FEve.obs <- as.data.frame(diversity2$FEve) FEve.null <- apply(na.omit(xy.FEve),2,mean) sd.FEve.null <- apply(xy.FEve,2,sd) IV.FEve <- as.data.frame((2 * (FEve.obs / (FEve.obs + FEve.null)))-1)

FRic.obs <- as.data.frame(diversity2$FRic)

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FRic.null <- apply(na.omit(xy.FRic),2,mean) sd.FRic.null <- apply(xy.FRic,2,sd) IV.FRic <- as.data.frame((2 * (FRic.obs / (FRic.obs + FRic.null)))-1)

FDiv.obs <- as.data.frame(diversity2$FDiv) FDiv.null <- apply(na.omit(xy.FDiv),2,mean) sd.FDiv.null <- apply(xy.FDiv,2,sd) IV.FDiv <- as.data.frame((2 * (FDiv.obs / (FDiv.obs + FDiv.null)))-1) ## outputs of the index of variance of FD indices out.iv <- as.data.frame(cbind(FD.obs, IV.FD, FEve.obs, IV.FEve, FRic.obs, diversity2$qual.FRic, IV.FRic, FDiv.obs, IV.FDiv, diversity$SR_FD)) colnames(out.iv) <- c("FD", "IV.FD", "FEve", "IV.FEve", "FRic", "qual.FRic", "IV.FRic", "FDiv", "IV.FDiv", "SR")

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APPENDIX B: LINEAR MIXED MODELS AND VARIABLES

Linear Mixed Model (LMM)

Variables

Variables were selected to represent botanical and landscape characteristics. Some variables were excluded because of missing data points which caused sites or distances to be missing from the LMM analysis. Many of the variables included came from the microplot botanical surveys, so the LMM excluded the distance 2000-3000 m from the analysis.

Aspect

Compass bearings and direction for the aspect was changed into a categorical variable of

North (316-360, 0-45), East (46-135), South (136-225), and West (226-315).

Index of Disturbance

To create an Index of Human Disturbance, I scaled the variables: number of cuts on trees, trails, cattle dung, and evidence of fire. Variables were scaled by dividing the number of occurrences by the maximum number of occurrence for that variable and weighted by 0.25 so that each variable had a maximum value of 0.25. The four scaled variables were then added together for each subplot and this value was averaged by the distance from the edge. The index is from 0-1, with values closer to 1 indicating higher human disturbance.

Tree Diversity

Tree diversity was calculated using the Shannon Diversity Index: ln , where pi is the proportional abundance of species.

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Supplemental Table 4 Vegetation and topographic characteristics for each site at the edge. Numbers in brackets indicate value range and ± indicates standard deviation. Densities are averaged from plots. † Variable used in the linear mixed model analysis.

Edge Afotaka1 Ampozasaha Mangevo Sahateza Torotosy Tsinjorano Valohoaka Vohiparara Vegetation Mean tree height 13.31 ± 12.36 ± 3.78 16.69 ± 5.68 12.45 ± 14.79 ± 15.62 ± 4.42 13.87 ± 3.90 10.45 ± 2.98 (m) 4.87 3.56 4.98 Mean DBH (cm) 20.74 ± 16.68 ± 6.59 19.32 ± 16.88 ± 18.50 ± 20.40 ± 11.58 19.07 ± 9.81 15.18 ± 5.15 22.56 11.10 6.53 8.77 Tree density/ha 816.63 ± 713.01 ± 827.60 ± 916.73 ± 1022.83 ± 702.48 ± 891.27 ± 814.87 ± (>10 cm DBH) 381.24 345.23 385.25 414.13 476.97 362.72 364.75 303.89 Density fallen 377.58 ± 500.81 ± 500.81 335.29 ± 594.18 122.93 ± 445.63 ± 114.59 trees/ha† 229.49 285.55 225.50 164.90 294.83 Density dead 30.73 12.73 76.39 0 161.28 294.16 220.69 284.36 ± trees, standing/ha 215.88 Shrub density/ha 1865.95 ± 1205.33 ± 564.47 1103.47 ± 0 741.99 1226.55 ± 539.00 ± 2012.67 1184.88 1709.64 3029.40 393.77 Tree diversity† 3.30 2.79 3.31 3.18 3.31 3.24 3.01 2.75 Disturbance Density cut 0 0 4.24 0 169.76 0 97.61 ± 76.39 ± 213.05 trees/ha† 467.74 Index of 0.038 ± 0.037 ± 0.064 0.010 ± 0.013 ± 0.065 ± 0.034 ± 0.054 0.022 ± 0.049 0.069 ± 0.098 Disturbance 0.061 0.033 0.038 0.077 Topographic Elevation (m) † 675.81 996.75 ( 901- 682.76 (588- 1119.71 892.83 905.10 (856- 867.29 (822- 1098.34 (541- 1119) 758) (1003-1195) (773-984) 988) 967) (1030-1887) 1000) Slope 17.36 ± 21.61 ± 8.13 27.76 ± 11.91 ± 17.68 ± 14.70 ± 5.09 19.80 ± 6.17 14.13 ± 6.49 5.99 14.91 6.24 5.56 Aspect South North North North East West North East

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Supplemental Table 4 Con’t Vegetation and topographic characteristics for each site in the interior forest

Interior Afotaka1 Ampozasaha Mangevo Sahateza Torotosy Tsinjorano Valohoaka Vohiparara Vegetation Mean tree height 13.55 ± 12.28 ± 3.30 14.56 ± 6.30 12.88 ± 4.42 14.37 ± 14.66 ± 5.57 14.86 ± 5.02 13.68 ± 3.75 (m) 5.23 4.83 Mean DBH (cm)† 19.68 ± 17.12 ± 6.50 21.14 ± 18.44 ± 9.76 17.93 ± 23.60 ± 40.69 23.15 ± 16.25 20.10 ± 9.06 12.73 14.69 7.41 Tree density/ha 735.08 ± 801.29 ± 757.06 ± 1085.69 ± 1083.97 ± 611.15 ± 892.96 ± 782.62 ± (>10 cm DBH) 384.58 377.22 341.70 555.28 413.11 292.30 489.33 416.38

Density fallen 402.34 ± 302.18 ± 342.40 581.56 ± 615.97 88.28 ± 344.62 ± 298.79 trees/ha† 222.55 225.22 422.59 118.36 229.08 Density dead 25.46 27.16 77.43 6.88 246.04 152.16 117.14 288.60 ± trees, standing/ha 207.57 Shrub density/ha 20.32.09 ± 1213.82 ± 1968.36 ± 3126.31 ± 452.52 ± 444.78 ± 1434.51 ± 607.76 ± 2067.43 1271.92 1968.79 2555.45 1231.41 723.10 2114.15 795.84 Tree diversity† 3.11 2.96 3.39 3.26 3.32 3.22 3.32 3.12 Disturbance Density cut 0 6.79 0 0 246.04 0 22.07 ± 82.07 13.58 ± 44.76 trees/ha† Index of 0 0.078 ± 0.12 0 0 0.053 ± 0.0006 ± 0.0012 ± 0.024 ± 0.049 Disturbance 0.068 0.0034 0.0047 Topographic Elevation (m)† 989.59 1069.81 915.73 (660- 1125.66 987.23 1079.03 (923- 1089.59 (888- 1076.72 (768-1150) (977-1150) 1100) (1001-1194) (820-1994) 1209) 1225) (1010-1160) Slope 16.65 ± 19.42 ± 5.53 27.48 ± 14.76 ± 7.11 17.98 ± 11.98 ± 5.34 17.42 ± 5.01 16.27 ± 7.96 6.64 11.65 7.22 Aspect North South North South South West East West 1 Ambinanindranofotaka

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Linear Mixed Model Results

Supplemental Table 5 Results of linear mixed model using the model including fallen tree density and location in predicting lemur functional diversity.

Variable Slope SE t value p Intercept -0.55 0.30 -1.82 0.081 Location: Interior 0.82 0.37 2.23 0.041 Fallen tree density 0.36 0.18 2.03 0.063

Supplemental Table 6 Results of the linear mixed model for functional richness using fallen tree density and elevation in the model

Variable Slope SE t value p Intercept 0 0.016 0 1.00 Fallen tree density 0.048 0.016 2.98 0.0069 Elevation 0.046 0.016 2.82 0.0099

Supplemental Table 7 Linear mixed model results using the model that includes tree diversity and elevation in predicting functional evenness

Variable Slope SE t value p Intercept 0.76 0.017 44.03 0.047-9 Tree diversity 0.071 0.017 4.18 0.00051 Elevation 0.045 0.018 2.55 0.018

Supplemental Table 8 Linear mixed model results for functional divergence using tree diameter (DBH), tree diversity, and elevation

Variable Slope SE t value p Intercept 0 0.12 0 1.00 DBH -0.59 0.12 -4.58 0.00015 Tree diversity 0.61 0.15 4.22 0.00035 Elevation 0.32 0.015 2.17 0.041

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APPENDIX C: VALOHOAKA LEMUR DENSITY AND ABUNDANCE

Lemur Group Density and Encounter Rates at Valohoaka Using the program DISTANCE (Thomas et al. 2010), group density was calculated for species for which there were enough sightings to calculate sighting distances; i.e., Cheirogaleus crossleyi and Microcebus rufus were run using a uniform cosine distribution, while other species used a half normal cosine model. If there were not data to calculate density (i.e., 60 encounters per species), the histogram-inspection technique was used (Whitesides et al. 1988).

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Supplementary Table 9 Lemur group densities and average cluster size by transect and year at Valohoaka, Ranomafana National Park. Brackets indicate confidence intervals.± is standard error, dash indicates the cluster size had no variance.

Species Transect A Transect B Transect C Overall Mean Distance from Edge (m) 1500-2000 2250-2750 3000-3500 Cluster Size Diurnal Propithecus edwardsi 2004 1.93 (1.58-2.34) 1.93 (1.58-2.34) 3.85 (3.17-4.68) 2.57 (1.04-6.33) 3.25 ± 0.63 2015 8.86 (7.29-10.78) 4.43 (3.64-5.39) 0 4.43 (0.48-40.65) 3.33 ± 0.67 Varecia variegata 2004 0 0 1.65 (1.38-1.97) 0.55 (0.016-18.89) 1 (-) editorum 2015 0 0 3.78 (3.17-4.52) 1.26 (0.037-43.44) 1 (-) Eulemur rubriventer 2004 4.40 (3.81-5.08) 2.20 (1.91-2.54) 11.00 (9.53-12.71) 5.87 (0.98-34.98) 2.38 ± 0.32 2015 10.12 (8.76-11.69) 0 5.06 (4.38) 5.06 (0.53-48.33) 2.67 ± 0.67 Eulemur rufifrons 2004 0 0 5.51 (4.49-6.76) 1.84 (0.054-62.39) 5.33 ± 0.33 2015 0 0 4.22 (3.44-5.18) 1.41 (0.041-47.83) 4 (-) Hapalemur griseus 2004 0 4.92 (4.14-5.86) 0 1.64 (0.048-56.67) 3.50 ± 0.50 ranomafanensis 2015 0 5.66 (4.76-6.74) 0 1.89 (0.055-65.17) 1 (-) Nocturnal Cheirogaleus crossleyi 2004 40.41 (32.07- 26.94 (21.38- 26.94 (21.38- 31.43 (19.90- 1 (-) 50.92) 33.94) 33.94) 49.64) 2015 0 13.47 (10.69- 26.94 (21.38- 13.47 (1.52- 1 (-) 16.97) 33.94) 119.44) Microcebus rufus 2004 0 44.00 (38.36- 22.00 (19.18- 22.00 (2.29- 1 (-) 50.46) 25.23) 211.03) 2015 22.00 (19.18- 88.00 (76.72- 65.99 (57.53- 58.66 (15.85- 1.13 ± 0.13 25.23) 100.92) 75.69) 217.16) Avahi peyrierasi1 2004 0 0 0 Absent 0 2015 26.91 13.45 26.91 22.42 1.40 ± 0.55 1 Avahi peyrierasi density was calculated using the histogram-inspection technique

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Supplementary Table 10 Encounter rates of lemurs (individuals/km) at Valohoaka, Ranomafana National Park.

Species Transect A Transect B Transect C Overall Distance from Edge (m) 1500-2000 2250-2750 3000-3500 Diurnal Propithecus edwardsi 2004 0.26 0.17 0.7 0.38 2015 1.6 0.4 - 0.67 Varecia variegata 2004 - - 0.087 0.029 editorum 2015 - - 0.2 0.067 Eulemur rubriventer 2004 0.43 0.087 1.13 0.55 2015 0.8 - 0.8 0.53 Eulemur rufifrons 2004 - - 1.39 0.46 2015 - - 0.8 0.27 Hapalemur griseus 2004 - 0.61 - 0.20 ranomafanensis 2015 - 0.2 - 0.067 Nocturnal Cheirogaleus crossleyi 2004 1.5 1 1 1.17 2015 - 1 2 0.5 Microcebus rufus 2004 - 1 0.5 0.5 2005 0.5 2 2 1.5 Avahi peyrierasi 2004 - - - Absent 2015 1 1 1.5 1.17

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APPENDIX D: VUONG TESTS

Supplementary Table 11 Vuong test results comparing between zero-inflated Poisson and Poisson model for Eulemur rubriventer

z-statistic p Raw PRM1 > ZIP2 0.66 0.25 AIC-corrected PRM > ZIP 21468.34 2 e-16 BIC-corrected PRM > ZIP 49323.95 2 e-16 1 Poisson model 2 Zero-inflated Poisson model

Supplementary Table 12 Vuong test results comparing between zero-inflated Poisson and Poisson model for Propithecus edwardsi

z-statistic p Raw PRM1 > ZIP2 1.26 0.10 AIC-corrected PRM > ZIP 5.43 2 e-16 BIC-corrected PRM > ZIP 1.25 2 e-16 1 Poisson model 2 Zero-inflated Poisson model

Supplementary Table 13 Vuong test comparing zero-inflated Poisson and Poisson model for Cheirogaleus crossleyi

z-statistic p Raw ZIP2 > PRM1 1.25 0.11 AIC-corrected PRM > ZIP -1.13 0.13 BIC-corrected PRM > ZIP -2.54 0.0056 1 Poisson model 2 Zero-inflated Poisson model

Supplementary Table 14 Vuong test comparing zero-inflated Poisson and Poisson model for Microcebus rufus

z-statistic p Raw PRM1 > ZIP2 -1.02 0.15 AIC-corrected PRM > ZIP -1.44 <2 e-16 BIC-corrected PRM > ZIP -2.30 <2 e-16 1 Poisson model 2 Zero-inflated Poisson model

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