Ecological Interactions of Anolis Cristatellus And

ECOLOGICAL INTERACTIONS OF ANOLIS CRISTATELLUS AND ANOLIS KRUGI IN TWO SECONDARY TROPICAL KARST FORESTS AT THE NORTHERN KARST BELT OF PUERTO RICO: OCCUPANCY ESTIMATES AND DEGREE OF OMNIVORY/FRUGIVORY

Sondra I. Vega-Castillo A thesis submitted to the Biology Intercampus Graduate Program

DEPARTMENT OF BIOLOGY FACULTY OF NATURAL SCIENCES UNIVERSITY OF PUERTO RICO RIO PIEDRAS CAMPUS

In partial fulfillment of the requirements for the degree of

DOCTOR IN PHILOSOPHY

May 2014 Río Piedras, Puerto Rico

TABLE OF CONTENTS

LIST OF TABLES ...... v LIST OF FIGURES ...... vii LIST OF APPENDICES ...... xii ABSTRACT ...... xiii BIOGRAPHY ...... xv ACKNOWLEDGEMENTS ...... 3 CHAPTER I ...... 5 INTRODUCTION, OBJECTIVES AND OVERVIEW OF CHAPTERS ...... 5 Introduction ...... 6 Objective of the Thesis ...... 8 Overview of Chapters ...... 9 Chapter II ...... 10 INFLUENCE OF HABITAT STRUCTURE AND QUALITY ON OCCUPANCY AND ABUNDANCE OF TWO ANOLES ECOMORPHS, ANOLIS CRISTATELLUS AND ANOLIS KRUGI, IN SECONDARY KARST FORESTS OF NORTHERN PUERTO RICO...... 10 Abstract ...... 11 Introduction ...... 12 Methods ...... 14 Study areas ...... 14 Focal species ...... 15 Demographic parameters and habitat characterization ...... 15 Statistical analysis ...... 17 Results ...... 20 Habitat characterization ...... 20 Prey availability ...... 21 Occupancy and abundance ...... 21 Discussion ...... 24 TABLES ...... 30 FIGURES ...... 45 APPENDICES ...... 58

CHAPTER III ...... 63 OMNIVORY AND TROPHIC POSITION OF ANOLIS CRISTATELLUS AND ANOLIS KRUGI AT TWO SECONDARY KARST FOREST ON NORTHERN PUERTO RICO ...... 63 Abstract ...... 64 Introduction ...... 65 Methods ...... 68 Study areas ...... 68 Samples collection ...... 68 Stable Isotopes Analysis ...... 69 Food resources availability ...... 70 Statistical analysis ...... 71 Results ...... 73 Food resources availability ...... 74 Discussion ...... 76 TABLES ...... 80 FIGURES ...... 93 CHAPTER IV ...... 110 EFFECTS OF SEASONAL CHANGES IN FOOD AVAILABILITY ON THE DIET OF ANOLIS CRISTATELLUSAND A. KRUGI AT TWO KARST FORESTS AT NORTHERN PUERTO RICO ...... 110 Abstract ...... 111 Introduction ...... 112 Methods ...... 115 Study areas ...... 115 Samples collection ...... 115 Stable Isotopes Analysis ...... 116 Food resources availability ...... 118 Statistical Analyses ...... 119 Results ...... 120 Food resources availability ...... 122 Discussion ...... 124 TABLES ...... 129 FIGURES ...... 136

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CHAPTER V ...... 175 CONCLUSION ...... 175 Conclusion...... 176 LITERATURE CITED ...... 178

LIST OF TABLES CHAPTER II

Table 1. Ecomorph characteristics for Caribbean anoles (modified from Losos 2009).

Table 2. Description of site covariates used in occupancy models for Anolis cristatellus and Anolis krugi. Data were collected in north-central karst region, Puerto Rico, 2012.

Table 3. Model notation and description for candidate models of Anolis cristatellus and A. krugi site occupancy at two private natural reserves. Data were collected in north central karst region, Puerto Rico, 2012.

Table 4. Average and standard error of vegetation variables measured at anoles surveys plots at Mata de Platano and El Tallonal Private Reserves. Data were collected in north central karst region, Puerto Rico, 2012.

Table 5. Candidate models for occupancy analysis for Anolis cristatellus and A. krug ranked by Akaike’s Information Criterion values. Site covariates included structural habitat, environmental and food availability. Data were collected in north-central karst region, Puerto Rico, 2012.

Table 6. Untransformed parameter estimates of coefficients (Beta's ± SE) for parameter and covariates from the top (most supported) multi-season occupancy models for Anolis cristatellus and A. krugi in Secondary Karst Forests in Northern Puerto Rico, 2012. Parameters include occupancy (Psi), gamma (colonization), epsilon (extinction), and detection (p) probabilities, and covariates. Seasons are defined as 6 time intervals (Jan, Mar, May, July, Aug, Oct) in 2012. The influence of a covariate (i.e. ˆ coefficient) was deemed to have a strong support if the 95% confidence interval (CI) did not include zero.

Table 7. Akaike weights (wi) for five competing multi-season occupancy models (Akaike’s

Information Criterion wi  2) of A. krugi. Data were collected in north-central karst

region, Puerto Rico, 2012. Akaike weights (wi) were used to assess the relative importance of covariates.

CHAPTER III

Table 1. Body measurements of males and females of Anolis cristatellus (n=112) and A. krugi (n=74) at Mata de Platano Private Reserve and El Tallonal Private Reserve, two private natural reserves at north-central karst region, Puerto Rico, 2012.

Table 2. Comparison of stable isotope values (‰) of Anolis lizards per categories (adults and juveniles) and per sex sampled at Mata de Plátano Reserve and El Tallonal Private Reserves at north-central karst region, Puerto Rico, 2012.

Table 3. Average stable isotope values (‰) of Anolis lizards (Anolis cristatellus, n= 76; A. krugi, n=30) and plants sampled at Mata de Plátano Reserve and El Tallonal Private Reserves at north-central karst region, Puerto Rico, 2012.

Table 4. Average trophic position for Anolis cristatellus (n=112) and A. krugi (n=74) by sampling period at Mata de Platano Private Reserve and El Tallonal Private Reserve, two private natural reserves at north-central karst region, Puerto Rico, 2012.

Table 5. Spearman’s Rank correlation values for log10-transformed morphometric measures of Anolis cristatellus (n=112) and A. krugi (n=74) against trophic position at Mata de Platano Private Reserve and El Tallonal Private Reserve, two private natural reserves at north central karst region, Puerto Rico, 2012.

Table 6. Fructification periods for the plant species producers of fleshy fruits identifiedas potential food resources for anoles at at Mata de Platano Private Reserve (n=10) and El Tallonal Private Reserve (n=10) two private natural reserves at north-central karst region, Puerto Rico, 2012.

CHAPTER IV

Table1. Stable isotope values (‰) of producers (n=17), invertebrates (twelve arthropods orders) and Anolis lizards (Anolis cristatellus, n= 76; A. krugi, n=30) sampled at Mata de Plátano Reserve, a private natural reserve at north-central karst region, Puerto Rico, 2012.

Table 2. Stable isotope values (‰) of producers (n=23), invertebrates (twelve arthropods orders) and Anolis lizards (Anolis cristatellus, n= 78; A. krugi, n=75) sampled a El Tallonal Reserve, a private natural reserve at north-central karst region, Puerto Rico, 2012.

Table 3. Fructification periods for the plant species producers of fleshy fruits identifiedas potential food resources for anoles at Mata de Platano Private Reserve (n=10) and El Tallonal Private Reserve (n=10), two private natural reserves at north-central karst region, Puerto Rico, 2012.

LIST OF FIGURES CHAPTER II

Figure 1. Locations of study sites in the karst belt of northern Puerto Rico. Red rectangles represent plots within the reserves (yellow lines)

Figure 2. Monthly average temperature and rainfall for Mata de Platano Private Reserves. Data were collected in north-central karst region, Puerto Rico, 2012.

Figure 3. Monthly average temperature and rainfall for El Tallonal Private Reserves. Data were collected in north-central karst region, Puerto Rico, 2012.

Figure 4. Total abundance of arthropods at Mata de Platano Private Reserve and El Tallonal Private Reserve, two private natural reserves at north-central karst region, Puerto Rico, 2012. Data for both reserves was pooled since no significant differences was found between reserves.

Figure 5. Occupancy estimates as a function of average DBH of mid-story trees and average prey abundance on occupancy estimates of Anolis cristatellus in two private natural reserves at north-central karst region, Puerto Rico, 2012

Figure 6. Additive effects of sapling richness (Beta = -0.77 ± 0.47) and average prey abundance (Beta = 0.62 ± 0.43) on occupancy estimates of Anolis krugi in two private natural reserves at north-central karst region, Puerto Rico, 2012.

CHAPTER III

Figure 1. Locations of study sites in the karst belt of northern Puerto Rico. Red rectangles represent plots within the reserves (yellow lines).

Figure 2. Mean trophic position (± 1 SE) of Anolis cristatellus and A. krugi at Mata de Platano Private Reserve and El Tallonal Private Reserve, two private natural reserves at north-central karst region, Puerto Rico, 2012. Trophic position of anoles 15 were calculated relative to the baseline δN value with the equation TP = (δ Norg 15 15 δ N baseline) /3.4) + 1, where TP is trophic position, δ N of producers (plants leaves) 15 15 at each study area were used as baseline (δ N baseline), δ Norg is the mean value for

lizards tissues and 3.4 ‰ correspond to the average standard trophic fractionation in 15N per trophic level determined in previous studies.

Figure 3. Trophic position (± 1 SE) of A. cristatellus by sampling periods at Mata de Platano Private Reserve, a private natural reserve at north-central karst region, Puerto Rico, 2012. Trophic position were calculated relative to the baseline δN 15 15 value with the equation TP = (δ Norg – δ N baseline) /3.4) + 1, where TP is trophic

vii

position, δ15N of producers (plants leaves) at each study area were used as baseline

15 15 (δ N baseline), δ Norg is the mean value for lizards tissues and 3.4 ‰ correspond to the average standard trophic fractionation in 15N per trophic level determined in previous studies.

Figure 4. Trophic position (± 1 SE) of A. cristatellus by sampling periods at El Tallonal Private Reserve, a private natural reserve at north-central karst region, Puerto Rico, 2012. Trophic position were calculated relative to the baseline δN value with the 15 15 15 equation TP = (δ Norg – δ N baseline) /3.4) + 1, where TP is trophic position, δ N of 15 producers (plants leaves) at each study area were used as baseline (δ N baseline),

15 δ Norg is the mean value for lizards tissues and 3.4 ‰ correspond to the average standard trophic fractionation in 15N per trophic level determined in previous studies.

Figure 5. Trophic position (± 1 SE) of A. krugi by sampling periods at Mata de Platano Private Reserve, a private natural reserve at north-central karst region, Puerto Rico, 2012. Trophic position were calculated relative to the baseline δN value with the 15 15 15 equation TP = (δ Norg – δ N baseline) /3.4) + 1, where TP is trophic position, δ N of 15 producers (plants leaves) at each study area were used as baseline (δ N baseline),

15 δ Norg is the mean value for lizards tissues and 3.4 ‰ correspond to the average standard trophic fractionation in 15N per trophic level determined in previous studies.

Figure 6. Trophic position (± 1 SE) of A. krugi by sampling periods at El Tallonal Private Reserve, Arecibo, a private natural reserve at north-central karst region, Puerto Rico, 2012. Trophic position were calculated relative to the baseline δN value with 15 15 the equation TP = (δ Norg – δ N baseline) /3.4) + 1, where TP is trophic position, δ15N of producers (plants leaves) at each study area were used as baseline (δ15N

15 baseline), δ Norg is the mean value for lizards tissues and 3.4 ‰ correspond to the average standard trophic fractionation in 15N per trophic level determined in previous studies.

Figure 7. Relative abundance of arthropods order by periods. Data from both reserves were pooled since not significant difference was found.

Figure 8. Phenology of fleshy fruits plants at Mata de Platano Private Reserve (n=10) and El Tallonal Private Reserve (n=10), two private natural reserves at north-central karst region, Puerto Rico, 2012.

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CHAPTER IV

Figure 1. Locations of study sites in the karst belt of northern Puerto Rico. Red rectangles represent plots within the reserves (yellow lines).

Figure 2. The relationship between the natural abundance of δ15N and δ13C (± SE) values for Anolis krugi for the four sampling periods (A. Mata de Platano Private Reserve, B. El Tallonal Private Reserve, two private natural reserves at north-central karst region, Puerto Rico, 2012).

Figure 3. The relationship between the natural abundance of δ15N and δ13C (± SE) values for Anolis cristatellus for the four sampling periods (A. Mata de Platano Private Reserve, B. El Tallonal Private Reserve, two private natural reserves at north central karst region, Puerto Rico, 2012).

Figure 4. Carbon:Nitrogen (C:N) ratios of Anolis cristatellus and A. krugi for the four periods at Mata de Platano Private Reserve, a private natural reserve at north central karst region, Puerto Rico, 2012.. Dashed lines represent mean values.

Figure 5. Carbon:Nitrogen (C:N) ratios of A. cristatellus and A. krugi for the four periods at El Tallonal Private Reserve, a private natural reserve at north central karst region, Puerto Rico, 2012. Dashed lines represent mean values.

Figure 6. Polygons for natural abundance δ13C and δ15 N of nine potential food sources for Anolis cristatellus on May 2012 at Mata de Plátano Private Reserve in Arecibo, P.R. (after correcting for trophic fractionation). Histograms show the distribution of feasible contributions from each source to anole diet according with Isosource isotopic model (values are 1–99 percentile ranges for the distributions).

Figure 7. Polygons for natural abundance of δ13C and δ15 N of nine potential food sources for Anolis krugi on May 2012 at Mata de Plátano Private Reserve in Arecibo, P.R. (after correcting for trophic fractionation). Histograms show the distribution of feasible contribution from each source to anole diet according with Isosource isotopic model (values are 1–99 percentile ranges for the distributions).

Figure 8. Polygons for natural abundance of δ13C and δ15 N of nine potential food sources for Anolis cristatellus on September 2012 at Mata de Plátano Private Reserve in Arecibo, P.R. (after correcting for trophic fractionation). Histograms show the distribution of feasible contributions from each source to anole diet according with Isosource isotopic model (values are 1–99 percentile ranges for the distributions).

Figure 9. Polygons for natural abundance of δ13C and δ15 N of nine potential food sources for Anolis krugi on September 2012 at Mata de Plátano Private Reserve in Arecibo, P.R. (after correcting for trophic fractionation). Histograms show the distribution of feasible contributions from each source to anole diet according with Isosource isotopic model (values are 1–99 percentile ranges for the distributions).

Figure 10. Polygons for natural abundance of δ13C and δ15 N of nine potential food sources for Anolis cristatellus on January 2013 at Mata de Plátano Private Reserve in Arecibo, P.R. (after correcting for trophic fractionation). Histograms show the distribution of feasible contributions from each source to anole diet according with Isosource isotopic model (values are 1–99 percentile ranges for the distributions).

Figure 11. Polygons for natural abundance of δ13C and δ15 N of nine potential food sources for Anolis krugi on January 2013 at Mata de Plátano Private Reserve in Arecibo, P.R. (after correcting for trophic fractionation). Histograms show the distribution of feasible contributions from each source to anole diet according with Isosource isotopic model (values are 1–99 percentile ranges for the distributions).

Figure 12. Polygons for natural abundance of δ13C and δ15 N of nine potential food sources for Anolis cristatellus on May 2012 at El Tallonal Private Reserve in Arecibo, P.R. (after correcting for trophic fractionation). Histograms show the distribution of feasible contributions from each source to anole diet according with Isosource isotopic model (values are 1–99 percentile ranges for the distributions).

Figure 13. Polygons for natural abundance of δ13C and δ15 N of nine potential food sources for Anolis krugi on May 2012 at El Tallonal Private Reserve in Arecibo, P.R. (after correcting for trophic fractionation). Histograms show the distribution of feasible contributions from each source to anole diet according with Isosource isotopic model (values are 1–99 percentile ranges for the distributions).

Figure 14. Polygons for natural abundance of δ13C and δ15 N of nine potential food sources for Anolis cristatellus on September 2012 at El Tallonal Private Reserve in Arecibo, P.R. (after correcting for trophic fractionation). Histograms show the distribution of feasible contributions from each source to anole diet according with Isosource isotopic model (values are 1–99 percentile ranges for the distributions).

Figure 15. Polygons for natural abundance of δ13C and δ15 N of nine potential food sources for Anolis krugi on September 2012 at El Tallonal Private Reserve in Arecibo, P.R. (after correcting for trophic fractionation). Histograms show the distribution of feasible contributions from each source to anole diet according with Isosource isotopic model (values are 1–99 percentile ranges for the distributions).

Figure 16. Polygons for natural abundance of δ13C and δ15 N of nine potential food sources for Anolis cristatellus on January 2013 at El Tallonal Private Reserve in Arecibo, P.R. (after correcting for trophic fractionation). Histograms show the distribution of feasible contributions from each source to anole diet according with Isosource isotopic model (values are 1–99 percentile ranges for the distributions).

Figure 17. Polygons for natural abundance of δ13C and δ15 N of nine potential food sources for Anolis krugi on January 2013 at El Tallonal Private Reserve in Arecibo, P.R. (after correcting for trophic fractionation). Histograms show the distribution of feasible contributions from each source to anole diet according with Isosource isotopic model (values are 1–99 percentile ranges for the distributions).

Figure 18. Relative abundance of arthropods by order by periods. Data from both reserves were pooled since not significant difference was found.

Figure 19. Phenology of fleshy fruits plants at Mata de Platano Private Reserve (n=10) and El Tallonal Private Reserve (n=10), two private natural reserves at north-central karst region, Puerto Rico, 2012.

LIST OF APPENDICES

APPENDIX I. Royle-Nichols Abundance Induced Heterogeneity Models by periods for Anolis cristatellus at Mata de Platano Private Reserve and El Tallonal Private Reserve ranked by their associated AIC values.

APPENDIX II. Royle-Nichols Abundance Induced Heterogeneity Models by periods for Anolis krugi at Mata de Platano Private Reserve and El Tallonal Private Reserve ranked by their associated AIC values.

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ABSTRACT

Anolis lizards in the Caribbean islands are a major component of diurnal communities. The natural history of Anolis lizards is well studied, however information about their feeding habits and trophic ecology remains incomplete. Consequently their precise role in ecosystem function and dynamics is poorly understood. In spite of the fact that fruits have been reported on dietary studies, their importance and contribution in the degree of omnivory/frugivory of the lizards is unknown. The purpose of this study was threefold: (1) determine the occurrence and abundance of two sympatric species of the genus Anolis, Anolis cristatellus (Duméril and Bibron) and Anolis krugi (Peters) in mature secondary forests, principal habitat of the region, (2) evaluate their trophic position, and (3) the degree and annual dynamics of omnivory/frugivory of the two species using stable istopes (13C and 15N). The work was carried out at two natural private reserves within the northern karst region of Puerto Rico. Anolis cristatellus and A. krugi occurred sympatrically, however occupancy estimates and abundance differed substantially between species and were influenced primarily by structural features of the habitat. A. cristatellus was the most abundant species at both sites with estimates of occupancy greater than 90%, and positively influence by midstory trees. Occupancy rates for A. krugi were low (55-

66%) compared with A. cristatellus, and negatively influenced by richness of saplings at plots but positively influenced by food availability. Isotopic analyses for A. cristatellus and

A. krugi revealed that both species have a broad diet with the inclusion of a great diversity of prey and fruits when available. The isotopic composition varies significantly between species. Mean values for trophic position for both species ranged from 2.0 and 3.0, which suggests that they are omnivores. However, fruits seemed to represent an important food

xiii resource for A. krugi, which showed the lowest trophic position (2.34). Results from source partition models for both species recognized temporal differences in the contribution of some groups of arthropods. My research contributes to the knowledge of omnivory/frugivory on anoles and lizards in general. It also confirms the importance of microhabitat structure on occurrence of A. cristatellus and A. krugi. These information contributes to the understanding of the functional role of Anolis lizards in the dynamic and structure of food webs and ecosystems of Caribbean forests.

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BIOGRAPHY

Sondra Ivelisse Vega Castillo was born in Humacao, Puerto Rico, in the year 1972. She is the first of three children (Alex Xavier and Sandra Liz) whose parents are Olga Castillo

Ramírez and Benjamín Vega Berríos. She obtained her high school diploma at Ana Roqué de

Duprey High School in Humacao in in 1990. In 1995, she obtained a Bachelor's Degree in

Applied Microbiology Biology from the University of Puerto Rico (UPR) at Humacao and in

2000 a Master's Degree in Biology from UPR Mayagüez. She married Alberto R. Puente Rolón in December 1998 and procreated their beloved children Alejandro Darío and Andira del Alba.

In 2008, she began her doctoral studies and since then has focused on gathering information on omnivory/frugivory of Anolis lizards at karst forests.

ECOLOGICAL INTERACTIONS OF ANOLIS CRISTATELLUS

AND ANOLIS KRUGI IN TWO SECONDARY TROPICAL KARST FORESTS

AT THE NORTHERN KARST BELT OF PUERTO RICO:

OCCUPANCY ESTIMATES AND DEGREE OF OMNIVORY/FRUGIVORY

DEDICATED TO MY BELOVED FAMILY AND UNCONDITIONAL FRIENDS,

WHO HAVE BEEN WITH ME IN JOYS, SORROWS AND VICISSITUDES

ACKNOWLEDGEMENTS

I would like to thank my thesis committee Elvira Cuevas Viera, Elvia Meléndez-

Ackerman, Eugenio Santiago, Manuel Leal and Virginia Sanz for their advice and guidance, especially to my committee chair, Elvira, for all her advice, support and patience during the last five years. Her guidance enabled me to discover stable isotopes analysis and understand it’s important and applicability. I also want to thank The Center for Applied Tropical Ecology and

Conservation, University of Puerto Rico at Rio Piedras, who’s funded processing of the stable isotope samples (grant NSF HRD 0734826) and the Department of Natural and Environmental

Resources Puerto Rico Commonwealth for issuing permit O-VS-PVS15-SJ-00604-21052013. I give special thanks to Abel Vale for his support and for allowing me to conduct research in El

Tallonal. Likewise, I thank the administration of the Mata de Platano Natural Reserve (Dr.

Armando Rodríguez from Interamerican University-Bayamón) for allowing me to conduct research there.

My deepest gratitude to Dr. Jaime Collazo from North Carolina State University, for all his statistical advice, help, support and patience. Thanks to Eneilis Mulero and Deborah López for the field assistance and also to all the students that helped me process tissues for stable isotope analysis and do arthropod sorting. Thanks to my friends José Sustache Sustache, Ramón

Luis Rivera and Hanna López for the invaluable help with vegetation analysis, especially to

Sustache who has always been there to help me. My most sincere gratitude to Manuel Delgado who helped me with fruit samples and found all those scientific articles that I needed and could not find by my own. Thanks to my bachelor mentor, Dr. Luis Nieves, who introduced me to the world of amphibians and reptiles, and to Dr. Fernando Bird, my master degree advisor, who was fundamental in my professional growth within herpetology and ecology. I am most grateful to

3 my beloved family: Alberto, Alejandro and Andira for their unconditional help, support and understanding through all these years, and to my parents for their constant support in all my goals and dreams. Sonia Reyes, Servy Serrano, Raquel Vargas, Ana Gómez and Iris Ríos, I will live eternally grateful for your unconditional support, motivation and help.

CHAPTER I

INTRODUCTION, OBJECTIVES AND OVERVIEW OF CHAPTERS

Introduction

Lizards are the most diverse and numerous group within reptiles, represented by more than

9,760 species (Reptile database 2012). Their ecological, physiological, and behavioral adaptations allow them to occur in almost every ecosystem (Pianka and Vitt 2008). Due to their abundance, diversity and widespread distribution, lizards are an important component of ecosystem structure and dynamics. Within food webs, lizards play a dual role since they are higher-order consumers but also important preys. In terms of their feeding habits, lizards consume a wide spectrum of food resources. Numerous groups function as predators primarily feeding on invertebrates or small vertebrates, and are therefore considered as strictly carnivorous. A small proportion of species also include plant material in their diets and are considered omnivorous. A even smaller proportion of species are primarily herbivorous and have evolved a diet dependent on plant foliage (Cooper and Vitt, 2002, Vitt and Pianka 2007, Vitt and Caldwell 2009).

Omnivory is defined as feeding on more than one trophic level (Pimm and Lawton 1978,

Polis and Strong 1996), a deﬁnition that includes ‘‘predators’’ that feed on both preys and selected plant tissues, as is the case of omnivorous lizards (Anderson 2007). Numerous omnivore species perform best on mixed diets of plants and prey when compared to restricted feeding on either diet (Coll 1998). Omnivores can harvest both types of food sources in variable proportions (i.e. focused on more abundant food sources but less nutritious, and less abundant food sources but more nutritious, (Coll and Guershon 2002). Although in many cases, a specialized digestive system is required to process and assimilate plant and animal tissues, omnivores are very common among some vertebrate groups (Polis et al. 1989, Winemiller 1990,

Martinez 1991, Polis 1991, Diehl 1993). In lizards, however, omnivory is considered unusual; according to Cooper and Vitt (2002), who’s reviewed the diet of over 450 lizard species, only

12.1% of these can be considered omnivorous. Due to the lack of morphological or physiological specialization to digests cellulose, omnivorous lizards consume only easily digestible plant parts such as flowers, fleshy fruits, nectar or sap (Pérez-Mellado and Casas 1997,

Whitaker, 1987, Cooper and Vitt, 2002). Fleshy fruits are particularly important as a food resource for omnivorous lizards because they are low in fiber, highly nutritious, rich in soluble carbohydrates and high in water content (Pérez-Mellado and Casas 1997, Jordano 2000; Valido et al., 2003). In ecosystems where prey is scarce, lizards may eat fleshy fruits to decrease energy loss and attain a positive energetic balance (Cooper and Vitt 2002).

In Puerto Rico, Anolis lizards are abundant and have been described as important consumers in diurnal food webs (Reagan 1986). For example, Anolis stratulus, exist at high densities with more than 21,000 individuals per hectare (Reagan 1996). Although historically the genus Anolis has been considered as insectivorous, this assumption is changing as evidence from dietary studies favors a generalist-feeding mode (Losos 2009). The consumption of fruits has been reported for six intermediate size species, A. evermanni (Reagan, 1996, Lister, 1981),

A. monensis (Schwartz and Henderson 1991), A. stratulus, A. gundlachi and A. krugi (Vega and

Puente 2011), as well as for the giant species A. cuvieri (Perez-Rivera, 1985 and Losos, 1990).

Also, nectar consumption have been reported for A. stratulus (Perry and Lazell, 1997 and Rios-

Lopez, 2004). On the other hand, there is no evidence, for Puerto Rico, of consumption of fruits by A. cristatellus. However, Lazell (1997) reported the sub-species A. cristatellus wileyae consuming fruits of Melocactus intortus and Trichostigma octandra on Ghana Island.

Regardless of numerous investigations on Puerto Rican anoles, and despite the report of seeds in the stomach contents or fecal pellets of some species, little is known about the degree of omnivory/frugivory on Puerto Rican anole lizards. The occurrence of omnivory is important to

7 understand food web dynamics (Holt and Polis 1997, Vandermeer 2006). In general, it has two main effects in food webs; first omnivory diffuses the effects of consumption and productivity across the trophic spectrum since it increases web connection (Polis and Strong 1996). Second, it may lead to competitive interactions when populations of these consumers increase when consuming “nontraditional” resources, promoting top-down control (Polis and Strong 1996).

The natural history of Anolis lizards is well studied; however, information regarding their feeding habits and consequently, their trophic ecology remains incomplete (Losos 2009). The knowledge about omnivory/frugivory in anoles is necessary in order to better comprehend the functional role of Anolis lizards in the dynamics and structure of food webs and in ecosystem function. This study is of particular importance since it is the first to evaluate feeding ecology of anoles in forests within the karst belt of the northern limestone of Puerto Rico. The northern limestone cover ca. 142, 544 ha and is considered a critical natural area in Puerto Rico (Lugo et al. 2001). Although this project is focused on anoles lizard species on the northern karst region of Puerto Rico, the findings are of relevance for the understanding of ecosystems where anoles are also the dominant component of diurnal communities.

Objective of the Thesis

To address omnivory in anoles lizards, my objectives were to (1) determine the occurrence and abundance of two sympatric species of the genus Anolis, Anolis cristatellus

(Duméril and Bibron) and Anolis krugi (Peters) in mature secondary forests, (2) evaluate their trophic position, and (3) determine the degree and annual dynamics of omnivory/frugivory of the two species using stable isotopes (13C and 15N) and their relative isotopic composition.

Overview of Chapters

In Chapter 2, I focus on estimates of occupancy and abundance for A. cristatellus and A. krugi as a function of food resource abundance and habitat structure at the private natural reserves Mata de Platano and El Tallonal. In Chapter 3, I studied the isotopic composition ant trophic position of both anoles species. Chapter 4 focuses on the diet of A. cristatellus and A. krugi and effects of food resource availability in the functional diet of both anoles. Finally,

Chapter 5 summarizes my conclusions. Results of my work will improve our understanding of the role of anoles in ecosystems function and dynamics.

Chapter II

INFLUENCE OF HABITAT STRUCTURE AND QUALITY ON OCCUPANCY AND ABUNDANCE OF TWO ANOLES ECOMORPHS, ANOLIS CRISTATELLUS AND ANOLIS KRUGI, IN SECONDARY KARST FORESTS OF NORTHERN PUERTO RICO.

Abstract

An important goal of ecology is to determine the factors and processes that explain observed patterns of species distribution and abundance. Competition and predation have been recognized as a major force inﬂuencing lizard abundance in natural ecosystems but there is strong evidence indicating that habitat structure and food availability are also a major influence on lizard population dynamics. Here, I examined site occupancy and abundance of two anoles lizards,

Anolis cristatellus and A. krugi at the northern karst belt of Puerto Rico. Anolis cristatellus and

Anolis krugi occur sympatrically at northern karst forests and were detected in study areas using the habitat consistently with its ecomorphological classification. Occupancy estimates and abundance differ substantially between species and were influenced primarily by structural features of the habitat. Anolis cristatellus was the most abundant of the two species, also exhibiting high rates of occupancy in study areas. Conversely, occupancy and abundance estimates for A. krugi were low. Consistent with their ecomorphology, occupancy estimates of

A. krugi were influenced by low-laying vegetation whereas for A. cristatellus it was the diameter at breast height of mid-story trees. This study expanded our understanding about the relationship between ecomorphology, habitat correlates and demography for two anoles in Puerto Rico.

Introduction

Anolis is one of the largest genera of vertebrates of the world, with nearly half of the species occurring on Caribbean islands (Roughgarden 1995; Losos 2009). In the Greater

Antilles, the genera radiated extensively (Losos 2009). Within each island, differentiation occurred independently resulting in species adapted to different structural microhabitats in terms of morphology, ecology and behaviorally. Based on these adaptations, anoles are grouped in categories named ecomorphs, where each ecomorph reflects the structural characteristics of the microhabitat in which often occurs (Williams 1972, 1983, Losos 1990, Losos et al. 1998). In

Puerto Rico, Anolis lizards are abundant and have been described as an important component in diurnal prey webs (Reagan 1986). Actually, the island harbors twelve species classified in ﬁve of the six ecological-morphological categories (Williams 1972; Williams 1983; Losos 1990;

Schwartz and Henderson 1991; Losos et al. 1998; Rivero 1998).

Although interspecific interactions are undoubtedly a prominent force determining anoles ecology and evolution (Losos 2009), as habitat specialists, structure and quality are also crucial in determining their distribution and population dynamics. Habitat structure, for example, affect distribution, foraging, thermoregulation and escape from predators (Moermond 1979; Huey

1991, Regalado 1998). Likewise, prey abundance, an expression of habitat quality, has the potential to influence fitness, and thus, limit lizard abundance (Lister 1981; Reagan 1996).

Because seasonal ﬂuctuations in prey abundance are associated with seasonal rainfall (Lister

1981; Steward and Woolbright 1996), weather is also implicated in the variation of anoles densities.

Despite the influence of habitat on demographic parameters of anoles, this relationship remains poorly understood in Puerto Rico, particularly in the northern karst region. Indeed, to

12 my knowledge there is no study focusing on this aspect of their ecology in this region. One would expect that habitat correlates influencing demographic parameters would conform to the ecomorphology of the species in question. I investigated this hypothesis by focusing on two sympatric anoles, A. cristatellus and A. krugi, in the northern karst belt of Puerto Rico.

Specifically, I determined which components of vegetation structure and quality had greater influence on their occupancy rates. Anolis cristatellus is a trunk-ground ecomorph and would be expected to be associated principally with broader superficies like tree trunks, whereas the grass- bush ecomorph, A. krugi, with the understory layer and narrow perches. I also considered the possible influence of other ecological covariates (e.g., prey abundance, location, rainfall) to gain a better sense of the relative importance of habitat structure and quality. I also estimated the population size of A. cristatellus and A. krugi in study areas, and determined if estimates were correlated to ecological covariates. My interest in abundance stemmed from its value as an indicator of population status and to other fields of research with direct application of population size such as functional roles and impact of anoles in prey webs. Occupancy, on the other hand, represents the state variable used to quantify the distribution of a species, and patch extinction and colonization rates, also estimated in this study, represent the underlying vital rates governing the process of patch dynamics.

In this study I estimated demographic parameters using occupancy-based analytical approaches (Bailey et al. 2014). These approaches do not assume that a species within a sampling unit is detected with a probability of 1. Standard methods to estimate lizard occupancy and abundance commonly include traps, direct searching during the day or night, or mark- recapture techniques. In the case of mark-recapture methods, for example, capture rates and catch-per-unit efforts across sites are then used to infer relative animal densities (McDiarmid et

13 al. 2012). In structurally complex forests or habitats, however, these methods could result ineffective, time demanding, and may induce biases. Estimates of occupancy or abundance without adjusting for imperfect detection (< 1), often referred to as naïve estimates, tend to be biased towards low values because individuals present at a sampling site can go undetected

(MacKenzie et al. 2006; Bailey et al. 2014). In addition, inferences about the strength of or influence of environmental covariates on parameters of interest could be spurious (MacKenzie et al. 2002; MacKenzie et al. 2006). This is particularly important with A. krugi, a small anole adapted to dense and clumped microhabitat.

Methods

Study areas

This study was conducted in the northern limestone karts belt of the main island of the archipelago of Puerto Rico (Figure 1). The karst belt covers ca 142, 544 ha, contains higher than average biodiversity, and is considered a critical natural area of the island (Lugo 2005). The study sites were two private natural reserves located in the municipality of Arecibo, characterized by forests at different successional stages (Figure 1). These were Mata de Plátano

Private Reserve (MPPR) (18º 24’46.63” N, 66 º 43’37.32” W), which consists of ca 53 ha, and

El Tallonal Private Reserve (ETPR), which covers ca 114 ha (18º 24’28.75” N, 66 º 43’54.51”

W). Both study sites are within the sub-tropical moist forest life zone, which covers 58% of the

Island. This zone has an average annual temperature of 25.5°C, with an average annual precipitation of 1,295 mm (Holdridge 1967), and a canopy height of about 20 m (Ewel and

Whitmore 1973). The dry season is from January to March, and the wettest period is from July to September (López and Villanueva 2006).

Focal species

Anolis cristatellus is a stocky medium size (56-76 mm) anole classified as trunk-ground ecomorph (Table 1) (Williams 1983, Rivero 1998). This species is widely distributed in the island, inhabiting forested as well as open habitat from sea level to mid-elevation on the Puerto

Rican Bank (Williams 1972, Rivero 1998, Henderson and Powell 2009). Within shaded habitats, this species lives on tree trunks, does not bask and is classified as thermoconformer (Huey 1974;

Hertz 1992). Anolis krugi, in contrast, is a slender small (36-45 mm), grass-bush anole (Table

1), commonly found on narrow low-laying dense vegetation within forested areas (Rivero 1998,

Henderson and Powell 2009). This species is associated with shaded areas within mesic forests from near sea level as well as up to ca 1300 m of elevation (Rivero 1998, Henderson and Powell

2009, Rodríguez et al. 2010). Both species are sexually dimorphic, with males bigger and heavier than females.

Anoles lizards are territorial, and its home range size is correlates with body size (Losos

2009). The estimates of average surface area for males of A. cristatellus is 19 m2 (Philibosian,

1975), estimates larger than or similar to other anoles in Caribbean islands (e.g. Schoener and

Schoener, 1982). There are not home range estimates for A. krugi, but, territories for grass-bush anoles are considered to be small than other ecomorphs (Losos 2009).

Demographic parameters and habitat characterization

Two 36 x 72 m plots (2,592 m2) were established on hillsides at each study site. Within each plot, fifteen 6 x 6 m quadrants spaced at least 10 m apart, were randomly chosen to survey lizards. Separation between quadrants guarantees that surveys quadrants are independent of each other. Two observers surveyed lizards concurrently at each study site, during four minutes on

15 each plot. Surveys were conducted between 900h to 1400h for three consecutive days every two months from January 2012 to November 2012. Lizard surveys were conducted under a multi- season occupancy robust design framework (Pollock 1982; MacKenzie et al. 2006, 2009). The schedule followed in this study yielded five primary sampling periods; observations made during three consecutive days constituted the secondary sampling periods. To minimize any systematic bias due to time of surveys among samplings, the order of visits at each study site within a study area was alternated.

Monthly estimates of prey abundance were obtained from areas around the quadrants used for surveys using three different trapping methods (Malaise, leaf litter collection and sweep- net). Flying arthropods were captured using two Malaise traps installed at each study site and operated for a 48-h period each month. Traps were located at least 20 m from each other. Leaf litter at each study site was collected from 20-0.25 m2 random plots around quadrants of surveys once a month. Leaf litter was placed into paper bags and animals were extracted from litter in the laboratory using Berlese funnels (Southwood et al. 1979) for 48 h. Berlese funnels separated arthropods from leaf litter when organisms moving downwards to escape from light and heat.

Sweep-net sampling was used to estimate the abundance of arthropods exploiting foliage, twig, and bark substrates. Vegetation at one meter along three -2 x 30 m transects were swept with standard muslin net. All arthropods were preserved in 70% ethanol until they were processed.

Then, they were sorted using stereoscopes into classes or orders. Only arthropods reported in the scientific literature as component of the diet of other anoles species with the same or similar ecomorphology were considered as potential prey and used for the analysis (Table 2).

The same quadrants used for surveys were also used to characterize habitat. Woody vegetation density (stems with diameter at breast height >1.0 cm) was determined using the

16 point-centered quarter method (Higging et al. 1994). The centers of lizards’ survey quadrants were used as center points to measure the distance to mid-story and over-story trees within each of the four cardinal directions (north, south, east and west) around the points. Diameter at breast height (DBH, cm) and height (m) were measured to all mid-story and over-story trees within survey quadrants. All seedlings, saplings and shrubs were identified and counted within quadrants to determine density and richness of plants species. Canopy and ground cover were measured within quadrants at each corner and at the center using an ocular tube (% of cover). In addition, rainfall, minimal and maximal air temperature and relative humidity were measured automatically by data loggers placed at each forest site.

Statistical analysis

Multi-season occupancy models were used to estimate occupancy, patch (quadrant) extinction and colonization, and detection probabilities for each primary sampling period

(MacKenzie et al. 2009). Lizard count data were converted into an encounter history of presence

(1) and non-presence (0). For example, an encounter history of 101, 111, 110 for three different seasons indicates that at least 1 individual of a species was detected on the first and third survey the first season, but not on the second survey. During the second season, at least one individual was detected on every survey. During the final season, at least one individual was detected on the first and second surveys, but not on the third or last survey. Occupancy is the probability that a quadrant is occupied by the species after adjusting for detection probability. Local probability of extinction is the probability that a quadrant occupied by the species in primary period t is not occupied in the next primary period (t+1). Conversely, local probability of colonization is the probability that a quadrant not occupied by the species in primary period t is occupied in the following primary period (t+1). These parameters were modeled as a function of 11 covariates

(Table 2 and 3). Because weather variables are time varying, I modeled them as sampling covariates; where the average of the temperature and other weather variables corresponded to each consecutive day within each primary sampling periods. Multi-season occupancy models assumed that: 1) quadrants were “closed” to changes in occupancy during secondary sampling periods (i.e., 3 days), 2) there were no false detections, and 3) detections across quadrants were independent (MacKenzie et al. 2002). These assumptions were met as it was unlikely that major changes in occupancy occurred within 3 consecutive days, observations were made by experienced herpetologists, and separation among quadrants minimized behavioral interactions.

A Royle/Nichols Abundance Induced Heterogeneity model was used to estimate the mean site abundance of lizards (Royle and Nichols 2003). The model estimates the proportion of area occupied when heterogeneity in detection probability exists as result of variation in abundance of the species studied. The estimated population size of each species per study site was calculated by multiplying the estimated mean quadrant abundance by the number of quadrants in study site (30). The Royle/Nichols model assumed that: 1) the number of lizards at a particular quadrant followed a defined spatial distribution for which ˆ indicates the mean abundance across all quadrants; and 2) the probability of detecting lizards in each quadrant was

ˆ related to the species' inherent detection probability, rˆ , and the quadrant abundance, Ni (Royle and Nichols 2003). Survey data for each primary period for A. krugi were suitable for a Poisson- based model ( P > 0.05), but not for A. cristatellus. Simulation studies, however, suggest that estimates are robust to deviations from a Poisson model, particularly if the species' inherent detection probability “r” is high as it was the case for A. cristatellus (Royle and Nichols 2003).

The software PRESENCE (v6.2, Hines 2006) was used to estimate model parameters for both species. Analyses started by assessing whether detection was constant over the primary

18 study periods or else time-specific (i.e. seasonal). This was followed by an assessment of the influence of selected covariates on detection (Table 2 and 3). The model with highest support was then used to model other parameters in the models (Franklin et al. 2004). For multi-season models, initial occupancy was modeled by study site, and followed by other covariates (Table 3).

Patch extinction and colonization probability were modeled as a constant or time-specific

(season). Models in the candidate set were only those with estimable parameters. Akaike’s

Information Criterion (AIC) was used to evaluate the support in the data for model sets and the strength of each covariate’s effect on occupancy and abundance (Burnham and Anderson 2002).

The relationship between the probability of occupancy and covariates at the segment level was established using a logistic model (logit link) in program PRESENCE (Hines 2006). The influence of a covariate was deemed strong if the 95% CIs did not overlap zero. The difference in AIC units between the best-supported model and any other model was used to calculate model weights (AIC wgt), which indicate the relative likelihood of the model given the data (Burnham and Anderson 2002). Models with AIC wgt  2 had the highest support. Akaike weights (wi) were calculated for A. krugi because 73% of the variance was captured by 5 competing models

(AIC wgt  2) (Table 7). This technique was used to assess the relative importance of covariates in competing models (Burnham and Anderson 2002).

Arthropods data were log-transformed before analysis to meet assumptions of normality.

Weather variables (e.g., precipitation, humidity), arthropod abundance and microhabitat characteristics of plots between study areas were compared using analysis of variance in JMP

(V# 2011). Relationships between the aforementioned variables and lizard abundance were evaluated using Pearson’s correlation analysis (JMP V# 2011). In this case, monthly average of weather variables were used for the analysis.

Results

Habitat characterization

Study sites were characterized by secondary forests at different successional stages, and thus, exhibited differences in some aspects of their structure. There were significant diﬀerences in canopy cover between study sites (F = 11.33, df = 1, p < 0.001); mean canopy cover for

MPPR was 95.4% (1.32  SE) and 86.6% (2.23  SE) for ETPR. There were also differences for seedling richness (F = 12.28, df = 1, p < 0.001), sapling richness (F = 4.33, df = 1, p < 0.001) and shrub richness (F = 8.50, df = 1, p < 0.001). In each of these cases, ETPR had higher richness values (Table 4). However, there were no differences in the total number of seedling, sapling or shrubs between study sites. Mid-story tree species at MPPR had greater DBH and height than

ETPR (F = 13.17, df = 1, p < 0.001 and F = 37.60, df = 1, p < 0.001, respectively) (Table 4). In contrast, over-story tree species at ETPR were taller and had larger DBH (F = 12.57, df = 1, p <

0.001 and F = 6.02, df = 1, p = 0.015, respectively) (Table 4). There were no differences between study sites for estimates of ground cover of leaf litter, rocks and vegetation measured on the ground layer. There were significant differences in rainfall between study sites; ETPR received more rain during 2012 than MPPR (F = 5.4, df = 1, p = 0.029) (Figures 2 and 3). ETPR also had lower temperatures (F = 10.98, df = 1, p = 0.003) (Figures 2 and 3) and higher humidity

(F = 137.58, df = 1, p < 0.001).

Prey availability

A total of 32,137 arthropods, considered potential prey for lizards (Table 2), were captured at study areas (16,306 at MPPR; 15,831 at ETPR). Arthropods were assigned to 15 orders, and one general category for larvae, which includes larvae of different groups (e.g.

Lepidoptera and Coleoptera). The predominant groups at the study areas were Diptera and

Hymenoptera, followed by Aranae and Hemiptera. There were no differences in arthropod abundance between study areas (F = 0.043, df =1, p = 0.84).

Occupancy and abundance

The model with highest support for A. cristatellus (AICwgt = 0.51, Table 5) indicated that occupancy in quadrants was high with a weak positive influence of DBH of mid-story trees and weak negative influence of prey abundance (Table 5 and 6). A competing model (AIC < 2.0), accounting for another 29% of the variation in the data, featured the same model terms except that it included a weak interaction between mid-story trees and prey abundance (Beta 95% CIs overlapped zero). Detection probability varied by primary sampling occasion, and was negatively influenced by the density of shrubs in quadrants (β = -0.33 ± 0.07). The estimated occupancy probability for A. cristatellus was high at both study sites (MPPR = 0.96 ± 0.03;

ETPR = 0.95 ± 0.05). The probability that a quadrant within a plot was colonized (t to t+1) was constant over the study and high (0.89 ± 0.09). The probability that a quadrant within plot went extinct was also constant but low (0.05 ± 0.02).

For A. krugi variation in the data was best explained by a model whose occupancy was weakly influenced by richness of saplings and prey abundance, and whose detection probability varied by season and influenced by site, rain and density of sapling (AICwgt = 0.24, Table 5 and

6). Average prey abundance had a positive influence on occupancy (β = 0.62 ± 0.45) while

21 richness of saplings had a negative inﬂuence (β = -0.77 ± 0.47; Figure 3). Another 49% of the variation in the data was explained by four competing models (AIC < 2.0). Competing models featured richness of saplings, prey abundance, site and the interaction between prey abundance and sapling richness (Table 5). Of these covariates, sapling richness (wi = 0.62) and prey (wi

=0.52) were the most important in explaining variation in A krugi occupancy over time (Table 7).

The estimated occupancy probability at MPPR was 0.55 ± 0.14 and 0.66 ± 0.14 for ETPR. The estimated detection probability per quadrant at MPPR was 0.23 ± 0.05 and 0.32 ± 0.06 for

ETPR. Detection probability increased strongly with sapling density within quadrants (β = 0.27

± 0.09). The probability that a quadrant within plots was colonized was constant and low (0.10 ±

0.04) and so was the probability that a quadrant within plots went extinct (0.19 ± 0.05).

The estimated mean site abundance per plot for A. cristatellus at MPPR was 5.73 ± 0.92.

An abundance estimate of population size across all plots at MPPR was 171.90 (95% CI =87.30-

359.10), corresponding to a density of 0.15 A. cristatellus/m2. While the estimated mean plot abundance for this species at ETPR was 6.54 ± 1.23. At ETPR the estimated abundance for all plots surveyed was 196.20 (95% CI =99.0-413.70), corresponding to a density of 0.18 A. cristatellus /m2. In the case of A. krugi, the estimated mean plot abundance at MPPR was 0.63 ±

0.19; with an abundance estimate of population size across all plots of 19.03 (95% CI =7.59-

39.45), corresponding to a density of 0.02 A. krugi/m2. The estimated mean site abundance for plots at ETPR was 1.58 ± 0.47; with an estimated abundance for plots surveyed of 47.45 (95%

CI =22.5-94.5), corresponding to a density of 0.04 A. krugi/m2.

The relationship between the abundance of arthropods and rainfall between study sites was not significant (n = 6 primary periods, p > 0.05). For A cristatellus, the correlation coefficient (r) at MPPR was 0.441 (n = 6 primary periods, p = 0.381) and 0.349 at ETPR (n = 6,

22 p = 0.497). For A. krugi the correlation coefficient (r) at MPPR was 0.164 (n = 6, p = 0.757) and

0.352 at ETPR (n = 6, p = 0.494). Likewise, there was a non-significant correlation between the abundance of arthropods and anoles at both study sites (A. cristatellus: r = 0.056, p = 0.861, and

A. krugi: r = -0.287, p = 0.365).

Discussion

Anolis cristatellus and Anolis krugi occurred sympatrically in two northern karst forests in Puerto Rico, associated to habitat according with their ecomorphological classification as hypothesized. As trunk-ground, Anolis cristatellus typically use tree trunks perching at various meters from the ground, while the grass-bush species, A. krugi, perch often at lowest height vegetation. Consistent with their microhabitat specialization, at study sites, occupancy estimates of A. krugi were influenced by low-laying vegetation whereas for A. cristatellus it was the diameter at breast height of mid-story trees.

Anolis cristatellus was the most abundant species in both sites with concomitant estimates of occupancy > 90%, remaining constant among primary sampling periods. The higher occupancy rates and abundance recorded for this species were not surprising since A. cristatellus is the most common and widespread anole in the island, occurring in a wide variety of habitats including forests interspersed with open areas (Williams 1972, Rivero 1998). That trees play a key role in determining occupancy rates was consistent with the use of microhabitat by this species. Structural habitat, defined by perch diameter and height, is one of the three axes by which anoles partition habitat where they occur (Losos 1990, Losos 2009). As a trunk-ground ecomorph, A. cristatellus has long hindlimbs adapted to occupy habitats with broad perches as tree trunks (Rivero 1998). The effect of perch diameter is very pronounced for species with long limb, particularly on locomotors performance because these species usually experience declines in speed on narrow surfaces as well as short jump distances (Losos and Sinervo 1989, Irschick and Losos 1999, Spezzano and Jayne 2004). In general, anoles lizards prefer to use microhabitat where they can achieve maximal locomotor performance (Irschick and Losos 1999). Locomotor performance is a determinant of social dominance and affects foraging skills, predator evasion

24 and reproductive success (Garland and Losos 1994, Irschick and Garland 2001, Perry et al.

2004).

Anolis cristatellus is a trunk-ground specialist with the potential of reach better locomotors performance on wider surfaces, thus, it was somewhat surprising that the DBH of mid-story trees (2.5-9.9 cm DBH) not over-story ones (≥ 10.0 cm DBH) was more influential on occupancy rates. This seeming discrepancy does not falsify that A. cristatellus used trees of wide surfaces (high to medium DBH), but rather, it points at the fact that the species was using forests whose structure reflected changes in landuse over time. The northern karst region, like most of the vegetation cover of Puerto Rico, suffered massive deforestation in the past (Birdsey and Weaver 1987, Rivera and Aide 1998). Actually, the region consists of secondary forests made up of a combination of native and introduced naturalized trees species in different stages of regeneration (Lugo et al. 2001, Marcano Vega et al. 2002, Aukema et al. 2007, Fonseca 2014).

In the case of MPPR and ETPR, both were used for agriculture and cattle grazing until the

1950’s (ETPR) and 1980’s (MPPR). Although forest regeneration began earlier at ETPR than

MPPR, secondary forests at ETPR are characterized by a high density of mid-story trees

(Fonseca 2014) much like what characterizes MPPR. Because mid-story trees were the most available perches for A. cristatellus, it was not surprising that they favored higher DBH trees within this size-class, a feature that probably helps them reach maximal performance. Prey abundance had a weak and negative influence on occupancy, but this occurred only at the lowest

DBH range of mid-size trees. This suggested that tree size, not prey, exerted a stronger influence on occupancy.

Occupancy rates for A. krugi were low (0.55 ± 0.14 and 0.66 ± 0.14) compared with A. cristatellus, and negatively influenced by richness of saplings but positively influenced by prey

25 abundance. Grass-bush anoles have short forelimbs and long hindlimbs adapted to move over narrow low-lying dense vegetation (Williams 1983, Losos 2009). Trees, shrubs or seedlings were not determinants of occurrence of A. krugi within plots at study sites. The higher occupancy rates observed at ETPR (79-92%) were at plots with low sapling richness, and were substantially higher (>90%) at plots where only saplings of Castilla elastica (Moraceae) occurred. Castilla elastica is an introduced naturalized tree common in ETPR and others secondary karst forests at northern Puerto Rico (Fonseca 2014). This tree species has large hairy oblong leaves 25.4-50.8 cm long and 10.2-20.3 cm broad (Littler et al. 1977), contrasting with the narrow perches reported for this species. Although there was no influence of sapling density on occupancy, usually, saplings of C. elastica occurs at very high densities creating a dense microhabitat.

At MPPR, higher occupancy also was observed in plots with low sapling richness (6-7 species) but where Anthurium crenatum (Araceae) or Pharus latifolius (Poaceae) were the predominant plants. Anthurium crenatum is a common understory herb at karst moist forest that has oblong-elliptic or oblanceolate leaves 40-80 cm long and 15-25 cm wide (Acevedo

Rodríguez and Strong 2005, Crook 2013). Pharus latifolius is a common grass in northern limestone hills that reach heights of 30 to 100 cm with elliptic leaves 15–30 cm long and 30–80 mm wide (Hitchcock, 1936). Although A. krugi at both study sites occurred at habitat with low dense vegetation, in terms of structural microhabitat, perch diameter appear differs substantially from the traditional perches commonly used by this species in others locations as well as by grass-bush anoles in general (e.g. narrow stems of grasses or bushes) (Rivero 1998, Johnson et al. 2006, Henderson and Powell 2009, Losos 2009). This species has short forelimbs and long hindlimbs adapted to move over relatively narrow perches. Detailed studies are needed in order

26 to understand the locomotors performance of A. krugi at habitat with broader perches. Since the ability to move effectively through the habitat guarantee ecologically important activities such as dispersal, resource use, escape from predators, and territorial defense (Turchin 1998).

Although anoles are capable of using perches of various diameters (Irschick et al. 2005,

Rodriguez-Robles et al. 2005, Losos 2009), they exhibit less flexibility in the use of other features of the habitat. For example, visibility has a strong influence on habitat selectivity for A. krugi. This species selects microhabitat with low range of visibility, in some cases, irrespective its abundance (Johnson et al. 2006). The higher estimates of occupancy for A. krugi at MPPR and ETPR were at plots where the predominant vegetation has large leaf blades and grows in clusters, vegetation with the potential of create close and low visibility microhabitat, as the preferred by this species in other locations. It seem that structural elements of this type of vegetation (e.g. visibility, perch diameter), and not just sapling richness is the factor determining occupancy at plots. In anoles, microhabitat structure not only defines the spatial distribution of lizards, but it also has the potential to regulate other aspects of their natural history (e.g. foraging, thermoregulation). In Cuba, for example, habitat structure determines escape behavior and strategies used to deal with predators. On big trunks, some species run around the trunk to avoid predator, but jump to another tree or perch when perched on small diameter trees (Regalado

1998). Further research are needed to determine which structural element of this particular vegetation is the determinant in A. krugi occupancy in study areas and also to determine variation in habitat use at this particular microhabitat.

Microhabitat structure undoubtedly is a prominent force determining occupancy in A. krugi (Table 5); however, prey abundance in plots appeared to mitigate the negative effects of habitat structure. Prey abundance positively influenced occupancy rates for A. krugi. For

27 example, plots with the same or similar sampling richness will have higher occupancy rates if they contain higher prey abundance (Figure 3). In lizards, prey limitation has an adverse effects on body condition and growth, playing a vital role in maintaining fitness (Licht 1974, Andrew

1976, Schoener and Schoener 1978, Lister 1981, Fergusson et al. 1983, Reagan et al. 1996).

Although there was no signiﬁcant correlation between the average abundance of arthropods and anoles, occupancy results suggested that prey abundance is a key component of quality of microhabitat, allowing high occurrence of A. krugi at those plots. This seeming discrepancy in the relationship between average abundance of arthropods, abundance of anoles and occupancy estimates could be explained by the differences in scale and resolution between occupancy and abundance estimates. While occupancy require at least the detection of one individual of the species per quadrant during a particular sampling occasion, abundance use numeric values subject to a higher variation.

Despite the occurrence of both species at study sites, A. krugi was very rare in comparison with A. cristatellus. Low abundances of A. krugi might reflect low abundance of dense microhabitat at study areas. In this study the sampling areas were located at hillsides, where grasses as well as other herbaceous vegetation were uncommon, and therefore, the microhabitat for this species was also infrequent. The findings of this study supported this possibility since the higher occurrences occurred in plots where the grass Pharus latifolius occurred (e.g., MPPR). Considering that anoles are also highly territorial lizards (Losos 2009), the abundance of a particular microhabitat might be a key factor controlling population abundance.

Information of predation rates was not available for any anoles species at MPPR or

ETPR. However, lizard cuckoos (Coccyzus vieilloti, Coccyzus minor), Puerto Rican racers

(Bornikenophis portoricensis), Puerto Rican boa (Chilabothrus inornatus) and the giant anoles

(A. cuvieri) were abundant predators at both reserves (pers. obs.). The high abundance of potential predators suggested that there could be high predation pressure, especially on the medium size A. cristatellus. For example, cuckoos and A. cuvieri might exert high predation pressure because both spend time foraging in the mid and understory, specifically in the morning

(pers. obs.).

Anole communities in the Caribbean consist of species adapted to different structural microhabitats or ecomorphs (Williams 1972, 1983, Losos 1990, Losos et al. 1998). This study provided supporting evidence linking ecomorphology, habitat correlates and selected demographic parameters of Anolis cristatellus and Anolis krugi in the northern karst belt of

Puerto Rico. It also highlighted that habitat quality (e.g. structure and prey abundance) plays a role in accounting for variation in occupancy rates. Interspecific interactions and predation are major determinants of the evolution and ecology anoles (Losos 2009), processes mediated by habitat structure. Gaining new insights on the relationship between these processes and habitat correlates will require a greater understanding of factors like predation pressure and species- specific habitat requirements for species like A. krugi. This study is the ﬁrst to apply recent theoretical developments to estimate occupancy (MacKenzie et al. 2002) on anoles lizards.

CHAPTER II

TABLES

Table 1. Ecomorph characteristics for Caribbean anoles (modified from Losos 2009).

Body size Movement Ecomorph Limb lengh Tail lengh Structural microhabitat (maximum in mm) rate

Crown- Large (130-191) Short Long High trunks and branches Low giant

Grass-bush Small (33-51) Long hindlimbs Very long Low, narrow supports Low

Trunk Small (40-58) Intermediate Short Trunks High

Trunk- Small to intermediate Short Long Trunks, branches, leaves, High crown (44-84) eye level to high

Trunk- Intermediate (55-79) Long hindlimbs Long Broad, intermediate to Low ground low surfaces

Twig Small to intermediate Very short Short Narrow supports High (41-80)

Table 2. Description of site covariates used in occupancy models for Anolis cristatellus and

Anolis krugi. Data were collected in north-central karst region, Puerto Rico, 2012.

Covariates Covariates Description

Site Study sites: Mata de Platano Private Reserve (MPPR) or El Tallonal Private Reserve (ETPR). Rain Average rainfall corresponding to each consecutive day within each primary sampling periods at study sites. Temperature Average temperature corresponding to each consecutive day within each primary sampling periods at study sites. Humidity Average humidity corresponding to each consecutive day within each primary sampling periods at study sites. Prey abundance Mean monthly abundance of arthropods within the orders Aranae, Blattodea, Coleoptera, Diptera, Dermaptera, Hymenoptera, Hemiptera, Lepidoptera, Mantodea, Neuroptera, Orthoptera, Phasmatodea, Psocoptera, Scorpiones and Thysanoptera, and larvae of Lepidoptera and Coleoptera. Sapling Density Density of trees with diameter at least 25 mm but less that 125 mm at quadrants of surveys. Sapling Richness Count of trees species with diameter at least 25 mm but less that 125 mm at quadrants of surveys. Shrubs Density Density of small multi-branched woody perennial plants at quadrants of surveys. Mid-story trees Average of DBH of trees with a diameter at breast height ranging from 2.5 to 9.9 cm. Over-story trees Average of DBH of trees with a diameter at breast height equal or over to 10.0 cm. Cover Mean canopy cover estimates at quadrants of surveys within study sites.

Table 3. Model notation and description for candidate models of Anolis cristatellus and A.

krugi site occupancy at two natural private reserves. Data were collected in north central

karst region, Puerto Rico, 2012.

Model Notation Parameter Description

Anolis cristatellus

psi(Mid-story DBH) The probability of site occupancy depends of average DBH of mid-story trees at plots. psi(Site) The probability of site occupancy depends on the location (MPPR or ETPR). psi(Over-story DBH) The probability of site occupancy depends of average DBH of over-story trees at plots. p(Shrubs Den) Detection probability depends on the density of shrubs at plots. p(Humidity) Detection probability depends on the humidity at plots. p(Sapling Den) Detection probability depends on the density of sapling at plots. gamma() The probability of colonization is constant. eps() The probability of extinction is constant. eps(Prey) Extinction probability depends on prey abundance. eps(t) Extinction probability are survey-dependent.

Anolis krugi

psi(Prey+Sapling Rich.) The probability of site occupancy depends of prey abundance and sapling richness. psi(Sapling Rich.) The probability of site occupancy depends of sapling richness. psi(Prey) The probability of site occupancy depends of prey abundance. psi(Prey +Sapling Rich..+Site) The probability of site occupancy depends of prey abundance, sapling richness and site. psi(Prey +Sapling Rich.+Inter) The probability of site occupancy depends of prey abundance, sapling richness and the interaction of both variables. psi(Mid-story DBH) The probability of site occupancy depends of average DBH of mid-story trees at plots. psi(Humidity) The probability of site occupancy depends of average humidity. gamma () The probability of colonization is constant eps() The probability of extinction is constant. p() Detection probability is constant. p(Site+Rain+Sapling Den) Detection probability depends on site, rain and density of sapling. p(Site+Humidity) Detection probability depends on site and average humidity. p(Site+Cover) Detection probability depends on site and average canopy cover. p(Site+Temperature) Detection probability depends on site and average temperature.

Table 4. Average and standard error of vegetation variables measured at anoles surveys plots at

Mata de Platano and El Tallonal Private Reserves. Data were collected in north central

karst region, Puerto Rico, 2012.

Habitat variables Mean Standard error

Mata de Platano

Mid-story DBH 2.35* 0.086

Mid-story height 4.37* 0.189

Over-story DBH 12.15 0.871

Over-story height 10.07 0.279

Seedling Richness 0.33 0.088

Sapling Richness 14.13 0.681

Shrubs Richness 7.70 0.782

El Tallonal

Mid-story DBH 1.94 0.143

Mid-story height 2.99 0.260

Over-story DBH 15.20* 1.773

Over-story height 12.95* 0.889

Seedling Richness 1.07* 0.191

Sapling Richness 11.66* 0.958

Shrubs Richness 11.76* 1.279

*Statistically significant.

Table 5. Candidate models for occupancy analysis for Anolis cristatellus and A. krug ranked by

Akaike’s Information Criterion values. Site covariates included structural habitat,

environmental and food availability. Data were collected in north-central karst region,

Puerto Rico, 2012.

Species Model AIC ΔAIC AIC w Model Parameters -2 Log Likelihood Likelihood Anolis psi(Mid-story DBH + Prey),gamma(),eps(),p(Shrubs Den) 1247.24 0.00 0.5111 1.0000 7 1233.24 cristatellus psi(Mid-story DBH + Prey + Inter),gamma(),eps(),p(Shrubs Den) 1248.34 1.10 0.2949 0.5769 8 1232.34 psi(Mid-story DBH),gamma(),eps(),p(Shrubs Den) 1251.10 3.86 0.0742 0.1451 6 1239.10 psi(Mid-story DBH),gamma(),eps(Prey),p(Shrubs Den) 1251.76 4.52 0.0533 0.1044 7 1237.76 psi(Prey),gamma(),eps(Prey),p(Shrubs Den) 1252.06 4.82 0.0459 0.0898 6 1240.06 psi(Mid-story DBH),gamma(),eps(t),p(Shrubs Den) 1255.34 8.10 0.0089 0.0174 10 1235.34 psi,gamma(),eps(),p(Shrubs Den) 1256.19 8.95 0.0058 0.0114 5 1246.19 psi(Site),gamma(),eps(),p(Shrubs Den) 1258.18 9.96 0.0035 0.0069 6 1245.20 psi(Over-story DBH),gamma(),eps(),p(Shrubs Den) 1263.43 10.94 0.0022 0.0042 6 1246.18 psi,gamma(),eps(),p(Humidity) 1268.01 16.19 0.0002 0.0003 5 1253.43 psi,gamma(),eps(),p(Sapling Den) 1273.30 20.77 0 0 5 1258.01 psi,gamma(),eps(),p() 1273.57 26.06 0 0 4 1265.30 psi,gamma(),eps(),p(Cover) 1274.04 26.33 0 0 5 1263.57 psi,gamma(),eps(),p(Rain) 1274.22 26.80 0 0 5 1264.04 psi,gamma(),eps(),p(Site) 1274.48 26.98 0 0 9 1256.22 psi,gamma(),eps(),p(Temperature) 1274.22 27.24 0 0 5 1264.48 psi,gamma(),eps(),p(Over-story DBH) 1275.16 27.92 0 0 5 1265.16 psi,gamma(),eps(),p(Mid-story DBH) 1275.19 28.03 0 0 5 1265.27

psi(Prey+Sapling Rich),gamma(),eps(),p(Site+Rain+Sapling Den) 820.78 0.00 0.2428 1.0000 13 794.78 Anolis psi(Sapling Rich.),gamma(),eps(),p(Site+Rain+Sapling Den) 821.19 0.41 0.1978 0.8146 12 797.19 krugi psi(Prey),gamma(),eps(),p(Site+Rain+Sapling Den) 822.47 1.69 0.1043 0.4296 12 798.47 psi(Prey+Sapling Rich.+Site),gamma(),eps(),p(Site+Rain+Sapling Den) 822.68 1.90 0.0939 0.3867 14 794.68 psi(Prey+Sapling Rich.+Inter),gamma(),eps(),p(Site+Rain+Sapling Den) 822.78 2.00 0.0893 0.3679 14 794.78 psi(Sapling Rich.),gamma(),eps(Prey),p(Site+Rain+Sapling Den) 823.13 2.35 0.0750 0.3088 13 797.13 psi,gamma(),eps(),p(Site+Rain+Sapling Den) 823.23 2.45 0.0713 0.2938 11 801.23 psi(Mid-story DBH),gamma(),eps(),p(Site+Rain+Sapling Den) 823.93 3.15 0.0503 0.2070 12 799.93 psi(Site),gamma(),eps(),p(Site+Rain+Sapling Den) 824.50 3.72 0.0378 0.1557 12 800.50 psi(Humidity),gamma(),eps(),p(Site+Rain+Sapling Den) 825.21 4.43 0.0265 0.1092 12 801.21 psi(Prey+ Sapling Rich.),gamma(),eps(t),p(Site+Rain+Sapling Den) 827.20 6.42 0.0098 0.0404 17 793.20 psi,gamma(),eps(),p(Site) 833.97 13.19 0.0003 0.0014 9 815.97 psi,gamma(),eps(),p() 834.49 13.71 0.0003 0.0011 4 826.49 psi,gamma(),eps(),p(Site+Humidity) 835.49 14.71 0.0002 0.0006 10 815.49 psi,gamma(),eps(),p(Site+Cover) 835.51 14.73 0.0002 0.0006 10 815.51 psi,gamma(),eps(),p(Site+Temperature) 835.69 14.91 0.0001 0.0006 10 815.69

Parameters include occupancy (Psi), gamma (colonization), epsilon (extinction), and detection (p) probabilities, and covariates. Seasons are defined as 6 time intervals (Jan,

Mar, May, July, Aug, Oct) in 2012. The relationship between the probability of occupancy and covariates was established using a logistic model (logit link) in program

PRESENCE (Hines 2006). Covariates are listed in Table 1. The influence of a covariate

(i.e. ˆ coefficient) was deemed to have a strong support if the 95% confidence interval

(CI) did not include zero.

______

Model Terms Estimate Standard Error______Anolis cristatellus Psi 6.200 2.441 Psi (DBH mid-story) 2.898 1.517 Psi (Prey) -2.198 1.588 gam 2.105 0.876 eps -3.027 0.340 p ( ) 1.133 0.078 p ( ) Shrubs Den -0.329 0.071 Anolis krugi

Psi 0.545 0.478 Psi (Sapling Rich.) -0.774 0.473 Psi (Prey) 0.624 0.428 gam -2.169 0.420 eps -1.457 0.328 p (season 1) -2.522 0.283 p (season 2) -4.251 0.325 p (season 3) -4.440 0.337 p (season 4) -2.204 0.300 p (season 5) -2.278 0.344 p (season 6) -3.195 0.371 p (Sapling Den) 0.273 0.092 p (Rain) 0.018 0.001

Table 7. Akaike weights (wi) for five competing multi-season occupancy models (Akaike’s

Information Criterion wi  2) of A. krugi. Akaike weights (wi) were used to assess the

relative importance of covariates. Data were collected in north-central karst region,

Puerto Rico, 2012.

Model Food Sapling Site Interaction (Sapling

Richness Richness*Food)

psi(Food+Sapling Rich.),gamma(),eps(),p(Site+Rain+Sapling Den) 0.0 0.27 0.27 0.0 psi(Sapling Rich.),gamma(),eps(),p(Site+Rain+Sapling Den) 0.0 0.0 0.22 0.0 psi(Food),gamma(),eps(),p(Site+Rain+Sapling Den) 0.0 0.12 0.0 0.0 psi(Food+Sapling Rich.+Site),gamma(),eps(),p(Site+Rain+Sapling

Den) 0.0 0.11 0.11 0.11 psi(Food+Sapling Rich.+Inter),gamma(),eps(),p(Site+Rain+Sapling Den)

0.10 0.10 0.10 0.0 Sum of AIC weights 0.59 0.69 0.11 0.10

CHAPTER II

FIGURES

Figure 1. Locations of study sites in the karst belt of northern Puerto Rico. Red rectangles represent plots within the reserves (yellow lines).

MPPR

ETPR

Figure 2. Monthly average temperature and rainfall for Mata de Platano Private

Reserves. Data were collected in north-central karst region, Puerto Rico, 2012.

Rain (mm) Rain

Temperature (ͦC) Temperature

Months

Figure 3. Monthly average temperature and rainfall for El Tallonal Private Reserves.

Data were collected in north-central karst region, Puerto Rico, 2012.

Rain Rain

(mm)

Temperature (ͦC) Temperature

Months

Figure 4. Monthly abundance of arthropods at Mata de Platano Private Reserve and El

Tallonal Private Reserve, two private natural reserves at north-central karst region,

Puerto Rico, 2012. Data for both reserves was pooled since no significant differences

were found between reserves.

Figure 5. Occupancy estimates as a function of average DBH of mid-story trees and average

prey abundance on occupancy estimates of Anolis cristatellus at two private natural

reserves at north-central karst region, Puerto Rico, 2012.

Figure 6. Additive effects of sapling richness (β = -0.77 ± 0.47) and average prey abundance

(β = 0.62 ± 0.43) on occupancy estimates of Anolis krugi in two private natural

reserves at north-central karst region, Puerto Rico, 2012.

CHAPTER II

APPENDICES

APPENDIX I

Royle-Nichols Abundance Induced Heterogeneity Models by periods for A. cristatellus at Mata de Platano Private Reserve and El Tallonal Private Reserve ranked by their associated AIC values.

Month Model AIC ΔAIC AIC w Model Parameters -2 LogLikelihood Likelihood January λ (.), r (.) 548.81 0.00 0.4228 1.0000 2 544.81 λ (.), r (Site) 550.15 1.34 0.2163 0.5117 3 544.15 λ (Site), r (.) 550.61 1.80 0.1719 0.4066 3 544.61 λ (.), r (t) 551.25 2.44 0.1248 0.2952 4 543.25 λ (.), r (t+ Site) 552.58 3.77 0.0642 0.1518 5 542.58

March λ (.), r (t) 565.21 0.00 0.6138 1.0000 4 557.21 λ (.), r (t+ Site) 566.28 1.07 0.3595 0.5857 5 556.28 λ (.), r (.) 573.12 7.91 0.0118 0.0192 2 569.12 λ (.), r (Site) 573.95 8.74 0.0078 0.0127 3 567.95 λ (Site), r (.) 574.09 8.88 0.0072 0.0118 3 568.09

May λ (Site), r (.) 517.34 0.00 0.3094 1.0000 3 511.34 λ (.), r (Site) 517.38 0.04 0.3032 0.9802 3 511.38 λ (.), r (.) 517.41 0.07 0.2987 0.9656 2 513.41 λ (.), r (t+ Site) 521.21 3.87 0.0447 0.1444 5 511.21 λ (.), r (t) 521.24 3.90 0.044 0.1423 4 513.24

July λ (.), r (t+ Site) 458.97 0.00 0.4429 1.0000 5 448.97 λ (Site), r (.) 459.22 0.25 0.3908 0.8825 3 453.22 λ (.), r (Site) 460.93 1.96 0.1662 0.3753 3 454.93 λ (.), r (t) 477.05 18.08 0.0001 0.0001 4 469.05 λ (.), r (.) 477.81 18.84 0 0.0001 2 473.81

September λ (.), r (.) 577.29 0.00 0.4415 1.0000 2 573.29 λ (Site), r (.) 578.76 1.47 0.2117 0.4795 3 572.76 λ (.), r (Site) 579.23 1.94 0.1674 0.3791 3 573.23 λ (.), r (t) 579.73 2.44 0.1303 0.2952 4 571.73 λ (.), r (t+Site) 581.68 4.39 0.0492 0.1114 5 571.68

November λ (.), r (.) 557.69 0.00 0.4142 1.0000 2 553.69 λ (.), r (Site) 558.20 0.51 0.321 0.7749 3 552.20 λ (Site), r (.) 559.62 1.93 0.1578 0.3810 3 553.62 λ (.), r (t) 561.55 3.86 0.0601 0.1451 4 553.55 λ (.), r (t+Site) 562.05 4.36 0.0468 0.1130 5 552.05

APPENDIX II

Royle-Nichols Abundance Induced Heterogeneity Models by periods for A. krugi at Mata de

Platano Private Reserve and El Tallonal Private Reserve ranked by their associated AIC values.

Month Model AIC ΔAIC AIC w Model Parameters -2 LogLikelihood Likelihood January λ (.), r (.) 207.19 0.00 0.4966 1.0000 2 203.19 λ (.), r (site) 209.15 1.96 0.1864 0.3753 3 203.15 λ (site), r (.) 209.19 2.00 0.1827 0.3679 3 203.19 λ (.), r (t) 210.44 3.25 0.0978 0.1969 4 202.44 λ (.), r (t+site) 212.41 0.0365 5 5.22 0.0735 202.41

March λ (.), r (t+site) 266.62 0.00 0.5665 1.0000 5 256.62 λ (.), r (t) 267.17 0.55 0.4303 0.7596 4 259.17 λ (site), r (.) 278.47 11.85 0.0015 0.0027 3 272.47 λ (.), r (.) 278.86 12.24 0.0012 0.0022 2 274.86 λ (.), r (site) 280.86 0.0005 3 274.86 14.24 0.0008

May λ (site), r (.) 174.58 0.00 0.5077 1.0000 3 168.58 λ (.), r (site) 175.31 0.73 0.3524 0.6942 3 169.31 λ (.), r (t+site) 177.2 2.62 0.137 0.2698 5 167.2 λ (.), r (.) 185.58 11.00 0.0021 0.0041 2 181.58 λ (.), r (t) 187.37 0.0008 4 179.37 12.79 0.0017

July λ (site), r (.) 233.97 0.00 0.705 1.0000 3 227.97 λ (.), r (site) 236.16 2.19 0.2358 0.3345 3 230.16 λ (.), r (t+site) 239.36 5.39 0.0476 0.0675 5 229.36 λ (.), r (.) 242.56 8.59 0.0096 0.0136 2 238.56 λ (.), r (t) 245.72 0.0020 4 237.72 11.75 0.0028

September λ (.), r (site) 190.00 0.00 0.6619 1.0000 3 184.00 λ (.), r (t+site) 192.23 2.23 0.2171 0.3279 5 182.23 λ (site), r(.) 193.43 3.43 0.1191 0.1800 3 187.43 λ (.), r (.) 202.31 12.31 0.0014 0.0021 2 198.31 λ (.), r (t) 196.54 204.54 0.0005 4 14.54 0.0007

November λ (.), r (t+site) 218.63 0.00 0.7496 1.0000 5 208.63 λ (site), r (.) 220.84 2.21 0.2483 0.3312 3 214.84 λ (.), r (site) 230.42 11.79 0.0021 0.0028 3 224.42 λ (.), r (t) 245.76 27.13 0.0000 0.0000 4 237.76 λ (.), r (.) 248.14 29.51 0.0000 2 0.0000 244.14

CHAPTER III

OMNIVORY AND TROPHIC POSITION OF ANOLIS CRISTATELLUS AND ANOLIS

KRUGI AT TWO SECONDARY KARST FOREST ON NORTHERN PUERTO RICO

Abstract

The degree of omnivory within members of an ecosystem is an important aspect that can influence food web dynamics, interspecific interactions, and trophic structure. In the archipelago of Puerto Rico, lizards of the genus Anolis are a dominant component of ecosystems and have been described as important consumers in diurnal food webs. Field observations have shown that they add novel food resources to the diet, however, the degree of omnivory remains unknown. I assessed the natural abundance of δ13C and δ15N in muscle tissues from two species of anole lizards in two secondary karts forests during the 2012 and calculated their trophic position using the stable isotope signature of food resources (arthropods and fruits). Isotopic analyses for A. cristatellus and A. krugi revealed that both species are omnivorous, however, the isotopic composition of anoles tissue and its trophic position varies significantly between species and sites. At both sites, A. krugi presented the more depleted signature for 15 N and therefore the lowest trophic position. Results suggests a high inclusion of fruits to the diet at both study sites.

Introduction

Lizards are characterized by their dietary diversity. Numerous groups depredate strictly over invertebrates or small vertebrates, others, that include more or less proportion of plant material in the diet, are categorized as omnivorous; in lesser extent are the herbivores species, that have evolved a diet of strict plant material (Cooper and Vitt, 2002, Valido and Olesen 2007,

Vitt and Pianka 2007, Vitt and Caldwell 2009). Omnivory has been progressively recognized for many species of lizards, omnivorous lizards supplement their diet with easily digestible plant material such as flowers, fleshy fruits, pollen and nectar that might be seasonal abundant and very nutritious (Cooper and Vitt, 2002, Valido and Olesen 2007). According to Cooper and Vitt

(2002), in ecosystems where preys are scarce and lizards are in negative energy balance, eating fleshy fruits might allow lizards to reduce net energetic loss or even attain positive energetic balance. Even though the consumption of those novel food resources is known for mainland and islands species, the occurrence is considered an island phenomenon (Olesen and Valido 2003,

Valido and Olesen, 2007).

Although historically, the genus Anolis has been considered as insectivorous, this assumption is changing as evidence from dietary studies favors a generalist-feeding mode (Losos

2009). Most of the anoles prey on a wide spectrum of invertebrates, including spiders, annelids, mollusks, millipedes, centipedes and even small vertebrates (Losos 2009). Differences in diets within and among anoles may be to reflect the differences in body size, microhabitat occupied, resource availability and energy requirements of species (Losos 2009). In terms of consumption of plant products, it’s known that at least 17 species of Anolis add nectar, fruits or seeds to its diet (Herrel et al. 2004). In the archipelago of Puerto Rico, anole lizards behave as a dietary generalist, eating mostly invertebrates as arthropods and mollusks (Reagan 1996). Although, the

65 important of fruits in diet is unknown, six species among four of the five ecomorphs present on the island have been reported consuming fruits (see Herrel et al. 2004, Losos 2009, Vega and

Puente 2011).

Even though several studies have examined the diets of the Puerto Rican anoles, all of them have been focused on quantifying stomach or fecal pellet contents, revealing only the food items being consumed immediately or during a particular time. These traditional methods tend to underestimate consumption of highly digestive material or rare preys (Stapp 2002). The precise knowledge of the food consumed by species is essential to establish its role in the ecosystems as well as to describe energy flow, nutrient cycling and the dynamics of ecosystems

(Reagan and Waide, 1996). Stable isotope analysis, especially carbon and nitrogen, is a useful tool that allows to track energy and mass through communities, identify ecological interactions and elucidate feeding relationships (Peterson and Fry 1987, Kling et al. 1992, Cabana and

Rasmussen 1996, Post 2002, Layman et al. 2012). This method is based in the relationship between stable isotope ratios of organism and those of their diets (DeNiro and Epstein 1978,

1981, Peterson and Fry 1987, Post 2002), representing assimilation rather than ingested food. In general, consumer tissues are enriched in 15N relative to their diet, resulting in an indicator of a consumer trophic position (Miniwaga and Wada 1984, Peterson and Fry 1987, Post 2002). Also, due to its limited fractionation, δ13C is used to determine primary production sources in a trophic network (Peterson and Fry 1987, Post 2002, Rubenstein and Hobson 2004).

Stable isotopes analysis has been used extensively in invertebrates, fishes, birds and mammals. In the case of reptiles, isotopic analyses are used widely on sea turtles (e.g. Seminoff et al.2006, Arthur et al. 2008, Reich et al. 2008, McClellan et al. 2010), and to lesser extent on lizards and snakes. In lizards, this approach has been applied to determine the trophic ecology of

66 the monitor lizard Varanus mabitang (Struck et al. 2002), to evaluate the impact of marine materials on the diet and abundance of Uta stansburiana in California (Barrett et al. 2005), to quantify the relative importance of primary production from plants with different photosynthesis pathways for the lizard community of the Chihuahuan Desert (Warne et al. 2010a), to evaluate the diet of Liolaemus pictus at two different localities at Chile (Vidal and Sabat 2010), and to determined carbon incorporation rates and diet-to-tissue discrimination in tissues of Sceloporus undulatus and Crotaphytus collaris (Warne et al. 2010b). The use of isotopic analysis in anoles has been limited to two studies at Bahamian islands; Takimoto et al. (2008) used the technique to test the roles of disturbance and ecosystem size in determining food-chain length of terrestrial food webs where Anolis sagrei are top predators, and Spiller et al. (2010) measured the effects of seasonal seaweed deposition on species, including anoles. Anolis lizards are excellent models to apply stable isotopes techniques because they are morphological, behavioral and ecological heterogeneous, differ significantly in habitat use and are the dominant components of ecosystems in Caribbean islands (Williams 1969, Moermond 1979).

Given the importance of Anolis lizards in diurnal food webs, documentation of omnivory in anoles is essential for a better understanding of ecosystems since omnivory can influence food web dynamics, interspecific interactions, and trophic structure. One would expect that if prey abundance fluctuates throughout time, at low prey abundance anoles lizards will add unusual food resources, such as fleshy fruits to their diets to meet energy requirements. The addition of fruits to the diet will result in an omnivory signature. One the other hand, since body size is associated with fruit consumption, an omnivory signature will be expected in individuals with intermediate and large of snout-vent length. To investigated the relationship between omnivory, prey abundance and body size I used stable isotopes of carbon (δ13C) and nitrogen (δ15N) to

67 assess the isotopic composition of muscle tissue of A. cristatellus and A. krugi and determine the trophic position of each species within two karst at the northern karst belt of Puerto Rico.

Methods

Study areas

This study was conducted at two secondary forests within the northern limestone karst belt of the main island of the archipelago of Puerto Rico (Figure 1). Both study sites are considered as private natural reserves and are located at the municipality of Arecibo within the sub-tropical moist forest life zone. This zone covers 58% of the total area of Puerto Rico and has an average annual temperature of 25.5°C, and an average annual precipitation of 1,295 mm

(Holdridge 1967), with a canopy height of about 20 m (Ewel and Whitmore 1973). The dry season is from January to March, and the wettest period is from July to September (López y

Villanueva 2006). Mata de Plátano Private Reserve (MPPR) (18º 24’46.63” N, 66 º 43’37.32”

W) consists of ca. 53 ha, while El Tallonal Private Reserve (ETPR) contains ca. 114 ha (18º

24’28.75” N, 66 º 43’54.51” W). The two reserves are located next to each other (Figure 1) and consist of forest at different successional stages.

Samples collection

Lizards were captured, every four months, by noose pole or hand at each study site. All lizards were sexed, measured snout-vent-length using a metric ruler (SVL; to 1.0 mm), weighed with a Pesola spring scale (nearest g), and measured head dimension (head width, length, and height) with a digital caliper (precision ± 0.01 mm, Mitutoyo).

A piece of ~2 cm sample of the tail from each lizard was collected before release at their site of capture. In reptiles, the isotopic signature of tissue reflects the diet of the last 4 to 6 months (Seminoff et al. 2007). I sampled only individuals with intact tails, avoiding clip

68 regenerated tails. All samples were kept individually packed and frozen until processing procedure. Plant leaves were used as the isotopic baseline at each study areas to establish the spatial and temporal variation in isotopic signatures and calculated the trophic positions of lizards (Post 2002). Ten leaves from each selected plant species were collected and combined into a single sample for stable isotope analysis.

Stable Isotopes Analysis

At the laboratory, all samples (tails and leaves) were dried for 48 hours at 60 °C and ground into a homogenous powder using a Retsch M-200 frequency grinder. Samples ranging from 0.9 - 1.3 mg of dried tissue in the case of lizards and 2.0 mg for plants were accurately weighed and loaded in tin capsules (5 X 8 mm, Elementar America). Analyses of the different samples were performed to determine the carbon and nitrogen isotope composition using a GV

Isoprime Isotope Ratio Mass Spectrometer (Elementar, Hanau, Germany) coupled with a

Eurovector (Eurovector, Milan, Italy) at the Laboratory of Stable Isotope Ecology in Tropical

Ecosystems, Department of Biology, University of Miami. International standards of known isotopic composition were included to correct for analytical and instrumental variations. Isotope ratios of each sample will are expressed as:

푅푆푎푚푝푙푒 훿13퐶 표푟 훿15푁 = ( − 1) × 1000 푅푆푡푎푛푑푎푟푑

69 nitrogen it is air nitrogen. All δ 13C values of invertebrates and lizard samples were corrected for lipids following Post et al. (2007) using the relationship between C:N ratio.

The trophic positions (TP) of anoles species at study areas were estimated relative to the

15 15 baseline δN value with the equation TP = (δ Norg - δ N baseline) /3.4) + 1 (Post 2002, Takimoto et

15 15 al. 2008). The δ N of producers (plants leaves) at each study area were used as baseline (δ N

15 baseline), δ Norg is the mean value for lizards tissues and 3.4 ‰ correspond to the average standard trophic fractionation in 15N per trophic level determined in previous studies (DeNiro and Epstein

1981, Post 2002). Although the trophic fractionation of 3.4 ‰ is not specific to lizards, it has been widely applicable in many studies and used previously to estimate trophic position in anoles

(Takimoto et al. 2008).

Food resources availability

Fruit availability was addressed by identifying, within each study area, plants producers of fleshy fruits with the potential of being food resources for lizards, and counting ripe fruits monthly for a complete year. The phenological status of 5 adult plants of each species was scored using a ripe fruit abundance index (FAI). Fruit abundance index for each plant species were established following Saracco et al. 2004. The categories of the FAI used to estimate the monthly fruit abundance were 0 = no ripe fruits, 1=1-10 ripe fruits, 2=11 – 100 ripe fruits, 3=101

– 1,000 ripe fruits, 4=1,001 –10,000 ripe fruits, 5=10,001 – 100,000 ripe fruits, and 6=>100,000 ripe fruits. The FAI of plants species per month was then summed.

70 traps were located at least 20 m apart from each other. Leaf litter for each sampling area was collected from 20-0.25 m2 random plots once a month. Leaf litter was placed into paper bags and animals were extracted from litter using Berlese funnels (Southwood, 1978) for 48 h. Berlese funnels separated arthropods from leaf litter when organisms moving downwards to escape from light and heat. All arthropods were preserved in 70% ethanol until they were processed. Sweep- net sampling was used to estimate the abundance of arthropods exploiting foliage, twig, and bark substrates. Vegetation at one meter along three -2 x 30 m transects was swept with standard muslin net; all invertebrates removed from the net were preserved in 70% ethanol. Arthropods were sorted using stereoscopes into classes or orders. Only arthropods reported in the scientific literature as component of the diet of anoles species with the same or similar ecomorphology were considered as potential prey and used for the analysis.

Statistical analysis

One-way analysis of variance was used to compare isotope ratios within and between species when data were normally distributed; when not normally distributed a Kruskal-Wallis

One Way Analysis of Variance on Ranks was performance. When data were normally distributed a t-test was used to compare TP of species within study sites and between the same species from reserves; when not normally distributed a Mann-Whitney U-test was performed.

All morphometric data were log 10 transformed before analysis to fulfill the assumption of normality. Multivariate analysis of variance (MANOVA) was used to compare morphometric variables of each species to determine differences between sites. Spearman’s Rank correlations were used to determine the relation among TP and residual values of log10-transformed morphometric variables. Residual values of morphometric variables were used because SVL and head variables were linear correlated. Differences in body size were separate from differences in

71 head dimension measurements using least squares linear regression to regress log-transformed

SVL body against each of the log-transformed variables (head dimension) per species. A split- plot ANOVA was used to evaluate whether the abundance of the different food types differs over time. Model terms were abundance of food (response variable), location, season, rainfall and season*location. Relations between average weather variables and monthly arthropods abundance were investigated using Pearson’s correlation coefﬁcients. Statistical analyses were conducted using JMP (V# 2011) and INFOSTAT software (Di Rienzo et al. 2011).

Results

A total of 259 Anolis were captured and measured, 154 for A. cristatellus (MPPR: 76,

ETPR: 78) and 105 for A. krugi (MPPR: 30, ETPR: 75). Mean adults A. cristatellus size ranged from 49.38 mm to 35.31 mm; in the case of A. krugi, ranged from 46.20 to 35.70 (Table 1) A

MANOVA for A. cristatellus did show significant differences in morphometric traits between sites: Wilks’ λ = 0.92, F = 2.21, p = 0.072, neither for A. krugi: Wilks’ λ = 0.92, F = 1.54, p =

0.192.

A piece of tail from all the individuals captured were collected and processed for stable isotopes analyses. For both stable isotopes values, there are no significant differences between sexes or categories (adults and juveniles) neither at MPPR (Table 2) or ETPR (Table 2) for both species. The data were therefore grouped per species for analyses of variation in isotope signatures within and between each study sites. The overall variation in δ13C and δ15 N for A. cristatellus and A. krugi at MPPR ranged from -26.82 to -21.35 ‰ and 2.12 to 7.59 ‰, and -

26.09 to -22.51 ‰ and 2.67 to 6.14 ‰, respectively (Table 3). In the case of ETPR, δ13C and

δ15 N values spanned from -26.34 to -17.90 ‰ and 3.22 to 6.40 ‰ for A. cristatellus, and from -

27.30 to -19.14 ‰ and 2.57 to 6.29 ‰ for A. krugi (Table 3). There were no significant spatial differences between study sites (δ13C: F=1.44, df =3, P=0.2319; δ15 N: F=0.27, df =3, P=0.603).

The mean isotopic signature for plants at MPPR was -32.18 ± 1.01 ‰ for δ13C and -1.50 ± 0.99

‰ for δ15 N (Table 3). In the case of ETPR, mean δ13C was -33.90 ± 0.53 ‰ and for δ15 N was -

0.60 ± 0.43‰ (Table 3).

The trophic position (TP) model for the two anoles species at MPPR based on stable isotopes indicated that both species it likely feeds on a combination of prey and plant products; the average TP for A. cristatellus was 2.71 and 2.65 for A. krugi. There are no significant

73 differences between the trophic position of species at MPPR (t = 1.849, df =117, P = 0.067)

(Figure 2). At ETPR, anoles species also are within the secondary level, but the TP for A. krugi was significantly less than that of A. cristatellus (t = 3.291, df = 154, P = 0.001) (Figure 2).

Comparisons between reserves indicated significant differences for the TP of both species, showing lowest TP at ETPR when compared to MPPR (A. cristatellus, t = -4.935, df = 154, P =

< 0.001; A. krugi, t = 3.979, df = 105, P = < 0.001) (Figure 2). A. krugi at ETPR showed the lowermost TP with an average of 2.45. When TP is compared between sampling periods for each species within reserve, a significant difference was found for A. cristatellus at MPPR (F =

2.97, df = 3, P = 0.037) (Table 4, Figure 3), but not at ETPR (F = 2.43, df =3, P = 0.071) (Table

4, Figure 4). In the case of A. krugi, there are not a significant differences at MPPR (H = 7.39, df

=3, P = 0.060) (Table 4, Figure 5), but there are significant difference at ETPR (F = 7.660, df =3,

P < 0.001) (Table 4, Figure 6). The significant difference at MPPR for A. cristatellus is given by

September, while for A. krugi at ETPR is by May. There was no correlation between log10- transformed morphometric variables and TP, except for log10-transformed head length of A. krugi at MPPR and head length of A. cristatellus at ETPR (Table 5).

Food resources availability

A total of 32,137 arthropods, considered as potential preys, (16,306 for MPPR and 15,

831 for ETPR), were captured at study areas and assigned to 15 orders and one general category for larvae, which includes larvae of different groups of arthropods (e.g. Lepidoptera and

Coleoptera). The predominant groups at study areas were Diptera and Hymenoptera followed by

Aranae and Hemiptera. There were no significant differences in arthropods abundance between study areas (F = 0.0431, df = 1, P = 0.8366). When the community data is pooled by periods, there were significant differences among seasons (F = 8.44, df = 3, P = 0.0009), with higher

74 abundance values between the September to December period relative to the others periods

(Figure 7). In spite of the significant value between September to December, only the order

Diptera account for the higher arthropods abundance. The correlation analysis among the monthly average abundance of each group of arthropods against the monthly average rainfall revealed no signiﬁcant association neither for MPPR nor ETPR (all p's > 0.05).

Ten plant species, producers of fleshy fruits, where identified as potential food resources for anoles at each study areas. The availability of fleshy fruits varied over the study period

(Table 6). Ripe fruits were most abundant during May to August at ETPR primarily as a result of the massive fructification of Castilla elastica and Spondias mombins (ca. 10,000 for each species) (Figure 8). In the case of MPPR, the abundance was slightly similar for May-August period and September-December (Figure 8), Spondias mombins was the responsible for the high abundance of fruits at both periods.

Discussion

This study provides the first isotopic assessment for anoles species inhabiting the limestone karst region of Puerto Rico. Conventional methods for diet reconstruction (e.g. gut contents and fecal analysis) are frequently restrictive since they only provide information about what an animal has eaten during a recent and brief window of time. The potential of isotopic analysis to reveal important insights of trophic relationships among organisms lies in the capacity of stable isotopes to reflect assimilation of energy sources integrated over time and space (Layman et al. 2011, Layman et al. 2012). Isotopic analyses for A. cristatellus and A. krugi revealed that both species are omnivores but the isotopic composition varies significantly between species. In general, herbivores are expected to have lower δ 15N values than carnivores, and omnivores should have δ 15N values intermediate between those of herbivores and carnivores

(Kelly 2000). At both sites, A. krugi presented the more depleted signature for 15 N, suggesting that this species add a significant amount of fruits to the diet.

The differences in nitrogen signatures between anoles species indicate a variation of its relative position in the food web on study sites. Trophic position is the energy-weighted mean number of links between primary producers and a consumer, and is one of the most used metric in studies employing stable isotopes (Vander Zanden and Rasmussen 1999, Post 2002, Layman et al. 2012). Usually, TP is expressed as a noninteger value and can account for omnivory and complexity. Results from mean TP indicated that both species have values between 2.0 and 3.0

(Table 2), values corresponding to omnivores (intermediates between primary and secondary consumers). However, TPs varies significantly between species at ETPR and within the same species from different sites. Both species exhibited the lowest values at ETPR, and A. krugi from this site showed the lowest TP: 2.45, near the theoretical TP of a strict herbivore (Figure 2).

Differences in TP among species or individuals at the same habitat could be the results of morphological variation or habitat specialization (Matthews et al. 2010). Anolis lizards differs on morphology, behavior and structural habitat use (Williams 1972, Williams 1983, Losos

1990); thus, variation observed in TPs might be mirror of the differences between ecomorphs. In terms of morphology, there are differences in body size between A. cristatellus and A. krugi.

Despite the difference there is no relationship between SVL and TP in either species. There is also no relationship between the dimensions of the head and TP; except for a weak positive relationship with head length in A. krugi from MPPR and A. cristatellus from ETPR.

As habitat specialist, Anolis species are adapted to different structural microhabitats

(Williams 1972, Williams 1983, Losos 1990, Losos et al. 1998). At both reserves, A. cristatellus use principally mid-story trees whereas A. krugi is associated with understory dense vegetation

(Vega-Castillo see Chapter 2). Therefore, although A. cristatellus and A. krugi co-occur, differences in microhabitat lead to differences in access to food resources. It seems that differences in TP between species could be related with the microhabitat occupied by the species. At ETPR, for example, occurrence of A. krugi at ETPR is associated with the presence of Castilla elastica (Vega-Castillo see Chapter 2), a tree species that massive produce fleshy fruits during May to July (Table 6, Figure 8). While at MPPR the higher occurrence is related with Anthurium crenatum, a plant that produce fleshy fruits during May to December (Table 6).

Previous isotopic research has demonstrated that the consumption of fruits by lizards have the effects of decreases trophic levels (Vidal and Sabat 2010). At both reserves, this species showed the lower TPs on September and January 2013 (Table 4) periods that are preceded by high abundance of fruits (Figure 8); and when the isotopic signatures reﬂect the addition of fruits

77 available at previous months (Vega-Castillo see Chapter 4). It seem that the lowest TP exhibited by A. krugi is consequence of fruit availability and not by prey fluctuations as hypothesized.

An alternative explanation to low TP could be that primary consumers provided the main energy source for anoles. Takimoto et al. (2010) argued that the inclusion of a significant amount of preys that occupy lower positions in the food web (herbivores preys) could decrease the TP of anoles inhabiting small Bahamian islands. In these islands, anoles caused extirpation or dramatic reduction of spiders favoring high occurrence of herbivorous prey. Contrary to

Bahamian anoles populations, at MPPR and ETPR, anoles have available a variety of potential preys that include from primary consumers to predatory arthropods (Vega-Castillo see Chapter

2). Resource models indicate that both herbivorous arthropods as predators contribute to the diet of A. cristatellus and A. krugi at both study sites (Vega-Castillo see Chapter 4). Even, partition models indicated that the omnivorous orders Blattodea and Coleoptera are the most important food sources for both anoles (Vega-Castillo see Chapter 4). Appears that the consumption of these two groups do not account for decrease in TPs rather has to do with frugivory.

In summary, A. cristatellus and A. krugi at MPPR and ETPR are omnivores but the degree of omnivory varied between species and among individuals of the same species. Lowest

TP at some periods seems to be related with fruits resource availability at microhabitat since preys availability is not confined to particular groups of consumers. Omnivorous consumers have the advantage to reap the most abundant but less nutritious resources and also the less abundant but more nutritious food sources in variable proportions (Coll and Guershon 2002).

Since the degree of omnivory within members of an ecosystem can influence food web dynamics and trophic structure (Morin and Lawler 1996, Polis and Strong 1996, Blüthgen et al. 2003), documenting the occurrence of omnivory in ecosystems is essential to comprehend ecosystem

78 functioning and stability (Dunne et al. 2002, Williams and Martínez 2004). Finding about the lowest TP of A. krugi is particular important since among West Indian anoles, smallest ecomorph are described as strict insectivores (Losos 2009). The results presented here might be helpful to understand the role of anoles on ecosystem, and therefore, understand ecosystem function and dynamics.

CHAPTER III

TABLES

Table 1. Average values and standard errors for six body traits of males and females of Anolis

cristatellus (n=112) and A. krugi (n=74) at Mata de Platano Private Reserve and El

Tallonal Private Reserve, two private natural reserves at north-central karst region, Puerto

Rico, 2012.

Table 2. Comparison of stable isotope values (‰) of Anolis lizards per categories (adults and

juveniles) and per sex sampled at Mata de Plátano Reserve and El Tallonal Private

Reserves at north-central karst region, Puerto Rico, 2012.

Species Comparison by categories Comparison by sex

Test value df P-value Test Value df P-value MPPR Anolis cristatellus δ13C H = 0.729 2 0.695 t = 1.265 41 0.213 δ15 N F = 1.859 2 0.163 t = -0.260 41 0.796 Anolis krugi δ13C F = 0.237 2 0.791 t = -0.568 20 0.577 δ15 N F = 0.234 2 0.793 t = 0.0189 20 0.985 ETPR Anolis cristatellus δ13C H = 1.210 2 0.546 T = 363.0 40 0.294 δ15 N F = 0.533 2 0.589 t = -1.045 40 0.302 Anolis krugi δ13C H = 1.863 2 0.394 T = 591.0 46 0.959 δ15 N F = 0.305 2 0.738 t = -0.0520 46 0.959

Table 3. Average stable isotope values (‰) of Anolis lizards (Anolis cristatellus, n= 76; A.

krugi, n=30) and plants sampled at Mata de Plátano Reserve and El Tallonal Private

Reserves at north-central karst region, Puerto Rico, 2012.

13 15 δ C (‰) δ N (‰)

Species (n) Average ± SE min max Average ± SE min max

Mata de Platano

A. cristatellus (76) -23.94 ± 0.12 -26.82 -21.35 4.91 ± 0.10 2.12 7.59 A. krugi (30) -24.65 ± 0.15 -26.09 -22.51 4.56 ± 0.14 2.67 6.14 Plants (7) -32.18 ± 1.01 -34.63 -28.33 -1.50 ± 0.99 -6.52 0.97

El Tallonal

A. cristatellus (78) -23.61 ± 0.17 -26.34 -17.90 4.92 ± 0.08 3.22 6.40 A. krugi (75) -24.27 ± 0.17 -27.30 -19.14 4.53 ± 0.09 2.57 6.29 Plants (11) -33.98 ± 0.53 -35.89 -30.99 -0.647 ± 0.43 -2.68 1.66

Sampling period MPPR ETPR

Anolis Anolis Anolis Anolis cristatellus krugi cristatellus krugi

January 2012 2.83 ± 0.092 2.73 ± 0.141 2.70 ± 0.071 2.37 ± 0.069

May 2.81 ± 0.053 2.70 ± 0.054 2.57 ± 0.034 2.62* ±0.0415

September 2.61* ± 0.046 2.53 ± 0.092 2.51 ± 0.048 2.45 ± 0.0343

January 2013 2.78 ± 0.043 2.63 ± 0.156 2.57 ± 0.327 2.34 ± 0.0509

* Significant differences.

Table 5. Spearman’s Rank correlation values for log10-transformed morphometric measures of

Anolis cristatellus (n=112) and A. krugi (n=74) against trophic position at Mata de

Platano Private Reserve and El Tallonal Private Reserve, two private natural reserves at

north central karst region, Puerto Rico, 2012.

Table 6. Fructification periods for the plant species producers of fleshy fruits identified

as potential food resources for anoles at at Mata de Platano Private Reserve (n=10)

and El Tallonal Private Reserve (n=10) two private natural reserves at north-central

karst region, Puerto Rico, 2012.

Plant species Jan-Apr May-Aug Sept-Dec

MPPR Anthurium crenatum - x x Casearia guianensis - - - Casearia sylvestris - - - Clusia rosea x - - Cordia laevigata - x x Eugenia ligustrina - x - Eugenia pseudopsidium x - x Faramea occidentalis x x x Ficus sp. x x - Spondias mombins - x x

ETPR Casearia guianensis - - - Casearia sylvestris - - - Castilla elástica - x - Clusia rosea x - - Coccoloba pyrifolia x - - Eugenia ligustrina - x - Eugenia pseudopsidium x - x Gyminda latifolia x - x Roystonea borinquena x x x Spondias mombins - x x

CHAPTER III

FIGURES

Figure 1. Locations of study sites in northern Puerto Rico. Red rectangles represent plots within

the reserves (yellow lines).

MPPR

ETPR

Figure 2. Mean trophic position (± 1 SE) of Anolis cristatellus and A. krugi at Mata de Platano

Private Reserve and El Tallonal Private Reserve, two private natural reserves at north

central karst region, Puerto Rico, 2012. Trophic position of anoles were calculated

15 15 relative to the baseline δN value with the equation TP = (δ Norg – δ N baseline) /3.4) + 1,

where TP is trophic position, δ15N of producers (plants leaves) at each study area were

15 15 used as baseline (δ N baseline), δ Norg is the mean value for lizards tissues and 3.4 ‰

correspond to the average standard trophic fractionation in 15N per trophic level

determined in previous studies.

Figure 3. Trophic position (± 1 SE) of A. cristatellus by sampling periods at Mata de Platano

Private Reserve, a private natural reserve at north-central karst region, Puerto Rico,

2012. Trophic position were calculated relative to the baseline δN value with the

15 15 15 equation TP = (δ Norg – δ N baseline) /3.4) + 1, where TP is trophic position, δ N of

15 15 producers (plants leaves) at each study area were used as baseline (δ N baseline), δ Norg is

the mean value for lizards tissues and 3.4 ‰ correspond to the average standard trophic

fractionation in 15N per trophic level determined in previous studies.

Figure 4. Trophic position (± 1 SE) of A. cristatellus by sampling periods at El Tallonal Private

Reserve, a private natural reserve at north-central karst region, Puerto Rico, 2012.

Trophic position were calculated relative to the baseline δN value with the equation TP =

15 15 15 (δ Norg – δ N baseline) /3.4) + 1, where TP is trophic position, δ N of producers (plants

15 15 leaves) at each study area were used as baseline (δ N baseline), δ Norg is the mean value

for lizards tissues and 3.4 ‰ correspond to the average standard trophic fractionation in

15N per trophic level determined in previous studies.

100

101

Figure 5. Trophic position (± 1 SE) of A. krugi by sampling periods at Mata de Platano Private

Reserve, a private natural reserve at north-central karst region, Puerto Rico, 2012.

Trophic position were calculated relative to the baseline δN value with the equation TP =

15 15 15 (δ Norg – δ N baseline) /3.4) + 1, where TP is trophic position, δ N of producers (plants

15 15 leaves) at each study area were used as baseline (δ N baseline), δ Norg is the mean value

for lizards tissues and 3.4 ‰ correspond to the average standard trophic fractionation in

15N per trophic level determined in previous studies.

102

103

Figure 6. Trophic position (± 1 SE) of A. krugi by sampling periods at El Tallonal Private

Reserve, Arecibo, a private natural reserve at north-central karst region, Puerto Rico,

2012. Trophic position were calculated relative to the baseline δN value with the

15 15 15 equation TP = (δ Norg – δ N baseline) /3.4) + 1, where TP is trophic position, δ N of

15 15 producers (plants leaves) at each study area were used as baseline (δ N baseline), δ Norg is

the mean value for lizards tissues and 3.4 ‰ correspond to the average standard trophic

fractionation in 15N per trophic level determined in previous studies.

104

105

Figure 7. Relative abundance of arthropods order by periods. Data from both reserves were

pooled since not significant difference was found.

106

6000

5000 Jan-April May-Aug

Sep-Dec 4000

3000

2000

Total Number of Arthropods of Number Total

1000

0 Aranae Blattodea Coleoptera Diptera Hemiptera Hymenoptera Isoptera Lepidoptera Orthoptera Others

107

Figure 8. Phenology of fleshy fruits plants at Mata de Platano Private Reserve (n=10) and

El Tallonal Private Reserve (n=10), two private natural reserves at north-central karst

region, Puerto Rico, 2012.

108

35000

Mata de Plätano 30000 El Tallonal

25000

20000

15000

Summed FAI Summed

10000

5000

0 Ene-Apr May-Aug Sept-Dec Sampling Periods

109

CHAPTER IV

EFFECTS OF SEASONAL CHANGES IN FOOD AVAILABILITY ON THE DIET OF ANOLIS CRISTATELLUSAND A. KRUGI AT TWO KARST FORESTS AT NORTHERN PUERTO RICO

110

Abstract

Food is one of the most essential resources that animals need from their surroundings, since it determines the energy available to perform the essential processes of life (Nagy 2005).

The use of stable isotopes in studies of diet offers advantages over conventional methods. Stable isotopes, especially carbon and nitrogen, provide signatures based on food assimilation over time, thus, reflect the diet over the period during which the tissue was synthesized. The objective of this study was to determine, using stable isotopes, the diet of Anolis cristatellus and

Anolis krugi at two karst forests at northern Puerto Rico. The effects of food resource availability was also analyzed. Mean isotope values were significantly different between anoles species, and in the case of A. krugi, change seasonality. Source-partition models showed that both species have a diverse diet and acquired energy from arthropods preys but also from fruits when are available. Although the models indicated fruits consumption by the two anoles, fruits seem to be particularly important for A. krugi.

111

Introduction

Food is one of the most essential resources that animals need from their surroundings, since it determines the energy available to perform the essential processes of life (Nagy 2005).

The study of diets has long been and continues to be an essential component to address ecological and evolutionary issues, as well as, for take decisions on wildlife management and conservation. In reptiles, dietary studies provided basic information that allows to make inferences in different knowledge areas, including, but not limited to, physiology, morphology, behavior, ecology and evolution (e.g. Pought 1973, Ferguson et al. 1983, Losos and Greene

1988, Pearson et al. 1993, Rodríguez-Robles et al. 1999a and Rodríguez-Robles et al. 1999b,

Cooper and Vitt 2002, Rodríguez-Robles 2002, Li et al. 2005).

Dietary studies in West Indian Anolis lizards have documented the consumption of fruits and others plants products (see Losos 2009). Omnivorous lizards regularly or occasionally consume easily digest vegetative products as fleshy fruits, nectar, pollen or sap that are low in fiber content but rich in water and nutrients (Cooper and Vitt, 2002, Olesen and Valido, 2003;

Valido and Nogales, 2003). Even though the consumption of those novel food resources is known for mainland and islands species, the occurrence is considered an island phenomenon

(Olesen and Valido 2003, Valido and Olesen, 2007). The evidence suggests that, in general, large body size, particularly head dimension, favors the consumption of fruits (Herrel et al.

2004a and b, Olesen and Valido 2004). Nevertheless, the ecological factors that determine whether insular lizards add fruits and other easily digest plant products into their diet remain unclear (Herrel et al. 2004, Valido and Olesen 2007). High densities of lizards, low exposure to predation and reduced prey availability are some of the suggested hypotheses explaining the

112 relationship between fruits consumption by lizards and insularity (Rand 1978, Perez-Mellado and

Cortis, 1993 and Van Damme, 1999, Olesen and Valido 2003).

Herrel et al (2004), studied the phenomena of omnivory/frugivory on Jamaican anoles and validated the correlation between body size and fruit consumption, but established that, in the case of Caribbean anoles, the evidence does not support any of the “traditional” hypotheses proposed to explain the phenomena. Despite the fact that Herrel et al (2004) argues against the food scarcity hypothesis, detailed analyses on preys abundance and anoles dietary are necessary to definitely corroborate food scarcity hypothesis. Especially when food supplementation experiments as well as other several studies established that anoles are food limited and that this limitation has adverse effects on body condition and growth (Licht 1974, Andrew 1976,

Schoener and Schoener 1978, Lister 1981, Stamp and Tanaka 1981, Reagan et al. 1982).

It is known that island areas have relatively fewer species of arthropods and lower number of individuals per species when compared with nearby mainland areas (Janzen, 1973,

Ebenman and Nilsson, 1982). Although tropical ecosystems lack of drastic yearly ﬂuctuations in temperature and rainfall often shows pronounced seasonal variation in which the dry period is interchanged with periods of heavy rain. Alternation of dry and rainy seasons in tropical forests influences prey availability, since many insects are sensitive to the changes in moisture that accompany such events (Schowalter 2006). The lack of moisture in the dry-season limits also plant growth, which negatively affects insect quantities during the season (Griffiths and Christian

1996). On the other hand, although rainfall patterns influence plant phenology, in general, the phenological complexity of plants at tropics leads the occurrence of flowers and fruits year around (Lawrence 1996). Differences in seasonal consumption of preys were shown in some anole species (Fleming and Hooker 1975, Floyd and Jenssen 1983, Fontenot et al. 2003,

113

Rodríguez-Schettino 2010). Anoles lizards are opportunistic feeders (Losos 2009), since those seasonality variations in the diet probably is reflected of exploitation of a seasonally abundant resource. The constant or seasonal scarcity of preys might lead predominantly arthropodivorous lizards to expand their trophic niche by the incorporation of those nontraditional food resources that are available (Janzen 1973, Olesen and Valido, 2003).

In Puerto Rico, the consumption of novel food resources is recognized for some species

(see Herrel et al. 2004, Losos 2009, Vega and Puente 2011); however, the contribution of fruits to the diet and the factors that promote the inclusion of fruits have never been studied. If food source vary seasonally, therefore importance of food item consumption will be associated with its abundance. Here, I evaluate, using stable isotopes, the diet of Anolis cristatellus and A. krugi at two forests within the northern karst region at Puerto Rico, and examine the effects of seasonal changes in food availability on the diet of these two species.

114

Methods

Study areas

This study was conducted at two secondary forests within the northern limestone karts belt of the main island of the archipelago of Puerto Rico (Figure 1). Both study sites are considered as private natural reserves and are located at the municipality of Arecibo within the sub-tropical moist forest life zone. This zone covers 58% of the total area of Puerto Rico and has an average annual temperature of 25.5°C, and an average annual precipitation of 1,295 mm

(Holdridge 1967), with a canopy height of about 20 m (Ewel and Whitmore 1973). The dry season is from January to March, and the wettest period is from July to September (López y

Villanueva 2006). Mata de Plátano Private Reserve (MPPR) (18º24’27” N, 66 º 42’61” W) consists of ca. 53 ha while El Tallonal Private Reserve (ETPR) contains ca. 114 ha (18º24’27” N,

66 º 43’53” W). The two reserves are located next to each other and consist of forest at different successional stages (Figure 1).

Samples collection

Lizards were captured by noose pole or hand at each study site, every four months

(January, May, September 2012 and January 2013). For each lizard, we collected the following information: snout vent length (SVL) to 1.0 mm using a metric rule; total mass (0.1 g) with a

Pesola spring scale; head width, length, and height with a digital caliper (Mitutoyo; precision ±

0.01 mm). I collected a piece of ~2 cm of the tail from each lizard captured at each study area.

After the sample collection, we release each animal at the capture site. In reptiles, the isotopic signature of tissue reflects the diet of the last 4 to 6 months (Seminoff et al. 2007). I only took samples from individuals which has intact tails, avoiding clip regenerated tails. All samples were kept individually packed and frozen until processing procedure was performed.

115

As part of the sampling, we collected plant leaves that were used to establish the isotopic baseline at each study areas and also allow us to establish the spatial and temporal variation in isotopic signatures. Ten leaves from each selected plant species were collected and combined into a single sample for stable isotope analysis. To establish the isotopic signature of available resources, fleshy fruits and arthropods identified as potential food resources were actively collected monthly at each study site In the case of fruits, five to ten ripe fruit from five different individuals of each plant species (when available) were combined for one sample for isotopic analysis. Small to medium bodied arthropods (e.g. ants, spiders and flies) required at least 30 to

50 individuals in order to provide an adequate amount of tissue for analysis. Potential preys with larger body size required fewer individuals. All potential resources were kept individually packed and frozen until processing procedure for stable isotope analysis was performed.

Stable Isotopes Analysis

At the laboratory, all samples (tails, arthropods, fruits and leaves) were dried for 48 hours at 60 °C and ground into a homogenous powder using a Retsch M-200 frequency grinder. Due to the stickiness of fleshy fruit, all fruits samples were ground with liquid nitrogen using a mortar and pestle. Samples ranging from 0.9 - 1.3 mg of dried tissue in the case of lizards and 2.0 mg for plants and fruits were accurately weighed and loaded in tin capsules (5 X 8 mm, Elementar

America). Analyses of the different samples were performed to determine the carbon and nitrogen isotope composition using a GV Isoprime Isotope Ratio Mass Spectrometer (Elementar,

Hanau, Germany) coupled with a Eurovector (Eurovector, Milan, Italy) at the Laboratory of

Stable Isotope Ecology in Tropical Ecosystems, Department of Biology, University of Miami.

International standards of known isotopic composition were included to correct for analytical and instrumental variations. Isotope ratios of each sample will are expressed as:

116

푅푆푎푚푝푙푒 훿13퐶 표푟 훿15푁 = ( − 1) × 1000 푅푆푡푎푛푑푎푟푑

Where 13C or 15N represent the isotopic abundance of carbon and nitrogen respectively, and RSample and RStandard represent the ratio of heavy to light isotopes from the sample and standard respectively. R Standard for carbon isotope ratios is the Vienna PDB standard and for nitrogen it is air nitrogen. δ 13C values of invertebrates and lizard samples were corrected for lipids following Post et al. (2007) using the relationship between C:N ratio.

I used IsoSource as source-partitioning model (Phillips and Gregg 2003) to evaluate the fractional contribution of nine sources to the diet of anoles. Based on mass balance equations, this model allows calculate the range of all possible source contributions when the number of potential sources is more than n + 1. The model uses the mean δ 13C and δ 15N values for each potential source, corrected for the discrimination factor of the mixture (consumer). Standard

trophic fractionation of 3.4 ‰ for 15N and 1.0 ‰ for 13C were used as the enrichment factor to run Isosource models. All models were run using an increment of 2% and a mass balance tolerance of ±0.1‰. Results from Isosource models for each species and at each sampling periods within study sites were used to construct polygons. Vertex of polygons represent potential food sources, and histograms show the distribution of feasible contributions from each source to the lizard diet. Values in histograms are 1–99 percentile ranges for these distributions.

Analyses were performed using isotopic ratios of food resources of step in relation with isotopic ratios of anoles; so, for example, the isotopic signature of food sources from January to April were used to run the models of May for each anoles and at each study site.

117

Food resources availability

Fruit availability was addressed by identifying, within each study area, plants producers of fleshy fruits with the potential of being food resources for lizards, and counting ripe fruits monthly for a complete year. The phenological status of 5 adult plants of each species was scored using a ripe fruit abundance index (FAI). Fruit abundance index for each plant species were established following Saracco et al. (2004). The categories of the FAI used to estimate the monthly fruit abundance were 0 = no ripe fruits, 1=1-10 ripe fruits, 2=11 – 100 ripe fruits, 3=101

– 1,000 ripe fruits, 4=1,001 –10,000 ripe fruits, 5=10,001 – 100,000 ripe fruits, and 6=>100,000 ripe fruits. The FAI of plants species per month was then summed.

Monthly variations in arthropod abundance was evaluated using three different trapping methods (Malaise, leaf litter collection and sweep-net) allowing estimates of arthropods relative abundance to which the lizards are exposed over the year. Two Malaise traps for capture flying arthropods were installed at each study site and were operated for a 48-h period each month; traps were located at least 20 m apart from each other. Leaf litter for each sampling area was collected from 20-0.25 m2 random plots once a month. Leaf litter was placed into paper bags and animals were extracted from litter using Berlese funnels (Southwood, 1978) for 48 h. Berlese funnels separated arthropods from leaf litter when organisms moving downwards to escape from light and heat. All arthropods were preserved in 70% ethanol until they were processed. Sweep- net sampling was used to estimate the abundance of arthropods exploiting foliage, twig, and bark substrates. Vegetation at one meter along three -2 x 30 m transects was swept with standard muslin net; all invertebrates removed from the net were preserved in 70% ethanol. Arthropods were sorted using stereoscopes into classes or orders. Only arthropods reported in the scientific

118 literature as component of the diet of anoles species with the same or similar ecomorphology were considered as potential prey and used for the analysis.

Statistical Analyses

When data were normally distributed, analyses of variance was performed to determine differences in isotope values among species, preys, and to evaluate if isotopes signatures of each anoles species differ significantly between locations. If the data were not normally distributed a

Kruskal-Wallis One Way Analysis of Variance on Ranks was performance. Analyses of anoles stable isotopes were performed using isotopic values of food resources of step in relation with isotopic ratios of anoles. A split-plot ANOVA was used to evaluate whether the abundance of the different food types differs over time. Model terms were abundance of food (response variable), location, season, rainfall and season*location. Relations between average weather variables and monthly arthropods abundance were investigated using Pearson’s correlation coefﬁcients. I used PERMANOVA, to testing the simultaneous response of δ13C and δ15 N factors in each species. PERMANOVA was performed in SAS version 2.4.1 (2011).

119

Results

Tail tissue was collected from 259 anoles. The distribution of samples were 154 for A. cristatellus (MPPR: 76, ETPR: 78) and 105 for A. krugi (MPPR: 30, ETPR: 75). The overall variation in δ13C and δ15 N for A. cristatellus and A. krugi at MPPR ranged from -26.82 to -21.35

‰ and 2.12 to 7.59 ‰, and -26.09 to -22.51 ‰ and 2.67 to 6.14 ‰, respectively (Table 1). In the case of ETPR, values spanned from -26.34 to -17.90 ‰ and 3.22 to 6.40 ‰ for A. cristatellus, and from -27.30 to -19.14 ‰ and 2.57 to 6.29 ‰ for A. krugi (Table 2). There were significant differences between species (δ13C: F=9.41, df =1, P = 0.002; δ15 N: F=11.25, df = 1, P

= 0.0009) with a significant season effects in both δ13C and δ15 N values (δ13C: F=2.81, df =3,

P=0.0402; δ15 N: F=3.0634, df = 3, P=0.0288). A PERMANOVA test incorporating both isotopes signature at the same time revealed, statistically significant differences for sites (F=1.56, df = 1, P=0.001) for A. krugi, and also between seasons (F=6.17, df = 3, P=0.001) (Figure 2).

There was no significant differences for A. critatellus neither spatial (F=1.756, df = 1, P=0.24) nor seasonal (F=1.852, df = 3, P=0.10) (Figure 3). The C:N ratios differ significantly among periods for A. cristatellus at MPPR (H=21.74, df = 3, P=<0.001) but not for A. krugi (H = 7.307, df = 3, P=0.063). In the case of ETPR, there were significant differences among time periods for A. cristatellus (H=16.32, df = 3, P = <0.001), and also for A. krugi (F=4.048, df = 3,

P=0.010). At both sites and for the two species the ratio of C:N was highly variable on

September (Figure 4 and 5).

Twelve arthropods groups identified as potential preys were analyzed to determine the ratios of both carbon and nitrogen stable isotopes (Table 1 and 2). Preys δ 13C ranged between -

29.46 to -22.93‰ for MPPR and between -30.50 to -23.11 for ETPR. In the case of δ15 N, signatures spanned from 0.73 to 6.10 at MPPR and for ETPR ranged from 0.28 to 5.18. For both

120 study sites, there were signiﬁcant differences between the preys isotopic signatures in δ13C

(MPPR: H=29.073, df = 11, P=<0.001; ETPR: H=31.769, df = 11, P=<0.001) and δ15 N ratios

(MPPR: F=36.485, df = 11, P=<0.001; ETPR: H=54.677, df = 11, P=<0.001). At both study sites Phasmatodea had the lowest δ13C values and Hemiptera the lowest δ15 N signature (Table 1 and 2). Isotopic signature for fruits at both sites are similar for δ13C and more depleted for δ15 N at ETPR (Table 1 and 2).

Isotopic models for both species at study areas showed a broad diet with predominance of certain preys in different periods. In all periods, the food resources selected as potential food items shaped a polygon in which lizards were at the center or falling near one end. In the case of

MPPR, the results for both species, for May, showed a significant contribution of Coleoptera (1–

99 the percentile: 18-63% for A. cristatellus and 12-58% for A. krugi) when compared with the other eight food sources (Figure 6 and 7). Hemiptera, Blattodea and Orthoptera had a lower but nevertheless important contribution to the diet of both lizards, ranging between 0-48% to 0-40 % for A. cristatellus, and from 0-60% to 0-40% for A. krugi (Figures 6 and 7). The other five food sources were definitely minor components of the diet (Figures 6 and 7). In September, all the food sources presented broad ranges of possible contributions to the diet of both species. For A. cristatellus, Hemiptera, Orthoptera and Blattodea were the groups that appeared to contribute the most to the diet (1–99 the percentile: 0-50 %, 0-46% and 0-42%, respectively) (Figure 8).

However, for A. krugi, Isoptera (0-68%) showed the more defined contribution followed by

Hemiptera (0-48%) and Blattodea (0-42 %) (Figure 9). For January, the model showed

Coleoptera and Blattodea as feasible sources for A. cristatellus, with 34-54% and 36-50%, respectively (Figure 10), the other food sources represented less than 16% of the diet. In the case of A. krugi, the model is also more informative and showed fruits and Blattodea as feasible

121 sources contributing 6-18% and 8-50% respectively, and Orthoptera and Hemiptera with equivalent contributions of 0-56% (Figure 11).

The results of the mixing models for ETPR, in May, showed Blattodea as a feasible source for both species, and appear constitute the main source for A. cristatellus (56-92%)

(Figure 12). In the case of A. krugi, Coleoptera represented an important secondary food source

(12-40%); the other potential food sources showed less precise dietary contributions (Figure 13).

The feasible ranges for all the potential food sources were diffuse for A. cristatellus in September

(Figure 14). In the case of A. krugi, fruits had the only feasible contribution representing 8-32% of the diet for this period (Figure 15). The feasible ranges for the other eight potential sources were widespread. Results for January are characterized by broad ranges of possible contributions to the diet for both species; Coleoptera, Hemiptera and Isoptera appeared to contribute more to the diet of both species than the other sources (Figure 16 and 17).

Food resources availability

A total of 32,137 arthropods, considered as potential preys, (16,306 for MPPR and 15,

831 for ETPR), were captured at study areas and assigned to 15 orders and one general category for larvae, which includes larvae of different groups of arthropods (e.g. Lepidoptera and

Coleoptera). The predominant groups at study areas were Diptera and Hymenoptera followed by

(Figure 18). In spite of the significant value between September to December, the order Diptera account for the higher arthropods abundance. The correlation analysis among the monthly

122 average abundance of each group of arthropods against the monthly average rainfall revealed no signiﬁcant association neither for MPPR nor ETPR (all p's > 0.05).

Ten plant species, producers of fleshy fruits, where identified as potential food resources for anoles at each study areas. The availability of fleshy fruits varied over the study period

(Table 4). Ripe fruits were most abundant during May to August at ETPR primarily as a result of the massive fructification of Castilla elastica and Spondias mombins (ca. 10,000 for each species) (Figure 19). In the case of MPPR, the abundance was slightly similar for May-August period and September-December (Figure 19), Spondias mombins was the responsible for the high abundance of fruits at both periods.

123

Discussion

Stable isotope analysis, especially carbon and nitrogen, is an alternative method that not only allows the reconstruction of diets, also permits to analyze the contributions of food resources, follow energy or mass ﬂow through ecosystems, estimate trophic position (i.e. the average level, relative to primary producers, at which an organism feeds), measure niche variation, and detect complex interactions, including trophic omnivory (Peterson and Fry 1987,

Kling et al. 1992, Cabana and Rasmussen 1996, Post 2002, Layman et al. 2012). Isotopic analyses for A. cristatellus and A. krugi revealed that the isotopic composition of anoles tissue varies significantly between species, and in the case of A. krugi, between periods. In the case of

A. cristatellus, the analyses incorporating both stable isotopes do not established spatial or temporal significant differences, suggesting that the composition of the diet is the same at both study site and through time. This species is a medium size lizard, widely distributed from open to forested areas and an opportunistic feeder able to exploit a great variety of food items.

In spite of the lack of seasonal significant differences, results from mixing models for A. cristatellus recognized temporal differences in the contribution of some groups of arthropods.

The models displayed, repeatedly, that Coleoptera and Blattodea are important components of the diet of A. cristatellus and also for A. krugi, independently of the site. Inclusively, in model for May at ETPR, Blattodea represent 56 to 92 % of the diet of A. cristatellus. In many cases prey incorporation is related with food availability (Pianka 1970, Sexton et al. 1972, Schoener et al. 1982, Floyd and Jenssen 1983, Bullock et al. 1993). However, this is not the case for

Coleoptera and Blattodea at MPPR or ETPR, since neither preys showed high abundance or seasonal variation in abundance during the study. Instead, the results suggest selection of these two groups independently of its abundance. The consumption of seasonally rare preys might be

124 related with optimal foraging models based on nutrient constraints, as demonstrated by Stamps et al. (1981) for juveniles of A. aeneus at Grenada. The low abundance of Blattodea and Coleopera might be the results of the high predatory pressure of both anoles over these two prey groups.

In anoles, seasonal shift in diet is associated with differences in prey availability (Losos

2009). Contrary to other periods, diet for September seems particularly diffuse for both species and at two sites. Partition models showed that, with the exception of fruits for A. krugi at ETPR, neither potential food sources had a maximum feasible contribution to the diet. Highly variable

C:N for this period support the idea of broadly diversiﬁed diets during the months prior to

September. It appears that a high abundance of prey during the months previous to September cause a shifts in the preference of prey moving both species to feed indiscriminately over a wide spectrum of prey. Anole lizards are opportunistic feeders since the broad diet on September probably reflects the exploitation of resources abundance. These results contrast with predictions by optimal foraging models that shown that lowered food abundance leads to a broader diet (Schoener 1974). This is the case, for example, of A. gundlachi, A. evermani and A. stratulus at Luquillo and Maricao, which expanded prey taxa component of the diet during periods of low arthropod abundance (Lister 1981).

Apparently, A. cristatellus and A. krugi incorporate in the diet a great variety of prey items independently of their abundance. At both study areas, Diptera and Hymenoptera followed by Aranae and Hemiptera were the most abundance groups. Even though all the models showed these four groups as potential food sources, none appeared to contribute significantly to the diet.

These results differ from other anoles dietary studies that established a positive association between seasonal prey consumption and prey availability (Fleming and Hooker 1975, Floyd and

Jenssen 1983, Fontenot et al. 2003, Rodríguez-Schettino 2010).

125

Although the models indicated fruits consumption by the two anoles, fruits seem to be particularly important for A. krugi. Models for September at ETPR and January at MPPR established that fruits represent a feasible component to the diet for this species. In the case of

MPPR, the consumption of fruits by A. krugi is evident since September but is unquestionable on

January when fruits and Blattodea represent feasible food resources. At ETPR, the mixing model identified fruits as a feasible resource for September. Arthropod abundance does not appears to be an important factor in determining fruits consumption in this species, since the abundance of preys did not reflect a decrease in abundance previously to September or January.

Hence, the inclusion of fruits to the diet seems to be linked to fruits abundance and availability.

Although both sites have fruits year around (Table 3), the increase in ripe fruit at MPPR was on

August and September, when Anthurium crenatum and Spondias mombins fruited, the latter with a massive fructification (> 10,000 fruits). In the case of ETPR, the abundance peaks of fruits correspondence to May and June, coinciding with the massive fructification of Castilla elastica

(>10,000). Indeed, after a peak of fruit availability, the isotopic composition of A. krugi muscle reflects the incorporation of nutrients from fruits. Previous research on occupancy estimates supported this hypothesis since the high occupancy for A. krugi was at plot where A. crenatum and C. elastica occur (Vega-Castillo see Chapter 2). It seems that those plots not only provide the microhabitat structure preferred by A. krugi but also novel food resources.

The consumption of fruits by A. krugi is not surprising, thus this species was reported before consuming fruits at Rio Abajo State Forest (Vega and Puente 2011). However, the findings raise new questions about the use and important of fruits in this small grass-bush ecomorph versus the medium size A. cristatellus a truck-ground ecomorph. Food resource is an axis of competition among anoles species (Losos 2008), where differences in body size as well

126 differences in microhabitat use determine the partition of food resources in sympatric species.

According with the evidence, larger lizards eat larger preys, and larger lizards usually has bigger head that allows them process and swallow larger food items, such as fleshy fruits (Herrel et al.

2004, Cooper and Vitt 2002, Olesen and Valido 2003 and 2007). Obviously, the size of A. krugi did not limit the incorporation of fruits in their diet, it may be add fruits in pieces and not as a whole fruit. The consumption of fruits then might be explained by the differences in the structural microhabitat occupied by the species. In the case of A. cristatellus, typically perch in tree trunks within meters of the ground, while A. krugi is on bushes and other low-lying vegetation. At MPPR and ETPR, A. krugi occupy plots associated to some plants producer of fleshy fruits (Vega-Castillo see Chapter 2), it appears to be that this species is able to access fruits more frequently than A. cristatellus. The dietary plasticity of both species might be particularly important in situations or habitat where preys decrease in abundance or individuals cannot meet energetic needs (Cooper and Vitt 2002).

The occurrence of lizards eating fruits has been described by many places around the world, including the Caribbean (e.g. Bahamas (Iverson 1985), New Zealand (Whitaker 1987),

New Caledonia (Bauer and Sadlier 1993), Balearic Islands (Traveset 1995), South Africa

(Whiting and Greeff 1997), Canary Islands (Valido and Nogales 1994, Nogales et al. 1998),

Central and South America (Traveset 1990, Figueira et al. 1994, Wilson et al. 1996, Vidal and

Sabat 2010), Jamaica (Herrel et al. 2004)). In Puerto Rico, the consumption of fruits has been reported for six species: A. evermanni (Reagan, 1996, Lister, 1981), A. monensis (Schwartz and

Henderson 1991), A. stratulus, A. gundlachi and A. krugi (Vega and Puente 2011) as well as for the giant species A. cuvieri (Perez-Rivera, 1985 and Losos, 1990).

127

Consumption of fruits in insular lizards has been related to large body size, high density of lizards, low predation and prey scarcity (Valido and Olesen 2006). Anolis krugi is a small size, body slender grass-bush species that inhabit forested areas on Puerto Rico. At MPPR and

ETPR A. krugi occurs at low densities (Vega-Castillo see Chapter 2), and, although no predation rate data is available, the high abundance of lizards cuckoos, Puerto Rican racers and Puerto

Rican boa in both reserves (Personal observation) suggest high predation pressure. Although arthropods availability varied seasonality at both study sites, the incorporation of fruits from this species occurs in periods when preys are highly abundance. Therefore, neither of the

“traditional” hypotheses seems to explain fruits consumption in A. krugi. Contrary to those hypotheses, the incorporation of fleshy fruits to the diet in periods of high abundance of prey implicates an opportunistic respond to fruits availability, a food resource that occurrence year around.

Although this study documents the consumption of fruits by A. krugi, more detailed studies are needed to better understand the importance of ﬂeshy fruits as an alimentary resource for this anole species. Isotopic measurements of exhaled CO2 or other tissue relate with carbohydrates allocation is needed since, in general, fleshy fruits are high in carbohydrates and low in proteins. If lizards allocate the carbohydrates of fruits into metabolism or energy storage and the carbon of protein in body tissue synthesis, as a consequence of the differential elemental routing, probably mixing models underestimates the real contribution of fruits to the diet of these two anoles. Undoubtedly, fruits represent an energy source for anoles lizards but at what extent and which is the anoles-plant interaction remain unknown. Further quantitative studies are needed to comprehend the role of Anolis as frugivorous as well as their potential as seed dispersers.

128

CHAPTER IV

TABLES

129

Table1. Stable isotope values (‰) of producers (n=17), invertebrates (twelve arthropods orders)

and Anolis lizards (Anolis cristatellus, n= 76; A. krugi, n=30) sampled at Mata de Plátano

Reserve, a private natural reserve at north-central karst region, Puerto Rico, 2012.

130

13 15 δ C (‰) δ N (‰)

Taxon (n) Means ± SE min max Means ± SE min max

Producers

Plants (7) -32.18 ± 1.01 -34.63 -28.30 -1.50 ± 0.99 -6.52 0.97

Fruits (10) -31.34 ± 0.74 -35.06 -28.33 -0.22 ± 0.82 -2.26 0.60

Invertebrates

Aranae (12) -24.62 ± 0.19 -26.15 -23.49 4.51 ± 0.14 3.81 5.23 Blattodea (7) -25.76 ± 0.41 -27.53 -24.23 -0.96 ± 0.33 -1.60 0.86 Coleoptera (8) -22.93 ± 0.45 -24.72 -20.89 3.08 ± 0.55 0.31 4.81 Isoptera (2) -26.04 ± 0.82 -26.86 -25.23 1.43 ± 0.89 0.54 2.32 Hemiptera (12) -24.62 ± 0.50 -27.14 -20.88 0.73 ± 0.32 -1.22 2.69 Hymenoptera (12) -24.24 ± 0.21 -25.40 -22.51 4.20 ± 0.26 2.90 6.28 Lepidoptera (9) -25.18 ± 0.75 -28.01 -21.01 3.83 ± 0.50 0.74 5.24 Diptera (12) -23.07 ± 0.67 -26.01 -18.32 6.10 ± 0.47 3.10 8.33 Orthoptera (12) -25.81 ± 0.31 -27.44 -23.63 1.53 ± 0.33 -1.70 2.56 Phasmatodea (6) -29.46 ± 0.60 -32.20 -28.03 2.25 ± 0.50 1.18 4.60 Mantodea (8) -25.10 ± 0.29 -26.63 -24.06 5.47 ± 0.64 3.35 8.67

Neuroptera (1) -24.82 ± ------1.43 ± ------

Anolis lizards

A. cristatellus (76) -23.94 ± 0.12 -26.82 -21.35 4.91 ± 0.10 2.12 7.59 A. krugi (30) -24.65 ± 0.15 -26.09 -22.51 4.56 ± 0.14 2.67 6.14

131

Table 2. Stable isotope values (‰) of producers (n=23), invertebrates (twelve arthropods orders)

and Anolis lizards (Anolis cristatellus, n= 78; A. krugi, n=75) sampled at El Tallonal

Reserve, a private natural reserve at north-central karst region, Puerto Rico, 2012.

132

13 15 δ C (‰) δ N (‰)

Taxon (n) Means ± SE min max Means ± SE min max

Producers

Plants (11) -33.90 ± 0.53 -35.89 -30.99 -0.60 ± 0.43 -2.68 1.66

Fruits (12) -31.80 ± 0.77 -28.21 -35.34 -1.20 ± 0.38 -4.15 0.70

Invertebrates

Aranae (11) -24.62 ± 0.26 -25.89 -23.60 4.06 ± 0.14 3.34 4.91 Blattodea (2) -25.76 ± 0.38 -26.15 -25.38 -1.42 ± 2.50 -3.92 1.08 Coleoptera (8) -23.28 ± 0.50 -25.17 -21.07 2.60 ± 0.55 1.06 5.50 Isoptera (3) -26.60 ± 1.55 -29.53 -24.24 0.28 ± 0.16 0.10 0.60 Hemiptera (11) -25.21 ± 0.53 -28.57 -22.11 0.38 ± 0.31 -1.30 1.84 Hymenoptera (10) -24.63 ± 0.26 -25.66 -23.32 4.25 ± 0.49 0.72 6.00 Lepidoptera (11) -25.58 ± 0.84 -31.11 -23.14 3.32 ± 0.53 -0.09 6.00 Diptera (13) -23.11 ± 0.40 -25.69 -20.59 5.18 ± 0.37 2.70 7.32 Orthoptera (11) -26.28 ± 0.31 -27.45 -24.76 1.11 ± 0.29 -0.42 3.09 Phasmatodea (3) -30.50 ± 0.62 -31.65 -29.52 0.63 ± 0.22 0.21 0.89 Mantodea (1) -24.88 ± ------5.55 ± ------

Neuroptera (5) -25.31 ± 1.47 -31.17 -23.37 3.16 ± 0.37 2.02 4.07

Anolis

A. cristatellus (78) -23.61 ± 0.17 -26.34 -17.90 4.92 ± 0.08 3.22 6.40 A. krugi (75) -24.27 ± 0.17 -27.30 -19.14 4.53 ± 0.09 2.57 6.29

133

Table 3. Fructification periods for the plant species producers of fleshy fruits identified

as potential food resources for anoles at at Mata de Platano Private Reserve (n=10)

and El Tallonal Private Reserve (n=10) two private natural reserves at north-central

karst region, Puerto Rico, 2012.

134

Plant species Jan-Apr May-Aug Sept-Dec

135

CHAPTER IV

FIGURES

136

Figure 1. Locations of study sites in northern Puerto Rico. Red rectangles represent plots within

the reserves (yellow lines).

137

MPPR

ETPR

138

Figure 2. The relationship between the natural abundance of δ15N and δ13C (± SE) values for

Anolis krugi for the four sampling periods (A. Mata de Platano Private Reserve, B. El

Tallonal Private Reserve, two private natural reserves at north-central karst region, Puerto

Rico, 2012).

139

140

Figure 3. The relationship between the natural abundance of δ15N and δ13C (± SE) values for

Anolis cristatellus for the four sampling periods (A. Mata de Platano Private Reserve, B.

El Tallonal Private Reserve, two private natural reserves at north central karst region,

Puerto Rico, 2012).

141

142

Figure 4. Carbon:Nitrogen (C:N) ratios of Anolis cristatellus and A. krugi for the four periods at

Mata de Platano Private Reserve, a private natural reserve at north central karst region,

Puerto Rico, 2012. Dashed lines represent mean values.

143

144

Figure 5. Carbon:Nitrogen (C:N) ratios of A. cristatellus and A. krugi for the four periods

at El Tallonal Private Reserve, a private natural reserve at north central karst region,

Puerto Rico, 2012. Dashed lines represent mean values.

145

146

Figure 6. Polygons for δ13C and δ15 N of nine potential food sources for Anolis cristatellus on

May 2012 at Mata de Plátano Private Reserve in Arecibo, P.R. (after correcting for

trophic fractionation). Histograms show the distribution of feasible contributions from

each source to anole diet according with Isosource isotopic model (values are 1–99

percentile ranges for the distributions).

147

148

Figure 7. Polygons for δ13C and δ15 N of nine potential food sources for Anolis krugi on May

2012 at Mata de Plátano Private Reserve in Arecibo, P.R. (after correcting for trophic

fractionation). Histograms show the distribution of feasible contributions from each

source to anole diet according with Isosource isotopic model (values are 1–99 percentile

ranges for the distributions).

149

150

Figure 8. Polygons for δ13C and δ15 N of nine potential food sources for Anolis cristatellus on

September 2012 at Mata de Plátano Private Reserve in Arecibo, P.R. (after correcting

for trophic fractionation). Histograms show the distribution of feasible contributions from

each source to anole diet according with Isosource isotopic model (values are 1–99

percentile ranges for the distributions).

151

152

Figure 9. Polygons for δ13C and δ15 N of nine potential food sources for Anolis krugi on

September 2012 at Mata de Plátano Private Reserve in Arecibo, P.R. (after correcting

for trophic fractionation). Histograms show the distribution of feasible contributions from

each source to anole diet according with Isosource isotopic model (values are 1–99

percentile ranges for the distributions).

153

154

Figure 10. Polygons for δ13C and δ15 N of nine potential food sources for Anolis cristatellus on

January 2013 at Mata de Plátano Private Reserve in Arecibo, P.R. (after correcting for

trophic fractionation). Histograms show the distribution of feasible contributions from

each source to anole diet according with Isosource isotopic model (values are 1–99

percentile ranges for the distributions).

155

156

Figure 11. Polygons for δ13C and δ15 N of nine potential food sources for Anolis krugi on January

2013 at Mata de Plátano Private Reserve in Arecibo, P.R. (after correcting for trophic

fractionation). Histograms show the distribution of feasible contributions from each

source to anole diet according with Isosource isotopic model (values are 1–99 percentile

ranges for the distributions).

157

158

Figure 12. Polygons for δ13C and δ15 N of nine potential food sources for Anolis cristatellus on

May 2012 at El Tallonal Private Reserve in Arecibo, P.R. (after correcting for trophic

fractionation). Histograms show the distribution of feasible contributions from each

source to anole diet according with Isosource isotopic model (values are 1–99 percentile

ranges for the distributions).

159

160

Figure 13. Polygons for δ13C and δ15 N of nine potential food sources for Anolis krugi on May

2012 at El Tallonal Private Reserve in Arecibo, P.R. (after correcting for trophic

fractionation). Histograms show the distribution of feasible contributions from each

source to anole diet according with Isosource isotopic model (values are 1–99 percentile

ranges for the distributions).

161

162

Figure 14. Polygons for δ13C and δ15 N of nine potential food sources for Anolis cristatellus on

September 2012 at El Tallonal Private Reserve in Arecibo, P.R. (after correcting for

trophic fractionation). Histograms show the distribution of feasible contributions from

each source to anole diet according with Isosource isotopic model (values are 1–99

percentile ranges for the distributions).

163

164

Figure 15. Polygons for δ13C and δ15 N of nine potential food sources for Anolis krugi on

September 2012 at El Tallonal Private Reserve in Arecibo, P.R. (after correcting for

trophic fractionation). Histograms show the distribution of feasible contributions from

each source to anole diet according with Isosource isotopic model (values are 1–99

percentile ranges for the distributions).

165

166

Figure 16. Polygons for δ13C and δ15 N of nine potential food sources for Anolis cristatellus on

January 2013 at El Tallonal Private Reserve in Arecibo, P.R. (after correcting for trophic

fractionation). Histograms show the distribution of feasible contributions from each

source to anole diet according with Isosource isotopic model (values are 1–99 percentile

ranges for the distributions).

167

168

Figure 17. Polygons for δ13C and δ15 N of nine potential food sources for Anolis krugi on January

2013 at El Tallonal Private Reserve in Arecibo, P.R. (after correcting for trophic

fractionation). Histograms show the distribution of feasible contributions from each

source to anole diet according with Isosource isotopic model (values are 1–99 percentile

ranges for the distributions).

169

170

Figure 18. Relative abundance of arthropods order by periods. Data from both reserves were

pooled since not significant difference was found.

171

6000

5000 Jan-April May-Aug

Sep-Dec 4000

3000

2000

Total Number of Arthropods of Number Total

1000

0 Aranae Blattodea Coleoptera Diptera Hemiptera Hymenoptera Isoptera Lepidoptera Orthoptera Others

172

Figure 19. Phenology of fleshy fruits plants at Mata de Platano Private Reserve (n=10) and

El Tallonal Private Reserve (n=10), two private natural reserves at north-central karst

region, Puerto Rico, 2012.

173

35000

Mata de Plätano 30000 El Tallonal

25000

20000

15000

Summed FAI Summed

10000

5000

0 Ene-Apr May-Aug Sept-Dec Sampling Periods

174

CHAPTER V

CONCLUSION

175

Conclusion

Anoles are a major component of diurnal communities at Caribbean islands and as preys and predators are a key element of ecosystems. The natural history of Anolis lizards is well studied; however, basic information about anoles ecology remains incomplete. This thesis provided valuable information on the occupancy and omnivory/frugivory of Anolis cristatellus and Anolis krugi at karts forests. My results confirmed the importance of microhabitat structure on the occurrence of A. cristatellus and A. krugi, and also suggested that food has an important role in habitat quality, particularly for A. krugi. In anoles, adaptation to particular microhabitat constrain habitat use but, in the case of A. krugi, is knowing that has preference for certain habitat characteristics in different habitats (Johnson 2006). Further studies are needed in order to determine which structural element, of the particular microhabitat occupied by A. krugi at study sites, is the determinant on occupancy of this species.

Results from trophic position analysis and source-partition models confirmed that both species are omnivores but the degree of omnivory/frugivory varied between species and within the same species at difference localities. Both species consumed at different levels in the food web and models suggested the incorporation of different proportions of prey and fruits into their diets. Sources partition models of A. krugi document fruits consumption, results that support the idea that fruits consumption is not related with body size. These is the first study documenting assimilation of fruits in muscle of a grass-bush anole, and establishing the importance of fruits as food resources for small ecomorphs. Traditional methods to infer omnivory/frugivory depend on detection of seed on stomach content, fecal pellets or direct observations of feeding habits.

Contrary to traditional methods, isotopes reﬂect average dietary records, allowing document consumption of highly digestible food source as flesh of fruits. Knowledge of the temporal and

176 spatial utilization of food resources is crucial for the understanding not only of population dynamics, but also to assess ecological eﬀects of environmental change such as global warming.

Understand the process of energy ﬂow and material cycling is complicated furthermore if omnivores utilize resources from several trophic levels but assimilate material from some particular resources. Omnivores could have trophic roles that differs from their functional roles

(Parkyn et al. 2001). According with partition models, A. krugi, function as omnivores by adding fruits to the diet, but also are an important predator of a great diversity of arthropods.

Further isotopic measurements using carbohydrates allocation tissues are needed in order to better understand the importance of fruits on the diet of this species, and with these better understand the role of anoles in energy flow and nutrient cycles. Undoubtedly, further quantitative studies on anoles-plant interaction are also needed to comprehend the role of anoles as frugivores as well as their potential role as seed dispersers. Given that Anolis lizards are an important component of Caribbean Islands, frugivory and seed dispersal by those lizards might be extremely important in the succession, regeneration and conservation of tropical forests.

Although this project is focused on anoles lizard species at Puerto Rico, the findings are of relevance for the understanding of ecological processes at others Caribbean islands.

177

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