Kari Lynn Schmidt Submitted in Partial Fulfillment of the Requirements For

SPATIAL AND TEMPORAL PATTERNS OF GENETIC VARIATION IN SCARLET MACAWS (ARA MACAO):

IMPLICATIONS FOR POPULATION MANAGEMENT IN LA SELVA MAYA, CENTRAL AMERICA

Kari Lynn Schmidt

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences

COLUMBIA UNIVERSITY 2013

Kari Lynn Schmidt

ABSTRACT

SPATIAL AND TEMPORAL PATTERNS OF GENETIC VARIATION IN SCARLET MACAWS (ARA MACAO):

IMPLICATIONS FOR POPULATION MANAGEMENT IN LA SELVA MAYA, CENTRAL AMERICA

Kari Lynn Schmidt

Advances in technology and molecular methodologies now provide an unprecedented view into the complex realm of natural populations by elucidating the degree and distribution of genetic variation, historical and contemporary processes driving differentiation, and individual behavior patterns. These critical biological parameters create a framework to enhance wildlife management initiatives, as illustrated here through the implementation of a model approach for the systematic genetic assessment of a group of scarlet macaws (Ara macao) under threat in La

Selva Maya, a tri-national system of protected areas in Mexico, Guatemala, and Belize. A total of 2172 base pairs across four mitochondrial data partitions were employed to test the current hypothesis of subspecific diversification. Phylogenetic reconstruction uncovered two phylogeographic units exhibiting distinct and complex evolutionary histories, emphasizing the importance of Central American populations to intraspecific diversity. Focusing on A. m. cyanoptera, mitochondrial control region sequences of 850 base pairs were examined within a hierarchical context to investigate patterns of genetic substructure at varying spatial scales (i.e. subspecific, regional, local) and extent of molecular variation, including potential temporal shifts in response to anthropogenic pressures. Population-level statistical tests detected evidence of recently restricted gene flow among nest sites in La Selva Maya, a stark contrast to the historical state of panmixia across the region; although overall levels of genetic variation remain high, a

decrease in diversity was noted among modern samples originating in the Chiquibul Forest

Reserve, Belize. Multilocus genotypes based on eight microsatellite markers were combined with haplotypic data to evaluate whether focal nest sites in the Maya Biosphere Reserve,

Guatemala represent distinct genetic clusters. Results from population genetic analyses argue against the presence of site fidelity at fine geographic scales. Examination of pairwise relative relatedness indices supports the observation of genetic connectivity across local breeding areas, while also revealing important insights into recent demographic trends, movement patterns, and breeding behaviors. In summary, this work demonstrates the continuity of biological and ecological influences across individual, local, regional, and continental scales, thus creating an empirical framework to refine population management goals and prioritize mitigation strategies in order to maximize conservation outcomes and foster long-term survival of wild scarlet macaws in La Selva Maya.

TABLE OF CONTENTS

LIST OF TABLES ii LIST OF FIGURES iv ACKNOWLEDGEMENTS vi DEDICATION ix PREFACE x

CHAPTER 1:

MODEL APPROACH FOR INTEGRATING GENETIC CONSIDERATIONS IN THE DESIGN AND IMPLEMENTATION OF POPULATION MANAGEMENT PROGRAMS MOLECULAR GENETICS AND CONSERVATION BIOLOGY 1 MODEL APPROACH FOR GENETIC EVALUATION 5 CASE STUDY: SCARLET MACAW 9 REFERENCES 23

CHAPTER 2: PHYLOGEOGRAPHIC ASSESSMENT OF SCARLET MACAWS (ARA MACAO): PATTERNS OF INTRASPECIFIC DIVERSITY AND IMPLICATIONS FOR CONSERVATION MANAGEMENT ABSTRACT 39 INTRODUCTION 40 MATERIALS AND METHODS 43 RESULTS 48 DISCUSSION 52 REFERENCES 76

CHAPTER 3: POPULATION DYNAMICS, GENETIC DIVERSITY AND DEMOGRAPHY OF SCARLET MACAWS (ARA MACAO CYANOPTERA) IN LA SELVA MAYA, CENTRAL AMERICA ABSTRACT 93 INTRODUCTION 94 MATERIALS AND METHODS 100 RESULTS 110 DISCUSSION 119 REFERENCES 144

CHAPTER 4: MAJOR CONCLUSIONS AND RECOMMENDATIONS FOR SCARLET MACAW (ARA MACAO CYANOPTERA) CONSERVATION MANAGEMENT IN LA SELVA MAYA, CENTRAL AMERICA SYNTHESIS OF GENETIC INSIGHTS INTO SPECIES BIOLOGY 175 EVALUATION OF CONSERVATION NEEDS 178

LIST OF TABLES

TABLE 2.1 85 Primers for PCR amplification and sequencing.

TABLE 2.2 89 Genetic variation within scarlet macaw subspecies and haplogroups.

TABLE 2.3 89 Indicators of demographic change in scarlet macaw subspecies.

APPENDIX I 90 List of museum specimens included in phylogeographic analyses.

TABLE 3.1 158 Primers for PCR amplification and sequencing.

TABLE 3.2 158 Indices of genetic variation for mitochondrial haplotypes and microsatellite genotypes in A. m. cyanoptera.

TABLE 3.3 159 AMOVA and global tests of differentiation for putative populations and sampling localities in La Selva Maya (LSM), Central America based on haplotypic data.

TABLE 3.4 160 Pairwise fixation indices for putative populations and sampling localities in LSM, Central America based on mitochondrial haplotypes.

TABLE 3.5 161 Indicators of demographic change in LSM, Central America.

TABLE 3.6 161 Genetic divergence among nest sites in the Maya Biosphere Reserve (MBR), Guatemala.

TABLE 3.7 163 Mitochondrial haplotype distribution across sampling years for nest sites in the MBR, Guatemala.

TABLE 3.8 163 Mitochondrial haplotype distribution for reoccupied nest cavities in the MBR, Guatemala across sampling years.

TABLE 3.9 164 Mean relatedness ± variance for 1000 simulated pairs of known relationship category based empirical allele frequencies based on allele frequencies sampled in the MBR, Guatemala.

TABLE 3.10 164 Rate of misclassification based on the midpoint method of Blouin et al. (1996).

TABLE 3.11 166 Evaluation of mean relatedness across nest sites in the MBR, Guatemala.

TABLE 3.12 167 Influence of shared haplotype and/or nest cavity on mean relatedness for scarlet macaws in the MBR, Guatemala.

TABLE 3.13 168 Pairs of putative relatives sampled in the MBR, Guatemala based on rxyLR and rxyML.

TABLE 3.14 169 Sex ratio for nestlings in the MBR, Guatemala.

APPENDIX II 169 List of museum specimens included in population genetic analyses.

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LIST OF FIGURES

FIGURE 1.1 37 Map illustrating recent changes in the geographic range of scarlet macaw (Ara macao) subspecies.

FIGURE 1.2 38 Map depicting the distribution of scarlet macaws in La Selva Maya (LSM), Central America.

FIGURE 2.1 86 Map showing sampling effort and geographic distribution of seven haplogroups detected across Central and South America.

FIGURE 2.2 87 Maximum likelihood and strict consensus Bayesian inference trees from concatenated data partitions.

FIGURE 3.1 156 Map depicting sampling localities for historical A. m. cyanoptera specimens.

FIGURE 3.2 157 Map illustrating modern sampling localities in LSM, Central America.

FIGURE 3.3 159 Median-joining network showing relationships among historical A. m. cyanoptera haplotypes.

FIGURE 3.4 160 Median-joining networks showing relationships among historical and modern haplotypes recovered in LSM, Central America.

FIGURE 3.5 162 Individual population membership demonstrating the relative contribution of putative genetic clusters for individuals sampled in the MBR, Guatemala.

FIGURE 3.6 165 Distribution of observed relatedness values recovered within the MBR, Guatemala overlaid with simulation distributions for 1000 simulated dyads of known relatedness categories.

FIGURE 3.7 166 Pairwise rxy plot showing mean ± variance, maximum and minimum values within the total sample and at focal nest sites in the MBR, Guatemala.

FIGURE 3.8 167 Pairwise rxy plot showing mean ± variance, maximum and minimum values showing relative influence of shared mitochondrial haplotypes and/or nest cavity.

ACKNOWLEDGEMENTS

My tenure as a graduate student has been an adventure like no other, taking me from the cornfields of the Midwest to the concrete jungle of New York City and lowland tropical forests of Central America. This journey would not have been possible without the companionship, encouragement, and assistance from a host of family, friends, and collaborators.

First and foremost I must thank my advisor, George Amato, for graciously taking me under his auspices, introducing me to the multidimensional world of conservation management, and sharing his passion for Neotropical psittacines. Admittedly it has been a long and winding road, with each experiencing their share of speed bumps along the way, but somehow we made it. I am extremely grateful for his patience and support throughout the dissertation.

I offer my gratitude to the other members of my committee for their participation in the project. Many thanks to Michael Russello for providing valuable advice in regard to manipulation of historical tissues and data analysis, and creating opportunities for outside collaborations. I also acknowledge Rob DeSalle, George Barrowclough, and Shahid Naeem for their helpful suggestions and contributions.

It was a privilege to work alongside a group of dedicated and talented graduate students and post-docs in the Ecology, Evolution, and Environmental Biology Department at Columbia

University and the Sackler Institute for Comparative Genomics at the American Museum of

Natural History. I want to thank Martin Mendez, Oscar Pineda, Charles Yackulic, Nicole

Mihnovets, Matt Leslie, Ines Carvalho, Isabela Dias-Freedman, and Anna Philips for their companionship, technical assistance, and support at various stages throughout the graduate student experience. Esther Quintero deserves special recognition for endless cups of coffee and conversations about research, academics, and life in general. Mary Blair has also been a good

friend; I am grateful for her efforts to provide perspective, and assistance with creating distribution maps.

I will forever be indebted to my “extended family” for being a constant source of unconditional friendship, encouragement, and strength. A heart-felt thank you to Erica Riedesel for always being the voice of reason and strictly enforcing the stipulations outlined in the infamous “napkin treaty”. I must also thank Nathaniel Hope and Matt Dickson for finding endless ways to make me smile, either through marathon phone calls or otherwise “random” comments. My deepest gratitude goes to Yula Kapetanakos for her optimism and generosity, and welcoming me with open arms and a bottle of wine when I needed a retreat from the city. I am eternally grateful for the words of wisdom and support offered by my “cous” Lona (Vandy)

Vandervort. Finally, a very special thank you to Melvin Mérida for always reminding me that

“despues de la tormenta, siempre sale el sol.”

Several local in-country collaborators were instrumental in the success of this work; I extend my gratitude to the governments of Guatemala (Consejo Nacional de Áreas Protegidas;

CONAP) and Belize (Forest Department) for research and export permits. I offer my deepest thanks to Roan McNab for allowing me the opportunity to work with the Wildlife Conservation

Society-Guatemala Program. This project would not have been possible without Rony García,

Gabriela Ponce, Marcial (Chalo) Córdova, and the rest of the WCS-Guatemala field team and office staff. Their dedication, resourcefulness, enthusiasm, and passion continue to inspire, and my experiences in the Maya Biosphere Reserve, Guatemala will forever shape my perspectives on in situ conservation and management. I am also grateful to Rafael Manzanero, Lenny Gentle, and field staff and rangers of the Friends of Conservation for facilitating fieldwork and permits in the Chiquibul Forest Reserve, Belize.

vii

I extend a sincere thank you to Donald Brightsmith, Robin Bjork, and Caroline Stahala for sharing their extensive knowledge of Neotropical psittacine biology, conservation and management; their insights and advice greatly enhanced this work. Kara Gebhardt kindly shared

DNA samples and unpublished primer sequences and genotypic data during the initial stages of project development. I am also grateful to Charles Britt and Mark McReynolds for valuable information regarding scarlet macaw behavior and demography in Belize, and access to critical

‘hard-to-find’ references. My thanks also go to Otto Monge for interesting conversations on the historical and contemporary distributions of scarlet macaws in Costa Rica.

The geographic and temporal scope of the project was only attained via the generous contribution of historical tissues from numerous museum and university collections. Specifically,

I thank the curators and collections managers from the Academy of Natural Sciences, American

Museum of Natural History, Field Museum of Natural History, California Academy of Sciences,

Carnegie Museum of Natural History, Field Museum of Natural History, Harvard Museum of

Comparative Zoology, Louisiana State University Museum of Natural Sciences, National

Museum of Natural History, UCLA-Dickey Collection, University of Michigan Museum of

Zoology and Yale Peabody Museum.

I am also extremely thankful for the administrative assistance provided by Lourdes

Gautier and Mohammad Faiz. This work was made possible by financial support from the

Ecology, Evolution and Environmental Biology Department at Columbia University, the Sackler

Institute for Comparative Genomics at the American Museum of Natural History, and the Frank

M. Chapman Memorial Fund.

viii

DEDICATION

I would not be where I am today without the love and support of my family, therefore I dedicate this work in their honor. To the memory of my maternal grandparents, Raymond and Irma

Ulrich, for their unwavering faith in my abilities, and teaching me the importance of integrity, hard work, and a sense of humor. I miss you both so much! To my parents, Robert and Bette

Schmidt, for fostering opportunities to take the path less traveled and always believing in me. To my siblings, Lisa, Dave, Al, Mike, Jason, Corey, Andy and Darin, for putting up with their sister’s crazy academic pursuits and encouraging me when times got tough, with no hesitation to go above and beyond the call of duty when needed. Finally, to my nieces and nephews as a reminder it does not matter if you come from a small town “in the middle of nowhere”, follow your dreams and anything is possible!

PREFACE

The primary objective of this dissertation is to underscore the importance of molecular genetic techniques in strengthening conservation management initiatives, using a threatened group of scarlet macaws (Ara macao cyanoptera) in La Selva Maya, a tri-national system of protected areas in Mexico, Guatemala and Belize, as a case study for implementation. Specifically, this work employs a hierarchical framework to investigate patterns of spatial and temporal genetic variation to elucidates the species’ evolutionary history, population dynamics, behavior, and influence of recent anthropogenic activities. Chapter 1 introduces a proposed model approach to characterize fundamental biological parameters, evaluate conservation needs, and prioritize mitigation strategies, emphasizing the vital role of molecular genetic data. Chapter 2 presents a phylogeographic assessment of scarlet macaw populations across the entire species’ range, with dual consideration of current taxonomic designations and distribution of genetic diversity within subspecific taxa. Chapter 3 combines mitochondrial and microsatellite data sets to elucidate population dynamics within La Selva Maya, investigating temporal shifts in genetic diversity and population substructure, and providing critical information in regard to demographic trends and breeding behaviors. Chapter 4 synthesizes major research findings, outlines conservation needs, and lists specific recommendations for scarlet macaw population management in La Selva Maya.

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CHAPTER 1:

MODEL APPROACH FOR INTEGRATING GENETIC CONSIDERATIONS IN THE DESIGN AND

IMPLEMENTATION OF POPULATION MANAGEMENT PROGRAMS

MOLECULAR GENETICS AND CONSERVATION BIOLOGY

Conservation biology is a crisis discipline requiring immediate action by wildlife managers and government authorities, reflecting the near ubiquitous human footprint on global landscapes

(Sanderson et al. 2002) and unprecedented loss of the world’s biodiversity (Ehrlich 1995;

Lawton et al. 1995; WCMC 1992). Adding to a sense of urgency, the deleterious impacts of human-mediated activities cannot simply be reduced to an inventory of species extinctions, but extends to disrupted symbiotic relationships (Kiers et al. 2010), degraded ecosystem services

(Worm et al. 2006), and other critical interspecific interactions (Soulé et al. 2003). While conservation actions aim to identify and ameliorate anthropogenic (e.g. habitat conversion, exploitation) and biological (e.g. invasive species, emergent diseases, hybridization) threats to wildlife, these efforts are not always straightforward given the complex realm of natural populations. Plasticity in phenotypic and behavioral traits may obscure the extent of evolutionary divergence, thus confounding attempts to designate conservation priorities and draft protective legislation (Brown et al. 2007; Haig et al. 2006; Russello et al. 2005; Wright & Wilkinson 2001).

Moreover, estimates of important population parameters (e.g. dispersal, demography, life history characteristics) are difficult to ascertain based on traditional field-based methods; sightings for nocturnal, elusive, and low-density species are often infrequent and unreliable (Goyette et al.

2011; Guschanski et al. 2009; Munari et al. 2011), and morphological similarities may preclude the unambiguous identification of individuals, sexes and/or age classes (Hilburn & Higgins

2000; Pollard et al. 2010; Wingard et al. 2011). Incorporation of radio and satellite technology to track movement patterns was a major advancement for population studies, yet these efforts are routinely constrained by loss of signal [e.g. dense vegetation, steep topography, limited battery life (Planes & Lemer 2011)], representation of a single age class [e.g. juveniles (Carreón-Arroyo

2006; Lindsey et al. 1991; Salinas-Melgoza & Renton 2007)], and small sample sizes (Rudnick et al. 2009). Despite logistical challenges, field observations provide useful insights into the organismal and population phenomena described above; however, these techniques have limited utility with assessing the genetic health of a population (i.e. inbreeding, genetic bottlenecks), notwithstanding rare instances of severe physical abnormalities or marked declines in fitness and survival rates (Brock & White 1992; Hedrick & Kalinowski 2000; O'Brien 1994). Accurate characterization of natural population dynamics is paramount for conservation initiatives; uncertainties in equilibrium baseline values may obscure detection of deviations prompted by deleterious human activities.

In recent decades, molecular genetics have proven invaluable for refining inferences of macroevolutionary patterns and microevolutionary processes, thus providing a means to place conservation management decisions within a broader contextual framework (DeSalle & Amato

2004). Specifically, genetic approaches assist in resolving issues of taxonomic uncertainty and designation of conservation units via explicit diagnosis of unique lineages (Freeland et al. 2007;

Goldstein et al. 2000; Hofkin et al. 2003; Russello et al. 2010). Interestingly, increased use of

DNA-based analyses has incited a burgeoning literature reporting cryptic intraspecific variation among morphologically similar taxa, including historically well-studied megafauna (Brown et al.

2007; Eaton et al. 2009; Russello et al. 2005). Within each taxonomic unit, statistical analyses of mitochondrial haplotype and allele frequency distributions empirically test hypotheses of spatial

3 genetic associations, thus refining targets for management actions (Mucci et al. 2010; Zielinski et al. 2012). In addition, molecular methods assist in differentiating census (i.e. total number of individuals) and effective (i.e. number of individuals contributing offspring to the next generation) population sizes (Luikart et al. 2010), an important consideration when evaluating recent demographic changes (Bishop et al. 2009; Miller & Waits 2003) and establishing restoration goals (Leonard et al. 2005). Similarly, it is not possible to predict the consequences of declines in effective population size a priori, given the stochastic nature of genetic bottlenecks, thus laboratory approaches provide direct measures of molecular variation (Alonso et al. 2009;

Hailer et al. 2006; Matocq & Villablanca 2001). DNA-based techniques also have the ability to assess the extent hybridization with abundant sympatric (Cordingley et al. 2009; Kyle et al.

2006), domestic (Chazara et al. 2010; O’Brien et al. 2009), or invasive species (King et al. 2011;

Zalapa et al. 2009) threatens the genetic integrity of low density endangered taxa. Moreover, reference databases of inter- and intraspecific genetic variation are an invaluable resource for law enforcement initiatives; species identification, in the case of animal parts or derivatives

(Abercrombie et al. 2005; Alacs et al. 2010; Coghlan et al. 2012; Eaton et al. 2010), or provenance assignment, for confiscated live exemplars (Faria et al. 2008; Manel et al. 2002;

Salinas et al. 2011), helps managers focus attention on the most heavily exploited taxa and/or populations.

Despite the important role of conservation genetics, limitations may arise when attempting to elucidate patterns of diversity and formative processes based solely on molecular data generated from extant samples. For example, threatened or endangered species often exist in small, fragmented, and/or isolated populations (Caballero et al. 2010; Saccheri et al. 1998;

Zachos & Hartl 2011), where the presence of inbreeding or loss of variation via genetic drift may

4 potentially skew underlying patterns of diversification. Moreover, local extirpation events introduce spatial gaps across the historical species’ range; biased or incomplete contemporary data sets may lead to erroneous inferences of basic biological parameters and recent evolutionary history, posing a formidable challenge to both researchers and managers (Leonard & Wayne

2008). For many species, however, these limitations may be circumvented to a large degree via examination of exemplars curated within museums and other natural history collections.

Historical genetic material provides an opportunity to characterize extinct populations (Culver et al. 2000; Godoy et al. 2004; Leonard et al. 2005; Miller & Waits 2003; Russello et al. 2010), while direct sampling of the same population pre- and post-decline establishes more accurate baselines to evaluate the impacts of recent human activities (Bourke et al. 2010; Bouzat 2000;

Martínez-Cruz et al. 2007; Miller et al. 2006; Nyström et al. 2006; Tracy & Jamieson 2011).

Importantly, advances in molecular and computational methodologies, ease of sampling, and reduced laboratory costs make it possible to move beyond examination of higher level parameters (i.e. taxonomy, population biology) to more individual-based analyses, creating new avenues for population monitoring (de Barba et al. 2010; Jaquiery et al. 2011). Genetic approaches offer several key advantages over field-based tracking techniques, including noninvasive sampling, creation of unique and permanent genetic tags, larger sample sizes, ability to differentiate sexes (i.e. for sexually monomorphic species or immature individuals), and cost effectiveness (de Barba et al. 2010; Rudnick et al. 2009; Rudnick et al. 2005; Schwartz et al.

2007; Stenglein et al. 2011). However, field observations have an important role to play in monitoring efforts by serving as an independent means to corroborate genetic data, thus strengthening inferences of movement and dispersal patterns, especially across fragmented landscapes (Leidner & Haddad 2011). Similarly, integration of complementary molecular and

5 field-based approaches helps refine estimates of multigenerational pedigrees within wild populations (Jones & Wang 2010). These analyses become more powerful when coupled with longitudinal studies; monitoring critical biological parameters (e.g. sex ratio, reproductive success, survival, recruitment, effective population size, dispersal) over consecutive years yields unprecedented insights into a population’s response to anthropogenic and biological threats, as well as gauge the efficacy of conservation initiatives (de Barba et al. 2010).

Within this context, consideration of spatial and temporal genetic variation at multiple hierarchical levels (e.g. species, subspecies, population, individual) holds immense potential for wildlife management by inferring key ecological, behavioral and life history traits influencing species biology. By using this empirical framework to evaluate conservation needs, assess current mitigation strategies, and prioritize future initiatives, local managers can ensure limited resources are allocated in the most appropriate manner to maximize conservation successes.

MODEL APPROACH FOR GENETIC EVALUATION

The systematic assessment of spatial and temporal genetic variation presented here reflects the disparity in conservation biology between the hierarchical structure of natural systems and insular design of management initiatives (Lankau 2011). In situ population dynamics are highly fluid (i.e. shifting ecological landscapes shape movement patterns, resulting in complex relationships across evolutionary timescales) and may encompass large geographic distances, whereas direct management actions are generally implemented on a local scale, constrained by political, logistical and socioeconomic factors (Sodhi et al. 2011). Consequently, individual management programs are unable to adequately protect imperiled taxa across their global distributions, with possible exception of highly restricted endemics. Therefore, it is imperative to

6 recognize the intended target for local conservation initiatives (i.e. group of individuals receiving direct, hands-on actions by local managers), herein referred to as the focal management target (FMT), may not represent an isolated biological entity, but likely maintains taxonomic, genetic, and demographic associations with neighboring conspecifics. The extent to which the

FMT is nested within broader population dynamics and overlaid with human-mediated threats will significantly influence the assessment of conservation needs and evaluation of program outcomes, given reported local trends might not reflect overall population responses.

Application of the proposed model approach begins with evaluation of phylogeographic patterns across the entire putative species’ range. Joint consideration of phylogenetic reconstructions and population aggregation analysis (PAA) serves to resolve taxonomic uncertainties by delineating specific and subspecific boundaries (Eaton et al. 2009; Goldstein et al. 2000), thus establishing an essential platform for drafting protective legislation for the FMT

(Haig et al. 2006). The accuracy and ease of assigning the FMT to a scientific epithet is largely a descriptive exercise and may not adequately reflect regional complexities in historical demographic trends (Crandall et al. 2000; Zink 2004), especially within taxa ranging over highly heterogeneous landscapes (Bickford et al. 2007). Detection of cryptic genetic diversity, as exemplified by the presence of unique evolutionary lineages or disproportionate representation of intraspecific variation, would advocate elevating the FMT’s conservation status (Murphy et al.

2011; Russello et al. 2010; Wenner et al. 2012).

After characterizing evolutionary significance, frequency-based fixation indices and

Bayesian analyses applied at multiple spatial scales (e.g. subspecific, regional, local) are employed to infer the extent of connectivity between the FMT and surrounding conspecifics

(Alonso et al. 2009; Efremov 2007; Hefti-Gautschi et al. 2009). In addition, examination of

7 pairwise relative relatedness indices, based on multilocus microsatellite genotypes, elucidates patterns of geospatial clustering within and among family groups, serving as an independent means to corroborate estimates of regional gene flow (Jones & Wang 2010; Rudnick et al. 2009).

The geographic distribution of genetic discontinuities will significantly impact the design of conservation actions because program outcomes are contingent on the overall response of demographically independent populations to management efforts (Efremov 2007; Palsbøll et al.

2007), therefore local implementation of recovery projects may attain suboptimal results if the

FMT is nested within broader population dynamics. These effects are further amplified if genetic and demographic associations extend beyond political boundaries, especially when accompanied by regional differences in enforcement of environmental policies, programmatic development, community support, and socioeconomic pressures (Sodhi et al. 2011). Moreover, changes in the distribution of preferred habitats and subsequent alterations to intrapopulation dynamics, within or otherwise involving the FMT, threaten long-term viability of the whole by reducing effective population sizes (Athrey et al. 2011; Broquet et al. 2010; England et al. 2010; Martínez-Cruz et al. 2007) and increasing susceptibility to stochastic (e.g. genetic, demographic, environmental, catastrophic) events. If molecular inferences detect recent population fragmentation, conservation practitioners should strive to support demographic equilibrium by reinforcing natural patterns of gene flow within and among population remnants, including the FMT

(Martínez-Cruz et al. 2007; Miller & Waits 2003; Nyström et al. 2006).

Definition of accurate baseline values and monitoring temporal trends for demographic and genetic parameters are quintessential for evaluating conservation needs and efficacy of management actions (de Barba et al. 2010; Nichols & Williams 2006; Schwartz et al. 2007), thus these efforts represent the final of the proposed model approach. Inbreeding influences survival

8 and fecundity rates, skewed age distributions and sex ratios threaten long-term viability, and the paucity of molecular variation reduces evolutionary potential (Blomqvist et al. 2010; Keller &

Waller 2002; Montgomery et al. 2000; Newman & Pilson 1997); however these important variables are often difficult to define via traditional field observations. Summary statistics quantifying molecular variation across mitochondrial and nuclear genomes provide an empirical, albeit generalized, assessment of genetic erosion within the FMT and other remaining population fragments (Athrey et al. 2011). Individual-based analyses are used to better characterize the reticulated networks found at or below the population-level, with an emphasis on demography and behavior (de Barba et al. 2010; Rudnick et al. 2009). For example, indices of pairwise relatedness inferred from microsatellite loci assist in establishing population pedigrees and prevalence of inbreeding within the FMT (Berger-Wolf et al. 2007; Jones & Wang 2010;

Pemberton 2008). Moreover, each exemplar sampled is assigned a unique and permanent genetic identification consisting of mitochondrial haplotype, multilocus genotype, and molecular sex determination (Rudnick et al. 2005); longitudinal studies generate reference databases to refine census and effective population size estimates (Luikart et al. 2010), track individual movements across the landscape (Planes & Lemer 2011), and elucidate key life history traits (Miño et al.

2011; Stenglein et al. 2011). Similarly, integration of molecular information with breeding records may reveal differential fecundity rates among genetic lineages (de Barba et al. 2010;

Rosenbaum et al. 2002), an additional factor potentially driving diversification of population remnants and influence local survival rates. As discussed above, it is imperative to examine demographic and genetic parameters beyond the FMT; trends observed at fine spatial scales may not reflect the overall population response to management actions, thus confounding efforts to define recovery goals and prioritize mitigation strategies (Eggert et al. 2010).

CASE STUDY: SCARLET MACAW

Scarlet macaws (A. macao) serves as a pragmatic case study to demonstrate the utility of molecular genetic data in elucidating key aspects of a species’ biology and recent evolutionary history, and how a systematic hierarchical examination of molecular variation creates a contextual framework to refine conservation goals and optimize mitigation strategies.

With a historical range extending from southern Mexico to Bolivia and eastern Brazil (Fig.

1.1A), scarlet macaws exhibit the largest distribution of any Neotropical psittacine. While preferring lower elevations (i.e. > 500 meters), the species occupies a wide variety of habitat types (e.g. deciduous and humid forests, open and gallery woodlands), where active nest cavities generally occur at higher densities near major water sources, including rivers, lakes and lagoons

(Boyle et al. 2008; Clark & Clark 2000). Scarlet macaws subsist on the fruits, nuts and seeds from a variety of plant species (Forshaw 2006); seasonal movements in response to staggered phenological events exemplify the species’ high capacity for dispersal (Karubian et al. 2005;

Renton 2002; Rodas 2002). Reproductive rates are low, a possible reflection of limited availability of suitable cavities (Munn 1992; Renton & Brightsmith 2009; Vaughan et al. 2003).

Field observations suggest only 20% of the population may breed in any given year (Brightsmith

2005; Munn 1992; Wright et al. 2001), and each successful nest attempt averages one to two fledglings (Iñigo-Elías 1996; Nycander et al. 1995; Renton & Brightsmith 2009). Individuals reach sexual maturity at approximately five years of age, with an average reproductive lifespan of 20 years (Brouwer et al. 2000; Clubb 1992). As with many Neotropical psittacine species, A. macao is sexually monomorphic (Forshaw 2006). All age classes (e.g. immature, reproductively active, senescent) demonstrating similar morphological characters post-fledging, posing a significant challenge for identifying individuals and determining age class distributions in situ

(Hilburn & Higgins 2000).

The colorful and charismatic nature of the scarlet macaw has fostered a long history of religious and economic significance; Pre-Columbian societies used images of scarlet macaws to adorn pottery and murals, and incorporated macaw feathers in religious objects and ceremonial wear (Benson 1997; Carrasco Vargas et al. 2009; Reina & Kensinger 1991). Admiration for the species was not limited to local cultures. Skeletal remains of numerous scarlet macaws have been recovered from ancient aviaries as far north as the United States, thousands of miles from the species’ native range in the lowland tropics of Central and South America (Creel & McCusick

1994; Minnis et al. 1993; Somerville et al. 2010). Modern exploitation of scarlet macaw populations is primarily driven by socioeconomic gains, rather than cultural symbolism, incited by international demand for exotic species as companion animals (Thomsen & Mulliken 1992;

Wright et al. 2001). Importation rates peaked in late 20th Century, with customs records reporting over 3,300 scarlet macaws entering the United States alone between 1976 and 1986 (Wiedenfeld

1994). Concerns of unsustainable harvests championed legislation within two major foreign markets (i.e. United States and Europe) banning the international trade of avian species; yet, local and regional demand within range countries continues to threaten the long-term viability of wild psittacines (Cantú-Guzmán et al. 2007; Gastanaga et al. 2011; Gonzalez 2003).

Exploitation pressures are a serious threat to scarlet macaw populations via direct and indirect influences on demographic stability and reproductive potential. Systematic removal of nestlings significantly reduces annual recruitment rates, with prolonged periods of intense exploitation likely skewing the age distribution toward older individuals (Clum 2008). Moreover, local managers may underestimate the magnitude of human-introduced generation gaps, due to the species’ longevity and high adult survival rates (Marsden & Pilgrim 2003); ongoing nesting

11 attempts by aging breeding pairs gives the illusion of a relatively stable demographic structure.

However, with few young scarlet macaws to ensue breeding as older age classes reach senescence, populations are at risk of experiencing rapid and severe demographic declines, including possible genetic bottlenecks, which may occur long after the primary threat (i.e. nest poaching) has subsided (Bourke et al. 2010; Hailer et al. 2006). Lingering effects of commercial exploitation on scarlet macaw populations are also associated with extraction methods employed by poachers; in certain locations, nest trees are purposely felled to facilitate access to cavities

(Britt 2011). These actions effectively destroying a vital and limited resource (Cornelius et al.

2008), degrading critical A. macao breeding areas through the cumulative loss of nest cavities.

Scarlet macaws are obligate cavity nesters (Brightsmith 2005; Renton & Brightsmith 2009); therefore, the loss of suitable cavities, compounded by subsequent heightened inter- and intraspecific competition, reduce inherently low reproductive rates, slowing population growth, and hindering conservation recovery efforts.

Whereas nest poaching specifically targets scarlet macaw populations, the generalized impacts of deforestation are no less powerful in driving demographic declines. Large swaths of lowland forests are continuously cleared throughout the Neotropics, with an average annual loss of 1.2% and 0.5% reported for Central and South America, respectively, between 2000 and 2005

(FAO 2005). Although proximate and ultimate drivers are multifaceted and vary by region, Geist and Lambin (2002) found land conversion in Latin America is primarily associated with infrastructural (e.g. settlements, transport) and, more importantly, agricultural (e.g. cultivation, ranching) factors. Moreover, studies have shown significant correlations between human demography (e.g. population density) and accessibility (e.g. road density) in determining the extent and rate of tropical forest loss (Mena et al. 2006). For example, observed deforestation

12 rates near heavily populated areas in the Bolivian Amazon were threefold greater than values recovered for regions where human settlements occur at low density (Marsik et al. 2011).

Similarly, 90% of land conversion events occurs within a 5 kilometer buffer of roadways (Viña et al. 2004), although the influence of accessibility on habitat modifications tapers off at greater distances from population centers (Marsik et al. 2011). Local sociopolitical environments also affect changes in forest cover; remote sensing images taken over a 23-year period show

Colombian lowlands are lost at twice the rate as adjoining habitats in Ecuador (Viña et al. 2004).

Population fragmentation, caused by large-scale conversion of preferred humid tropical forests, has pronounced ramifications on long-term viability of scarlet macaws throughout their global distribution. First, loss of migration corridor restricts movements across the landscape, thus limiting access to prime foraging and/or nesting areas; permanent occupancy in suboptimal habitats may result in decreased reproductive outputs (Puzin et al. 2011), higher predatory pressure (Chalfoun et al. 2002), and lower survival and stochastic population growth rates

(Nicolè et al. 2011). Moreover, deforestation disrupts gene flow (Reed et al. 2011), thus altering underlying patterns of spatial substructure, reducing effective population sizes and heightening susceptibility to inbreeding and loss of molecular variation (Athrey et al. 2011; Couvet 2002;

Martínez-Cruz et al. 2007), with negative consequences for individual fitness and overall evolutionary potential (Johansson et al. 2007; Keller & Waller 2002; Montgomery et al. 2000).

Depending on the extent of land conversion, remaining habitat fragments may be of insufficient size or quality to support scarlet macaws, as evidenced by reported extirpation events across significant portions of the species’ range where deforestation is most severe (i.e. Central

America: Fig. 1.1B), including the local extinction of A. macao in El Salvador (Juniper & Parr

1998; Wiedenfeld 1994).

Focal Management Target: Maya Biosphere Reserve, Guatemala

Conservation management initiatives are imperative in Central America, where scarlet macaw populations are on the verge of extinction; commercial exploitation and land conversion occur at alarming rates, reflecting the region’s high human population density (CEPAL 2010), associated socioeconomic needs, and poor governance of environmental policies. Among the more developed scarlet macaw conservation and management projects in the region, the Wildlife

Conservation Society – Guatemala Program (WCS-Guatemala) actively monitors demographic trends and annual reproductive success at nesting sites in the Maya Biosphere Reserve (MBR), while working with local communities to raise awareness of environmental issues and conservation needs (Garcia et al. 2008). Since project initiation in 2002, WCS-Guatemala has taken steps to broaden the scope of scarlet macaw conservation actions; moving beyond anti- poaching and habitat protection patrols, the program’s multifaceted approach now includes more intensive field-based techniques such as veterinary interventions and supplemental feeding.

Although annual fledging rates in the MBR have steadily risen in response to the dedicated efforts of WCS-Guatemala, it is unclear how reported conservation successes relate to long-term viability of scarlet macaws on local and regional scales. Furthermore, concerns remain regarding the possibility of inbreeding and genetic bottlenecks in the MBR, inciting discussions of whether genetic management initiatives are also warranted (Garcia et al. 2008). This work aimed to address these uncertainties by employing the proposed model approach for a hierarchical analysis of spatial and temporal genetic variation within scarlet macaws, designating breeding areas in the MBR, Guatemala as the focal management target (differentiated below by *).

Primary objectives were two-fold: to elucidate general biological parameters important for guiding conservation efforts and place the MBR* within the context of broader population

14 dynamics in LSM. This detailed molecular data set will provide WCS-Guatemala a vital platform to refine local conservation goals, identify critical deficiencies in protective legislation, evaluate current management actions, and prioritize future programmatic development (e.g. potential genetic recovery program).

To date, intraspecific variation in scarlet macaws is characterized as two poorly differentiated subspecies, based primarily on the description of few subjective morphological traits (Wiedenfeld 1994). The nominate subspecies (Ara macao macao) ranges throughout South

America and into lower Central America, whereas the second (Ara macao cyanoptera) is found across upper Central America, with a putative zone of intergradation along the border of

Nicaragua and Costa Rica. Within this taxonomic framework, the scarlet macaws under threat in the MBR*, Guatemala are classified as A. m. cyanoptera; however, this designation is of little consequence for global protective legislation, where both subspecific taxa are jointly considered a single species of ‘Least Concern’ by the International Union for Conservation of Nature

(IUCN; www.iucnredlist.org). The uniform treatment of scarlet macaws throughout the species’ range may be an inappropriate approach for evaluating the conservation status of local populations, given the intense commercial exploitation pressures, population fragmentation, and demographic declines reported in Central America (Cantú-Guzmán et al. 2007; Garcia et al.

2008; Iñigo-Elías 1996; Wiedenfeld 1994), and potential for cryptic variation to arise across the highly heterogeneous Neotropics (Bickford et al. 2007).

Within each taxonomic unit, little is known regarding population-level dynamics (e.g. spatial substructure, genetic variation, demography); this is especially important given the MBR* constitutes the core of a larger complex of protected areas in Mexico, Guatemala and Belize, collectively referred to as La Selva Maya (LSM: Fig. 1.2). Being the largest contiguous patch of

15 lowland moist forest remaining in Central America (Mendoza & Dirzo 1999; Sanderson et al.

2002), LSM represents the last stronghold for A. m. cyanoptera. Surveys of existing habitats have identified three primary nest sites: 1) Maya Biosphere Reserve, Guatemala, 2) Chiquibul

Forest Reserve, Belize, and 3) Usumacinta River drainage basin, along the border between

Mexico and Guatemala (Britt 2011; Garcia et al. 2008; Iñigo-Elías 1996). Each breeding area is characterized by a unique set of physical and ecological features, and subject to varying combinations of specific and general human-mediated threats; interactions between these biological and anthropogenic factors within and among scarlet macaw nest areas will have significant ramifications for local and regional conservation efforts.

Maya Biosphere Reserve (MBR*), Guatemala: Extending across 2.11 million hectares, the MBR is relatively flat, with maximum elevations reaching approximately 200 meters above sea level, dominated by closed canopy tropical evergreen and seasonal broad-leaf forests, and typified by shallow limestone soil with calcareous rocks (Vreugdenhil et al. 2002). During the breeding season, ~200 scarlet macaws congregate in the western block of the reserve, namely the

Laguna del Tigre National Park, and surrounding Biological Corridor and Forest Concessions

(Fig. 1.2), to nest among the area’s widespread lakes and lagoons (Carreón-Arroyo & Iñigo-Elías

1998; Clum 2008; Garcia et al. 2008). Guatemala’s human population is growing at the highest rate of any Latin American country, with a national increase of 2.46% reported for 2009

(CEPAL 2010). Specifically within the MBR, human settlements experienced a 30-fold expansion (i.e. 20,000 to 600,000) from 1960 to 2006 (McNab & Ramos 2006) and areas adjacent to active scarlet macaw nest sites support a permanent population of approximately

18,000 people (Ramos et al. 2001), along with an unknown number of illegal settlers. Continued human growth in the MBR exerts increasing pressure on local ecosystems; unauthorized

16 colonization, illegal land conversion for ranching or agricultural purposes, and unsustainable natural resource extractions, floral and faunal, constitute the most important on-going threats

(Hughell & Butterfield 2008). These detrimental activities are most intense among critical scarlet macaw nest sites in the western MBR, where a significant portion of forest cover has been lost and areas with intact canopies are often degraded due to selective harvesting pressures (McNab

& Ramos 2006). However, robust habitat protection efforts and visible field presence of WCS-

Guatemala have made significant strides in mitigating anthropogenic threats in portions of the

MBR, as exemplified by no net increase in deforestation rates reported among sites managed by

WCS-Guatemala from 2001-2006 (McNab 2011) and an order of magnitude reduction in the area of forest burnt by human-mediated fires since program initiation (Garcia et al. 2008).

Similar advances have also been noted for exploitation of scarlet macaws in Guatemala; WCS field staff have not documented evidence of nest failure due to poaching among monitored cavities since anti-poaching patrols began in 2004, a stark contrast to historically high levels of exploitation in the region (McNab 2011). These conservation successes are attributed, in part, to the accessibility of scarlet macaw breeding areas and strong collaborative partnerships with local communities. Although the dedicated efforts of WCS-Guatemala have helped protect key scarlet macaw habitats in the MBR, it is important to recognize anthropogenic threats remain a serious concern and many areas remain highly volatile; the intensity and nature of illicit activities (e.g. drug trafficking) deem portions of the Laguna del Tigre National Park too dangerous for effective on-the-ground conservation management (Garcia et al. 2008; McNab 2008).

Chiquibul Forest Reserve (CFR), Belize: Estimated at ~200 individuals (Garcia et al.

2008; Kainer 1991; Manzanero 1991), scarlet macaws in Belize inhabit areas along the Macal and Raspaculo Rivers nestled within the Central Maya Mountains (Fig. 1.2). Covering an area of

0.167 million hectares, the CFR is dominated by tropical evergreen and seasonal broad-leaf forests ranging to an elevation of approximately 1120 meters above sea level (Penn et al. 2004).

Despite the second highest national human population growth rates in Latin America [i.e. 2.06% as of 2009 (CEPAL 2010)], no permanent human settlements are located within CFR boundaries and these protected areas have remained largely intact over the past 30 years (Cherrington et al.

2010). One notable exception occurred in 2005; interests promoting hydroelectric energy championed construction of the Chalillo Dam, flooding approximately 953 hectares of pristine lowland forests along the confluence of the Macal and Raspaculo Rivers (Reid et al. 2000), including a significant proportion of the only known scarlet macaw breeding area in the country.

Effects of the Chalillo reservoir on local landscapes have subsequently stabilized, whereas commercial exploitation currently represents the greatest active threat to scarlet macaws in the

CFR. Local law enforcement seized 10 scarlet macaw nestlings being smuggled out of Belize in

2009 (Manzanero 2009), and the following year chicks were removed from 45% of monitored nests (Britt 2011). Poaching in the CFR is driven by the influx of Guatemalans “xateros” from border communities crossing into these protected areas to legally and illegally extract xaté

(Genus: Chamaedorea), an understory palm used extensively in the international floriculture industry (Bridgewater et al. 2006). Poaching occurs opportunistically as “xateros” traverse the

CFR in search of their primary economic target; therefore the perspective of scarlet macaws as supplementary revenue fosters little concern for sustainable harvesting, as exemplified by nest trees frequently being cut down to facilitate extraction of chicks (Britt 2011; Manzanero 2009).

These actions amplify the degradative effects of recent flooding on scarlet macaw breeding habitats in the CFR; the reduction of available nest cavities hinders current and future reproductive outputs, ultimately slowing growth rates. Despite the acute need for scarlet macaw

18 monitoring efforts and anti-poaching patrols in the CFR, local conservation managers have a limited field presence due to the logistical and financial constraints posed by the area’s large size, thick riparian vegetation, rugged terrain, and few primary access roads (Manzanero 2009).

Usumacinta River Drainage Basin (URDB), Mexico/Guatemala: Scarlet macaws in

Mexico are distributed among two disjoint locations, Oaxaca and the Usumacinta River drainage system bordering Guatemala; the former is a small, presumed isolated, group of ~50 wild scarlet macaws (Binford 1989; Iñigo-Elías et al. 2001), whereas the latter is of more consequence to broader population dynamics in La Selva Maya. The URDB consists of territories in La Selva

Lacandona in Chiapas and the adjacent Sierra del Lacandón in Guatemala (Fig. 1.2), covering approximately 0.5 million hectares of largely pristine lowland tropical forest (Carreón-Arroyo

2006). Accounts differ on the number of exemplars ranging across the region; previous studies postulated up to 400 scarlet macaws inhabit these riparian systems (Howell & Webb 1995; Iñigo-

Elías 1996), however more recent estimates based on simulated carrying capacity have reduced this value to ~140 individuals (Garcia et al. 2008). Although national human growth rates in

Mexico are less pronounced relative to Guatemala and Belize, with a reported annual increase of

1.05% (CEPAL 2010), local growth rates of 5.7% within La Selva Lacandona (INE 2000) are a considerable threat to the survival of scarlet macaws in the URDB. High market demands for the international exotic pet trade, coupled with close geographic proximity to the United States, have provided intense economic incentives for nest poaching in Mexico. For example, law enforcement officials seized four scarlet macaw chicks en route to the United States between

1990-1993, and an additional 10 exemplars were confiscated between 1997-2000 (Iñigo-Elías et al. 2001). More recently, an extensive and lucrative market for wild psittacines within Mexico has sustained commercial exploitation pressures throughout the region; extrapolations from data

19 gathered between 2005-2006 suggest ~50 scarlet macaw chicks, harvested within the URDB and/or smuggled from Central American countries, are handled by Mexican poachers and traders on an annual basis (Cantú-Guzmán et al. 2007). Deforestation and habitat degradation also represent significant risk factors for long-term viability of scarlet macaws in the URDB, with the loss of limited nest cavities and critical foraging areas negatively impacting local fecundity and survival rates. While protected areas in the region have maintained approximately 95% forest cover (CI 2002), the majority of these habitats (e.g. Montes Azules Biosphere Reserve) occur at higher elevations outside the natural range of scarlet macaws; in contrast, up to 60% of non- protected lowland forests in the URDB have been cleared for anthropogenic purposes (CI 2002).

Conflicts related to drug trafficking are among the most influential factors constraining effective conservation management in the region; weak governance and instability in areas where Mexican cartels are highly active preclude a permanent field presence by local managers (Garcia et al.

2008; Iñigo-Elías 2010; McNab 2008) and curtail opportunities for community involvement in habitat protection and anti-poaching patrols in the URDB.

The degree of historical and contemporary genetic substructure among the MBR*, CFR, and URDB is currently unknown, yet has important ramifications for scarlet macaw conservation management in the MBR*. Recent molecular work conducted by Gebhardt (2007) revealed genetic homogeneity for multiple macaw species, including A. macao, across widespread tracts of relatively undisturbed habitats in the Amazonian lowlands of Peru, highlighting the potential for regional connectivity. Radio telemetry studies have confirmed seasonal movements between the MBR* and URDB, in response to annual phenological cycles (Carreón-Arroyo 2006; Iñigo-

Elías et al. 2001; Rodas et al. 2001), presenting an inherent biological mechanism to facilitate gene flow throughout LSM. However, large-scale conversion of vital foraging areas, nesting

20 sites and migration corridors, first prompted by government sponsored development programs in the 1970s (Iñigo-Elías 1996; Renton 2000), may have prompted severe population fragmentation across Mexico, Guatemala and Belize. As stated above, it appears sufficient forest corridors remain to facilitate seasonal movements between the MBR* and URDB, yet the extent of connectivity with scarlet macaws nesting in the CFR is questionable given the observation of presumed aborted migration attempts based on satellite-tag data. Upon completion of the breeding season in the CFR, collared scarlet macaws moved westward toward known communal foraging areas along the URDB; however, after entering the denuded landscape of central

Guatemala (Fig. 1.2), monitored individuals promptly returned to the riparian habitats of the

Central Maya Mountains (Britt 2012). If documented movements are associated with historical gene flow, recent alterations would reduce the effective population size of wild scarlet macaws in LSM, shifting genetic and demographic equilibrium baselines and increasing local extinction risk (Martínez-Cruz et al. 2007).

Regardless of hypothesized changes in gene flow across Mexico, Guatemala and Belize, concerns of genetic and demographic instability remain paramount for local conservation programs given reports of intense commercial exploitation in the region; the MBR*, CFR, and

URDB have all experienced increased nest failure rates in recent decades due to poaching (Britt

2011; Garcia et al. 2008; Iñigo-Elías 1996). Significant losses in reproductive investment (e.g.

45% of monitored nest cavities were poached in the CFR, Belize during the 2010 breeding season) over consecutive years would skew the demographic distribution toward an aging population (Clum 2008). Deviations from an equilibrium state may result in inbreeding, genetic drift and susceptibility to random fluctuations in sex ratio, thus threatening long-term viability of scarlet macaws in LSM (Keller & Waller 2002; Montgomery et al. 2000; Nyström et al. 2006).

Despite the importance of these biological parameters, ambiguities in individual identification

(Hilburn & Higgins 2000), inconsistencies in age determination methodologies (Haussmann et al. 2003), and the species’ extended lifespan (Brouwer et al. 2000; Clubb 1992) hinder efforts to evaluate demography and genetic health solely based on contemporary trends inferred from field-based observations. For example, breeding records have revealed relative constancy in the number of annual reproductive attempts in the MBR* (26.8 ± 3.35) since WCS-Guatemala standardized surveying methods in 2007 (McNab 2011); these data, coupled with the recent mitigation of nest poaching in the area, suggest a stable local breeding population. However, it is plausible continued reproduction by older individuals may be delaying the manifestation of a demographic bottleneck in the MBR*, Guatemala, with the full extent of instabilities only becoming apparent after aging reproductive pairs reach senescence (Marsden & Pilgrim 2003).

Similar uncertainties exist regarding the genetic status of scarlet macaws in the MBR*, and the broader LSM; allele frequencies documented within an aging population may inflate contemporary levels of molecular diversity and a marked turnover in active breeding pairs may prompt a rapid and unexpected genetic bottleneck (Bishop et al. 2009; Hailer et al. 2006).

Conversely, heterogeneity in reproductive success, resulting from artificially enhanced fecundity rates among individuals preferentially nesting in cavities unknown or inaccessible to poachers, may accelerate the loss of genetic diversity across Mexico, Guatemala and Belize, and these trends would become more pronounced if the species’ demonstrates nest fidelity.

Further complexities arise with establishing recovery goals and management strategies given remnant scarlet macaw populations will likely exhibit differential responses to anthropogenic threats due to the inherent stochasticity of biological systems and idiosyncrasy of local conditions (e.g. ecology, severity of threats, socioeconomic and political constraints, on-site

22 presence of conservation practitioners). Depending on the extent of isolation, detrimental human activities occurring at one location (e.g. intensive nest poaching in the CFR, unsustainable land conversion in the URDB) may have direct, but unseen, influences on the success of conservation programs at other sites (e.g. MBR*). Therefore, it is imperative to characterize the extent of historical and ongoing genetic affinities between known breeding areas, as well as associated demographic parameters and life history traits, to prioritize conservation actions and determine whether the MBR*, CFR, and URDB merit independent local management or if broader transnational collaborative programs are necessary to promote long-term viability of scarlet macaws in LSM.

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Figure 1.1: Map illustrating recent changes in the geographic ranges of scarlet macaw subspecies. Light Grey: A. m. cyanoptera; Dark Grey: A. m. macao. A) Historical distribution Juniper and Parr (1998); B) Current distribution based on observations reported in Wiedenfeld (1994) and Iñigo-Elias (2001); Arrow: La Selva Maya (LSM), Central America.

Figure 1.2: Map depicting the distribution of scarlet macaws in La Selva Maya, modified from Iñigo-Elias et al. (2001). Light Grey: Historical distribution reported in 1950; Dark Grey: Distribution reported in 2001; Blue: Focal management target in the Maya Biosphere Reserve (MBR), Guatemala; Diagonal Stripe (Mexico/Guatemala): Usumacinta River drainage basin (URDB); Diagonal Stripe (Belize): Chiquibul Forest Reserve (CFR), Chiquibul National Forest and Red Bank; Dashed Line: National protected areas.

CHAPTER 2:

PHYLOGEOGRAPHIC ASSESSMENT OF SCARLET MACAWS (ARA MACAO): PATTERNS OF

INTRASPECIFIC DIVERSITY AND IMPLICATIONS FOR CONSERVATION MANAGEMENT

ABSTRACT

Taxonomic designations represent an important element for establishing conservation management priorities; however, assignment to a specific or subspecific epithet based on phenotypic variants may not accurately reflect patterns of genetic divergence or complexities in historical population dynamics, especially within widespread taxa. Scarlet macaws (Ara macao) have the largest geographic distribution of any Neotropical psittacine, occupying a variety of lowland habitats from Mexico to Brazil. Two subspecies are currently recognized based on wing cord length and plumage coloration, with formal descriptions suggesting genetic introgression in southern Nicaragua and northern Costa Rica. The present study aimed to investigate the extent of intraspecific diversification by analyzing 2,172 base pairs of mitochondrial sequence data from

108 exemplars, including contemporary (n = 27) and historical (n = 81) tissues. Phylogenetic reconstruction revealed two major phylogeographic groups generally concordant with taxonomic designations, notwithstanding a southern shift in the subspecific boundary and lack of support for the putative hybrid zone. Sequence analysis also uncovered contrasting patterns of genetic diversity and historical demography, likely driven by climatic cycles and regional differences in topography. Overall, these results demonstrate a complex evolutionary history for scarlet macaws, highlight the limited utility of morphological characters to distinguish subspecies, and emphasize the importance of highly endangered Central American populations to overall intraspecific diversity.

INTRODUCTION

Taxonomic designations are a fundamental element for the development of conservation initiatives by definition of management targets (IUCN 1998; Mace 2004; McNeely 2002) and creation of a platform to establish local, national and international protective legislation (Haig et al. 2006). Accuracy in identifying appropriate conservation targets is of utmost importance, as management decisions based on taxonomic misclassifications may have serious repercussions on the cost and scope of program implementation (Palsbøll et al. 2007), in addition to considerable biological and legal consequences. Managing several distinct taxa as a single unit could result in the local extinction of populations harboring significant genetic diversity or unique evolutionary lineages (Daugherty et al. 1990). Conversely, a single taxon maintained as multiple independent units inflates conservation management costs, limiting the capacity of practitioners to manage effectively (Kitchener 2010). In addition, hybridization between distinct taxa risks alteration of the underlying genetic composition of threatened populations (Drew et al. 2003), disruption of advantageous allele combinations, or introduction of maladaptive genes (Allendorf et al. 2001;

Edmands 2007). Introgression further confounds management efforts by potentially jeopardizing the legal protection status of threatened taxa due to unresolved questions regarding the conservation significance of hybrids (Allendorf et al. 2001; Haig et al. 2004).

Despite the importance of taxonomic designations to wildlife management efforts, descriptions based on traditional methodologies do not always accurately reflect underlying evolutionary histories (Crandall et al. 2000; Moritz 1994; Zink 2004) and thus prove to be a suboptimal means of prioritizing populations for conservation attention. Additional complexities arise when these approaches are applied at the intraspecific level; subspecific classifications, particularly among avian taxa, are generally based on regional variation of morphological or

41 behavioral characters. It is difficult to discern if subtle differences in observed traits (e.g. plumage, body mass, song) are the manifestation of phenotypic/behavioral plasticity, isolation by distance, or evidence of independent evolutionary trajectories. Novel molecular methodologies have become an ever more central tool in resolving issues of taxonomic uncertainty via direct diagnosis of unique lineages, providing a more robust empirical framework for the designation of conservation units (Freeland et al. 2007; Goldstein et al. 2000; Hofkin et al. 2003; Russello et al.

2010). Interestingly, increased use of DNA-based approaches has incited a burgeoning literature reporting cryptic intraspecific variation among morphologically similar taxa, including historically well-studied megafauna (Brown et al. 2007; Eaton et al. 2009; Russello et al. 2005).

Description of these previously unrecognized genetic discontinuities highlight the complexity of population histories and need for consideration not only of taxonomic designations, but patterns of diversification within each distinct unit.

Study System

Scarlet macaws (Ara macao) are among the most colorful and charismatic of all Neotropical psittacines. Notwithstanding a preference for lower elevations (i.e. < 500 meters), scarlet macaws demonstrate broad habitat tolerance by subsisting on a highly diverse diet of fruits and seeds

(Renton 2006; Vaughan et al. 2006), and occurring in deciduous, humid, pine and terra firme forests, in addition to open and gallery woodlands (Forshaw 2006). Historically, the species was common throughout its entire range (i.e. southern Mexico through Brazil), however direct and indirect exploitation pressures (e.g. capture for the international exotic pet trade and deforestation/habitat degradation, respectively) have contributed to widespread demographic declines and local extinctions, most notably in Central America (Forshaw 2006; Wiedenfeld

1994). High human population densities, on average 4.8 fold greater than South America

(CEPAL 2010), and associated resource demands put tremendous strain on the region’s remaining natural systems, highlighting the need for enhanced conservation efforts. Despite marked regional differences in the severity of anthropogenic threats and population fragmentation, scarlet macaws are regarded as a single taxonomic unit; listed under Appendix I of the Convention on the International Trade of Endangered Species (CITES; www.cites.org), and considered a species of ‘Least Concern’ by the International Union for Conservation of

Nature (IUCN; www.iucnredlist.org). The uniform treatment of populations may be an inappropriate approach for evaluating the conservation status of scarlet macaws, given the potential for cryptic variation to arise across the highly heterogeneous Neotropics (Bickford et al.

2007). Therefore, it is of utmost importance to assess the extent of intraspecific variation and characterize the evolutionary history of the species to optimize conservation management initiatives for scarlet macaws.

Formally described by Linnaeus in 1758, the scarlet macaw (Ara macao) has traditionally been considered a monotypic species (Forshaw 2006); yet in recent decades the aviculture community has attested phenotypic variants warrant independent taxonomic consideration

(Smith 1991). Abramson and Thomsen (1995) suggested scarlet macaws be divided into three distinct subspecies given differences in body size and wing coloration (i.e. width of yellow band and presence or absence of green plumage on median and secondary wing-coverts); however, observations were made in captivity without presentation of empirical data on morphometrics, genetics, or geographic distributions. Currently, two subspecies are recognized following the classification scheme proposed by Wiedenfeld (1994) based on an examination of museum specimens with known provenance. Within these formal descriptions, the author maintained

43 body size and qualitative measures of wing coloration as the primary observable characters differentiating subspecific taxa. Ara macao cyanoptera ranges throughout the upper portion of the species’ range (i.e. southern Mexico to central Nicaragua); exemplars are characterized as being more physically robust and exhibiting a wide band of yellow on the wings, with a general absence of green plumage. Scarlet macaws found at more southern latitudes (i.e. southern

Nicaragua to Brazil) are classified as Ara macao macao; distinguished by reduced yellow and more pronounced green coloration, in addition to overall smaller size. A steep decline in wing cord length, coupled with the intragradation of color variants, observed in southern Nicaragua and northern Costa Rica prompted Wiedenfeld to designate this area a natural hybrid zone between A. m. cyanoptera and A. m. macao.

The present study examined genetic variation among scarlet macaws sampled throughout their historical geographic range; utilizing mtDNA sequence data to evaluate the distinctiveness of putative subspecific taxa, quantify the extent of genetic diversity, draw inferences of past demography, and provide insights relevant for on-going conservation management efforts.

MATERIALS AND METHODS

Sample Collection, DNA Amplification and Sequencing

Genetic samples were obtained from 108 scarlet macaws distributed across the majority of the species’ historical range (Appendix I). DNA was extracted from feather, blood, and tissue samples collected from wild individuals in the Laguna del Tigre National Park, Guatemala (n =

11), Chiquibul National Forest Reserve, Belize (n = 5), and Iwonkrama Reserve, Guyana (n = 1).

A collaborator (K. Gebhardt, University of Idaho, USA) provided DNA extracts from feathers

44 acquired along clay licks in the Tambopata National Reserve, Peru (n = 10). All modern tissues were collected in accordance to local, national and international regulations. Remaining tissues were toe pads from study skins located at 11 natural history collections across the United

States (n = 81). Genetic material was also obtained from a single blue-and-gold macaw (Ara ararauna) and green-wing macaw (Ara chloroptera) to serve as outgroup taxa for phylogenetic reconstruction.

Total genomic DNA was extracted from blood samples using DNeasy tissue extraction kits (QIAGEN Inc.) as per manufacturer protocol. Modifications were made to optimize DNA yield from feather and toe pad samples, consisting of a 48 h digestion, pre-heating elution buffer to 70ºC, a 30 min incubation of elution buffer on spin columns prior to centrifugation, and reduced elution volumes. Whole pieces of toe pad were subject to a bleach bath (10% bleach for

5 min) and three subsequent water washes prior to DNA extraction as described above.

DNA was amplified and sequenced at four mitochondrial gene regions [12s rDNA (12S),

16s rDNA (16S), cytochrome oxidase I (COI), and cytochrome b (cytb)]. Table 2.1 lists primers used for amplification and sequencing; internal primers were designed using OligoAnalyzer 3.1

(Integrated DNA Technologies) for each gene region to generate short overlapping amplicons from historical specimens. All polymerase chain reactions (PCR) were performed on epGradient

S Mastercycler thermocyclers (Eppendorf). Previously published primers were employed to PCR fragments from modern tissue in a total reaction volume of 15 µl and included: ~20-50 ng DNA,

10 mM Tris-HCl (pH 9.0), 50 mM KCl, 1.5 mM MgCl2, 200 µM dNTPs, 0.65 µM of each primer, and 0.5 U of Taq DNA polymerase (Fisher Scientific). Thermocycler conditions were as described in Tavares (2004), with an annealing temperature of 50ºC. Total volume of 25 µl was used for amplifications from historical tissues, reactions included: ~20-50 ng DNA, 10 mM Tris-

HCl (pH 8.3), 50 mM KCl, 1.5 mM MgCl2, 10 µg bovine serum albumen, 200 µM dNTPs, 0.6

µM of each primer, and 0.5 U of AmpliTaq Gold DNA polymerase (Applied Biosystems). PCR of short fragments was carried out under the following conditions: 95ºC (10 min), 35 cycles of

95ºC (30 sec), 50ºC (30 sec), 72ºC (45 sec), and a final extension of 72ºC (7 min). All gene regions were sequenced in both directions using BigDye 3.1 chemistry (PerkinElmer) on an ABI

3730xl sequencer (Applied Biosystems). Sequences were visualized, edited and aligned with

Sequencher 4.8 (Gene Codes Corp.).

Standard historical DNA protocols were followed while manipulating samples from museum specimens (Gilbert et al. 2005; Wandeler et al. 2007). DNA extractions were conducted in a workspace dedicated to historical tissues, with independent sets of reagents used for modern and historical samples to reduce the risk of contamination from exogenous DNA and PCR amplified products. Multiple negative extraction and PCR controls, along with amplification of short, overlapping fragments (~200 bp) were employed to screen for contamination. PCR reactions were repeated to minimize the incorporation of errors due to miscoding lesions.

Population Aggregation Analysis

The discrete character-based methodology of Population Aggregation Analysis (PAA, Davis &

Nixon 1992) was used to test the current hypothesis of intraspecific diversity in scarlet macaws.

With this approach, populations sharing a suite of fixed nucleotide differences are aggregated into diagnosably distinct taxa (i.e. phylogenetic species, sensu Cracraft 1983). Character fixation provides a contextual framework to infer monophyly by demonstrating terminal taxa are united through hierarchical, rather than reticulate, genealogies (Goldstein et al. 2000). To evaluate the a priori hypothesis of two distinct units within scarlet macaws (A. m. cyanoptera and A. m. macao)

46 proposed by Wiedenfeld (1994), a character matrix was generated from concatenated mtDNA haplotypes using MacClade 4.06 (Sinauer Associates Inc.) and screened for presence or absence of fixed and alternate character differences among putative subspecific taxa.

Phylogenetic Analysis

Prior to phylogenetic reconstruction, partial fragments of protein-coding regions were compared to the published complete COI and cytb amino acid sequences of Agapornis roseicollis

(GenBank accession number: EU410486) to assess proper translation. Congruence between molecular data partitions was determined by 100 replicates of the partition homogeneity test

(PHT, Farris et al. 1994), as implemented in PAUP* (Swofford 2003). Two methods of phylogenetic inference were used to evaluate hierarchical relationships among haplotypes

[maximum likelihood (ML) and Bayesian inference (BI)], with explicit treatment of mutation models during reconstruction. The best-fit model of nucleotide substitution for the scarlet macaw data set was selected using jModeltest (Posada 2008) by rating likelihood scores generated from tree optimization for each of 56 candidate models based on either Akaike information criterion

(AIC) or Bayesian information criterion (BIC) (Posada & Buckley 2004). All analyses were conducted with the complete concatenated data set and gaps (indels) were treated as a fifth character state. ML phylogenetic reconstruction was conducted in PHYML 3.0 (Guindon &

Gascuel 2003) assuming a HYK model of nucleotide substitution and empirical determination of the proportion of invariable sites (I), selected as the AIC best-fit model by jModeltest. Measures of nodal support for ML analyses were generated from 100 non-parametric bootstrap replicates and each run initiated from a random starting tree. Mr Bayes 3.1.2 (Altekar et al. 2004; Ronquist

& Huelsenbeck 2003) was used to reconstruct phylogenetic relationships using BI assuming a

HYK + I nucleotide substitution model according to the BIC, as implemented in jModeltest. A

Markov chain Monte Carlo process was set for five simultaneous chains with 10,000,000 total generations, each starting from a random tree and using the default heating scheme. Markov chains were sampled every 200 generations, with the initial 10% of reconstructed trees discarded as burn-in.

Molecular Diversity

Corrected genetic distances (K2P, Kimura 1980) were calculated among each aggregated population identified in this study as executed in MEGA 4.0 (Kumar et al. 2008). The number of haplotypes (h), haplotype (Hd) and nucleotide (π) diversity, number of segregating sites (S), and number of singleton mutations were quantified in DnaSP 5.10.01 (Librado & Rozas 2009) to further investigate patterns of intraspecific genetic variation. Regions of the nucleotide matrix with gaps or missing data are excluded from the inference of genetic distances and diversity indices using MEGA 4.0 and DnaSP 5.10.01, respectively. Therefore only samples with >95% complete haplotype sequences were used in these analyses to maximize the number of sites considered, providing a more comprehensive overview of molecular variation.

Demographic History

Demographic histories for scarlet macaws in Central and South America were evaluated from the truncated nucleotide data set using class I (i.e. frequency of segregating sites), and class II (i.e. haplotype distribution) statistics to test for departures in selective neutrality and population equilibrium (Ramos-Onsins & Rozas 2002). Tajima’s D and R2 were selected among class I

48 statistics to evaluate the placement of segregating sites along genealogies, as the excess of recent mutations along external branches is characteristic of recent population growth (Slatkin &

Hudson 1991; Tajima 1989a, b). Fu’s Fs (1997), a class II statistic, uses information from the haplotype distribution to detect genetic signatures of demographic changes; low values are associated with the excess of singleton mutations in an expanding population. These statistical tests have been shown to outperform other methodologies in rejecting the null hypothesis of constant population size (Ramos-Onsins & Rozas 2002). All demographic inferences were implemented in DnaSP 5.10.01 (Librado & Rozas 2009) with 10,000 replicates, assuming an infinite sites model and no recombination.

RESULTS

Data Quality

Mitochondrial haplotypes consisted of 2,172 aligned nucleotide characters distributed across four gene regions: 12S (336 bp), 16S (519 bp), COI (589 bp), and cytb (728 bp). Seventy-one haplotypes were detected among concatenated mtDNA sequences from 108 scarlet macaws sampled across the contemporary and historical species’ range. A total of 110 variable sites

(5.1%) were observed within the nucleotide matrix, including a single indel one base pair in length. Complementary haplotypes were also successfully generated from a blue-and-gold macaw (Ara ararauna) and green-wing macaw (Ara chloroptera). Single-banded PCR products of expected size were obtained for all amplifications with previously published external primer pairs (modern samples) and novel internal primer pairs for small overlapping fragments

(historical samples). Multiple independent DNA extractions and PCR amplifications from

49 museum tissues resulted in 3x to 8x sequence coverage for amplicons exhibiting unique single base pair mutations.

Population Aggregation Analysis

Examination of the character matrix yielded unambiguous support for the hypothesized intraspecific taxonomic division (i.e. A. m. cyanoptera and A. m. macao) presented by

Wiedenfeld (1994). Aligned mitochondrial sequences revealed four single nucleotide diagnostic characters distributed across gene regions: 12S (1), 16S (0), COI (1), and cytb (2). Closer inspection of mtDNA sequence data revealed seven clusters of closely related haplotypes [herein referred to as haplogroups (i.e. Haplo1, Haplo2, etc.)]. A. m. cyanoptera is comprised of five distinct haplogroups, each pair being differentiated by four to eight fixed characters, with considerable geographic overlap (Fig. 2.1). Haplo2 was found throughout the entire subspecies’ range from Mexico to northeastern Costa Rica, whereas the remaining four haplogroups exhibited more restricted distributions. Haplo1 and Haplo6 were both detected in Mexico and

Guatemala, although the former was likewise found in Belize. Haplo3 and Haplo5 demonstrated similar ranges along the Pacific slope of El Salvador, Honduras and Nicaragua; however, Haplo3 was also recovered on Isla Coiba, Panama (Fig. 2.1). In contrast, A. m. macao is characterized by two haplogroups, distinguished by two fixed nucleotide mutations, with discrete geographic ranges delimited by the Andean cordilleras (Fig. 2.1). Trans-Andean populations in Colombia and lower Central America belong to Haplo7, and Haplo4 is widely distributed throughout the

Amazon basin.

Phylogenetic Analysis

No incongruence was detected between data partitions following PHT analysis and all subsequent phylogenetic reconstructions were conducted using a single concatenated sequence data set. Near identical topologies were recovered for maximum likelihood and Bayesian inference, demonstrating insensitivity to inference methodology (Fig. 2.2). Phylogenetic reconstruction revealed two major monophyletic groups, generally concordant with current subspecific taxonomy. Sampled haplotypes within A. m. cyanoptera form a well-supported monophyletic clade [ML bootstrap support (BSML) = 89, and BI posterior probability (PPBI) =

0.99; Fig. 2.2]. Each cyanoptera haplogroup clusters into a monophyletic group with moderate to

high bootstrap support (BSML = 58-87; Fig. 2.2) and high posterior probabilities (PPBI = 0.91-1.0;

Fig. 2.2), with exception of Haplo6 represented by a single haplotype. Evolutionary relationships among the five haplogroups are poorly resolved, ML denotes Haplo2 as sister to all other haplogroups, whereas all cyanoptera haplogroups collapse into a single polytomy upon BI analysis (Fig. 2.2). The reconstructed topology revealed low to moderate nodal support for A. m. macao (BSML = 47, PPBI = 0.66; Fig. 2.2). Trans-Andean haplotypes form a monophyletic group within the otherwise paraphyletic Haplo4 clade with moderate bootstrap support (BSML = 74;

Fig. 2.2) and high posterior probabilities (PPBI = 0.95; Fig. 2.2). Furthermore, Haplo7 haplotypes of Central American origin are monophyletic with similar nodal support (BSML = 65, PPBI =

0.90; Fig. 2.2).

Molecular Diversity

Fifty-seven mitochondrial haplotypes were identified for the truncated data set of 86 scarlet macaws (i.e. exemplars with >95% complete sequences). The number of nucleotide positions

51 included in computation of diversity indices varied depending on the relative completeness

(i.e. number of gaps or missing data) of the sample subset under consideration (Table 2.2).

Average genetic distances between the two putative subspecific taxa equaled 0.7%; distances among individual haplogroups ranged from 0.2% (Haplo3/Haplo1, Haplo3/Haplo5, and

Haplo3/Haplo6) to 0.8% (Haplo2/Haplo4 and Haplo2/Haplo7). Comparisons between A. macao subsp. and outgroup taxa revealed average genetic distances of 2.8% (A. chloroptera) and 7.7%

(A. ararauna). Comparable levels of haplotype and nucleotide diversity were observed for A. m. cyanoptera (Hd = 0.95; π = 0.00314) and A. m. macao (Hd = 0.98; π = 0.00315). A reduction in haplotype diversity was noted for four cyanoptera haplogroups upon independent analysis, ranging from 0.00 to 0.86, and nucleotide diversity declined in concert (Table 2.2). Conversely, haplogroups within A. m. macao maintain high haplotype diversity when considered separately,

Hd = 0.98 and 0.93 for Haplo4 and Haplo7, respectively. Lower nucleotide diversity was recovered within each individual macao haplogroup, albeit estimates are equivalent or higher than values observed for cyanoptera (Table 2.2). A total of 64 polymorphisms (59% singleton mutations) were detected among macao haplotypes, contrasted with 35 polymorphisms (29% singleton mutations) within cyanoptera.

Demographic History

Statistical analyses revealed differential demographic trends for each scarlet macaw subspecies

(Table 2.3). Genetic signatures typically associated with recent population expansion were recovered within A. m. macao; mitochondrial haplotypes exhibited a high number of singleton mutations and yielded highly significant values for Tajima’s (D = -1.942, P = 0.001), Fu’s (Fs = -

27.57, P = 0.00) tests of neutrality, and the R2 statistic (R2 = 0.042, P = 0.00). Neutral patterns of

52 variation were observed for A. m. cyanoptera, as both Tajima’s test (D = -0.825, P = 0.22) and the R2 statistic (R2 = 0.085, P = 0.19) failed to reject the null hypothesis of constant population size. The more sensitive Fu’s neutrality test (Fs = -5.911, P = 0.05) was found to be significant, albeit marginally. Insufficient sample sizes precluded independent assessment of the demographic histories for each individual haplogroup.

DISCUSSION

Evaluation of Current Taxonomy

Phylogenetic analyses identified two major phylogeographic groups within scarlet macaws, evidenced by the presence of both diagnostic characters and reciprocal monophyly. These phylogenetic species are broadly concordant with current taxonomic designations, A. m. cyanoptera and A. m. macao, albeit with notable exceptions. First, Wiedenfeld (1994) delineates the subspecific boundary in Central Nicaragua, whereas this study suggests the range of A. m. cyanoptera extends to the Caribbean slope of Costa Rica (Fig. 2.1). Second, the observed distribution of mtDNA haplotypes exhibits a general pattern of geographic segregation, rather than co-occurrence, of cyanoptera and macao lineages in lower Central America (Fig. 2.1), thus arguing against introgression. Despite broad congruence with published subspecies descriptions

(i.e. diagnosis of two phylogenetic groups), these discrepancies underscore the potential limitations of employing morphological traits to describe intraspecific variation.

Based on the distribution of mitochondrial haplotypes, the geographical limits of A. m. cyanoptera and A. m. macao are delimited by the central cordilleras of Costa Rica and Panama

(Fig. 2.1, inset). This volcanic mountain system ranges in elevation from 500 to over 3,800

53 meters and transects the country with near complete isolation of the scarlet macaw’s preferred lowland habitats along Pacific and Caribbean slopes (Fig 2.1, inset), implying areas of significant topographic variation limit genetic connectivity among populations. It is important to note, however, the data presented here reflect maternal genealogies only; gene flow mediated by male- biased dispersal would not be detected in the presence of female philopatry (Avise et al. 1992;

Baker et al. 2009), thus obscuring the signal of subspecific hybridization. Examination of the broader species’ range reveals mitochondrial haplogroups are distributed across relatively large geographic areas, with noted genetic discontinuities associated with prominent geologic features

(Fig. 2.1, Fig 2.2). These pattern are contrary to the spatial clustering expected if female scarlet macaws remained within natal nest sites, but consistent with recent studies suggesting female philopatry plays a limited role in population differentiation for Neotropical psittacines (Faria et al. 2008; Gebhardt 2007; Wright et al. 2005; but see also Caparroz et al. 2009). Moreover, the central cordilleras of lower Central America have also been shown instrumental in driving genetic differentiation within an array of other Neotropical taxa (Cavers et al. 2003; Demastes et al. 1996; Hoffmann & Baker 2003; Johnson et al. 2007).

Within his taxonomic descriptions, Wiedenfeld (1994) also included a zone of intragradation in southern Nicaragua and northern Costa Rica, presenting a steep cline between morphological variants (i.e. wing cord length) in lower Central America as evidence of gene flow between subspecific taxa (Harrison 1993). In the event of in situ hybridization between A. m. cyanoptera and A. m. macao, intermediate phenotypes would be associated with geographic overlap of subspecific haplotypes within the contact zone, as seen for the burrowing parrot

(Cyandiseus patagonus: Masello et al. 2011). Furthermore, populations should become more genetically disparate across space, with co-occurrence of cyanoptera and macao lineages

54 decreasing in frequency with increased geographic distances (Barton & Gale 1993; Harrison

1993). Under this model, introgression would be most intense where subspecies’ distributions adjoin, resulting in higher probability of recovering both cyanoptera and macao haplotypes at the

Nicaragua/Costa Rica border, and with reduced detection rates across space. However, this study recovered the opposite trend; intermediate phenotypes were not associated with considerable geographic overlap of subspecific phylogenetic lineages within the proposed contact zone (Fig.

2.1). For example, no cyanoptera haplotypes were detected on mainland Central America south of the Costa Rican cordilleras (n = 8); instead, the only reported instance of cyanoptera lineages within the macao distribution occurred on Isla Coiba, a large island located off the Pacific slope of Panama, over 300 km from the nearest empirically confirmed locality for A. m. cyanoptera

(Fig. 2.1). Interestingly, 80% of exemplars sampled on Isla Coiba (n = 5) belonged to cyanoptera

Haplo3. Acknowledging a small sample size, this pattern of high co-occurrence between subspecific lineages at distant locales is atypical for a stable hybrid zone; however, the absence of cyanoptera haplotypes along mainland lower Central America is not the only confounding factor. For example, intervening habitats are flanked by two prominent geographic features; as discussed above, the central cordilleras separate Caribbean and Pacific slopes of Costa Rica and

Isla Coiba, as an insular system, implies reduced connectivity with mainland populations (Fig.

2.1). Therefore, the limited extent of geographic overlap between A. m. cyanoptera and A. m. macao should not be interpreted as definitive evidence of subspecific hybridization. Moreover, unusual circumstances regarding insular specimens suggests an alternative explanation for the presence of cyanoptera haplotypes on Isla Coiba (see below).

Although Wiedenfeld observed a cline in morphological traits across scarlet macaw populations, limited and potentially biased sampling may have exaggerated the degree of

55 phenotypic differentiation in lower Central America. First, few individuals (n = 31) and characters (n = 4) were examined in the morphological analysis, with poor representation of a substantial portion of the species’ range (i.e. a single representative each from Mexico,

Guatemala, Belize and Brazil, while no exemplars originated from Venezuela, Guyana and

Suriname). According to Wiedenfeld’s (1994) subspecies descriptions, the primary morphological character differentiating scarlet macaw taxa is the extent of green tips on secondary wing coverts; A. m. cyanoptera is described as having little to no green coloration, whereas A. m. macao exhibits small to substantial amounts of green plumage. Interestingly, the author himself noted the inherent ambiguity and subjectivity of this trait, presumably most pronounced in lower Central America, and provided no methodology to quantify ‘little’ versus

‘small’, instead concluded morphological intragradation was evidence of introgression between subspecific taxa. Change in wing cord length was the only quantitative trait shown to produce the morphologic cline indicative of hybridization, however it is important to note these data represent a single line of evidence; corroboration among multiple independent characters would provide stronger support for claims of introgression (Harrison 1993). Moreover, closer inspection of specimen information for exemplars included in Wiedenfeld’s (1994) morphological assessment revealed evidence of sex-biased sampling, potentially accounting for the steep decline in wing length observed in lower Central America. Study skins originating from the Northern extent of the species’ range (i.e. Mexico through Nicaragua) were predominantly male, whereas the majority from lower Central America (i.e. Costa Rica and Panama) were females; South American samples were comprised of an equal number of both sexes. While

Wiedenfeld (1994) did not recover statistically significant sexual dimorphism within scarlet macaws, males were on average larger than females. Therefore, the geographic clustering of

56 females in the central portion of the species’ range may have overstated the observed reduction in wing cord length with decreased latitude.

Potential problematic sampling notwithstanding, recent works have questioned the utility of morphological characters for taxonomic investigations in Neotropical psittacines. Specifically,

Eberhard and Bermingham (2004) and Ribas (2007) highlight the unreliability of feather coloration as a robust character for discriminating discrete taxa in the genus Amazona. Tavares et al. (2006) further emphasize the limitations of morphology for the inference of evolutionary relationships among Neotropical parrots (tribe Arini) by reporting many of the most commonly used characters (e.g. tail length, tail shape, bicolored ‘pericyclic’ iris, exposed nostrils) are phylogenetically uninformative.

Patterns of Diversification

While the identification of evolutionarily meaningful taxonomic units is an important step in the development of conservation programs, understanding how genetic variation is partitioned among units provides critical insights into the prioritization of management initiatives. In the case of the scarlet macaw, molecular genetic data indicate the species underwent a rapid diversification with contrasting demographic histories for cyanoptera and macao. Short internode and terminal branches recovered from phylogenetic reconstruction (Fig. 2.2), coupled with small genetic distances suggest subspecific lineages split in the recent past. This observation is consistent with an apparent influx of tropical lowland taxa into Central America during the

Pleistocene (Culver et al. 2000; Eizirik et al. 2001; Hynkova et al. 2009; Johnson et al. 2007;

Lerner et al. 2009). Webb and Rancy (1996) proposed the Panamanian land bridge was initially dominated by savanna-like vegetation, thus constraining dispersal of forest-dwelling species

57 until mature lowland ecosystems developed. Despite recent origins, scarlet macaw subspecies exhibit considerable phylogeographic structure in the form of seven diagnosably distinct mtDNA haplogroups (Fig. 2.2). Evidence of complex evolutionary histories within young lineages is not without precedence, the Pleistocene epoch is known to have played an active role in the intraspecific diversification of avian species (Avise & Walker 1998). Fluctuations in temperature and precipitation associated with Milankovitch cycles are widely considered key factors governing the historical biogeography of lowland forests across the Neotropics. For example, cool-dry phases prompted large-scale vegetation changes as moist tropical ecosystems were replaced by more arid habitats, creating a system of isolated forest refugia (Haffer 1969). In contrast, Nores (1999) suggested rising sea levels flooded low lying areas during intense warm- wet periods. It is important to note local climate patterns are heavily influenced by land area, surface relief, proximity to large bodies of water, and air currents (Daly 2006), therefore the location and magnitude of fine-scale environmental shifts would have varied across the

Neotropics (Webb & Bartlein 1992). These regional differences in intensity of population fragmentation during the Pleistocene, and associated changes in demography, provide a logical theoretical framework to explain patterns of genetic variation and distribution of haplogroups observed within A. m. cyanoptera and A. m. macao.

Comparable values of total haplotype (Hd) and nucleotide (π) diversity were recovered for A. m. macao and A. m. cyanoptera, yet independent estimation of genetic variation within each haplogroup reveals a superficial similarity between subspecific taxa. The presence of several distinct haplogroups within A. m. cyanoptera appears to inflate levels of overall sequence variation, as diversity indices decline for the majority of cyanoptera haplogroups when treated as separate units (Table 2.2). The nucleotide data set for cyanoptera includes relatively few

58 segregating sites and singleton mutations, with most polymorphisms representing fixed character differences between haplogroups. This pattern of high inter- and low intrahaplogroup diversity is suggestive of a complex demographic history for A. m. cyanoptera, a reasonable hypothesis given the physical characteristics of the subspecies’ range. Central America is a relatively small and elongated landmass punctuated by an extensive backbone of highlands; these prominent geologic features not only constrain the distribution of tropical humid habitats, but also alter local weather patterns, intensifying changes in paleoecological assemblages during the

Pleistocene.

Across the majority of upper Central America, lowland forest ecosystems form a perimeter around the central massifs; each versant exhibits distinctly different geographic characteristics, with possible important implications for the historical demography of A. m. cyanoptera. Along the Caribbean slope, substantial tracts of tropical moist habitats are interspersed among the cordilleras and extend well beyond the coastal plains (Olson et al. 2001).

As a result, catastrophic glacial melting notwithstanding, the increase in global sea level proposed by Nores (1999) during intense warm-wet cycles likely had marginal effects on scarlet macaw populations in the region. However, the retraction of preferred forest habitats would have played a more critical role in the evolutionary history of A. m. cyanoptera lineages. Recent paynological studies of sediment cores from Lake Petén-Itzá, located in the lowlands of northern

Guatemala, have confirmed the periodic conversion from humid to more arid habitats in response to climatic oscillations (Bush et al. 2009; Correa-Metrio et al. 2012; Hillesheim et al. 2005); similar events likely occurred throughout A. m. cyanoptera’s range in Central America. As savanna-like habitats advanced in cool-dry cycles of the Pleistocene, remnant tropical wet forests may have survived by retreating further inland to areas located along the base of the cordilleras,

59 where moisture-laden prevailing winds are stalled by the change in surface relief (Haffer

2008). Based on a molecular genetic assessment of Poulsenia armata, Aide and Rivera (1998) suggested Central American lowland moist habitats also persisted in riparian areas along the

Caribbean slope. Several major rivers distributed across southern Mexico and northern Central

America [e.g. Coatzacoalcos and Usumacinta (Mexico), Coco (Honduras), San Juan

(Nicaragua)] may have helped sustain humid forests in periods of reduced precipitation and temperature. Coastal plains along the Pacific versant of upper Central America are greatly reduced in area, relative to the Caribbean slope, due to the close proximity of the cordilleras to the shoreline. Coupled with the rain shadow effect created by the extensive highland systems, it is uncertain whether these shortened watercourses supported sufficient lowland forests during cool-dry periods for viable scarlet macaw populations. Moreover, these coastal areas may have been susceptible to flooding during intense warm-wet periods (Nores 1999), further influencing the distribution of tropical lowland habitats along the Pacific slope. Regardless of the exact location of Pleistocene forest refugia in southern Mexico and upper Central America, the retraction of tropical humid ecosystems implies the distribution of ancestral A. m. cyanoptera was fragmented into a series of isolated remnant populations. Periodic vicariance events represent a reasonable mechanism driving intraspecific diversification, resulting in the five distinct lineages reported here. Moreover, these allopatric refugia provide a logical explanation for the lack of hierarchy noted among well-supported monophyletic cyanoptera haplogroups

(Fig. 2.2). Theoretically, population sizes remained small during periods of isolation, facilitating the accumulation of diagnostic characters and extinction of ancestral haplotypes via genetic drift.

Contemporary geographic overlap of cyanoptera haplogroups (Fig. 2.1) supports the hypothesis of recent population expansion across Central America, as lowland forests

60 regenerated and attained their current distribution. Interestingly, neutrality tests provide little support for demographic disequilibrium within A. m. cyanoptera. Observed values for Tajima’s

D and Ramos-Onsins and Rosas’ R2 failed to reject the null hypothesis of constant population size. Simulation studies have shown Fu’s Fs outperforms other analytical methods for data sets with a comparable sample size and number of segregating sites (Ramos-Onsins & Rozas 2002), and weakly significant deviations from demographic equilibrium were detected using this methodology. However, conservative interpretation of these data suggests the effective population size for A. m. cyanoptera has been reasonably stable in the recent past. It is possible the complex system of highlands found in Central America limited opportunities for demographic growth by constraining the extent of lowland tropical forests, as a result scarlet macaw populations in the region may have quickly reestablished a state of equilibrium.

Alternatively, the simultaneous consideration of sequence data from all cyanoptera haplogroups may confound the inference of demographic trends within the subspecies. Despite taxonomic links due to present-day gene flow, differential patterns of variation suggest historical demographic independence of allopatric haplogroups. For example, lack of singleton mutations in cyanoptera Haplo1 and Haplo6 is indicative of small effective population size or recent bottleneck event (i.e. loss of low frequency haplotypes due to genetic drift). The relatively large percentage of singleton mutations found in cyanoptera Haplo2, Haplo3, and Haplo5, however, implies recent population growth (i.e. accumulation of new mutations as the population expands). Averaging these values may weaken the genetic signal of demographic disequilibrium in A. m. cyanoptera (Table 2.2, Table 2.3) and obscure underlying complexities in population history. Unfortunately, the independent assessment of each cyanoptera haplogroup is not

61 possible with the current data set; more extensive sampling is needed to elucidate higher resolution demographic trends within A. m. cyanoptera.

A markedly different pattern of genetic variation emerges upon examination of scarlet macaw populations across the southern portion of the species’ range. A. m. macao is represented by only two haplogroups and values of haplotype (Hd) and nucleotide (π) diversity recovered within each lineage are comparable to total subspecific diversity; moreover, the number of segregating sites and singleton mutations found among macao haplotypes far exceed those of A. m. cyanoptera (Table. 2.2). High levels of molecular variation recorded for A. m. macao is consistent with the observation other wide-ranging lowland Neotropical taxa [e.g. jaguar

(Panthera onca: Eizirik et al. 2001), puma (Puma concolor: Culver et al. 2000), snail kite

(Rostrhamus sociabilis: Haas et al. 2009), hook-billed kite (Chondrohierax uncinatus: Johnson et al. 2007) and harpy eagle (Harpia harpyja: Lerner et al. 2009)] exhibit greater diversity within

Amazonian populations relative to Central American conspecifics. A pattern of low inter- and high intrahaplogroup diversity was also recovered within A. m. macao; singleton mutations occur with high frequency among macao haplotypes, with few fixed nucleotide substitutions differentiating haplogroups. Accumulation of substantial genetic variation indicates the historical effective population size of A. m. macao remained relatively stable despite intense paleoclimatic oscillations, a stark contrast to the demographic instability hypothesized for cyanoptera haplogroups. Scarlet macaw subspecies also differ in regard to the spatial distribution of genetic lineages; considerable overlap is observed within A. m. cyanoptera, whereas macao haplogroups are geographically isolated (Fig. 2.1). Several key aspects of A. m. macao’s range may account for discrepant patterns of historical demography and genetic diversity between subspecies. For example, A. m. macao is found across an area significantly larger than conspecifics in northern

Central America (Forshaw 2006). Moreover, prominent geologic formations (i.e. Isthmus of

Panama, Andes Mountains) partition the subspecies’ distribution into three disjunct regions: the

Pacific versant of Costa Rica and Panama, Magdalena River Valley in Colombia, and tropical humid forests in the Amazon lowlands (Forshaw 2006). As discussed above, the physical attributes of an area (e.g. land area, surface relief, proximity to water, air currents) influence fine- scale weather patterns; therefore it is probable demographic trends for macao populations within each region differed due to the idiosyncratic response of ecological assemblages to climatic fluctuations.

Amazonia is the largest of A. m. macao’s three geographic partitions and represents the nucleus of the subspecies’ range, spanning an area an order of magnitude greater in size than the entirety of Central America. The region’s dynamic geologic history was likely highly instrumental in shaping patterns of sequence variation observed within the endemic macao haplogroup, Haplo4. As tectonic uplifts gave rise to the Andean cordilleras, hydrologic pathways shifted throughout the Amazon lowlands, resulting in a complex system of wetland and lacustrine habitats (Hoorn et al. 2010; Latrubesse et al. 2010). During the Pleistocene, the accumulation of eroded highland sediments continued to alter drainage patterns and led to the formation of the Amazon River and its tributaries (Hoorn et al. 2010; Ribas et al. 2012). These changes in the basin’s hydrologic structure prompted an ecological transition from permanently flooded aquatic landscapes to fluvial channels and terra firme forests (Hoorn et al. 2010;

Latrubesse et al. 2010; Rossetti et al. 2005). Despite marked changes in distributional patterns

(i.e. diffuse/standing to concentrated/flowing), water resources remained abundant throughout

Amazonia during the Pleistocene epoch, potentially tempering the effects of oscillating paleoclimatic conditions on tropical lowland species. As seen in northern Central America,

63 sediment cores and paynological analyses indicate savanna-like habitats periodically appeared across portions of the Amazon in association with decreased temperature and precipitation

(Enters et al. 2010; Hessler et al. 2010). However, it is unlikely the spread of arid habitats would have been uniform across Amazonia; the density and breadth of major rivers within the basin

[e.g. Amazon, Madiera, Negro, Purus, and Xingu (Brazil)], combined with the presence of remnant lacustrine and wetland systems, would have supported a widespread network of gallery and lake-margin forests across the central lowlands (Haffer 2008; Latrubesse et al. 2010).

Ecological assemblages located at the periphery of the Amazonian drainage system may have been more susceptible to cool-dry climatic cycles, though the surface relief introduced by the

Andean cordilleras and ancient Guiana and Brazilian Highlands would have helped maintained moist conditions, thus sustaining humid habitats in these areas (Haffer 2008). Geologic analyses suggest warm-wet periods had a similarly limited effect on the distribution of lowland forest ecosystems within the Amazon Basin during the Pleistocene; patterns of sedimentary deposits revealed no evidence for large inland water bodies with the lowland basin, thus arguing against

Nores’ (1999) proposed scenario of recent marine incursions associated with increased sea levels

(Latrubesse et al. 2010; Rossetti et al. 2005). Molecular genetic signatures observed within macao Haplo4, indicative of a large historical effective population size, reflect this relative stability of tropical lowland forests within the Amazon. For example, the accumulation of considerable sequence variation within mitochondrial haplotypes implies substantial populations of scarlet macaws persisted within the complex mosaic of tropical wet forest assemblages, even during the most extreme glacial cycles (Table 2.2). The broad distribution of closely related haplotypes and lack of hierarchical structure [i.e. mitochondrial haplotypes collapse into a polytomy upon phylogenetic analysis (Fig. 2.2)] within the Haplo4 lineage further support the

64 hypothesis that geologic and climatic perturbations were insufficient to disrupt genetic connectivity throughout the vast Amazonia lowland forests. Continued gene flow throughout the

Pleistocene, facilitated by the scarlet macaw’s volant nature and widespread distribution of suitable habitats within the Amazon Basin, would counter the effects of genetic drift and buffer against the loss of variation.

Populations of A. m. macao are also found along the Magdalena River of Colombia and

Pacific slope of lower Central America, separated from lowland Amazonia by the immense

Andean cordilleras and from each other by the Panamanian Isthmus. These trans-Andean localities share key physical attributes (e.g. geographic extent, proximity to marine systems and highland areas) distinctly different from the remaining portions of the scarlet macaw’s range, with important implications for the survival of ancestral scarlet macaw populations. First, the distribution of A. m. macao within these areas is markedly smaller than upper Central America and the Amazon Basin, highly restricted by the presence of prominent geographic barriers. In northwestern Colombia, scarlet macaws inhabit moist tropical habitats along the mid to lower

Magdalena River Valley, and associated coastal floodplain, flanked by the Central and Oriental

Cordilleras of the Northern Andes (Hilty & Brown 1986). Similarly, tropical moist ecosystems of lower Central America are confined to a narrow band bounded by the central cordilleras and

Pacific Ocean (Olson et al. 2001). Given the low elevation of preferred scarlet macaw habitats and close proximity to oceanic areas, both regions are vulnerable to coastal flooding; periodic marine incursions, prompted by warm-wet cycles during the Pleistocene as proposed by Nores

(1999), held the potential to significantly reduce the extent of humid lowland habitats within an already limited distribution. Moreover, geographical characteristics of A. m. macao’s trans-

Andean range may have evoked a more pronounced response from humid forest ecosystems

65 during cool-dry phases. Although the Pacific versant of Costa Rica and Panama, and the

Magdalena River Valley are both adjacent to areas with notable topographical variation, features hypothesized to locally mitigate effects of decreased temperature and rainfall (Haffer 2008); yet sediment cores and pollen analyses confirm the appearance of more arid habitats in Panama

(Piperno & Jones 2003). Moreover, several recent studies report extremely low level of genetic diversity for the Magdalena river turtle (Podocnemis lewyana); authors attribute this paucity to repeated historical bottlenecks associated with the decline of aquatic habitats during adverse climate cycles (Restrepo et al. 2008; Vargas-Ramírez et al. 2012), implying a simultaneous loss of humid forest ecosystems in the region. One potential explanation for the decline of lowland habitats in Central America and northwestern Colombia is that these regions are located along the leeward slope of the surrounding cordilleras; the resulting rain shadow would further reduce precipitation levels and exacerbate the spread of savanna-like vegetation (Galvis et al. 1997;

Palka 2005). While the extent of forest loss during the Pleistocene is not fully understood (Haffer

2008), potential impacts on scarlet macaw populations in lower Central America and the

Magdalena River Valley would have been twofold. First, continuous fluctuations in the distribution of tropical forest ecosystems, heavily influenced by both cool-dry and warm-wet climatic cycles, may have resulted in severe demographic instability. Moreover, it is theoretically possible coastal lowland habitats fell below the critical threshold needed to support a viable population of scarlet macaws. Interestingly, patterns of genetic variation observed within macao

Haplo7 are indicative demographic stability, a surprising result given dynamic shifts in ecological assemblages throughout the Pleistocene. Population bottlenecks are expected to reduce overall genetic variation as rare alleles are lost via genetic drift; yet trans-Andean populations exhibit high haplotype and nucleotide diversity, including numerous singleton

66 mutations (Table 2.2), suggestive of a relatively large historical effective population size.

However, if A. m. macao only recently colonized the Magdalena River Valley and lower Central

America, arriving after the most severe paleoclimatic events, ancestral populations may have avoided acute demographic declines and thus retained considerable molecular variation.

Closer examination of the entire A. m. macao mitochondrial data set provides support for

Haplo7 being a young evolutionary lineage, while creating a contextual framework to explain broader patterns of intrasubspecific diversification. Two diagnostic nucleotide substitutions differentiate trans- and cis-Andean lineages of A. m. macao, with a single fixed mutation distinguishing Central and South American populations. While highlighting the importance of geologic features in shaping patterns of intraspecific diversity in scarlet macaws, these few characters are a stark contrast to the four to eight diagnostic characters differentiating each pair of cyanoptera lineages in upper Central America, suggesting a relatively recent split between macao haplogroups. The lack of reciprocal monophyly within A. m. macao is also consistent with a nascent diversification event; phylogenetic analyses revealed Haplo4 to be paraphyletic, whereas macao haplotypes sampled in Costa Rica and Panama are nested within the broader trans-Andean clade (Fig. 2.2). Interestingly, the limited hierarchical structure observed among macao haplotypes tracks the apparent stepwise geographic expansion of the subspecies’ range, likely fueled by intense population growth. Highly significant neutrality statistics indicate a dramatic increase in effective population size for A. m. macao; Tajima’s D, Ramos-Onsins and

Rozas’ R2 and Fu’s Fs uniformly reject the null hypothesis of demographic equilibrium. As discussed above, molecular genetic data indicate ancestral macao populations remained relatively large and stable throughout the Pleistocene, especially in lowland Amazonia, as evidenced by the accumulation of substantial genetic diversity. As favorable climatic conditions

67 returned, new habitats created by the regeneration of tropical humid forests provided opportunities for growth within A. m. macao. The high percentage of singleton mutations and widespread distribution of closely related haplotypes throughout Amazonia are a genetic hallmark of this recent demographic expansion (i.e. genetic homogeneity resulting from insufficient temporal or spatial isolation). Movement away from the Amazon Basin into trans-

Andean locales may have been in response to ecological saturation of the central lowlands, where rapidly expanding populations of A. m. macao would have intensified intra- and interspecific competition for limited resources [e.g. nest cavities (Brightsmith 2005; Renton &

Brightsmith 2009)].

Interestingly, empirical patterns of sequence differentiation and general lack of hierarchical structure among mitochondrial haplotypes suggest the movement of A. m. macao across the Andes Mountains and into lower Central America was a secondary colonization event, independent from an earlier invasion by ancestral A. m. cyanoptera. Recurrent migrations out of

South America is not a novel concept; several authors have reported genetic signatures indicative of multiple invasions across the Panamanian Isthmus for a diverse array of Neotropical taxa

(Baumgarten & Williamson 2007; Culver et al. 2000; Eizirik et al. 2001; Johnson et al. 2007).

Although these studies support the general conclusion colonization of Central America was not a singular event for many species, it is important to note the specific processes influencing population histories are idiosyncratic to each individual taxon. In the case of scarlet macaws, it appears demographic instability, prompted by the continual expansion and contraction of tropical humid forests, may have led to the local extinction of ancestral cyanoptera lineages along the

Pacific versant of Costa Rica and Panama. However, this raises the question of why A. m. cyanoptera failed to reinvade lower Central America, despite geographic proximity, when

68 climatic conditions improved and humid tropical forests regenerated across the region.

Potential hypotheses include the delayed demographic recovery of A. m. cyanoptera as humid tropical forests regenerated across the region, thus favoring reinvasion by rapidly expanding A. m. macao populations. Moreover, the narrow corridor between subspecies’ distribution in northwest Costa Rica, bounded by the central cordilleras and Pacific Ocean (Fig. 2.1), may have restricted the southward movement of cyanoptera lineages. Alternatively, a small remnant population of A. m. cyanoptera may have persisted in lower Central America, despite climatic perturbations, yet succumbed to genetic swamping upon the arrival of A. m. macao. Although the current data set is insufficient to test among these hypotheses, examination of nuclear loci may provide additional insights into the timing and processes involved in generating the observed distribution of scarlet macaw haplotypes.

Although interpretation of demographic trends for macao haplogroups appears more straightforward than A. m. cyanoptera, two important factors must be considered. First, neutrality tests evaluate demographic changes based on excess of recent mutations within the nucleotide matrix. Given extensive sampling of museum specimens, the large number of singleton mutations noted in A. m. macao may reflect spurious nucleotide modifications, the result of degradative processes (e.g. hydrolytic deamination) rather than biologically meaningful variation

(Gilbert et al. 2003; Sefc et al. 2007), inflating inferences of genetic diversity and population expansion. However, care was taken during PCR amplification and sequence editing to minimize the presence of nucleotide errors. Moreover, miscoding lesions are equally likely to occur in all historical samples given the random nature of degradative attacks on DNA molecules. It is important to note cyanoptera haplotypes do not exhibit the high frequency of singleton mutations seen in A. m. macao, even though both data sets contain similar numbers of historical tissues.

These data suggest miscoding lesions are not responsible for rejection of demographic equilibrium in A. m. macao. Second, the presence of undetected fine-scale genetic structure within Haplo4 and Haplo7, coupled with less intensive sampling across the region, may exaggerate the number of unique haplotypes and misrepresent population histories across the extensive range of A. m. macao. Yet, each statistical method for evaluating extent of population expansion utilizes different information within the mtDNA sequence data set (i.e. number of singleton mutations, ratio of singletons to total segregating sites, and excess of low frequency haplotypes), thus are not equally sensitive to complexities in population discontinuities.

Therefore, documented consistency of significant results across analytical methods demonstrates strength of the genetic signal for demographic expansion in A. m. macao (Table 2.3).

Isla Coiba, Panama: Misidentified Origin or Biogeographic Anomaly?

Genetic results from one sample locality are inconsistent with broader phylogeographic patterns of diversity, warranting further consideration. Isla Coiba, the largest island in the Veragua

Archipelago, is situated 22.5 km off the Pacific coast of Panama (Fig 2.1, inset); under

Wiedenfeld’s taxonomic scheme, scarlet macaws inhabiting Isla Coiba are members of the nominate subspecies, A. m. macao. Interestingly, molecular analyses revealed four specimens collected on the island (FMNH19625, FMNH19626, FMNH19627 and USNM460654) carry mtDNA haplotypes characteristic of cyanoptera Haplo3, whereas only a single exemplar (FMNH

19628) clusters within the expected haplogroup macao Haplo7 (Fig 2.1, Fig 2.2). Introgression represents one potential explanation for the observed geographic overlap of cyanoptera and macao haplotypes; however, as discussed above, fine-scale patterns noted within the mitochondrial data set argue against hybridization between scarlet macaw subspecies.

Alternatively, it is possible aberrant Panamanian specimens have become associated with erroneous locality data; validation of specimen information is among the most common challenges of working with historical tissues (Martínkova & Searle 2006; Palkovacs et al. 2004).

Faded or torn voucher tags may result in lost data (e.g. locality, date, collector), and creation of replacement tags or transcription of hand-written records into digital format provides opportunities for introduction of typographical errors. In these situations, original collection legers may serve as an alternative resource for verifying voucher information. Of greater concern is the growing literature reporting intentional mislabeling of specimens for financial or professional gain; although presumably rare, falsified data significantly confounds research efforts (Boessenkool et al. 2010; Dalton 2005; Olson 2008).

Three anomalous study skins (FMNH19625, FMNH19626, FMNH1927) were prepared by Joseph H. Batty, a professional collector known to falsify information on a series of specimens allegedly obtained in the Veragua Archipelago, Panama during 1901-1902 (Olson

2008). Suspected motivation for this deceitful behavior lies with the primary beneficiary of this work; Walter Rothschild, a British aristocrat with a keen interest in insular species, purchased specimens collected by Batty on Isla Coiba and surrounding islands. Accessing the Veragua

Archipelago was notoriously difficult and expensive; therefore, claiming insular origin for representatives collected from more accessible mainland populations may have appeared a lucrative alternative to contending with logistical constraints and harsh field conditions. In his assessment of J. H. Batty’s Panamanian expeditions, Olson (2008) noted all specimens with questionable locality data are associated with Rothschild’s personal collection, with very few exceptions. However, rather than simply disregard Batty’s aberrant scarlet macaws skins as fraudulent data points, consideration of the final anomalous exemplar hints at a far more

71 complex scenario. The fourth Coiba specimen nested within cyanoptera Haplo3

(USNM460654) is not associated with J. H. Batty, but instead was collected by Alexander

Wetmore, a renowned ornithologist and former Secretary of the Smithsonian Institute. During his professional tenure, Wetmore dedicated considerable time and effort to document Panama’s rich avian diversity, as exemplified by his four-volume work on the topic, thus would have been well versed on the natural distributions of endemic taxa. Given this expertise, Wetmore openly challenged the origin of numerous Batty specimens, especially those claimed to be from the

Veragua Archipelago (Olson 2008). In light of Wetmore’s acclaimed work ethic and meticulous field notes, the scarlet macaw specimen collected under his auspices should be considered reliable; this exemplar also exhibits an unexpected cyanoptera haplotype, thus prompting reexamination of the study skins collected on Isla Coiba.

Closer examination of all five specimens sampled reported Isla Coiba origins (i.e. four putative cyanoptera and one putative macao) reveals evidence to suggest the unexpected detection of lineages from northern Central America may not simply reflect erroneous locality data, instead supports the intriguing hypothesis the island itself represents a biogeographic anomaly. For example, genetic data suggest aberrant specimens prepared by both Batty and

Wetmore were harvested from the same locality, a highly significant finding given the uncertainty surrounding Batty’s skins and time lapse between collecting expeditions (i.e.

Wetmore worked in the Veragua Archipelago 50 years after Batty). All cyanoptera specimens from Isla Coiba carry closely related mitochondrial haplotypes, exemplified by a shared single diagnostic nucleotide character (i.e. suggesting geographic isolation), few segregating sites, and monophyletic clustering within the Haplo3 clade (Fig. 2.2). Moreover, the presence of known

Isla Coiba endemics found within the Batty collection indicates he made at least one trip to the

72 island; therefore based on collection dates from confirmed island endemics, Olson (2008) sought to reconstruct Batty’s itinerary for the 1901-1902 expeditions in the Veragua Archipelago and create a timeline for evaluating the authenticity of specimen data. Interestingly, the only

Coiba specimen consistent with broader phylogeographic patterns (i.e. belonging to the macao

Haplo7 lineage) is among study skins with dubious collection dates, therefore there is a high probability this exemplar originated on mainland Panama. In contrast, Isla Coiba scarlet macaws within the cyanoptera Haplo3 lineage are associated with more reliable dates (i.e. higher likelihood Batty was in the archipelago); this observation is concordant with the Coiba origin for

Wetmore’s cyanoptera specimen and further confirms a common geographic source for these aberrant samples. It is also worth mentioning a genetic discord between insular and continental populations in lower Central America may not be unique to scarlet macaws; during a taxonomic assessment of howler monkeys (Genus: Alouatta), Cortes-Ortiz et al. (2003) found individuals sampled on Isla Coiba exhibited greater genetic affinity to populations in Mexico and northern

Costa Rica than individuals on the adjacent mainland of Panama. Acknowledging the small sample size for the Alouatta study and present work, these data raise an interesting series of questions regarding the timing, source and mechanisms responsible for colonization of Isla

Coiba by Neotropical megafauna.

Further investigation of contemporary genetic samples is needed to elucidate the geographic distribution of A. m. cyanoptera in lower Central America and clarify the evolutionary relationship of scarlet macaws inhabiting Isla Coiba within broader patterns of intraspecific diversity. Analysis of mitochondrial haplotypes would reveal whether cyanoptera lineages are present within the extant insular population; if confirmed, these data would also provide valuable insights into whether A. m. cyanoptera is restricted to Isla Coiba, highlighting

73 potentially underappreciated complexities to the island’s biogeographic history (e.g. Does this population represent a unique refuge of ancestral A. m. cyanoptera in lower Central America?), or that the perceived absence of cyanoptera lineages along the Pacific slope of Costa Rica and

Panama is simply an artifact of low haplotype frequencies and/or limited sampling. In tandem, multilocus microsatellite genotypes would create a more robust framework to infer fine-scale patterns of population substructure and extent of in situ introgression in lower Central America.

Conservation Implications

In addition to providing further insights into Neotropical biogeography, recognition of the convoluted evolutionary history of scarlet macaws has significant implications for population management. Analysis of mitochondrial sequences did not prompt major taxonomic revision, but confirmed presence of two distinct phylogeographic units generally concordant with current subspecific designations. These data also failed to document evidence of a putative hybrid zone, advocating independent management efforts for each subspecies. In addition, complexities in the demographic history of scarlet macaw populations highlight the need for more detailed evaluation of intraspecific genetic variation when identifying prioritizing conservation targets.

Adding a sense of urgency to scarlet macaw conservation in Central America is the presence of cryptic genetic variation and importance of these populations to overall species’ diversity. For example, A. m. cyanoptera is endemic to the region and represents five of seven unique mitochondrial haplogroups identified within the species. Furthermore, detection of limited gene flow across the Panamanian Isthmus also warrants heightened conservation attention for A. m. macao in Costa Rica and Panama, despite taxonomic links to conspecifics in

South America. Scarlet macaw populations are under severe threat throughout Central America

74 due to the high incidence of poaching and large-scale conversion of lowland forest habitats to agricultural lands (Forshaw 2006). As of the 1960s, the species was already extirpated from El

Salvador; several other populations are currently on the verge of collapse (Forshaw 2006;

Wiedenfeld 1994). Given the haplotypic data presented here reflect the historical status of intraspecific diversity (i.e. 76% of sampled exemplars were museum skins collected 50-142 years ago), additional molecular studies are needed to assess the genetic health of contemporary populations. Moreover, without enhanced conservation management efforts to counter on-going demographic declines, Central American populations of scarlet macaws are at risk for the local or global extinction of haplotypes and/or evolutionary distinct haplogroups.

Examination of a recent conservation program in Costa Rica provides a case study underscoring the significance of phylogeographic data to local population management efforts.

Two extant remnant populations of wild scarlet macaws remain along the Pacific slope of Costa

Rica, within the Carara and Corcovado National Parks. To re-establish scarlet macaws at two additional sites, a series of captive releases were conducted between 1999 and 2001 using hand- reared chicks breed at two local institutions (Brightsmith et al. 2005). At the time of program development, it was not known the historical distributions of both subspecies’ extended within the political boundaries of Costa Rica; therefore, local managers did not investigate the taxonomic status of breeding pairs prior to implementation. Similarities in plumage coloration and size, due to the close geographic proximity of founding populations along the morphological cline, likely gave no reason to suspect multiple distinct evolutionary lineages may be present within the captive population. However, the present study failed to detect substantive evidence for in situ hybridization based on mtDNA haplotypes; if nuclear analyses confirm genetic segregation of A. m. cyanoptera and A. m. macao, the undocumented introgression among

75 distinct subspecific lineages within captive source populations and potential mixed ancestry of release candidates generates questions over the conservation significance of re-establishment efforts. Of additional concern are the genetic integrity and legal protection status of native scarlet macaws in Costa Rica, representatives of the genetically distinct macao Haplo7, given the close proximity of release sites to remnant populations and the potential for interbreeding between captive-reared (i.e. putative subspecific hybrids) and wild macaws.

The demonstrated utility of a comprehensive phylogeographic assessment to inform scarlet macaw conservation management programs has implications for other Neotropical species with large geographic distributions. In the coming decades, a growing number of species, especially in Central America, will likely need direct population management to ameliorate anthropogenic threats. However, several recent phylogeographic studies of wide-ranging lowland

Neotropical taxa have focused primarily on understanding patterns of diversification across the

Amazon basin, including comparatively few Mesoamerican exemplars with poor geographic representation of the subcontinent (Eberhard & Bermingham 2005; Haas et al. 2009; Hynkova et al. 2009; Lerner et al. 2009). Ironically, subsequent analyses reveal genetic homogeneity across

South America, while the complexity and distinctiveness of Central American populations remain unresolved due to inadequate sampling. Future studies should give special consideration to Mesoamerican locales to further elucidate comparative biogeographic patterns, providing an important contextual framework for the development of conservation initiatives throughout the region.

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Table 2.1: Primers for PCR amplification and sequencing

Gene Size (bp) Primer Sequence Reference 12S 336 12SL1735 GGATTAGATACCCCACTATGC Miyaki et al. (1998) 12SH2170 AGGGTGACGGGCGGTATGTACG Miyaki et al. (1998) Hist-12SR1 TGGGCTGTGGGCTGTTGGGCTCA This study Hist-12SF2 TGCCAAAACAGCCTATATACCG This study Hist-12SR2 ACCGGCTTAAAGGAGGCTTGGTTA This study 16S 519 16SL2702 CCTACCGAGCTGGGTGATAGCTGGTT Miyaki et al. (1998) 16SH3309 TGCGCTACCTTCGCACGGT Miyaki et al. (1998) Hist-16SR1 TTTGGGGGTGGCTGCTTTAAGG This study Hist-16SF2 CAACCCAAATGTGATAGTTAAGGG This study Hist-16SR2 CTAGCATTGATTCATCTATTGTTATAGG This study Hist-16SF3 CAAAGATTGCGTCAAAGCTCC This study Hist-16SR3 TTGACAGGGTGGGTTCAATGG This study Hist-16SF4 CCAGCCTGTATACCATTAACAG This study COI 589 COIA7988 AGTATAAGCGTCTGGGTAGTC Palumbi (1996) COIF7308 CCTGCAGGAGGAGGAGAYCC Palumbi (1996) Hist-COIF1 GAGTATCGTCGTGGTATGCCTGC This study Hist-COIR1 AACTCCTCACTAGACATTGCC This study Hist-COIF2 GACAGGACATAGTGGAAGTGTGC This study Hist-COIR2 GGAGGGACTATCAAATGAGACC This study Hist-COIF3 GATGGTGAATAGGAAGATGAATCC This study Hist-COIR3 GGTATGAGCAATACTATCAATCG This study Hist-COIF4 GGTGTCTACGTCTATTCCTACC This study cytb 728 CBL14957 AAAAAGCTTCCATCCAACATCTCAGCATGATGAAA Kocher et al. (1989) CBH15331 AAACTGCAGCCCCTCAGAATGATATTTGTCCTCA Kocher et al. (1989) CBL15298 TGAGGCCAAATATCATTCTGAGGGGC Cheng et al. (1994) CBL15764 CCTCCTAGTTTGTTGGGGATTGA Miyaki et al. (1998) Hist-cytbR1 CTGTGACAAAATCCCATTCCACCC This study Hist-cytbF2 TATGATTGTGAATCCTAGCAGG This study Hist-cytbR2 GATTCTCTGTAGACAACCCCACC This study Hist-cytbF3 GTAGGAAGGTTAAGTGGATGAG This study Hist-cytbF4 GATATAGGGGATGGCAGAGAATAG This study Hist-cytbR4 CTGTATCTACCTTCATATCGC This study Hist-cytbF5 AGGATGATACCTGTGTTTCAGG This study Hist-cytbR5 TCCTAGCTGCCCATTACACTGC This study Hist-cytbF6 GGAGGTTTCGAATTAGTCAACC This study

Figure 2.1: Map showing sampling effort and geographic distribution of seven haplogroups detected across Central and South America. Each dot represents a single specimen, with the exception of Laguna del Tigre National Park, Guatemala (n = 11); Chiquibul Forest Reserve, Belize (n = 6); Isla Coiba, Panama (n = 5); and Tambopata National Reserve, Peru (n = 10). Inset: Close-up of subspecific boundary. Arrow indicates the location of Isla Coiba, Panama. Grey: major areas ≥ 500 m in elevation.

Figure 2.2: A) Maximum Likelihood; B) Bayesian Inference. Colored circles designate bootstrap support and posterior probabilities. White: >60/0.60; Grey: >80/0.80; Black: >95/0.95. Aberrant haplotypes from Isla Coiba, Panama are shown in bold. Color scheme is consistent with haplogroup designations given in Fig. 2.1.

Table 2.2: Genetic variation within scarlet macaw subspecies and haplogroups.

-2 Subspecies Length (bp) n h Hd π x10 S Singletons a. A. m. cyanoptera 2016 39 21 0.95 (0.020) 0.314 (0.023) 35 10 Haplo1 2172 7 4 0.86 (0.102) 0.114 (0.022) 5 0 Haplo2 2016 21 9 0.85 (0.055) 0.087 (0.012) 10 7 Haplo3 2172 6 5 0.93 (0.122) 0.098 (0.016) 5 3 Haplo5 2172 3 2 0.67 (0.314) 0.123 (0.058) 4 4 Haplo6 2150 2 1 - - - -

b. A. m. macao 2066 47 36 0.98 (0.010) 0.315 (0.022) 64 38 Haplo4 2121 37 28 0.98 (0.014) 0.257 (0.030) 55 33 Haplo7 2101 10 8 0.93 (0.077) 0.123 (0.023) 10 7 h, number of haplotypes

Hd, haplotype diversity (SD) π, nucleotide diversity (SD) S, number of segregating sites

Table 2.3: Indicators of demographic change in scarlet macaw subspecies. Statistically significant values highlighted in bold.

Subspecies n Length (bp) D Fs R2 A. m. cyanoptera 39 2016 -0.825 -5.911* 0.085

A. m. macao 47 2066 -1.942** -27.57** 0.043** D, Tajima 1989

Fs, Fu 1997

R2, Ramos-Onsins and Rozas 2002 *p<0.05, **p<0.01

Appendix I: List of specimens included in the present study. Reference haplotypes for phylogenetic analyses highlighted in bold.

Local ID Contributing Institution Specimen Code Country Date Sample Type Reference a. Scarlet macaw (Ara macao) 255120 American Museum of Natural History aEcua1 Ecuador 1926 toe pad Peru4 279026 American Museum of Natural History aBraz2 Brazil 1930 toe pad 121459 American Museum of Natural History aColo3 Colombia 1913 toe pad 474212 American Museum of Natural History aCoRi4 Costa Rica 1891 toe pad 255121 American Museum of Natural History aEcua5 Ecuador 1925 toe pad 255122 American Museum of Natural History aEcua6 Ecuador 1926 toe pad 255124 American Museum of Natural History aEcua7 Ecuador 1926 toe pad 102717 American Museum of Natural History aNica8 Nicaragua 1908 toe pad 143776 American Museum of Natural History aNica9 Nicaragua 1917 toe pad 474211 American Museum of Natural History aPana10 Panama 1903 toe pad usCoRi4 474199 American Museum of Natural History aSuri11 Suriname 1905 toe pad 474200 American Museum of Natural History aSuri12 Suriname 1905 toe pad 474201 American Museum of Natural History aVene13 Venezuela 1898 toe pad 474202 American Museum of Natural History aVene14 Venezuela 1901 toe pad fSuri16 474203 American Museum of Natural History aVene15 Venezuela 1900 toe pad 474197 American Museum of Natural History aBraz16 Brazil 1906 toe pad Peru4 474198 American Museum of Natural History aBraz17 Brazil 1907 toe pad 429106 American Museum of Natural History aBraz18 Brazil 1931 toe pad 429108 American Museum of Natural History aBraz19 Brazil 1931 toe pad 278557 American Museum of Natural History aBraz20 Brazil 1930 toe pad 278559 American Museum of Natural History aBraz21 Brazil 1930 toe pad 278560 American Museum of Natural History aBraz22 Brazil 1930 toe pad 248513 Field Museum of Natural History fColo1 Colombia 1957 toe pad 248514 Field Museum of Natural History fColo2 Colombia 1957 toe pad 248515 Field Museum of Natural History fColo3 Colombia 1957 toe pad fColo2 248516 Field Museum of Natural History fColo4 Colombia 1957 toe pad fColo1 19683 Field Museum of Natural History fGuat5 Guatemala 1905 toe pad Guat4 19624 Field Museum of Natural History fGuya9 Guyana unk toe pad aSuri11 21867 Field Museum of Natural History fNica10 Nicaragua 1905 toe pad 19625 Field Museum of Natural History fPana11 Panama 1901 toe pad 19626 Field Museum of Natural History fPana12 Panama 1901 toe pad 19627 Field Museum of Natural History fPana13 Panama 1901 toe pad fPana11 19628 Field Museum of Natural History fPana14 Panama 1901 toe pad 187757 Field Museum of Natural History fPeru15 Peru 1947 toe pad 260146 Field Museum of Natural History fSuri16 Suriname 1960 toe pad 541324 National Museum of Natural History usBraz1 Brazil 1986 toe pad 410647 National Museum of Natural History usColo2 Colombia 1949 toe pad 410648 National Museum of Natural History usColo3 Colombia 1949 toe pad 198767 National Museum of Natural History usCoRi4 Costa Rica 1905 toe pad 361451 National Museum of Natural History usCoRi5 Costa Rica 1940 toe pad 112231 National Museum of Natural History usHond7 Honduras 1887 toe pad 112232 National Museum of Natural History usHond8 Honduras 1887 toe pad 237572 National Museum of Natural History usHond9 Honduras 1901 toe pad usHond8

57821 National Museum of Natural History usMex10 Mexico 1868 toe pad wMex2 040964 National Museum of Natural History usNica11 Nicaragua unk toe pad 460654 National Museum of Natural History usPana12 Panama 1956 toe pad 55921 Yale Peabody Museum yCoRi1 Costa Rica 1925 toe pad aNica9 55922 Yale Peabody Museum yCoRi2 Costa Rica 1925 toe pad 55923 Yale Peabody Museum yCoRi3 Costa Rica 1925 toe pad usHond8 55924 Yale Peabody Museum yCoRi4 Costa Rica 1926 toe pad 81420 Yale Peabody Museum yPeru5 Peru 1958 toe pad 81421 Yale Peabody Museum yPeru6 Peru 1958 toe pad 15669 UCLA-Dickey Collection ucES1 El Salvador 1925 toe pad 15670 UCLA-Dickey Collection ucES2 El Salvador 1925 toe pad usHond8 118782 Museum of Comparative Zoology mCoRi2 Costa Rica 1906 toe pad 137803 Museum of Comparative Zoology mEcua3 Ecuador 1925 toe pad Peru4 136567 Museum of Comparative Zoology mHond5 Honduras 1928 toe pad 72226 Museum of Comparative Zoology mMex6 Mexico 1868 toe pad 272843 Museum of Comparative Zoology mMex7 Mexico unk toe pad usHond8 107748 Museum of Comparative Zoology mPana8 Panama 1900 toe pad 109102 Museum of Comparative Zoology mPana9 Panama 1901 toe pad usCoRi4 143341 Museum of Comparative Zoology mSuri10 Suriname 1914 toe pad 145084 Academy of Natural Sciences apColo1 Colombia unk toe pad 75201 Academy of Natural Sciences apNica2 Nicaragua unk toe pad 75202 Academy of Natural Sciences apNica3 Nicaragua unk toe pad mHond5 187617 Academy of Natural Sciences apGuya4 Guyana 1996 tissue 20535 Louisiana State University lBelz1 Belize unk toe pad wMex2 29047 Louisiana State University lHond2 Honduras unk toe pad ucES1 31261 Louisiana State University lPeru3 Peru unk toe pad 31262 Louisiana State University lPeru4 Peru unk toe pad 34005 Louisiana State University lPeru5 Peru unk toe pad Peru4 114610 Louisiana State University lPeru6 Peru unk toe pad 114611 Louisiana State University lPeru7 Peru unk toe pad 80 Western Foundation for Vert. Zoology wMex2 Mexico unk toe pad 5056 Western Foundation for Vert. Zoology wMex3 Mexico unk toe pad 12837 Western Foundation for Vert. Zoology wMex4 Mexico unk toe pad 25001 California Academy of Science cMex1 Mexico 1905 toe pad wMex3 26056 California Academy of Science cMex2 Mexico 1905 toe pad wMex2 26057 California Academy of Science cMex3 Mexico 1905 toe pad wMex2 33543 California Academy of Science cHond4 Honduras 1930 toe pad usNica11 132210 University of Michigan Museum of Zoology umCoRi2 Costa Rica 1951 toe pad 135156 University of Michigan Museum of Zoology umSuri3 Suriname 1952 toe pad mSuri10 MAM27 Wildlife Conservation Society Guat1 Guatemala 2004 blood MAM30 Wildlife Conservation Society Guat4 Guatemala 2004 blood MAM31 Wildlife Conservation Society Guat5 Guatemala 2004 blood MAM33 Wildlife Conservation Society Guat7 Guatemala 2004 blood Guat1 MAM34 Wildlife Conservation Society Guat8 Guatemala 2004 blood MAM36 Wildlife Conservation Society Guat10 Guatemala 2004 blood Guat8 MAM37 Wildlife Conservation Society Guat11 Guatemala 2004 blood Guat5 MAM38 Wildlife Conservation Society Guat12 Guatemala 2004 blood MBR32 Wildlife Conservation Society Guat32 Guatemala 2007 feather Guat4 MBR33 Wildlife Conservation Society Guat33 Guatemala 2007 feather

MBR50 Wildlife Conservation Society Guat50 Guatemala 2009 feather wMex3 CBL1 Friends of Conservation and Development Belz1 Belize 2008 feather Guat4 CBL2 Friends of Conservation and Development Belz2 Belize 2008 feather Belz1 CBL3 Friends of Conservation and Development Belz3 Belize 2008 feather CBL4 Friends of Conservation and Development Belz4 Belize 2008 feather Belz6 CBL6 Friends of Conservation and Development Belz6 Belize 2008 feather T271 Uni. of Idaho/ Tambopata Research Center Peru1 Peru unk DNA extract T274 Uni. of Idaho/ Tambopata Research Center Peru2 Peru unk DNA extract Peru4 T277 Uni. of Idaho/ Tambopata Research Center Peru3 Peru unk DNA extract T281 Uni. of Idaho/ Tambopata Research Center Peru4 Peru unk DNA extract T300 Uni. of Idaho/ Tambopata Research Center Peru5 Peru unk DNA extract T303 Uni. of Idaho/ Tambopata Research Center Peru6 Peru unk DNA extract T332 Uni. of Idaho/ Tambopata Research Center Peru7 Peru unk DNA extract Peru4 T333 Uni. of Idaho/ Tambopata Research Center Peru8 Peru unk DNA extract T344 Uni. of Idaho/ Tambopata Research Center Peru9 Peru unk DNA extract T347 Uni. of Idaho/ Tambopata Research Center Peru10 Peru unk DNA extract Peru4

b. Blue-and-gold macaw (Ara ararauna) Aara1 American Museum of Natural History Aara Captive 2007 feather

c. Green-wing macaw (Ara chloroptera) 11002 American Museum of Natural History Achl Captive 2007 tissue

CHAPTER 3:

POPULATION DYNAMICS, GENETIC DIVERSITY AND DEMOGRAPHY OF SCARLET MACAWS (ARA

MACAO CYANOPTERA) IN LA SELVA MAYA, CENTRAL AMERICA

ABSTRACT

Molecular genetics plays a vital role in wildlife management via characterization of key biological parameters, thus creating an empirical framework to guide monitoring and mitigation efforts. Scarlet macaw (Ara macao cyanoptera) populations throughout Central America have experienced marked demographic declines due to intense anthropogenic pressures. La Selva

Maya (LSM), a tri-national system of protected areas in Mexico, Guatemala and Belize, represents the subspecies’ last stronghold, where the scope of human-mediated threats and local conservation programs differ across the region. The present study employed a hierarchical analysis of mtDNA haplotypes and nuclear genotypes to elucidate aspects of scarlet macaw biology to enhance ongoing conservation efforts. Population genetic analyses of sequence variation revealed breeding sites across LSM belong to a historically panmictic population, distinct from nesting areas in the southern portion of the subspecies’ range. In contrast, patterns indicative of restricted gene flow were found between contemporary sampling localities in

Guatemala and Belize. Diversity indices exhibited substantial genetic variation within both temporal samples, despite a distinct shift in haplotype frequencies. Moreover, pairwise relatedness estimates suggested high annual turnover of reproductive pairs at local nest sites in

Guatemala. These results underscore the importance of current management programs, while also advocating expansion of in situ activities (i.e. habitat protection, anti-poaching patrols, veterinary and husbandry efforts) and re-enforcement of trans-national collaborations in LSM.

INTRODUCTION

Molecular genetic techniques have proven invaluable for assisting wildlife management by unraveling the complexities of natural populations and impacts of recent human-mediated disturbances, thus providing a contextual framework to identify and evaluate the current status of conservation targets, design mitigation strategies, and assess program outcomes (Bourke et al.

2010; de Barba et al. 2010a). Demographically independent populations are the logical units for direct monitoring and management by conservation practitioners (Efremov 2007; Palsbøll et al.

2007), emphasizing a need for accurate delineation of spatial genetic structure. However, contemporary estimates of inter- and intrapopulation dynamics for endangered or threatened taxa may be influenced by demographic declines and fragmentation events; shifts in allele frequency distributions may erroneously lead to maintenance of separate stocks (Cozzolino et al. 2007;

Martínez-Cruz et al. 2007), hindering recovery efforts via misallocation of limited resources.

Fortunately historical tissues from museums and other natural history collections provide a unique opportunity for “retrospective monitoring” (Schwartz et al. 2007), establishing meaningful baseline values to empirically test for the recent appearance of spatial genetic heterogeneity (Martínez-Cruz et al. 2007; Nyström et al. 2006; Tracy & Jamieson 2011).

Moreover, it is important to document changes in effective population size, driven by direct exploitation and habitat fragmentation, given small isolated remnant populations exhibit greater susceptibility to inbreeding and extinction via stochastic (e.g. genetic, demographic, environmental, catastrophic) events (Reed 2004). Furthermore, temporal analyses are pivotal for determining whether contemporary indices of genomic variation deviate significantly from historical equilibrium values; populations depauperate of genetic diversity may experience lower

95 survival and fecundity rates and decreased evolutionary potential (Charlesworth & Willis

2009; Hedrick & Kalinowski 2000; Keller & Waller 2002).

Application of molecular genetic approaches extends beyond describing population-level phenomena; individual-based analyses employing unique multi-locus genetic IDs provide additional insights into critical life history traits, creating a platform to interpret broader population dynamics and optimize management actions (Bourke et al. 2010; da Silva et al. 2010;

Schwartz et al. 2007). For example, documenting how exemplars move across landscapes on fine geographic scales allows conservation practitioners to implement mitigation strategies to reinforce natural dispersal patterns (Martínez-Cruz et al. 2007; Miller & Waits 2003; Nyström et al. 2006). Moreover, statistical estimates of in situ population pedigrees are invaluable for management efforts via elucidation of social structure, mating systems, recruitment rates, and extent of inbreeding (Amavet et al. 2012; Becker et al. 2012; de Barba et al. 2010a; Jones &

Wang 2010; Miño et al. 2011; Rudnick et al. 2009); demographic and social dynamics are exceedingly important for human-mediated wildlife relocation programs by refining release protocols to facilitate integration of introduced individuals within existing population networks

(IUCN 1998).

As stated above, inferences of population structure, genomic diversity, demography, and behavior ascertained from hierarchical analysis of molecular genetic information provide an empirical framework to guide programmatic development; however, potential gaps between the scope of conservation needs and actions pose a significant challenge for wildlife management.

As an illustration, effective global protection of endangered populations within widespread taxa may require coordinated efforts across political boundaries (Castro et al. 2007; Lorenzen et al.

2006; Norén et al. 2011). In contrast, direct monitoring and mitigation initiatives (e.g. habitat

96 protection, anti-poaching, anti-predation, population augmentation) are generally implemented within a limited geographic area by local management authorities (Sodhi et al. 2011), and constrained by socioeconomic pressures, enforcement of national environmental policies, and availability of funding and personnel (Louette et al. 2011). Differential recovery rates among population remnants, driven by regional inconsistencies in conservation management, may introduce harmful source/sink dynamics [i.e. individuals are siphoned away from well-managed areas to sites where anthropogenic perturbations remain unabated (Andreasen et al. 2012)], obscuring demographic trends and threatening overall viability. Locally reported conservation successes (e.g. increased survival and recruitment), while encouraging, may be insufficient to sustain broader demographic equilibrium, resulting in further, albeit more gradual, reductions of effective population size. This potential for complex interactions between regional connectivity and variance in local human-mediated threats reiterates the importance of characterizing natural population dynamics and evaluating the efficacy of local management programs within the context of overall conservation needs.

Study System

Scarlet macaws (Ara macao) are among the most colorful and charismatic of all Neotropical psittacines. While demonstrating a strong preference for lower elevations (i.e. > 500 meters), the species occupies a broad range of habitat types (e.g. deciduous and humid forests, open and gallery woodlands), and undergoes seasonal movements in response to staggered phenological events (Karubian et al. 2005; Renton 2002; Rodas 2002). Although longevity allows for an extended reproductive lifespan [i.e. approximately 5 to 25 years of age (Brouwer et al. 2000;

Clubb 1992)], scarlet macaws exhibit relatively low reproductive rates. Field observations reveal

97 an average of one or two fledglings per nesting attempt (Iñigo-Elías 1996; Nycander et al.

1995; Renton & Brightsmith 2009; Vaughan et al. 2003) and infer 20% of the population breeds in any given year (Brightsmith 2005; Munn 1992; Wright et al. 2001). As with many psittacine species, A. macao is sexually monomorphic (Forshaw 2006), with all age classes (e.g. immature, reproductively active, senescent) sharing similar phenotypic characters post-fledging, hindering unambiguous in situ identification of individual scarlet macaws (Hilburn & Higgins 2000).

Populations are under threat throughout the species’ range, the product of direct and indirect exploitation pressures driven by local socioeconomic factors and poor governance of environmental policies. The colorful and charismatic nature of scarlet macaws has fostered a long history of cultural and economic significance, prompting extensive trade networks within and beyond range countries. Pre-Columbian societies harvested scarlet macaws for religious and other ceremonial purposes (Creel & McCusick 1994; Minnis et al. 1993; Somerville et al. 2010), whereas contemporary poaching efforts target nestlings to satiate market demands for exotic pets

(Cantú-Guzmán et al. 2007; Thomsen & Mulliken 1992; Wright et al. 2001). Degradation of breeding areas caused by secondary effects of commercial exploitation (e.g. destruction of nesting trees to facilitate chick extraction) further reduce long-term reproductive potential for these obligate cavity nesters (Britt 2011). Moreover, large swaths of lowland forests are continuously cleared throughout the Neotropics, with reported deforestation rates of 1.2% and

0.5% reported for Central and South America, respectively (FAO 2005); critical scarlet macaw breeding and foraging areas are converted primarily to meet the infrastructural (e.g. settlements, transport) and agricultural (e.g. cultivation, ranching) needs of local communities (Geist &

Lambin 2002). Demographic declines and extirpation events are most pronounced in Central

America (Forshaw 2006; Wiedenfeld 1994), where human population densities are on average

4.8 fold greater than South America (CEPAL 2010), thus supporting designation of the endemic subspecies, A. m. cyanoptera, as a taxon of conservation concern.

La Selva Maya (LSM), a tri-national system of protected areas in Mexico, Guatemala and

Belize, constitutes an important stronghold for the subspecies, yet this region has also experienced large-scale deforestation, the result of government sponsored development programs in the 1970s (Iñigo-Elías 1996; Renton 2000). Extensive surveys of existing habitat have identified three clusters of nest sites in LSM, each characterized by a unique set of physical and ecological features, and anthropogenic threats. 1) Maya Biosphere Reserve (MBR), Guatemala extends across 2.11 million hectares closed canopy tropical evergreen and seasonal broadleaf forests, interspersed by a widespread system of lakes and lagoons (Vreugdenhil et al. 2002).

Although nest poaching was historically prevalent, land conversion represents the greatest active threat to A. m. cyanoptera in these protected areas (Garcia et al. 2008); the local human population experienced a 30-fold expansion between 1960 and 2006 (McNab & Ramos 2006), with areas immediately adjacent to scarlet macaw nest sites supporting permanent settlements of approximately 18,000 people (Ramos et al. 2001). 2) Chiquibul Forest Reserve (CFR), Belize is nested within the central Maya Mountains, covering 0.167 million hectares of tropical evergreen and seasonal broadleaf forests (Penn et al. 2004). With exception of the Chalillo Dam, flooding

953 acres of scarlet macaw breeding habitats along the confluence of the Macal and Raspaculo

Rivers (Reid et al. 2000), these remote and relatively inaccessible lowland ecosystems remain largely intact; however high commercial exploitation limits recruitment rates in the CFR, with reports of poachers removing scarlet macaw chicks from 45% of monitored nest cavities (Britt

2011). 3) Usumacinta River drainage basin (URDB) consists of 0.5 million hectares of tropical lowland forests (Carreón-Arroyo 2006), divided among La Selva Lacandona, Mexico and

99 adjacent Sierra del Lacandon, Guatemala. High local human population growth [e.g. 5.7% within La Selva Lacandona (INE 2000)], place tremendous strain on surrounding ecosystems, where 60% of non-protected forests have been cleared for infrastructural and agricultural purposes (CI 2002); moreover, an extensive market for wild psittacines within Mexico has sustained commercial exploitation pressures, with estimates of approximately 50 to 60 scarlet macaw chicks handled annually by Mexican poachers and traders (Cantú-Guzmán et al. 2007).

The efficacy of conservation management programs, and ultimately the long-term viability of scarlet macaws in LSM, depends heavily on interactions between local anthropogenic threats and underlying population dynamics (i.e. gene flow, demography, and behavior). For example, the extent of regional genetic substructure is poorly understood; confirmed reports of individuals moving between the MBR and URDB in response to phenological shifts provide a plausible behavioral mechanism to facilitate regional genetic exchange (Carreón-Arroyo 2006;

Iñigo-Elías et al. 2001; Rodas et al. 2001), yet the presence of both resident and migratory groups raises the possibility of natal philopatry driving local population differentiation (Rodas

2002). If A. m. cyanoptera ranging throughout LSM comprise a naturally panmictic population, recently deforested areas (e.g. central Guatemala) may have served as critical migration corridors; subsequent genetic isolation of breeding areas would reduce the overall effective population size and increase extinction risk for population remnants across Mexico, Guatemala and Belize (Athrey et al. 2011; England et al. 2010; Martínez-Cruz et al. 2007). Commercial exploitation of scarlet macaw chicks may further contribute to demographic disequilibrium, heightening concerns for inbreeding, loss of genetic diversity, and diminished evolutionary potential in LSM (Groombridge et al. 2000; Keller & Waller 2002; Saccheri et al. 1998). In addition, differential reproductive success among nest sites, reflecting regional variance in the

100 intensity of anthropogenic threats, may have unexpected consequences for local conservation outcomes (Andreasen et al. 2012).

The present study examined molecular genetic data within a hierarchical framework to characterize population dynamics and molecular ecology of scarlet macaws in LSM, Central

America. Mitochondrial sequences were employed to infer the extent of gene flow throughout the historical geographic distribution of A. m. cyanoptera, temporal comparisons of regional substructure across sampling localities in Mexico, Guatemala and Belize, and contemporary genetic connectivity between local nest sites in the MBR, Guatemala. Control region haplotypes also served as a proxy to quantify extant molecular variation and test for recent genetic bottlenecks in LSM. Individual-based analytical methodologies applied to both mitochondrial and nuclear data sets assists efforts to monitor movement patterns, annual turnover in reproductive pairs, and prevalence of inbreeding in Guatemala. Moreover, integration of spatial and temporal analyses provides valuable insights for regional conservation management efforts.

MATERIALS AND METHODS

Sample Collection and DNA Extraction

Toe pads from museum specimens (n = 39), collected between 1868 and 1956, were used to examine the amount and distribution of genetic diversity across the historical range of A. m. cyanoptera (Figure 3.1, Appendix II). The current genetic status of scarlet macaws ranging in La

Selva Maya (LSM) was assessed from samples collected at four key nest sites in the Maya

Biosphere Reserve (MBR), Guatemala (n = 61) and one in the Chiquibul National Forest

Reserve (CFR), Belize (n = 13) between 2004 and 2009 (Figure 3.2). Contemporary samples

101 consisted of blood or plucked feathers from scarlet macaw nestlings and molted feathers acquired opportunistically within, or on the ground beneath, nest cavities. All modern samples were acquired in accordance to local, national and international regulations.

Total genomic DNA was extracted from blood samples using DNeasy tissue extraction kits (QIAGEN Inc.), as per manufacturer protocol. Modifications were made to optimize DNA yield from feather and toe pad samples, consisting of a 48 h digestion, pre-heating elution buffer to 70ºC, reduced elution volume, and 30 min incubation of elution buffer on spin columns prior to centrifugation. Whole pieces of toe pad were subject to a bleach bath (10% bleach for 5 min) and three subsequent water washes before proceeding with DNA extraction as described above.

Mitochondrial DNA sequencing

An 850 base pair segment of the mitochondrial control region was amplified and sequenced from contemporary samples as two fragments, using primer pairs CR380/AmCR3R and

AmCR3F/AmCR5R. In the case of DNA extracts from highly degraded modern samples or museum specimens, mtDNA sequences were generated with a set of five short overlapping amplicons, each fragment not exceeding 215 base pairs in length (Table 3.1). Internal primers were designed using OligoAnalyzer 3.1 (Integrated DNA Technologies). All polymerase chain reactions (PCR) were performed on epGradient S Mastercycler thermocyclers (Eppendorf).

Control region fragments were amplified from modern tissue in a total reaction volume of 15 µl and included: ~20-50 ng DNA, 10 mM Tris-HCl (pH 9.0), 50 mM KCl, 1.5 mM MgCl2, 200 µM dNTPs, 0.65 µM of each primer, and 0.5 U of Taq DNA polymerase (Fisher Scientific).

Thermocycler conditions were as described in Tavares (2004), with an annealing temperature of

50ºC. Total volume of 25 µl was used for amplifications from historical tissues, reactions

102 included: ~20-50 ng DNA, 10 mM Tris-HCl (pH 8.3), 50 mM KCl, 1.5 mM MgCl2, 10 µg bovine serum albumen, 200 µM dNTPs, 0.6 µM of each primer, and 0.5 U of AmpliTaq Gold

DNA polymerase (Applied Biosystems). PCR of short fragments were carried out under the following conditions: 95ºC (10 min), 35 cycles of 95ºC (30 sec), 50ºC (30 sec), 72ºC (45 sec), and a final extension of 72ºC (7 min). All gene regions were sequenced in both directions using

BigDye 3.1 chemistry (PerkinElmer) on an ABI 3730xl Sequencer (Applied Biosystems).

Sequences were visualized, edited and aligned with Sequencher 4.8 (Gene Codes Corp.).

Microsatellite genotyping

Genotypic data were collected at 11 microsatellite loci originally characterized in the St. Vincent

Amazon parrot (Amazona guildingii; AgGT21, AgGT17, AgGT19, AgGT32, AgGT42,

AgGT90) (Russello et al. 2001; Russello et al. 2005) and the blue-and-gold macaw (Ara ararauna; UnaCT43, UnaCT74, UnaCT32, UnaCT21, UnaCT41) (Caparroz et al. 2003), but redesigned to reduce amplicons size and minimize genotyping errors (Gebhardt & Waits 2008).

All forward primers were 5’-labeled with one of four fluorescent dyes (6-FAM, VIC, NED, PET) to facilitate automated genotyping. Multi-locus multiplex reactions using individually labeled primers, as outlined in Gebhardt and Waits (2008), were run on modern samples in 15 µl total volumes and included: ~20-50 ng DNA, 10 mM Tris-HCl (pH 8.3), 50 mM KCl, 1.5 mM MgCl2,

10 µg bovine serum albumen, 200 µM dNTPs, Panel A: 0.8 µM of each UnaCT21 and UnaCT74 primer, 0.3 µM of each UnaCT32 primer, and 0.4 µM of each UnaCT43 primer; Panel B: 0.3 µM of each AgGT21 and AgGT17 primer, 0.6 µM of each UnaCT41 primer, and 0.8 µM of each

AgGT90 primer; Panel C: 0.3 µM of each AgGT19 primer, 0.5 µM of each AgGT32 primer and

0.8 µM of each AgGT42 primer, and 0.5 U of AmpliTaq Gold DNA polymerase (Applied

103

Biosystems). In the current study, nested singleplex reactions were used to amplify microsatellite loci for problematic modern samples and museum specimens. Primary PCR reactions used non-labeled forward primers, with amplified products serving as template for subsequent PCR reactions utilizing the corresponding fluorescently labeled forward primers.

Total reaction volumes of 15 µl included: ~20-50 ng DNA, 10 mM Tris-HCl (pH 8.3), 50 mM

KCl, 1.5 mM MgCl2, 10 µg bovine serum albumen, 200 µM dNTPs, 0.65 µM of each primer, and 0.5 U of AmpliTaq Gold DNA polymerase (Applied Biosystems). Thermocycler conditions for multiplex and primary singleplex reactions were optimized using a ‘touchdown’ cycling program consisting of: 95ºC (5 min), 38 cycles of 95ºC (30 sec), annealing (30 sec), 72ºC (1 min), and a final extension of 72ºC (3 min). The initial 14 cycles of the ‘touchdown’ program reduced the annealing temperature from 60ºC to 53.5ºC, decreasing 0.5ºC per cycle, while the annealing temperature was held constant at 53.5ºC for the remaining cycles (Gebhardt & Waits

2008). Fragments were separated on an ABI 3730xl Genetic Analyzer and scored using

GENEMAPPER 4.0 (Applied Biosystems).

Quality control

Standard historical quality control protocols were followed while manipulating tissues from museum specimens. DNA extractions were conducted in a workspace dedicated to archival tissues and independent sets of reagents were used for modern and historical samples, reducing the risk of contamination from exogenous DNA and PCR amplified products. Multiple negative extraction and PCR controls were performed for mtDNA sequencing and autosomal genotyping.

The use of short, overlapping fragments was employed to increase amplification success of target regions of the mitochondrial genome in historical extracts.

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Additional precautions were taken to optimize data quality across the entire sample set. PCR reactions were repeated to minimize the incorporation of errors due to miscoding lesions or occurrence of null alleles. These efforts resulted in 3x to 8x sequence coverage for amplicons exhibiting unique single base pair mutations, and microsatellite loci were genotyped in triplicate. The program MICRO-CHECKER (Van Ooseterhaut et al. 2004) was used to screen the genotypic data set for evidence of null alleles. Tests for deviations from Hardy-Weinberg equilibrium and presence of linkage disequilibrium across autosomal loci were conducted in

GENEPOP 4.0 (Rousset 2008), based on 1,000 demorization iterations, 1,000 batches and

10,000 iterations per batch. Significance values were corrected for multiple comparisons using the sequential Bonferroni procedure (Rice 1989).

Genetic Variation

Haplotype (Hd) and nucleotide (π) diversity, number of haplotypes (h), and number of segregating sites (S) were calculated using DnaSP 5.10.01 (Librado & Rozas 2009) to quantify variation among A. m. cyanoptera mitochondrial haplotypes across the entire subspecies’ range.

Arlequin 3.1 (Excoffier et al. 2005) was used to estimate allelic diversity (A), expected (HE), and observed (HO) heterozygosity for each microsatellite locus.

Regional Patterns of Differentiation

A hierarchical and temporal approach was employed to investigate patterns of genetic substructure within A. m. cyanoptera, with an emphasis on understanding patterns of connectivity within LSM. Initial analyses involved determining whether scarlet macaws ranging

105 in Mexico, Guatemala, and Belize represent an independent biological unit, or if these areas are nested in broader population dynamics. Given the demonstrated importance of topographical variation (Chapter 2) in driving genetic differentiation in scarlet macaws, historical cyanoptera specimens were partitioned into two broad geographic groups (i.e. putative populations). These groups were delimited by an extensive system of the central highlands transecting the region from the Caribbean slope of northern Honduras to the Pacific versant of El Salvador. Resulting contractions in the scarlet macaw’s preferred lowland habitats along coastal regions are hypothesized to restrict gene flow between Mexico, Guatemala and Belize [i.e. La Selva Maya

(LSM); Fig. 3.1] and the southern extent of the subspecies’ distribution [i.e. A. m. cyanoptera

South (Amcy_S); Fig. 3.1]. Exemplars originating in LSM were further divided into three subgroups based on the geographic distribution of sampling localities [i.e. West (LSM_W),

Central (LSM_C), and East (LSM_E); Fig 3.1] to examine the extent of fine scale genetic discontinuities across the region. Separate analyses were conducted for historical (i.e. hLSM) and modern (i.e. mLSM) data sets to investigate potential temporal shifts in spatial structure across upper Central America.

Median-joining networks (Bandelt et al. 1999) constructed in Network

(http://www.fluxus-engineering.com) were used to visualize relationships among cyanoptera haplotypes and detect possible geographic associations. An analysis of molecular variance

(AMOVA; Excoffier et al. 1992) was performed in Arlequin to infer spatial genetic discontinuities across putative populations and sampling localities in LSM. Regional heterogeneity in haplotype distributions was also assessed via a global χ2-test and an Exact-test conducted in DnaSP and Arlequin, respectively. Pairwise FST and ϕST were generated to further evaluate geographic differentiation, testing the significance of empirical estimates against a null

106 distribution created from 10,000 nonparametric random permutations. While the former utilizes haplotype frequencies alone, the latter incorporates pairwise differences between mitochondrial sequences. The best fit model of nucleotide substitution for the control region data set [i.e. HKY + I, as selected by jModeltest (Posada 2008) based on Akaike information criterion

(AIC; Posada & Buckley 2004)] is not implemented in Arlequin, therefore the more inclusive

Tamura-Nei model (TrN; Tamura & Nei 1993) was used to generate pairwise distances.

Regional estimates of nuclear gene flow were not possible because a considerable proportion of samples failed to yield consistent and reproducible genotypes (see Results).

Demographic History

Demographic histories of scarlet macaws in LSM were inferred from mitochondrial control region haplotypes using class I (i.e. frequency of segregating sites), and class II (i.e. haplotype distribution) statistics to test for departures in selective neutrality and population equilibrium

(Ramos-Onsins & Rozas 2002). Tajima’s D and R2 were selected among class I statistics to evaluate the placement of segregating sites along genealogies, as the excess of recent mutations in external branches is characteristic of recent population growth (Slatkin & Hudson 1991;

Tajima 1989a, b). Fu’s Fs statistic (1997), class II, uses information from the haplotype distribution to detect genetic signatures of demographic changes, low values are associated with the excess of singleton mutations in an expanding population. Chosen statistical tests have been shown to outperform other methodologies in rejecting the null hypothesis of constant population size (Ramos-Onsins & Rozas 2002). All demographic inferences were implemented in DnaSP with 10,000 replicates, assuming an infinite sites model and no recombination.

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Local Patterns of Differentiation

Haplotypic and genotypic data from the mLSM_C sample set were examined in greater detail to elucidate patterns of fine-scale spatial structure in the Maya Biosphere Reserve (MBR),

Guatemala. Genetic sampling focused on four focal breeding sites, areas with dense clusters of potential and/or active scarlet macaw nest cavities [Perú (PE), Burral (BU), Corona (CO), and

Chuntuquí (CHU); Fig. 3.2]. Statistical analyses of population differentiation were performed to determine if breeding areas represent distinct genetic units; however, the nest site of Chuntuquí was not included here due to small sample size (n = 2). Sequence-based analyses (i.e. global χ2- test and Exact-test, AMOVA, and pairwise fixation indices FST and ϕST) were conducted as outlined above for the regional population assessment. A similar suite of analytical tests was used to examine the nuclear genome for signals of genetic differentiation. Specifically, spatial structure was measured from the microsatellite matrix via a global Exact-test and AMOVA; pairwise estimates of θ, an analog of FST assuming an infinite allele model of mutation, (Weir &

Cockerham 1984) were also generated from multi-locus genotypes in Arlequin. As above, significance of fixation indices was tested against a null distribution of 10,000 nonparametric random permutations of the data. Multi-locus microsatellite genotypes were also employed to test for fine-scale genetic differentiation using the Bayesian algorithm of Pritchard (2000), as implemented in STRUCTURE v2.3.2; the number of distinct genetic clusters (K) was estimated by grouping individuals as to minimize linkage disequilibrium and Hardy-Weinberg equilibrium within the data set. Simulations employed a straight admixture model with correlated allele frequencies to generate log likelihood scores for a range of clusters K from 1 to 5. Run length was set to 1,000,000 MCMC replicates after an initial burn-in of 500,000 with 20 replicates per value of K. Given spatial genetic heterogeneity may be difficult to detect in instances of recent

108 divergence, independent analyses were run with and without sampling localities included as priors to increase sensitivity, as per Hubisz et al. (2009).

Pairwise Estimates of Relative Relatedness

Pairwise estimates of relatedness were used to elucidate additional aspects of the species’ breeding biology (e.g. fidelity to a specific focal breeding site and/or nest cavity, annual turnover of reproductive pairs). Indices of pairwise relatedness were also employed to investigate the extent of inbreeding among scarlet macaws nesting in the MBR, Guatemala.

Multi-locus microsatellite genotypes were used to calculate relatedness estimates among individuals sampled at 23 nest cavities; for eight nest cavities, genetic samples were collected over two or more field seasons between 2004 and 2009. The software program KINGROUP v2.0.9 (Konovalov et al. 2004) was used to evaluate four common estimators [rxyQG (Queller &

Goodnight 1989), rxyLR (Lynch & Ritland 1999), rxyW (Wang 2002), rxyML (Maximum Likelihood,

Goodnight & Queller 1999)] by determining their relative power to distinguish unrelated individuals from first-order pairs. Simulations consisted of 1,000 dyads for each of four relatedness categories (unrelated, half sibling, full sibling, and parent/offspring), based on empirical population allele frequencies inferred from a microsatellite matrix with a single chick representing each putative sibling group. Estimator performance was evaluated by an assessment of sample mean and variance for simulated rxy values in each relatedness category, as described by Van de Castelle et al. (2001). The midpoint method of Blouin et al. (1996) was employed to determine the estimator with least overlap between simulated distributions for unrelated individuals and first-order relatives, thus minimizing the occurrence of type I and II errors. The

109 two best performing estimators were subsequently used to generate rxy estimates for each pair of individuals sampled in the MBR, Guatemala using KINGROUP.

Average relatedness values were partitioned into multiple subsets based on three independent variables; the relative influence of shared mitochondrial haplotype, nest cavity, and/or sample year on mean relatedness indices serves to elucidate in situ behavioral patterns and pedigree structure. First, mean rxy estimated across the entire data set tested the assumption scarlet macaws nest clusters in the MBR, Guatemala represent a random-mating population, with values significantly greater than zero implying the presence of inbreeding. Comparisons were also conducted among individuals sharing a common mitochondrial haplotype to identify potential family groups. Data partitions grouped by locality (i.e. breeding site and/or nest cavity) provide insights into the extent of annual turnover in reproductive pairs. Haplotype frequencies among and within breeding sites, and specific nest cavities were also calculated across sampling years to further examine annual movement patterns in the absence nuclear data (see Results).

Consideration of pairwise rxy estimates for individual dyads provides opportunities for genetic recapture events, thus refining observations of site and/or nest fidelity among adults, and also survival and recruitment rates for chicks. A 95% threshold was used to identify putative relatives among scarlet macaws sampled in the MBR, Guatemala within and across sampling years (Gonçalves da Silva et al. 2010). Briefly, the distribution of rxy values for simulated unrelated dyads was used to determine an empirical value above which pairs exhibit a ≤0.05 chance of being unrelated. Dyads exhibiting one or both rxy estimates above the 95% threshold were considered potential relatives; this more inclusive approach reflects the varying performance of relatedness estimators and broad distribution of relatedness values per relationship category. Once putative full sibling or parent/offspring pairs were identified, dyads

110 were manually inspected for shared alleles at all microsatellite loci (Milligan 2003) and maternal inheritance of mtDNA haplotypes.

Molecular Sexing

PCR-based methods were employed to determine sex assignments and ratios for each cohort of scarlet macaw nestlings sampled in Guatemala between 2004 and 2009. A modified sexing protocol, as described by Russello and Amato (2001), was employed using primers P2 and P8

(Griffiths et al. 1998). Primers flank a region of the sex-linked, chromo-helicase-DNA-binding

(CHD) gene known to exhibit differential intronic lengths on the W and Z chromosomes, yielding one fragment in homogametic males and two in heterogametic females. Molecular sexing was carried out using PureTaq Ready-to-Go PCR beads (GE Health Sciences), ~20-40 ng

DNA, and 0.2 µM of each primer in a total reaction volume of 20 µl. Thermocycler conditions were as follows: 95ºC (1 min), 40 cycles of 95ºC (30 sec), 50ºC (30 sec), 72ºC (30 sec), and a final extension of 72ºC (5 min). PCR products were visualized on a 2.5% agarose gel and scored for sex based on the number of correct size bands.

RESULTS

Haplotypic Data Quality and Variation

Single-banded PCR products of expected size were obtained for all control region fragments amplified from high quality contemporary specimens (i.e. plucked feathers and blood samples) using external primers. Similarly, novel internal primer pairs yielded single-banded amplicons for small overlapping fragments in historical and degraded contemporary samples. Multiple

111 independent DNA extractions and PCR amplifications from museum tissues and degraded samples resulted in 3x to 8x sequence coverage for fragments exhibiting unique single base pair mutations.

Reproducible haplotypes were generated for all historical tissues; however, modern samples exhibited variation in the quality of recovered sequence data, depending on age and collection method as reported by Gebhardt et al. (2009). Complete haplotypes were readily obtained from blood samples and plucked feathers, with exception of tissues collected in 2006.

Delays in securing exportation permits and suboptimal storage conditions in the field resulted in significant DNA degradation; similarly, a subset of molted feathers was also excluded from subsequent sequence analysis due to problematic amplification. Three samples exhibited a high percentage of missing data (17% to 63%) after repeated sequencing attempts; in addition, four samples yielded results suggestive of contamination (i.e. concatenation of small overlapping fragments revealed hybrid haplotypes). Among samples with complete haplotypes, duplicates were removed when determined to originate from the same individual [i.e. molted feathers collected concurrently from the same nest site (n = 7)] or be identical by descent [i.e. sibling groups (n = 14), and mother-offspring (n =2)] to reduce pseudo-replication in the sequence data set. However, museum specimens were considered independent sampling events and all resulting haplotypes were included in the final analyses.

A total of nine mtDNA sequences were recovered from the putative southern population of A. m. cyanoptera, including seven unique haplotypes (Table 3.2, Fig. 3.3), demonstrating high haplotype and moderate nucleotide diversity. Eleven haplotypes were identified for LSM data sets (Table 3.2), with changes in haplotype frequency between temporal samples (Fig. 3.4).

Despite changes in composition, comparably high levels of haplotype diversity, moderate

112 nucleotide diversity, and near identical number of segregating sites were recovered for the historical (Table 3.2) sequence matrices. Diversity indices remained high to moderate when the historical data set was partitioned by sampling locality, with exception of nucleotide diversity in the eastern portion of LSM (Table 3.2). Only three haplotypes were observed in the mLSM_E data set, compared to six in the historical sample, resulting in reduced diversity indices. With an increase in samples size, the number of haplotypes and haplotypic diversity among the mLSM_C sequences rose, although nucleotide diversity declined slightly (Table 3.2). One of two mLSM_C haplotypes was recovered within the modern data set; interestingly, the most common contemporary haplotype (CR1), representing 13 of 35 exemplars (37.1%), was not detected among museum specimens (Fig. 3.4).

Genotypic Data Quality and Variation

Of 11 microsatellite loci screened, one was found to be monomorphic (AgGT32) and subsequently removed from the current study. An additional two loci were not considered due to difficulties with amplification (AgGT90) and scoring (AgGT42); an observation consistent problematic genotyping reported for South American scarlet macaws (Gebhardt & Waits 2008).

No evidence of null alleles was detected among the remaining loci when screened using

MICRO-CHECKER (Van Ooseterhaut et al. 2004). Analyses conducted in GENEPOP 4.0

(Rousset 2008) did not report deviations from Hardy-Weinberg equilibrium among microsatellite genotypes, although pair-wise comparisons revealed significant linkage disequilibrium for one pair of autosomal loci (AgGT21 and AgGT17) following sequential Bonferroni correction.

Extremely low levels of variation within pairs of loci may explain repeated recovery of specific allele combinations; however, these loci were the most diverse in the current study, with 10 and

113

11 alleles respectively. Familial relationships serve as a plausible alternative explanation for the observed linkage disequilibrium (i.e. groups of alleles co-occurring among close relatives), given the large number of putative full siblings within the sample set [pairs (n = 9); trio (n = 1)].

Tests of Hardy-Weinberg equilibrium and linkage disequilibrium were rerun with two permutations of a truncated microsatellite matrix (i.e. including only one member of each sibling group) revealed no deviations from Hardy-Weinberg equilibrium or evidence of linkage disequilibrium. Therefore, all eight loci were included in the final nuclear data set.

Although microsatellite loci were amplified and scored in triplicate, considerable gaps remained within the genotypic matrix, especially among molted feathers and historical specimens. Only individuals successfully genotyped for a minimum of six loci were considered in the present study, resulting in exclusion of the entire mLSM_E data set (n = 13), a subset of mLSM_C samples (n = 24), and all historical specimens (n = 29). Less than 3.7% of data were missing in the final truncated microsatellite matrix (n = 37; eight loci), revealing high levels of allelic richness and heterozygosity (Table 3.2). However, all samples originated in Guatemala

(i.e. mLSM_C), thus precluding regional estimates of gene flow for nuclear loci.

Regional Patterns of Differentiation

Analysis of regional genetic differentiation revealed hierarchical and temporal patterns of spatial structure in A. m. cyanoptera. Median-joining networks recovered five clusters of haplotypes consistent with haplogroup designations defined in Chapter 2 (i.e. Haplo1, Haplo2, Haplo3,

Haplo5 and Haplo6; Fig 3.3, Fig 3.4). Two haplogroups were shared between hAmcy_S and hLSM, with one and two haplogroups unique to either putative population, respectively. Among historical exemplars, haplotypes for both sample sets were not strictly partitioned, but rather

114 distributed throughout the network (Fig. 3.3). Spatial structure was evident, however, from the high percentage of private haplotypes; only two of 18 mtDNA sequences were common to both populations. Moreover, sequences unique to hLSM were located on peripheral nodes, whereas shared haplotypes and those private to hAmcy_S tended to occur within the network core. Statistical analyses also suggest the presence of genetic structure across hAmcy_S and hLSM, as evidenced by statistically significant results from global tests of differentiation (χ2 =

30.33, df = 17, P = 0.024; Exact-test P = 0.001; Table 3.3) and AMOVA (P = 0.001; Table 3.3).

Highly significant pairwise FST and ϕST values further support this observation (Table 3.4).

Closer examination of haplotypes found in Mexico, Guatemala and Belize did not reveal structure within the haplotype network; rather the same or closely related sequences were distributed across sampling localities (Fig. 3.4). Although the global Exact-test reported significant spatial structure across the hLSM data set (P = 0.021; Table 3.3), other statistical analyses suggest exemplars ranging across the region represent a single biological unit. The χ2- test was non-significant (χ2 = 30.18, df = 20, P = 0.067; Table 3.3); moreover, greater genetic variation was distributed within, rather than among, historical sampling localities (P = 0.311;

Table 3.3). Fixation indices were non-significant for all pairwise comparisons between hLSM_W, hLSM_C, and hLSM_E, providing an additional line of evidence for haplotypic homogeneity across the region (Table 3.4).

Sequence data suggest a potential temporal shift in the composition of mitochondrial haplotypes in the LSM. Eight of 11 historical haplotypes were observed within Haplo2, with a single representative for Haplo1 and Haplo5, and two of Haplo6 (Fig. 3.4). This haplotype distribution was generally maintained for the mLSM_E data set; in contrast, a significant proportion of mLSM_C samples (37.1%) clustered within the historically undetected Haplo1.

115

Highly significant genetic structure was also documented among statistical analyses, including global tests (χ2 = 28.55, df = 10, P = 0.002, Exact-test P = 0.000; Table 3.3), AMOVA

(P = 0.000; Table 3.3) and both pairwise FST and ϕST indices (Table 3.4).

Demographic History

Neutrality tests revealed weak evidence of demographic change for the scarlet macaw population in LSM (Table 3.5). Fu’s Fs neutrality test rejected the null hypothesis of constant population size within the historical data set, albeit with minimal significance (P = 0.041); both Tajima’s D and the R2 statistic of Ramos-Onsins and Rozas were non-significant (P = 0.340; P = 0.360, respectively). Genetic signatures were also indicative of demographic equilibrium among the mLSM samples, as all indices failed to detect deviations from neutrality. Similar values were recovered when contemporary sampling localities in Belize and Guatemala were considered independently.

Local Patterns of Differentiation

No evidence of genetic differentiation was found across focal breeding areas in the MBR,

Guatemala using population-level statistical analyses. Global tests for mitochondrial sequences revealed non-significant differentiation between PE, BU, CO, and CHU (χ2 = 21.5, df = 16, P =

0.16; Exact-test P = 0.202; Table 3.6). AMOVA recovered greater variation within, rather than among, nest sites for both mtDNA and nuclear data sets (P = 0.179, P = 0.309, respectively;

Table 3.6). Similarly, fixation indices for all pairwise site comparisons were non-significant

(Table 3.6). Bayesian algorithms did not support focal nest sites as distinct genetic clusters;

116 highest mean log-likelihood values were recovered for K = 1. Plotting estimates of Q (i.e. membership coefficients per cluster) for K >1 revealed the representative proportion of each cluster assigned to an individual was roughly symmetrical across the data set (Fig. 3.5). Near identical results were obtained for both simulation runs, thus demonstrating insensitivity to sampling locality priors (data not shown).

Pairwise Estimates of Relative Relatedness

Relative relatedness estimators based on multi-locus genotypes varied in performance depending on the criteria under consideration. Simulations based on the indices of Queller and Goodnight

(1989) and Lynch and Ritland (1999) exhibited the fewest deviations from expected theoretical means for four relatedness categories [parent/offspring (PO): rT = 0.5, full sibling (FS): rT = 0.5, half sibling (HS): rT = 0.25, unrelated (UR): rT = 0; Table 3.9]. The maximum likelihood approach of Goodnight and Queller (1999) demonstrated the least variance in pairwise rxy estimates for familial pairs (i.e. PO, FS, HS); the algorithm of Lynch and Ritland performed best for unrelated dyads (Table 3.9). The latter also reported the lowest rate of misclassifying unrelated individuals as first-order relatives (Table 3.10). Based on overall performance, two indices, rxyLR and rxyML, were selected to examine pedigree relationships among scarlet macaws nesting in the MBR, Guatemala.

The distribution of observed rxy values for all wild samples peaked near zero for both relatedness estimators (Fig. 3.6; Fig 3.7). Analysis of sequence and autosomal data sets provided no evidence of philopatry (i.e. higher incidence of close relatives, or spatial clustering of mtDNA haplotypes) with samples partitioned by breeding area (Fig. 3.7, Table 3.6, Table 3.11; CHU excluded due to small samples size). Only one of six nest site averages deviated significantly

117 from theoretical expectations (rT = 0) for a population of unrelated individuals (PE: rxyLR = -

0.025, P value = 0.002; Fig. 3.7, Table 3.11). Factors such as shared nest cavity or mitochondrial haplotype had little influence on mean rxy values, with exception of presumed sibling groups.

Pairwise comparisons for 10 multi-chick clutches (n =12) revealed averages concordant with expected rT = 0.5 for first-order relatives (Fig. 3.8, Table 3.12). However, relatedness estimates inferred for all dyads sampled at the same nest cavity across years, excluding nest mates, did not differ from theoretical panmixia (rT = 0), similar results were recovered for pairs with a shared control region haplotypes but independent nest cavity, as well as all exemplars carrying the high frequency CR1 haplotype (Fig. 3.8; Table 3.12). Mean rxy values for dyads lacking both a common haplotype and nest cavity likewise failed to deviate significantly from theoretical rT = 0 for unrelated pairs (Fig. 3.8, Table 3.12). Examination of mitochondrial haplotype distributions across each breeding area failed to demonstrate a clustering pattern consistent with female philopatry; 83% of haplotypes recovered more than once (n = 6) were shared between nest sites

(Table 3.7). A high degree of turnover was also noted for sequences recovered from the same nest cavity across breeding seasons; only the most common control region haplotype (CR1) was detected in the same cavity across multiple years (Table 3.8).

Additional analyses employed empirically determined critical rxy values to screen for putative first-order relatives among scarlet macaws sampled in the MBR, Guatemala. Using the distribution derived from 1,000 simulated unrelated pairs, the 95% threshold value (i.e. rxy above which dyads have a ≤0.05 chance of being unrelated) was determined to be 0.281 for the approach of Lynch and Ritland (1999) and 0.351 for the maximum likelihood method of

Goodnight and Queller (1999) (Fig. 3.6). Of 666 total pairwise comparisons, 44 (6.6%) presented observed rxy values consistent with familial relationships (Table 3.13). Specifically, 19 dyads

118 were identified as putative relatives following a conservative interpretation of relatedness indices (i.e. both observed rxy values above their respective 95% threshold), including nine pairs of nest mates. Two additional sibling pairs were recovered among 25 dyads considered potential relatives based on more inclusive criteria (i.e. one observed rxy value above its respective 95% threshold). This approach failed to detect one known sibling pairs (8%), although the observed rxyLR of 0.25 was only slightly lower than the 95% threshold of 0.281. Among dyads without a priori hypotheses of relatedness, six demonstrated patterns indicative of first-order relatives (i.e. one shared allele per locus and, in instances of suspected maternal descent, a shared haplotype).

Lack of a common haplotype, incompatible multi-locus genotypes, or temporal constraints (i.e. insufficient time for chicks produced in previous years to reach sexual maturity and breed) resulted in indeterminate pedigree relationships for the remaining 27 pairs (Table 3.13).

Molecular Sexing

Of 41 scarlet macaw chicks sampled in the MBR, Guatemala across four breeding seasons, 33 were successfully sexed using molecular methodologies (Table 3.14). Although the number of males to females fluctuated slightly, no individual year significantly differed from an expected ratio of 1:1. This observation remained true when examining the net contribution of each sex over the entire sampling period. Moreover, deviations from an expected proportion of 1:2:1 for

M/M, M/F, and F/F sibling groups were non-significant across the entire sample [Total (4:3:3):

χ2 = 1.8, df = 2, P = 0.47]. Due to severe degradation DNA extracts, sex could not be determined for chicks sampled in 2006. A total of three females and two males were identified among molted feather samples, the remainder could not be assigned to either sex due to failed amplifications.

119

DISCUSSION

Hierarchical Assessment of Genetic Structure

Application of molecular genetic methodologies at multiple spatial scales indicates scarlet macaws (A. m. cyanoptera) ranging throughout LSM represent a meaningful biologic unit, genetically distinct from breeding areas distributed across the southern portion of the subspecies’ range. Genetic discontinuities were associated with the central highland system, where the species’ preferred habitats are greatly reduced along the Caribbean slope of northern Honduras and Pacific versant of El Salvador (Fig. 3.1), underscoring the importance of geologic features in shaping patterns of genetic structure within scarlet macaws, as first discussed in Chapter 2. Two control region haplotypes were recovered on either side of the central massif, suggesting low levels of unidirectional gene flow from Amcy_S into LSM. Several lines of evidence support this observation: both shared control region haplotypes occur at relatively high frequency in

Amcy_S, but are rare in LSM (Fig. 3.3); moreover, two haplogroups unique to LSM (Haplo 1 and Haplo6) were not recovered with the Amcy_S, whereas only a single haplogroup is endemic to the southern sample set (Fig. 3.3). Moreover, Amcy_S haplotypes, including the two shared with LSM, have core positions within the haplotype network, whereas endemic LSM haploytpes are distributed along the tips; this pattern suggests Amcy_S represents the ancestral population for LSM. Dispersal from Amcy_S may be facilitated by the frequent hurricanes passing across the Caribbean slope of upper Central America, generally in a westerly direction (NOAA 2011), in addition to the small patches of lowland habitats that form “stepping stones” along the northern coast of Honduras (Fig. 3.1). It is important to note the challenges of determining whether shared mitochondrial haplotypes reflect historical association or low levels of genetic connectivity between contemporary populations. Interestingly, the only previous study to

120 examine patterns of mitochondrial variation across the geographic distribution of A. m. cyanoptera comes to the opposite conclusion (García Feria 2009), arguing high levels of gene flow between LSM and the tropical lowland forests of La Mosquitia in northeastern Honduras.

However, differences in methodological approaches (e.g. gene regions, fragment size, statistical analyses) provide a reasonable explanation for discrepancies noted between studies. Increasing sample sizes and incorporating multi-locus nuclear genotypes would further clarify patterns of substructure within A. m. cyanoptera; nonetheless, repeated association of genetic discontinuities with restricted habitat distributions, as seen in Chapter 2, lends credibility to the findings presented here.

With historically widespread lowland tropical forests and few notable areas of significant topographical variation (Fig. 3.1), there is high potential for genetic homogeneity across scarlet macaw breeding areas in the LSM. Mitochondrial control region haplotypes sequenced from archival tissues were employed to test this hypothesis and generate a baseline scenario of spatial substructure across the region to compare with contemporary patterns (see below). Population genetic analyses detected a general lack of divergence among three discrete sampling localities

(Fig. 3.1). Median joining networks recovered no geographic clustering of mitochondrial sequences; instead closely related haplotypes are distributed across sampling sites, with both hLSM_C and hLSM_E sharing a haplotype with hLSM_W (Fig. 3.4). Among global tests of differentiation analyses, only the Exact-test yielded a significant result; conversely, AMOVA and χ2 did not detect genetic discontinuities, with pairwise fixation indices being similarly non- significant (Table. 3.3). Lack of genetic heterogeneity within the historical data set may reflect small sample size; theory predicts the most common haplotypes will have higher detection rates, thus underestimating the extent of spatial substructure. However, examination of molecular

121 variation across numerous Neotropical psittacines [blue-and-gold macaws (A. ararauna:

Caparroz et al. 2009), hyacinth macaw (Anodorhynchus hyacinithus: Faria et al. 2008), red-tailed

Amazon (Amazona brasiliensis: Caparroz et al. 2006), green-wing macaws (A. chloroptera:

Gebhardt 2007) and scarlet macaws (A. macao macao: Gebhardt 2007)] has revealed extensive gene flow across large areas devoid of prominent geologic features. While dispersal capabilities in most parrots, including scarlet macaws, are greatly facilitated by the species’ size and volant nature, this behavior is likely driven by spatial and temporal shifts in critical resources [e.g. fruiting phenology, water availability (Bjork 2004; Karubian et al. 2005; Powell et al. 1999;

Renton 2002)]. Empirical observation of radio-collared scarlet macaws in LSM reveals movements from focal nest sites in the Maya Biosphere Reserve (MBR), Guatemala to the

Usumacinta River drainage basin (URDB) along the border between Guatemala and Mexico during the non-breeding season (Carreón-Arroyo & Iñigo-Elías 1998; Rodas 2002; Rodas et al.

2001). It is important to recognize dispersal does not equal effective gene flow; however the seasonal congregation of scarlet macaws provides ample opportunities for the formation of pair bonds between individuals from disparate natal areas, thus facilitating regional genetic connectivity.

As stated above, ecological factors (e.g. distribution of humid lowland habitats, seasonal resource availability) are more influential than behavior and mobility in dictating individual movements across Neotropical psittacines (Evans et al. 2005; Myers & Vaughan 2004); therefore, recent reductions in the scarlet macaw’s preferred lowland habitats across LSM, most notably between breeding areas in Belize and Guatemala (Fig. 3.1), raise concerns of disrupted gene flow throughout the region (Clum 2008). Molecular genetic analyses confirmed this hypothesis by detecting significant genetic differentiation across contemporary sampling

122 localities across the central (mLSM_C) and eastern (mLSM_E) portions of LSM, a notable departure from baseline genetic homogeneity recovered from historical specimens (Fig. 3.2,

Table 3.3, Table 3.4). Examination of the haplotype network (Fig. 3.4) shows a distinct shift in haplotype frequencies across temporal data sets. For example, the most common control region haplotype sampled in mLSM_C (i.e. CR1) was not recovered among historical exemplars, yet currently is carried by 31.7% of individuals sampled between 2004 and 2009. Likewise, a reduction in the number of haplotypes was detected in mLSM_E, although the two predominant contemporary haplotypes were recovered within the historical data set for the same sampling locality (Fig. 3.4). Small sample sizes may confound interpretation of spatial structure, however limited sampling generally underestimates the extent of genetic discontinuities by introducing biases toward high frequency haplotypes; therefore the strong signal of differentiation observed between contemporary sampling localities is considered significant. Moreover, the apparent absence of CR1 in mLSM_E is an additional indicator of genetic isolation; given the frequency of occurrence in mLSM_C (37.1%) and sample size of mLSM_E (n = 9), at least three individuals would be expected to carry CR1 in mLSM_E if haplotype distributions were currently uniform across breeding sites.

Given the most significant reduction of preferred lowland habitats in LSM has occurred within the past 60 years (Iñigo-Elías et al. 2001), it is unlikely the temporal shifts observed in the contemporary haplotype distribution result from genetic drift. Scarlet macaws are a long-lived species (Brouwer et al. 2000) with overlapping generations, two important factors empirically shown to delay the manifestation of genetic bottlenecks and mitigate the effects of genetic drift

(Hailer et al. 2006). The LSM mitochondrial sequence matrix exhibits a similar number of segregating sites and singleton mutations across temporal samples, thus suggesting low

123 frequency haplotypes have not been lost via drift, but redistributed across the landscape; this finding is corroborated by non-significant neutrality tests (Table 3.5). Given longer periods of isolation, genetic drift may exacerbate fluctuations in haplotype frequencies and strengthen patterns of differentiation across LSM, as seen in the Spanish imperial eagle (Alquila adalberti:

Martínez-Cruz et al. 2007). However, examination of pairwise relative relatedness estimates between dyads sampled in the MBR, Guatemala indicate, to date, the marked demographic declines sustained by A. m. cyanoptera have not resulted in a genetic bottleneck (see Genetic

Health and Links to Demography). Therefore other formative processes may be responsible for the genetic signatures observed among breeding sites in Guatemala and Belize. Stochastic isolation serves as an alternative hypothesis to explain altered haplotype distributions in LSM; disrupted migration routes result in population fragmentation, where current haplotype distributions reflect the random subset of individuals present at the time of isolation. Moreover, habitat loss may have created a scenario of anthropogenic philopatry, where individuals and their offspring continue to breed in the same focal nest area over consecutive years due to an absence of suitable forest corridors between nest sites. As a case in point, preliminary data from an ongoing satellite tracking study of scarlet macaws in the Chiquibul Forest Reserve (CFR), Belize have revealed an inherent drive for individuals to move into central Guatemala, presumably toward known congregation areas within the URDB along the border of Mexico and Guatemala, during the non-breeding season (Britt 2012). Unfortunately at present, these areas are largely devoid of forest cover (Fig. 3.2) and satellite data show scarlet macaws promptly return to nest areas in CFR, Belize after crossing the Guatemalan border, where they remain for the duration of the non-breeding season (Britt 2012). Interestingly, vast expanses of pristine, albeit drier, lowland forests still exist throughout northeastern Guatemala and northern Belize providing a

124 potential migration corridor; however these habitats are not actively utilized by A. m. cyanoptera [Fig. 3.2; (Clum 2008; Iñigo-Elías et al. 2001)], highlighting a preference for moist habitats.

Site Fidelity and Nest Use

Assessment of haplotype variation revealed no population genetic structure between sampling localities in the LSM; however, these analyses provide only limited insights into population dynamics. Closer examination of contemporary samples collected from scarlet macaws nesting areas in the MBR, Guatemala provide a preliminary means to address data gaps and elucidate behavioral patterns in greater detail. First, the degraded nature of historical DNA extracts precluded generation of reliable multi-locus microsatellite genotypes to test for undetected patterns of male philopatry within the regional data set. Investigation of fine-scale genetic differentiation using both mitochondrial and nuclear markers from contemporary samples found no evidence of natal philopatry among focal breeding sites in the MBR. All global tests were non-significant, with pairwise fixation indices and Bayesian cluster analyses yielding similar results (Table 3.6; Fig. 3.5), corroborating initial findings of genetic connectivity across the region. Second, temporal shifts in haplotype frequencies imply disrupted dispersal patterns due to loss of migration corridors between the MBR, Guatemala and CFR, Belize. However, potential changes in philopatric tendencies may be too recent for detection using population-level analytical approaches (i.e. insufficient time for perceptible differences in allele frequencies across sample sets). If anthropogenic activities have reduced opportunities for movement between sites, each fragment would accumulate a greater number of close kin than expected for a panmictic population, as reproductive pairs and their offspring are forced to remain in natal

125 areas. Therefore, estimates of relative relatedness serve as an alternative methodology to investigate changes in migration patterns and anthropogenic philopatry. Mean rxy values for scarlet macaws sampled within each of three focal nest areas in the MBR were not statistically different from an rT = 0 for a random sample of unrelated individuals (Table 3.11); moreover, similar distributions of average relatedness were observed across focal breeding areas (Fig. 3.7).

Only the relatedness estimator of Lynch and Ritland (1999) for samples collected the

Guatemalan field site (PE) demonstrated a significant deviation from expectations; however, the observed mean (rxyLR = -0.025, P = 0.002; Table 3.11) is below the theoretical value (rT = 0) for unrelated individuals, hence not considered an indication of a higher incidence of kinship in

Peru. It is plausible this significant value is an artifact of data partitioning, given the observation

P values decrease with sample size and the pattern of significant rxLR estimates recurs for the largest data subset per comparison (see below). These data imply opportunities for larger movement patterns still exist for scarlet macaws breeding in the MBR, Guatemala, most likely within the neighboring URDB along the Mexico and Guatemala border. Increased spatial and temporal sampling is needed to more fully characterize regional and local movement patterns, and the relative importance of behavioral influences (i.e. sex-biased dispersal, anthropogenic philopatry) in LSM. For example, the close geographic proximity of nesting sites in the MBR

(i.e. <50 km; Fig. 3.2) would fail to detect genetic discontinuities occurring at larger spatial scales. Moreover, depending on rates of reproduction and continued habitat loss, changes in pedigree structure may be slow to develop, requiring a longer time series for effects to be seen.

Although molecular genetic methodologies highlight the inherent tendency of scarlet macaws to undergo large-scale migrations, these observations are generalized across the entire population and provide little information regarding how individuals use lowland landscapes.

126

Behavioral studies tracking movement patterns in raptors have shown idiosyncratic migration routes for individual birds with low levels of repeatability across years (Alerstam 2006; Vardanis et al. 2011). Vardanis (2011) argues this flexibility is driven by high unpredictability in critical environmental factors (e.g. wind speed, foraging sites) across broad geographic distances.

Interestingly, radio telemetry work with mealy parrots (Amazona farinosa) in the MBR,

Guatemala also demonstrated variability in individual movement patterns, with a proportion of the population opting out of seasonal migrations to remain in the breeding area year round (Bjork

2004). A multi-year study tracking scarlet macaws in the same general study area revealed a similar pattern of changes in behavioral strategies between breeding attempts; one adult fitted with a radio collar remained near nest areas in the Sierra del Lacandon, along the border between

Mexico and Guatemala, while traveling into the MBR in the following year (Rodas 2002; Rodas et al. 2001). Although proven a powerful tool in elucidating aspects of species’ biology important for management efforts, implementation of tracking studies using radio and satellite technology is generally limited in scope due to logistical and financial constraints (e.g. unit cost, battery life, difficult terrain, technical malfunctions), monitoring few individuals over relatively short time series (de Barba et al. 2010a). Genetic sampling provides a novel and permanent means to monitor in situ movement patterns, allowing inclusion of larger sample sizes; moreover, non-invasive sampling reduces the risk of altered behaviors due to handling or physical tags (Planes & Lemer 2011; Schwartz et al. 2007).

Multi-locus genotypes generated from genetic samples acquired at wild nest cavities in the MBR, Guatemala were employed to estimate relative relatedness indices to monitor individual movements across breeding seasons. Recognizing the potential reduction in power for genetic mark-recapture studies, the pairwise rxy approach was taken due to a highly constrained

127 sampling strategy. It is well recognized adult psittacines are difficult to capture (Meyers

1994), therefore fieldwork concentrated on procuring tissue samples from sedentary nestlings.

Molted feathers were also collected opportunistically in an attempt to increase sample size (de

Barba et al. 2010b) and generate genetic profiles for adults, however, data quality varies widely among these more degraded samples (Gebhardt et al. 2009). As a result, the final data set was biased toward a single age class (i.e. nestlings), included a priori knowledge that each sample represented a unique genetic ID (i.e. zero probability the individual was sampled in previous field seasons), and presented a considerable delay in potential recapture events (i.e. when/if nestlings reach sexual maturity at approximately five years of age). With continued genetic sampling as part of a longitudinal study, it is possible a significant portion of the reproductive population may be incorporated into the genetic database for individual mark-recapture analyses.

However, for the purposes of this study, the relative relatedness approach was considered a reasonable proxy for elucidating preliminary insights into individual movement patterns.

Among 666 pairwise comparisons within the MBR data set, only eight dyads (1.2%), representing nine individuals, were indicative of potential recapture events (Table 3.13). One female (CO32_a_F_07) sampled in 2007 as an adult at the nest “Cariba” shared relatedness coefficients suggestive of a first-order relative with a pair of chicks (CO30_c1_F_08 and

CO31_c2_F_08) hatched at the same site, but in the nest “Magnolia”. Moreover, all three individuals carry the same control region haplotype and share at least one allele per nuclear locus. Given patterns of inheritance, it is not possible with the current data set to determine whether CO32_a_F_07 represents the dam or sibling to CO30_c1_F_08 and CO31_c2_F_08, but these data are highly suggestive all three individuals are part of a single nuclear family group. A second adult (PE38_a_M_08) genetically tagged in 2008 at the nest “TE #20” shared high rxy

128 estimates for three chicks sampled over two consecutive years. The lone chick

(PE46_c_M_08) in nest “Diana” also sampled in 2008 shares a mitochondrial haplotype and one allele per locus with PE38_a_M_08; whereas the sibling pair (PE57_c1_M_09 and

PE58_c2_M_09) hatched in nest “P11-C” at the same site in 2009 only share one allele per locus with the adult male. The absence of a shared mitochondrial haplotype across both clutches excludes the possibility all three chicks being offspring of the same breeding pair. One hypothetical scenario to explain these patterns is PE38_a_M_08 being a sibling of

PE46_c_M_08, but the sire of PE57_c1_M_09 and PE58_c2_M_09; alternatively, the high degree of relatedness between the 2008 and 2009 clutches may represent an event of mate- switching or extra-pair paternity. The last dyad of interest is a male chick (PE10_c_M_04) hatched in 2004 at the nest “Lily” in Peru, who shares relatively high rxy estimates and at least one allele per locus with a chick (BU49_c_M_09) from nest “Lolita” in Burral. Interestingly, the two chicks do not share a control region sequence, falsifying a full sibling relationship; however,

PE10_c_M_04 would have reached the minimum age of sexual maturity (i.e. five years) in 2009, highlighting the possibility of a genetically documented recruitment event. Irrespective of exact kinship category, no putative first-order relatives were found to occupy the same nest cavity across years, a finding corroborated by the observation chicks sampled within the same nest do not exhibit higher mean pairwise rxy estimates relative to individuals from independent cavities, nest mates notwithstanding (Table 3.12, Fig. 3.8).

As mentioned above, DNA extracts were highly degraded for a subset of contemporary samples (i.e. old, molted feathers); in these cases, only mitochondrial haplotypes were available for analysis, thus preventing inclusion of these individuals in pairwise rxy analyses. To increase overall sample size and attempt better characterization of temporal patterns in nest use, albeit

129 very simplistically, haplotype distributions were generated showing the composition of maternal lineages present within each focal nest site per breeding season (Table 3.7). A similar haplotype distribution was also generated for the eight nest cavities where genetic samples were acquired over two or more field seasons (Table 3.8). Both analyses revealed a unique combination of control region sequences within each nest site or specific cavity across years, indicating high levels of annual turnover in reproductive pairs.

Acknowledging insights into individual behavior patterns are extremely preliminary, results obtained from population genetic analyses, mean and pairwise rxy analyses, and mitochondrial haplotype distributions support the same conclusion: scarlet macaws nesting in the

MBR, Guatemala do not demonstrate philopatric tendencies in regard to individual nest cavities, with limited evidence of breeding pairs returning to the same focal nest site. This observation raises the question of possible ecological factors driving reproductive pairs to seek alternative nest cavities between breeding attempts. Based on empirical observations in Tambopata, Peru,

Brightsmith (2005) argues competition represents one factor dictating nest use by scarlet macaws; first, the specific size requirements needed to accommodate this large bird preclude use of the majority of available cavities, while second, multiple psittacine species with concurrent breeding seasons intensifies demand for suitable nest sites (Renton 2004). However, interspecific competition among large psittacines does not apply to scarlet macaws breeding in Mexico,

Guatemala and Belize, given A. m. cyanoptera represents the only macaw species found among these moist tropical lowland forests (Forshaw 2006). While intraspecific competition is noted in

LSM (Garcia 2011; Iñigo-Elías 1996), the reported intensity is less severe relative to the

Peruvian Amazon where three large macaw species (A. ararauna, A. macao, and A. chloroptera) coexist. Interspecific competition for nest cavities with non-psittacine species, however, is

130 relatively common in the MBR (Garcia 2011); nest competitors include the collared forest falcon (Micrastur semitorquatus), bat falcon (Falco rufigularis), kinkajou (Potus flavus), barn owl (Tito alba), with an increasing number of cavities being colonized by Africanized bees (Apis mellifera). Many of these species are likewise known to prey upon scarlet macaw eggs and chicks; attacks by the collared forest falcon (Micrastur semitorquatus) are considered the primary cause of nest failure in the MBR, Guatemala (Garcia 2011). Interestingly, predatory attacks demonstrate a non-random distribution, where the same scarlet macaw nest cavities exhibit high annual predation rates (Garcia 2011); it is plausible breeding pairs may hesitate to reoccupy a nest cavity following predation event. Similarly, ectoparasites demonstrate a clustered distribution among potential scarlet macaw nest cavities in Guatemala (Garcia 2011).

Parasitic infestations are known to influence overall reproductive success and female quality in avian taxa (Tomas et al. 2007), therefore Stanback and Dervan (2001) hypothesize limited nest fidelity in some species may reflect an attempt to reduce parasite loads. Factors influencing cavity selection and use by scarlet macaws are little understood, providing several important avenues for future investigation.

Genetic Health and Links to Demography

Diversity indices recovered high levels of variation for both mitochondrial and nuclear markers within the modern LSM data set (Table 3.2), an observation consistent with reported values for scarlet macaws sampled in Peru and Brazil. Haplotypic, nucleotide and allelic diversity were slightly reduced for A. m. cyanoptera relative to A. m. macao [i.e. Hd = 0.87, π = 0.0056, A = 7.1

(Guatemala; this study) vs. Hd = 0.99, π = 0.031, A = 8.4 (Peru: Gebhardt 2007; Gebhardt &

Waits 2008) and A = 9.5 (Brazil; Presti et al. 2011)], however broader examination of four

131 macaw genera (Anodorhynchus, Ara, Cyanopsitta, and Orthopsittaca) revealed populations of A. macao to be the most genetically diverse (Caparroz et al. 2009; Faria et al. 2008; Gebhardt

2007; Gebhardt & Waits 2008; Presti et al. 2011). As discussed above, A. macao is a long-lived, volant, habitat generalist, which seemingly does not exhibit philopatric tendencies within its extensive geographic distribution; these characteristics support larger effective population sizes and foster accumulation of genetic variation by minimizing the effects of genetic drift. Positive correlations between diversity indices, range size, and habitat requirements were also observed across macaw species for all measures of molecular variation. For example, taxa with large distributions, but more restricted ecological niches, exhibit a reduction in mean number of alleles per locus [e.g. green-wing macaws: A = 5.1 and 5.2 (A. chloroptera: Gebhardt & Waits 2008;

Presti et al. 2011); blue-and-gold macaws: A = 4.3 and 5.3 (A. ararauna: Caparroz et al. 2009;

Gebhardt & Waits 2008); chestnut-fronted macaws: A = 3.67 (A. severus: Gebhardt & Waits

2008); red-bellied macaws: A = 4.0 (Orthopsittaca manilata: Gebhardt & Waits 2008); and hyacinth macaws: A = 2.3 and 3.8 (Anodorhynchus hyacinithus: Faria et al. 2008; Presti et al.

2011). Similarly, the lowest levels of nuclear variation were found among species confined to the smallest geographic ranges [Lear’s macaws: A = 2.8 (Anodorhynchus leari: Presti et al. 2011); and Spix’s macaws: A = 2.5 (Cyanopsitta spixii: Presti et al. 2011)].

Although scarlet macaw biology highlights the potential for large effective population sizes, widespread habitat destruction and commercial exploitation have lead to fragmentation and marked demographic declines, especially within A. m. cyanoptera (Juniper & Parr 1998;

Wiedenfeld 1994). Recent reductions in census numbers raise concerns over long-term viability of the subspecies in LSM, as inbreeding and loss of genetic variation are predicted to increase extinction risk by reducing reproductive success and overall evolutionary potential (Blomqvist et

132 al. 2010; Frankham 2003; Lacy 1997; Pertoldi et al. 2007). High diversity indices noted for scarlet macaws currently breeding in LSM, compared to South American taxa, suggest this remnant population has maintained considerable molecular variation; however, patterns of genetic diversity reflect unique historical events, shaped by idiosyncratic demographic histories, ecological interactions, and abiotic conditions (Bouzat 2010). Moreover, the final outcome of population bottlenecks are unpredictable, given the non-deterministic relationship between demography, genomic variation and fitness, especially when considering multi-locus and environmental interactions (Bouzat 2010). Therefore, sister species or demographically stable conspecifics are not appropriate reference proxies for evaluating the genetic health of threatened populations and impacts of anthropogenic activities. Alternatively, archival tissues from museum and natural history collections provide a more accurate means to evaluate changes in genetic variation by direct sampling of the same population pre- and post-decline (Bouzat 2000).

Due to problematic amplification of microsatellite markers from museum skins, temporal comparisons of genetic diversity for A. m. cyanoptera are limited to analysis of mitochondrial control region haplotypes; however, these data provide important preliminary insights into recent demographic trends in LSM. Composite values of sequence variation were near equivalent across historical and modern sample sets (hLSM: Hd = 0.895, π = 0.572, S = 19; mLSM: Hd =

0.866, π = 0.562, S = 18; Table 3.2), suggesting the intensity and/or duration of population declines have been insufficient to produce a measureable genetic bottleneck across the humid lowland forests of Mexico, Guatemala, and Belize. Although total levels of molecular variation indicate possible demographic stability in LSM, an observation consistent with non-significant neutrality tests (Table 3.5), these results should be interpreted with caution. Other large, long- lived avian species have demonstrated significant retention of molecular diversity after marked

133 demographic declines, thus arguing longevity may act as an intrinsic buffer against the rapid loss of genetic variation (Bourke et al. 2010; Hailer et al. 2006). For example, Bourke et al.

(2010) found the British golden eagle (Aquila chrysaetos), a species with life history traits similar to the scarlet macaw [e.g. five years to sexual maturity, overlapping generations, lifespan of >20 years, average clutch size of three to four eggs, socially, and presumably genetically, monogamous (Ferguson-Lees & Christe 2001)], maintained historical levels of heterozygosity and allelic diversity across 13 polymorphic loci after sustaining a nadir of 100-200 breeding pairs for eight generations.

Although initially encouraging, the presence of high genetic variation in long-lived species may mask effects of demographic instability introduced by habitat alteration and anthropogenic exploitation, resulting in a sudden and marked loss of diversity when census numbers fall below an unknown critical threshold. Specifically, systematic removal of scarlet macaw nestlings over extended periods of time has likely produced an uneven age distribution in

LSM (Britt 2011; Garcia et al. 2008), heavily skewed toward older individuals (Clum 2008).

Uncertainty in the magnitude of demographic disequilibrium raises concerns the high genomic variation observed for A. m. cyanoptera ranging across Mexico, Guatemala, and Belize reflects an inflated, retrospective measure of diversity rather than an accurate representation of current genetic status. Depending on in situ productivity (i.e. number of successful fledglings) during peak periods of nest poaching, few younger reproductive pairs may be available to replace older individuals as they reach senescence in LSM; a unexpected decline in breeding pairs, prompted by rapid generational turnover, has the potential to accelerate genetic erosion within an otherwise seemingly stable population. Marsden and Pilgrim (2003) stated similar concerns for weak correlations between current species abundance and extinction risk for hornbills and numerous

134 psittacine species in Papua New Guinea; long-lived taxa remained relatively common across field sites despite extremely low annual recruitment rates. To date, accurate age determination of non-captive born scarlet macaws is not possible; methodologies quantifying telomere length hold promise, however previous work revealed a non-deterministic relationship between cellular aging and chromosomal modifications among five avian species with varying lifespan

(Haussmann et al. 2003). Therefore, longitudinal studies employing multi-locus genetic IDs are needed to monitor demographic trends and turnover in reproductive pairs.

While the specific age distribution is unknown, statistical analysis of spatial genetic structure has already revealed evidence of demographic disequilibrium for A. m. cyanoptera in

LSM [i.e. restricted gene flow between central and eastern portions of the population’s range

(Fig 3.2; Table 3.3; Table 3.4)]. Independent estimation of diversity indices for geographically partitioned sample sites lends further support for recent changes in regional demography, while also underscoring the potential differential response of population fragments to variance in intensity of local anthropogenic perturbations. Temporal analysis of genetic diversity for scarlet macaws in LSM_E revealed a marked reduction in sequence variation for modern exemplars relative to archival tissues collected within the same sampling locality and composite hLSM diversity indices (Table 3.2). In contrast, assessment of historical and modern genetic tissues originating in LSM_C revealed haplotypic diversity and number of segregating sites increased through time (Table 3.2); although novel mutations or an influx of recent migrants may explain the greater sequence variation among modern tissues collected in the MBR, Guatemala, differences in temporal sample sizes represent a more probable alternative hypothesis.

Congruence between summary statistics for overall sequence variation within mLSM_C and total levels of historical haplotypic diversity in LSM, coupled with high contemporary variation

135 among nuclear loci, imply local demographic equilibrium (Table 3.2). However, further examination of the data set uncovered a distinct temporal shift in the composition and frequency of mitochondrial haplotypes. Among the most poignant examples is the increased prevalence of

CR1 in the MBR, Guatemala; this control region sequence was not found among any historical

A. m. cyanoptera exemplar, yet is currently the most common haplotype found within this sampling locality (Figure 3.4).

Apparent overrepresentation of a single mtDNA haplotype within a restricted geographic distribution raises concerns of possible inbreeding in LSM_C; however, uniparental inheritance of mitochondrial haplotypes limits their utility in resolving genealogical relationships. Nuclear loci represent a more powerful suite of molecular markers for detecting non-random mating by incorporating genetic information from both parents. For Neotropical psittacines, previous studies employed band sharing coefficients (BSC) inferred from DNA fingerprints to assess the degree of inbreeding (i.e. higher mean values cited as evidence for a greater proportion of close relatives within the population) across several taxa, revealing generally positive correlations between BSC and threatened status (Brock & White 1992; Caparroz et al. 2001a; Caparroz et al.

2001b; Nader et al. 1999). Although more informative than mitochondrial sequences, inferences of kinship based on minisatellite BSC are rudimentary at best, reflecting the anonymous nature of screened loci, ill-defined inheritance patterns, and unknown selective pressures (Frankham et al. 2002). Application of multi-locus microsatellite genotypes largely circumvents issues faced by previous molecular markers (i.e. greater variability, Mendelian inheritance, selective neutrality, and targeted amplification of known genomic fragments), greatly improving estimates of coancestry. Notwithstanding the more informative nature of genotypic data, Pemberton (2008) recommends cautious interpretation of rxy, citing limitations in statistical analysis, especially

136 when the data matrix includes few or low variability loci. However, Jones and Wang (2010) state imprecise estimates of relative relatedness of greater concern for evolutionary studies investigating biological phenomena, where exact multi-generational pedigrees are essential (e.g. inbreeding depression, heritability, cooperative breeding), and further argue pairwise relatedness indices are well-suited to elucidate population-level parameters.

Mitochondrial haplotypes, coupled with a simplistic dual analytical approach (i.e mean rxy, 95% threshold across two independent relatedness estimators) for nuclear genotypes, were employed to assess the prevalence of close kin in LSM_C (i.e. sampling localities in the MBR,

Guatemala; Fig. 3.2); unfortunately, the absence of contemporary tissues from Mexico, and the highly degraded nature of DNA extracts originating in the CFR, Belize, preclude a wider geographic scope for pedigree analysis in LSM. Among the available data, indices of relatedness inferred within multi-chick clutches served as a theoretical proof of method given scarlet macaws are believed to be socially and genetically monogamous, a hypothesis supported by molecular work conducted for other Neotropical psittacines (Caparroz et al. 2011; da Silva et al.

2010; Masello et al. 2002). All nest mate dyads shared mitochondrial haplotypes (Table 3.13), indicative of common maternal descent, and mean rxy estimates were consistent with expected values (rT = 0.5) for full siblings (Fig. 3.8, Table 3.12); seven of eight multi-chick clutches were successfully identified as putative relatives based on the 95% threshold approach (Table 3.13), yielding a reasonable empirical error rate of 0.125 given the limited sample size. Broader application of these methodologies across the entire mLSM_C sample set revealed average pairwise relatedness indices in agreement with theoretical expectations (rT = 0) for an outbred population (rxyLR = -0.018, rxyML = 0.016; Fig. 3.7); only 44 dyads (6.6%) yielded rxy estimates above one or both threshold values for familial groups (Table 3.13). The majority of non-nest

137 mate pairs included within the putative kin matrix (81.8%) demonstrated indeterminate pedigree relationships (i.e. lack of shared mitochondrial haplotype and/or one allele per autosomal locus), indicating low incidence of first-order relatives within the general population

(Table 3.13). Interestingly, independent analysis of dyads carrying the same control region haplotypes, including known nest mates, failed to increase mean relative relatedness coefficients, despite a higher likelihood of shared maternal heritage (Fig. 3.8, Table 3.12). Further truncation of the data set to only include individuals sharing the common CR1 haplotype yielded surprisingly similar results (Figure 3.8, Table 3.12); average rxy did not significantly deviate from theoretical expectations (rT = 0) for panmixia, thus suggesting inbreeding is not responsible for overrepresentation of this mtDNA sequence in LSM_C. The apparent paucity of close kin across data partitions, excluding presumed sibling groups (i.e. nest mates), is an encouraging sign that recent population fragmentation and intense commercial exploitation have not prompted an increased incidence of consanguineous pairings in LSM_C, and demographic instability does not pose an imminent threat to population viability in Mexico, Guatemala and Belize.

Nonetheless, current rxy estimates among scarlet macaws nesting in the MBR, Guatemala are rudimentary at best, thus definitive statements claiming the absence of inbreeding in LSM_C are premature. Genetic captures to date likely represent a small proportion of the overall population, given logistical and biochemical constraints hinder sample collection and analysis

(i.e. short time series, restricted geographic scope, DNA degradation in old molted feathers).

Biological and behavioral factors also confound inferences of pedigree structure in LSM; evidence suggesting lack of philopatric tendencies and suspected long recuperation periods between breeding attempts further reduce the probability of sampling close kin over a few field seasons. Moreover, assumptions of monogamy in scarlet macaws have not been tested

138 empirically; the presence of mate switching or extra-pair paternity would inflate the number of putative non-relatives within a limited data set biased toward nestlings. Similarly, the apparent lack of close kin in LSM_C may reflect a significant temporal shift in breeding pairs (i.e. rapid manifestation of a generational gap produced by historical commercial exploitation), however genetic profiles from reproductive adults are needed to evaluate turnover rates within the breeding population. Continued genetic monitoring, with broadened geographic distribution of sampling localities (i.e. CFR, URDB), would assist in resolving more robust estimates of population pedigrees and subsequent assessment of overall conservation needs for scarlet macaws in LSM.

Conservation Implications

The hierarchical assessment of molecular genetic data presented here creates an empirical framework to better characterize key biological parameters for A. m. cyanoptera at the population (e.g. spatial substructure, genetic health, demography) and individual (e.g. movement patterns, kinship, fitness) level. While reiterating the utility of descriptive studies to enhance conservation decision-making, by establishing biologically meaningful goals and prioritizing hands-on mitigation actions, this work also highlights the central role of long-term research for species and/or habitat protection. Longitudinal studies are necessary to unravel the complex realm of natural populations (Schwartz et al. 2007); the full extent of demographic and behavioral parameters of interest may only be observable over an extended time series, especially for long-lived, highly mobile, and/or elusive taxa. Similarly, demographic and genetic bottlenecks are generally aftereffects, developing over time in response to detrimental anthropogenic activities (e.g. population fragmentation, commercial exploitation), with the

139 potential to manifest after primary threats have been abated (Clum 2008). Continuous surveillance efforts allow local managers to monitor population trends in real time, thus shortening response times if significant deviations from equilibrium baselines are detected

(Athrey et al. 2011; Martínez-Cruz et al. 2007).

Although widely recognized as an important feedback mechanism for programmatic development, long-term scientific endeavors are often underappreciated for providing direct conservation benefits to wildlife populations; the physical presence of field researchers deters poaching and land conversion within protected areas, thus creating refugia for threatened and over-exploited species. For example, Campbell et al. (2011) found distance to research area was more powerful than human population density and forest type in explaining encounter rates (i.e. cumulative weighted Akaike information criteria ranging between 0.67 and 1.0) across numerous duiker and primate species in the Taï National Park, Ivory Coast, and reported strong negative correlations between signs of poaching (e.g. empty cartridges, snares, poacher’s trails and camps) and proximity to long-term research sites. A similar situation appears to be unfolding in the lowland tropical forests of Guatemala; in 2002, the Wildlife Conservation Society initiated a research program to locate active nest cavities and monitor reproductive success for scarlet macaws in the MBR, and subsequently expanded scientific efforts to include four additional key

“landscape species” [i.e. white-lipped peccary (Tayassu pecari), jaguar (Panthera onca), Baird’s tapir (Tapirus bairdii), and Mesoamerican river turtle (Dermatemys mawii)], resulting in a marked reduction in deforestation and commercial exploitation rates within areas frequented by field staff (McNab 2011). It is worth noting, the protective influence of local research and management action in Guatemala creates a critical safe haven for nesting scarlet macaws, with reverberating effects for the survival of A. m. cyanoptera on a regional scale. Given the apparent

140 absence of philopatric tendencies, these protected nest cavities are a potential resource for the broader LSM population, thus reducing inbreeding and genetic erosion by equalizing reproductive outputs as breeding pairs “rotate” through the MBR.

However, securing scarlet macaw nest sites represents one component of a multidisciplinary management approach employed in Guatemala, where habitat protection and anti- poaching patrols are augmented by direct hands-on interventions including anti-predation efforts, supplemental feeding, and veterinary treatment. Since implementation, these actions have prompted a dramatic increase in local reproductive success for scarlet macaws in the MBR, with the number of fledglings per active nest cavity rising from 0.07 in 2003 to 1.21 in 2011 (McNab

2011) and the observed nest survival rate of 61% (Britt 2011) far exceeds the minimum threshold

(i.e. 32%) simulated for population stability in LSM based on VORTEX viability models (Clum

2008). Interestingly, several studies have shown rapid growth after severe demographic bottlenecks has significant implications for population recovery by helping mitigate the loss of genetic diversity, particularly for long-lived species with overlapping generations (Hailer et al.

2006; Lerner et al. 2009). Kuro-o et al. (2010) found short-tailed albatross (Phoebastria albatrus), a species exhibiting an average generation of seven years and producing one egg per clutch, retained 90-92% of mitochondrial haplotypes and considerable haplotypic diversity (h =

0.96) despite experiencing an extreme nadir of ~50 to 60 individuals. In this case, the authors attribute maintenance of molecular variation to periods of exponential growth (i.e. up to 6.7% annually), arguing the severity of genetic bottlenecks is more strongly influenced by duration, rather than intensity, of demographic declines. Considering similarities in the life history traits of the short-tailed albatross and scarlet macaw, and high levels of contemporary genomic diversity recovered for A. m. cynaoptera in LSM_C, local conservation efforts should remain focused on

141 increasing in situ fledging and recruitment rates (i.e. bolster population growth) in an attempt to circumvent threats of genetic erosion.

While commending the recent successes achieved in the MBR, it is important to note the scope and intensity of management programs are not uniform across the region; inconsistencies in the scope and implementation of local conservation initiatives presumably have important implications for A. m. cyanoptera throughout LSM, most notably when assessing population responses to mitigation actions. As an illustration, research and monitoring effort in the CFR,

Belize are more problematic relative to breeding areas in Guatemala, reflecting the site’s remote location, rugged terrain, and minimal infrastructure, and intermittent field presence of conservation practitioners, resulting in the high incidence of nest failure due to poaching (i.e. 9 of 20 monitored nest cavities in 2010) along the Macal and Raspaculo Rivers (Britt 2011).

Continual harvesting of scarlet macaw nestlings widens the generational gap and perpetuates demographic disequilibrium across Belizean lowland forests; commercial exploitation has also driven productivity rates in the CFR [i.e. 25% nest survival in 2010 (Britt 2011)] below the theoretical critical threshold of 32% necessary for population stability (Clum 2008). Extraction methods (i.e. destruction of nest trees) further constrain long-term reproductive potential with significant consequences for population viability; specifically, systematic loss of available cavities in the CFR limits the number of breeding females, the most influential factor determining population growth for scarlet macaws in LSM (Clum 2008). Furthermore, in situ nest cavities are not an abundant resource for this large-bodied species [i.e. scarlet macaws rely on primary excavators to produce suitable nest hollows in mature trees of sufficient age and girth

(Cornelius et al. 2008; Renton & Brightsmith 2009)], and it may take years to generate a new cavity to replace one felled by poachers. While strengthening anti-poaching patrols may protect

142 nesting trees and bolster fledging rates, these efforts would only partially address the legacy of anthropogenic threats in the CFR, Belize; a possible shortage of scarlet macaw nest cavities, created by past decades of intense commercial exploitation, will likely continue to hinder reproductive rates and delay local population recovery. Heightening conservation concerns, preliminary evidence suggests LSM_E may be entering the early stages of population collapse, given field-based surveys and habitat modeling infer small census estimates (Garcia et al. 2008;

Manzanero 1991) and diversity indices reported here imply a recent reduction in molecular variation. Prolonged demographic declines are hypothesized to accelerate changes to the underlying demographic and genetic structure of this remnant population, increasing the probability of local extirpation in Belize.

Given scarlet macaw breeding areas are not independent entities, but nested within broader population dynamics, disparities among local management programs in the eastern and central portions of LSM may confound regional conservation efforts by obscuring overall demographic trends. For example, areas experiencing rapid growth (i.e. MBR, Guatemala) may serve as a source of individuals to reinforce small remnant populations with low nest survival rates (i.e. CFR, Belize). Although high productivity in the MBR hold the potential to slow population declines, uncertainty remains whether a surplus of fledglings in Guatemala is sufficient to establish demographic equilibrium and/or promote positive growth rates throughout

LSM. Of additional concern, no current estimates of reproductive success are available for the

URDB; the last assessment published in 1996 reported nest survival rates above the critical threshold for population stability (37%; Iñigo-Elías 1996), however it is likely this value has decreased in recent years due to intensification of illicit activities (e.g. drug trafficking, deforestation) along the Mexico/Guatemala border. The possibility of a second demographic sink

143 in LSM places increased pressure on management activities in the MBR, Guatemala, resulting in heightened extinction risk for A. m. cyanoptera throughout the region. In summary, it is imperative for wildlife managers to consider the conservation status of surrounding remnant populations when assessing the success of local programs, while also striving to enhance regional management efforts and strengthen transnational collaborations via enhanced communication, development of joint management objectives, and integration of mitigation actions.

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Figure 3.1: Map depicting sampling localities for historical specimens. Light Yellow: La Selva Maya West (hLSM_W); Light Blue: La Selva Maya Central (hLSM_C); Light Green: La Selva Maya East (hLSM_E); Dark Grey: A. m. cyanoptera South (hAmcy_S). Light Grey: Areas within the historical distribution of A. m. cyanoptera not included in the present study. Modified from Juniper and Parr (1998).

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Figure 3.2: Map illustrating contemporary nest sites and sampling localities in La Selva Maya. Dark Blue: Maya Biosphere Reserve, Guatemala (mLSM_C); Dark Green: Chiquibul Forest Reserve, Belize (mLSM_E). Focal nest sites in Guatemala: CO: Corona, BU: Burral, PE: Perú, CT: Chuntuquí. Light Grey: Reported distribution in 1950; Dark grey: Reported distribution in 2001. Diagonal Stripe: Breeding area and non-breeding communal foraging area along the Usumacinta River drainage basin (URDB), Guatemala/Mexico. Modified from Iñigo- Elias et al. (2001).

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Table 3.1: Primers for PCR amplification and sequencing.

Gene Size (bp) Primer Sequence Reference

Control Region 850 CRL380 GCCCATAAYTGGCCCGGGAACTT Tavares et al. 2004 AmCR1r GGACGGAGTACGCAAATAGCG This study

AmCR2F TCGGTCAGGCACATAACTTGG This study AmCR2r AGACGAACCCACACCTGAAACC This study AmCR3F CAGTGCAATGGGTATACAATTGG This study AmCR3r AAGTAACCCATGAGATTCAAACC This study AmCR4F CTCGCTTTACATCTCTTTCTCG This study AmCR4r ATTTAAGTGGAGATGTTAAAAAGAGG This study AmCR5F GACTATTCGATCAATGGTCGC This study AmCR5r ATTAAGGCAGAGGAGTCAGG This study

Table 3.2: Indices of genetic variation for mtDNA haplotypes and microsatellite genotypes in A. m. cyanoptera.

Mitochondrial DNA Microsatellites -2 Population n h Hd π x10 S n A HO HE a. Historical Amcy_S 19 9 0.901 (0.043) 0.488 (0.04) 12 - - - -

b. Historical LSM 20 11 0.895 (0.052) 0.572 (0.126) 19 - - - - hLSM_W 10 5 0.756 (0.130) 0.681 (0.176) 15 - - - - hLSM_C 3 2 0.667 (0.314) 0.706 (0.333) 9 - - - - hLSM_E 7 6 0.952 (0.096) 0.347 (0.079) 8 - - - -

c. Modern LSM 44 11 0.866 (0.028) 0.562 (0.034) 18 - - - - mLSM_C 35 10 0.834 (0.044) 0.580 (0.040) 17 37 7.11 0.713 0.696 mLSM_E 9 3 0.667 (0.105) 0.249 (0.136) 8 - - - - h, number of haplotypes

Hd, haplotype diversity (SD) π, nucleotide diveristy (SD) S, number of segregating sites A, mean number of alleles per locus

HO, observed heterozygosity

HE, expected heterozygosity

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Figure 3.3: Median-joining networks showing relationships among historical A. m. cyanoptera haplotypes. Circle diameter is proportional to the number of individuals carrying each haplotype; circles shown in the legend reference a single individual. Lines connecting haplotypes represent single base pair mutations. If multiple substitutions separate two haplotypes, the number of mutations steps is indicated by cross bars. Haplogroup labels follow numerical designations given in Chapter 2.

Table 3.3: Analysis of molecular variance (AMOVA) and global tests of differentiation for putative cyanoptera populations and sampling localities in La Selva Maya based on haplotypic data.

Population Source of variation df % of variation P value χ2 P value Exact-test P value a. Historical A. m. cyanoptera hAmcy_S Among populations 1 8.43 0.024** 0.024* 0.001** hLSM† Within populations 37 91.57 b. Historical La Selva Maya hLSM_W Among populations 2 1.68 0.311 0.067 0.021* hLSM_C Within populations 17 98.32 hLSM_E c. Modern La Selva Maya mLSM_C Among populations 1 20 0.000** 0.002** 0.000** mLSM_E Within populations 43 80 † Includes all historical La Selva Maya specimens *P<0.05, **P<0.01, Significant values shown in bold

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Figure 3.4: Median-joining networks showing relationships among haplotypes recovered in La Selva Maya. Circle diameter is proportional to the number of individuals carrying each haplotype; circles shown in the legend reference a single individual. Lines connecting haplotypes represent single base pair mutations. If multiple substitutions separate two haplotypes, the number of mutations steps is indicated by cross bars. Color scheme is consistent with Fig. 3.1 and 3.2, indicating historical and modern data sets. Haplogroup labels follow numerical designations given in Chapter 2.

Table 3.4: Pairwise fixation indices for putative cyanoptera populations and sampling localities in La Selva Maya based on mtDNA sequences.

Population 1 Population 2 Fixation indices

FST ϕST a. Historical A. m. cyanoptera hLSM† hAmcy_S 0.076** 0.084* b. Historical La Selva Maya hLSM_W hLSM_C 0.222 -0.482

hLSM_W hLSM_E 0.087 0.031 hLSM_C hLSM_E 0.154 0.092 c. Modern La Selva Maya mLSM_C mLSM_E 0.2** 0.218** † Includes all historical La Selva Maya specimens *P<0.05, **P<0.01, Significant values shown in bold

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Table 3.5: Indicators of demographic change in La Selva Maya.

Population n D Fs R2 a. Total La Selva Maya hLSM† 20 -0.525 -5.396* 0.118 mLSM†† 44 0.488 0.615 0.132 b. Modern La Selva Maya mLSM_E 9 -1.284 1.919 0.277 mLSM_C 35 0.641 0.807 0.145 D, Tajima 1989

Fs, Fu 1997

R2, Ramos-Onsins and Rozas 2002 † Includes all historical La Selva Maya samples †† Includes all modern La Selva Maya samples *P<0.05, **P<0.01, Significant values shown in bold

Table 3.6: Genetic divergence among nests sites in the Maya Biosphere Reserve, Guatemala.

Mitochondrial DNA Microsatellites Nest Site Source of variation df % of variation P value df % of variation P value a. Analysis of molecular variance Perú Among populations 2 3.4 0.179 2 0.12 0.309 Burral Within populations 31 96.6 67 99.88 Corona

Mitochondrial DNA Microsatellites

Nest Site 1 Nest Site 2 FST ϕST FST b. Fixation indices Perú Burral 0.021 0.148 0.002

Perú Corona 0.049 -0.061 -0.005 Burral Corona 0.022 0.067 0.009 *P<0.05, **P<0.01, Significant values shown in bold

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Figure 3.5: Individual population membership coefficients (Q) demonstrating the relative contribution of putative genetic clusters (K = 2 to 5) for individuals sampled at three focal nest sites in the Maya Biosphere Reserve, Guatemala.

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Table 3.7: Mitochondrial haplotype distributions across sampling years for nest sites in the Maya Biosphere Reserve, Guatemala.

Control Region Haplotype Distribution† Nest Site n Nests CR1 CR2 CR4 CR5 CR7 CR8 CR9 CR10 CR11 CR12 a. Burral 2004 5 3 2 1

2009 6 4 2 1 1 b. Chuntuquí 2009 2 1 1 c. Corona 2006 3 1 1 2007 2 2 1 1 2008 7 3 2 1 2009 2 2 2 d. Perú

2004 8 5 2 1 1 1 2006 7 4 1 1 1 1 2008 7 4 3 2 1 2009 6 3 2 1 1 Totals 55 13 2 4 6 1 4 2 1 1 1 † Pseudo-replicates removed

Table 3.8: Mitochondrial haplotype distribution for reoccupied nest cavities across sampling years. Control region haplotypes recovered from adults highlighted in bold.

Control Region Haplotype Nest Cavity Site 2004 2006 2007 2008 2009 Lolita Burral CR1 CR1 Rosario Burral CR2 CR5 Cariba Corona CR4 CR9 Magnolia Corona CR1 CR5 CR4 CR1 Artificial 10 Perú CR1 CR5, CR1 Lily Perú CR2 CR8, CR5 P11-C Perú CR9, CR8 CR10 R-13 Perú CR1 CR1

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Table 3.9: Mean relatedness (m) and standard deviation (SD) for 1000 simulated pairs of known relationship categories: parent/offspring (PO), full sibling (FS), half sibling (HS), and unrelated (UR) based on empirical allele frequencies. Smallest sampling variances per category highlighted in bold. P values show departures from theoretical relatedness values.

Relationship PO rT = 0.5 FS rT = 0.5 HS rT = 0.25 UR rT = 0 m SD P value m SD P value m SD P value m SD P value

rxyQG 0.482 0.140 0.000** 0.489 0.206 0.424 0.237 0.206 0.052 -0.005 0.215 0.425 rxyLR 0.488 0.187 0.053 0.495 0.231 0.901 0.244 0.216 0.105 -0.008 0.155 0.000** rxyW 0.48 0.292 0.129 0.587 0.283 0.000** 0.41 0.289 0.000** 0.304 0.462 0.000** rxyML 0.494 0.101 0.000** 0.507 0.195 0.024* 0.257 0.194 0.091 0.02 0.168 0.244 rxyQG, Goodnight and Queller 1999

rxyLR, Lynch and Ritland 1999

rxyW, Wang 2002 rxyML, Maximum Likelihood, Goodnight and Queller 1999 *P<0.05, **P<0.01

Table 3.10: Misclassification rate of unrelated (UR) as first-order relatives (PO: parent/offspring and FS: full sibling) based on the midpoint method, as described by Blouin et al. (1996). The best performing estimator for each comparison is highlighted in bold.

Relatedness Estimator

Simulated Misclassified as rxyQG rxyLR rxyW rxyML

UR PO 0.134 0.063 0.470 0.111 UR FS 0.133 0.062 0.443 0.105

r , Goodnight and Queller 1999 xyQG rxyLR, Lynch and Ritland 1999

rxyW, Wang 2002

rxyML, Maximum Likelihood, Goodnight and Queller 1999

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Figure 3.6: Distribution of observed relatedness values (closed circle) recovered within the Maya Biosphere Reserve, Guatemala overlaid with simulated distributions for 1000 unrelated (open circle), full sibling (open diamond) and parent/offspring (open triangle) dyads for two relatedness estimators. Gray dashed lines indicate 95% upper threshold values for simulated unrelated pairs (see text). A) Lynch and Ritland (1999), UR95 = 0.281; B) Maximum Likelihood, Goodnight and Queller (1999), UR95 = 0.351.

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Table 3.11: Evaluation of mean relatedness across focal nest sites in the Maya Biosphere Reserve, Guatemala. P values show departures from theoretical value for unrelated individuals (UR: rT = 0).

rxyLR rxyML Nest Site n m SD P value m SD P value a. One sample Wilcoxon ranked-sign test Peru 91 -0.025 0.220 0.002** 0.006 0.228 0.216 Burral 45 0.023 0.241 0.550 0.056 0.221 0.294 Corona 56 -0.006 0.197 0.236 0.020 0.234 0.466 *p<0.05, **p<0.01, Significant values shown in bold

Figure 3.7: Pairwise rxy plot showing the mean (± one standard deviation), minimum and maximum values within the total sample and each of three focal nest sites in the Maya Biosphere Reserve, Guatemala.

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Table 3.12: Influence of shared haplotypes and/or nest cavities on mean relatedness of scarlet macaws in the Maya Biosphere Reserve, Guatemala. P values show deviations from theoretical values for unrelated individuals (UR: rT = 0) and full siblings (FS: rT = 0.5).

rxyLR rxyML Group n m SD P value m SD P value a. One sample Wilcoxon ranked-sign test

HapNO/NestNO 537 -0.031 0.145 <0.0001 0.003 0.161 0.322

HapYES/NestNO 103 -0.010 0.149 0.120 0.020 0.154 0.615

HapNO/NestYES 11 -0.045 0.189 0.175 -0.003 0.210 0.365 CR1 only 85 0.001 0.155 0.151 0.026 0.156 0.642

† † HapYES/NestYES 12 0.541 0.297 0.733 0.619 0.209 0.092 (i.e. nest mates) † Mean rxy tested against rT = 0.5 *p<0.05, **p<0.01, Significant values shown in bold

Figure 3.8: Pairwise rxy plot showing the mean (± one standard deviation), minimum and maximum values showing the relative influence of shared mitochondrial haplotypes and/or nest cavity.

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Table 3.13: Pairs of putative relatives sampled in the Maya Biosphere Reserve, Guatemala based on rxy indices of Goodnight and Queller (1999; Maximum Likelihood) and Lynch and Ritland (1999). Putative relationship categories: parent/offspring (PO), full sibling (FS), indeterminate (IN). Abbreviations for field sites given in text. Shared control region haplotypes and/or nest cavities shown in bold.

Individual 1 Individual 2 ML LR

Sample ID CR Nest Sample ID CR Nest rxy P value rxy P value

a. Both rxy estimates above 95% threshold FS PE13_c2_M_04 7 Po. 2 PE12_c1_F_04 7 Po. 2 0.413 0.023 0.411 0.025 PE2_c2_F_04 1 Art. 10 PE1_c1_F_04 1 Art. 10 0.898 0 0.958 0 CO31_c2_F_08 4 Magnolia CO30_c1_F_08 4 Magnolia 0.621 0.001 0.458 0.016 CO35_c1_F_08 4 Elvia CO34_c2_M_08 4 Elvia 0.634 0 0.318 0.051 CO37_c2_M_08 9 Cariba CO29_c3_F_08 9 Cariba 0.614 0.001 0.528 0.007 PE4_c2_M_04 8 Art. 11 PE3_c1_M_04 8 Art. 11 0.553 0.002 0.567 0.005 BU48_c2_M_09 1 Steve BU47_c1_F_09 1 Steve 0.891 0 0.823 0 PE58_c2_M_09 10 P11-C PE57_c1_M_09 10 P11-C 0.748 0 0.86 0 BU9_c2_M_04 2 Rosario BU8_c1_M_04 2 Rosario 0.906 0 0.971 0

PO / FS CO32_a_F_07 4 Cariba CO30_c1_F_08 4 Magnolia 0.466 0.01 0.353 0.039 CO32_a_F_07 4 Cariba CO31_c2_F_08 4 Magnolia 0.555 0.002 0.572 0.004

PO PE38_a_M_08 5 TE #20 PE57_c1_M_09 10 P11-C 0.468 0.01 0.332 0.046 PE38_a_M_08 5 TE #20 PE58_c2_M_09 10 P11-C 0.35 0.041 0.35 0.04

IN PE44_c_F_08 8 Lily PE4_c2_M_04 8 Art. 11 0.354 0.039 0.381 0.031 PE44_c_F_08 8 Lily PE10_c_M_04 2 Lily 0.564 0.002 0.444 0.019 CHU59_c_M_09 1 Hilda CO30_c1_F_08 4 Magnolia 0.444 0.014 0.309 0.056 BU50_c_F_09 12 Po. 5 CO36_c1_M_08 9 Cariba 0.418 0.023 0.285 0.067 BU48_c2_M_09 1 Steve CO29_c3_F_08 9 Cariba 0.481 0.007 0.571 0.004 BU47_c1_F_09 1 Steve CO29_c3_F_08 9 Cariba 0.514 0.004 0.564 0.005

b. One rxy estimate above 95% threshold FS CO37_c2_M_08 9 Cariba CO36_c1_M_08 9 Cariba 0.444 0.014 0.229 0.096 CO36_c1_M_08 9 Cariba CO29_c3_F_08 9 Cariba 0.422 0.021 0.123 0.178

PO / FS PE38_a_M_08 5 TE #20 PE46_c_M_08 5 Diana 0.435 0.017 0.074 0.239

PO PE10_c_M_04 2 Lily BU49_c_M_09 1 Lolita 0.39 0.029 0.162 0.141

IN PE13_c1_M_04 7 Po. 2 BU7_c_F_04 1 Lolita 0.077 0.272 0.297 0.061 CO29_c3_F_08 9 Cariba PE1_c1_F_04 1 Art. 10 0.432 0.019 0.006 0.377 CO32_a_F_07 4 Cariba PE12_c1_F_04 7 Po. 2 0.27 0.069 0.285 0.067 CO32_a_F_07 4 Cariba PE13_c1_M_04 7 Po. 2 0.392 0.026 0.185 0.123 CO34_c2_M_08 4 Elvia PE1_c1_F_04 1 Art. 10 0.207 0.105 0.522 0.008 CO34_c2_M_08 4 Elvia PE2_c2_F_04 1 Art. 10 0.203 0.14 0.489 0.011 CO35_c1_F_08 4 Elvia BU6_c2_F_04 1 Leslie 0.349 0.041 0.326 0.048 CO36_c1_M_08 9 Cariba CO31_c2_F_08 4 Magnolia 0.036 0.351 0.328 0.047 PE38_a_M_08 5 TE #20 BU9_c2_M_04 2 Rosario 0.35 0.041 0.25 0.084 PE46_c_M_08 5 Diana CO35_c1_F_08 4 Elvia 0.435 0.017 0.183 0.125 PE46_c_M_08 5 Diana CO36_c1_M_08 9 Cariba 0.435 0.017 0.176 0.13 BU47_c1_F_09 1 Steve BU6_c2_F_04 1 Leslie 0.253 0.081 0.428 0.022 BU48_c2_M_09 1 Steve BU6_c2_F_04 1 Leslie 0.25 0.083 0.317 0.052 BU48_c2_M_09 1 Steve CO34_c2_M_08 4 Elvia 0.446 0.014 0.186 0.123 BU48_c2_M_09 1 Steve CO35_c1_F_08 4 Elvia 0.495 0.006 0.257 0.08 BU50_c_F_09 12 Po. 5 PE1_c1_F_04 1 Art. 10 0.39 0.029 0.262 0.078 BU50_c_F_09 12 Po. 5 PE2_c2_F_04 1 Art. 10 0.39 0.027 0.226 0.097 CHU59_c_M_09 1 Hilda PE12_c1_F_04 7 Po. 2 0.272 0.067 0.285 0.067 CHU59_c_M_09 1 Hilda PE13_c1_M_04 7 Po. 2 0.247 0.084 0.323 0.049 CHU59_c_M_09 1 Hilda CO31_c2_F_08 4 Magnolia 0.444 0.014 0.126 0.174 BU64_c1_F_09 5 Rosario PE12_c1_F_04 7 Po. 2 0.268 0.07 0.303 0.058

c. Known nest mates with neither rxy estimate above 95% threshold FS BU6_c2_F_04 1 Leslie BU5_c1_F_04 1 Leslie 0.283 0.062 0.25 0.085 a, adult c, chick (c1, c2, c3 indicate member of multi-chick clutch) M/F, sex assignment (male/female)

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Table 3.14: Sex ratio for nestlings in the Maya Biosphere Reserve, Guatemala.

Chick Sex Ratio Year n Males Females P value 2004 13 7 6 1 2006† 8 - - - 2008 9 4 5 1 2009 11 7 4 0.549 Total 18 15 0.729 † Extensive DNA degradation preclude sex assignment *P<0.05, **P<0.01, Significant values shown in bold

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Appendix II: List of historical specimens included in the present study.

Specimen ID Contributor Specimen code Country Date Sample Type a. La Selva Maya West (hLSM_W) 57821 National Museum of Natural History USNM10 Mexico 1868 toe pad 72226 Museum of Comparative Zoology MCZ6 Mexico 1868 toe pad 272843 Museum of Comparative Zoology MCZ7 Mexico unk toe pad 80 Western Foundation for Vert. Zoology WFVZ2 Mexico unk toe pad 5056 Western Foundation for Vert. Zoology WFVZ3 Mexico unk toe pad 12837 Western Foundation for Vert. Zoology WFVZ4 Mexico unk toe pad 25001 California Academy of Science CAS1 Mexico 1905 toe pad 26056 California Academy of Science CAS2 Mexico 1905 toe pad 26057 California Academy of Science CAS3 Mexico 1905 toe pad 94051 University of Michigan Museum of Zoology UMMZ1 Mexico 1937 toe pad

b. La Selva Maya Central (hLSM_C) 089998 National Museum of Natural History USNM6 Guatemala 1883 toe pad 10879 Western Foundation for Vert. Zoology WFVZ1 Mexico unk toe pad 10880 Western Foundation for Vert. Zoology WFVZ5 Mexico unk toe pad

c. La Selva Maya East (hLSM_E) 19683 Field Museum of Natural History FMNH5 Guatemala 1905 toe pad 19684 Field Museum of Natural History FMNH6 Guatemala 1905 toe pad 19685 Field Museum of Natural History FMNH7 Guatemala 1905 toe pad 39878 Field Museum of Natural History FMNH8 Guatemala unk toe pad 237572 National Museum of Natural History USNM9 Honduras 1901 toe pad 136567 Museum of Comparative Zoology MCZ5 Honduras 1928 toe pad 20535 Louisiana State University LSU 1 Belize unk toe pad

d. A. m. cyanoptera South (hAmcy_S) 102717 American Museum of Natural History AMNH8 Nicaragua 1908 toe pad 143776 American Museum of Natural History AMNH9 Nicaragua 1917 toe pad 21867 Field Museum of Natural History FMNH10 Nicaragua 1905 toe pad 19625 Field Museum of Natural History FMNH11 Panama 1901 toe pad 19626 Field Museum of Natural History FMNH12 Panama 1901 toe pad 19627 Field Museum of Natural History FMNH13 Panama 1901 toe pad 040964 National Museum of Natural History USNM11 Nicaragua unk toe pad 112231 National Museum of Natural History USNM7 Honduras 1887 toe pad 112232 National Museum of Natural History USNM8 Honduras 1887 toe pad 460654 National Museum of Natural History USNM12 Panama 1956 toe pad 55921 Yale Peabody Museum YPM1 Costa Rica 1925 toe pad 55922 Yale Peabody Museum YPM2 Costa Rica 1925 toe pad 55923 Yale Peabody Museum YPM3 Costa Rica 1925 toe pad 15669 UCLA-Dickey Collection UCLA1 El Salvador 1925 toe pad 15670 UCLA-Dickey Collection UCLA2 El Salvador 1925 toe pad 75201 Academy of Natural Sciences ANSP 2 Nicaragua unk toe pad 75202 Academy of Natural Sciences ANSP 3 Nicaragua unk toe pad 29047 Louisiana State University LSU 2 Honduras unk toe pad 33543 California Academy of Science CAS4 Honduras 1930 toe pad

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CHAPTER 4:

MAJOR CONCLUSIONS AND RECOMMENDATIONS FOR SCARLET MACAW (ARA MACAO CYANOPTERA)

CONSERVATION MANAGEMENT IN LA SELVA MAYA, CENTRAL AMERICA

This dissertation aimed to describe a model approach for the systematic examination of spatial and temporal genetic variation within a threatened taxon to elucidate fundamental questions regarding recent evolutionary history and species biology, while highlighting the utility of these molecular data in developing effective wildlife management strategies.

Conservation practitioners are charged with the difficult task of identifying and ameliorating anthropogenic (e.g. habitat conversion, exploitation) and biological (e.g. invasive species, emergent diseases, hybridization) threats to wildlife, yet efforts are confounded by a general dearth of information regarding the underlying complexities of natural populations and impact of recent human-mediated disturbances. Moreover, resources are limited for mitigation activities, thus local managers must ensure funds are allocated in the most appropriate manner to maximize conservation successes. Molecular genetics has come to be an important player in this process by describing macroevolutionary patterns and microevolutionary processes; examining the degree and distribution of molecular variation across multiple hierarchical levels (e.g. species, population, individual) creates an empirical framework to infer key ecological, behavioral, and life history traits influencing population dynamics, evaluate current conservation status, and define the inherent scope of management initiatives. The latter is especially important given the extended geographic distribution of many imperiled taxa and near ubiquitous human- mediated threats; global protection efforts are generally beyond the purview of individual management programs, with possible exception of highly restricted endemics. Therefore, it is

172 imperative to place the intended target for local conservation action, the focal management target (FMT), within the broader context of intraspecific diversity, because genetic affinities with surrounding conspecifics may have significant ramifications for the design of management interventions and overall conservation outcomes.

Study System

To illustrate the real world applicability of the proposed theoretical approach, this work defines the FMT (differentiated below by *) as a group of scarlet macaws (Ara macao) under threat in the Maya Biosphere Reserve (MBR*), Guatemala. Initiated in 2002, the Wildlife Conservation

Society (WCS) – Guatemala Program’s scarlet macaw project aimed to monitor local demographic trends, noting extremely low fledging rates and high incidence of land conversion near key nest sites. In subsequent years, WCS-Guatemala has made significant strides to ameliorate anthropogenic threats by working closely with local communities to raise awareness of environmental issues, promote involvement in conservation activities, and develop viable economic alternatives to unsustainable natural resource extraction. In tandem, local managers have emphasized the need to expand mitigation strategies to enhance reproductive success and support long-term viability in the MBR*. To better achieve this goal, WCS-Guatemala has expressed interest in employing molecular genetic analyses to refine local conservation priorities, evaluate ongoing in situ interventions, and identify critical deficiencies in current management strategies, including consideration of a potential genetic recovery program.

Several innate characteristics make the scarlet macaw an ideal case study to explore the role of molecular genetics in resolving biological uncertainties and guiding conservation management programs. For example, the species occupies a wide array of habitat types

173 throughout the highly heterogeneous Neotropics, albeit with a preference for humid lowland forests. Scarlet macaws also exhibit a high capacity for dispersal, as exemplified by seasonal movements in response to shifts in resource availability. Reaching sexual maturity at age five, this long-lived species continues to produce offspring for up to an additional 20 years; however, overall annual reproductive rates are low, with an average of one or two successful fledglings per nest attempt, thus naturally constraining population growth. As with many psittacines, scarlet macaws are sexually monomorphic, with all age classes (e.g. immature, reproductively active, senescent) demonstrating similar morphological characters post-fledging, thus confounding efforts to monitor individual movements and fecundity using field-based methodologies.

Although characterization of natural population dynamics itself presents a challenge to conservation practitioners, recent alterations introduced by anthropogenic activities (e.g. habitat modification, commercial exploitation) may lead to disruptions in population equilibrium and erroneous inferences of basic biological parameters. Large-scale land conversion presents a generalized threat to critical scarlet macaw nest sites, foraging areas, and migration corridors; these activities have resulted in numerous local extinction events, with unknown effects on underlying patterns of spatial genetic structure. Alternatively, habitat modifications can specifically target scarlet macaws and compound the deleterious effects of commercial exploitation, as exemplified by the loss of nest trees to facilitate extraction of chicks to meet demands of the exotic pet trade. Systematic removal of scarlet macaw nestlings slows annual population growth rates, whereas selective destruction of suitable nest cavities greatly reduces future reproductive potential. In the event cavities remain intact, sustained periods of increased nest failure may produce significant generational gaps within affected populations; intense poaching pressures may also drive differential reproductive success among family groups, if

174 nestlings taken from the same cavity over consecutive years are siblings. Yet regardless of specific mechanism, any changes to the population’s underlying demographic composition raise the risk of inbreeding and loss of genetic diversity.

While scarlet macaw populations have suffered marked census declines throughout the species’ range, the effects of recent human-mediated threats are most pronounced in Central

America. Vast tracts of prime habitats have been cleared to support the region’s high human population densities; La Selva Maya (LSM), a tri-national system of protected areas in Mexico,

Guatemala and Belize, is the largest fragment of lowland forests remaining in Central America.

Field surveys suggest three primary scarlet macaw breeding areas remain within LSM: 1)

Usumacinta River drainage basin (URDB), Mexico/Guatemala, 2) Maya Biosphere Reserve

(MBR*), Guatemala, and 3) Chiquibul Forest Reserve (CFR), Belize. Despite being in close geographic proximity (i.e. within 300 km), the intensity of habitat modifications and commercial exploitation vary significantly across the three sites, reflecting differences in national environmental policy, socioeconomic factors, general accessibility, and on-site presence of conservation practitioners. The relative influence of local variations in demographic stability and reproductive success on long-term viability of scarlet macaws breeding in the MBR*, Guatemala is largely dependent on whether management inconsistencies are site-specific or integrated into broader population dynamics; however, patterns of historical and contemporary genetic connectivity between Mexico, Guatemala and Belize are little understood. Radio telemetry studies have confirmed seasonal movements from the MBR* to the URDB; while the congregation of scarlet macaws within communal foraging sites during the non-breeding season does not equate effective gene flow, it represents a plausible mechanism for genetic connectivity.

Conversely, loss of intervening habitats between the CFR and remaining nest areas in LSM (i.e.

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MBR*, URDB) may limit opportunities for dispersal, thus resulting in the genetic isolation of scarlet macaws breeding in Belize.

The present study examined molecular genetic data within a hierarchical framework to characterize general biological parameters indicative of scarlet macaws, while also placing the focal management unit (i.e. MBR*, Guatemala) within the context of broader population dynamics in order to evaluate the extent of conservation need, monitor demographic trends, and identify critical management gaps.

SYNTHESIS OF GENETIC INSIGHTS INTO SPECIES BIOLOGY

Among the most relevant findings is the remarkable consistency of molecular results across analytical methodologies and hierarchical levels, with inferred behavioral and life history traits providing context for broader patterns of genetic diversification.

Scarlet macaws exhibit high dispersal capabilities and extensive habitat requirements, due to spatial and temporal shifts in critical resources (e.g. fruiting phenology, water availability), with little empirical evidence of fidelity to a specific nest cavity or breeding site

(Chapter 3: Site Fidelity and Nest Use). Moreover, the species demonstrates a wide habitat tolerance; therefore it is interesting to note the importance of abiotic factors (i.e. elevation and relative humidity) in constraining individual movements and gene flow. Specifically, the distribution of humid lowland forests appears to represent the quintessential force shaping population differentiation, with examination of historical population substructure within A. m. cyanoptera further supporting this hypothesis (Chapter 3: Hierarchical Assessment of Genetic

Structure). Scarlet macaw nest sites located across widespread moist tropical ecosystems with relatively little surface relief (e.g. LSM) are genetically homogeneous, whereas marked

176 contractions in preferred forest habitats associated with the complex highland system in upper Central America result in reduced genetic connectivity. However, frequency-based differences in mitochondrial haplotype distributions suggest permeable population boundaries, with the possibility of unidirectional gene flow facilitated by easterly air currents and small moist forest “stepping stones” along the Caribbean slope of Honduras. In contrast, population aggregation analyses conducted across the entire species’ range found fixed nucleotide characters associated with more robust geographic barriers (Chapter 2: Patterns of

Diversification). Populations separated by major geologic formations (i.e. Andean Mountains,

Isthmus of Panama, central cordilleras of Costa Rica and Panama) are diagnosably distinct; while undeniably important in generating and maintaining genetic boundaries between subspecies and macao lineages, topographical variation represents a single mechanism of physical separation.

Climatic oscillations likely altered the paleodistribution of ecological assemblages across low- lying Neotropical landscapes, most notably in Central America, thus promoting the vicariant origin of A. m. cyanoptera’s five haplogroups within isolated forest refugia. Recent population fragmentation highlights the influential power of habitat modifications by demonstrating measureable shifts in haplotype distributions can manifest among breeding areas within a few generations after the disappearance of key migration corridors (Chapter 3: Hierarchical

Assessment of Genetic Structure). High indices of sequence variation, allelic richness, and heterozygosity, presence of low frequency haplotypes, and minimal inbreeding reported among contemporary nest sites in the MBR*, Guatemala imply limited dispersal opportunities, rather than genetic drift, are responsible for observed changes in local molecular diversity (Chapter 3:

Genetic Health and Links to Demography), further emphasizing how disruptions in the distribution of preferred habitats drive genetic discontinuities in scarlet macaws.

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Conversely, empirical evidence of widespread spatial movements and panmixia across continuous tracts of humid lowland habitats provides a contextual framework to explain the significant accumulation of genetic diversity within A. m. cyanoptera. For example, as adverse climatic conditions subsided and moist forest ecosystems regenerated to reach their modern distributions in Central America, the geographic extent of ancestral cyanoptera populations expanded in tandem, thus establishing contact between previously allopatric lineages

(Chapter 2: Patterns of Diversification); introgression among these divergent genomes would bolster overall intrasubspecific variation. Moreover, large effective population sizes, supported by continued gene flow across disparate nest sites (Chapter 3: Site Fidelity and Nest Use), buffer against the loss of molecular diversity via genetic drift (Chapter 3: Genetic Health and Links to

Demography). Although the recent loss of critical habitats between eastern and central portions of LSM has altered historical mitochondrial haplotype frequencies, the noted absence of philopatric tendencies has important implications for helping maintain the genetic health within each remnant population. Exploitation pressures are unevenly distributed across the region, with local intensity dependent on proximity to human settlements, accessibility, and efficacy of protection efforts; within heavily exploited areas, poachers systematically target known scarlet macaw nest cavities over multiple years. However, based on current molecular data, these activities do not appear to prompt differential reproductive success among individuals, given family groups demonstrate a lack of fidelity to a specific cavity or focal nest site. Therefore a considerable portion of the population has an opportunity to nest within unknown, inaccessible and/or protected (e.g. MBR*, Guatemala) nest cavities in any given year, thus helping equalize recruitment among reproductive pairs, reduce inbreeding rates, and mitigate the loss of genetic diversity (Chapter 3: Genetic Health and Links to Demography).

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Longevity also plays an influential role in maintaining high levels of genomic variation within scarlet macaw populations (Chapter 3: Genetic Health and Links to

Demography). With an average reproductive lifespan of up to 20 years, recruitment is not contingent on a single nesting attempt; production of more offspring over time by individual breeding pairs increases the probability of genetic representation in the next generation, despite low annual reproduction rates and occasional nest failure events (e.g. predation, parasite infestation, structural damage to cavity). As fledging rates began falling below background levels in the 1980s, due to widespread harvesting of chicks to meet increasing demands of the international exotic pet trade, the prolonged reproductive period of scarlet macaws provided a potential intrinsic mechanism to mitigate genetic bottlenecks (Chapter 3: Genetic Health and

Links to Demography). For example, nest poaching in the MBR*, Guatemala was largely abated in the early 2000s; therefore, individuals fledging just prior to peak trade years may have continued to breed after exploitation pressures subsided in the area. These latter nesting attempts represent a final opportunity for recruitment of pre-exploitation molecular diversity in LSM, thus weakening the effects of genetic drift, albeit with greatly diminished lifetime reproductive success (i.e. high rates of nest failure due to poaching) as aging breeding pairs approach senescence.

EVALUATION OF CONSERVATION NEEDS

Analyses of spatial and temporal genetic variation argue for designating the MBR*, Guatemala as a high conservation priority, while also emphasizing several key areas of uncertainty in need of consideration when evaluating local program outcomes.

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Phylogenetic approaches revealed two genetically distinct taxonomic units, generally concordant with currently described subspecies, and classified scarlet macaws nesting in the

MBR*, Guatemala as A. m. cyanoptera (Chapter 2: Evaluation of Current Taxonomy).

Confirmation of cyanoptera populations as an independent evolutionary unit highlights the inadequacy of the International Union for Conservation of Nature’s (IUCN) current listing of scarlet macaws as a single species of ‘Least Concern’. Elevating the threatened status of A. m. cyanoptera would more accurately reflect the subspecies’ vulnerability to extinction due to intense human-mediated threats, a stark contrast to the relatively robust populations of A. m. macao distributed across South America (Chapter 3: Conservation Implications). Moreover, A. m. cyanoptera harbors five of seven unique mitochondrial haplogroups (Chapter 2: Patterns of

Diversification), underscoring the subspecies’ importance to overall intraspecific variation and adding a sense of urgency for local management initiatives. Conservation activities in LSM are especially critical, given two evolutionary lineages are endemic to these humid lowland habitats; both were found among contemporary nest sites in the MBR*, Guatemala (Chapter 3:

Hierarchical Assessment of Genetic Structure), emphasizing the need for effective conservation management.

Based on molecular inferences, a critical step in optimizing efforts would be the expansion and integration of local scarlet macaw conservation management programs, as the

MBR*, Guatemala appears to be nested within broader population dynamics. Non-significant tests for spatial substructure among museum skins collected at geographically disparate sampling sites imply, within a historical state of equilibrium, scarlet macaws ranging from the Isthmus of

Tehuantepec in Mexico to the Caribbean slope of Belize and Guatemala comprise a single demographic unit (Chapter 3: Hierarchical Assessment of Genetic Structure). Analysis of

180 modern samples revealed the absence of philopatric tendencies within the MBR*, further emphasizing the potential for regional gene flow (Chapter 3: Site Fidelity and Nest Use).

However, in recent decades, widespread conversion of lowland forests for infrastructural (e.g. settlements, transportation) and agricultural (e.g. cultivation, ranching) purposes has confined the distribution of A. m. cyanoptera to a tri-national system of protected areas centered around the

MBR*, Guatemala. Despite being the largest tract of relatively pristine scarlet macaw habitats remaining in Central America, the limited carrying capacity of LSM implies a de facto reduction in effective population size. Moreover, observed temporal shifts in haplotype frequencies and highly significant fixation indices suggest restricted gene flow between contemporary breeding areas in the CFR, Belize and the MBR*, presumably due to the loss of migration corridors across central Guatemala (Chapter 3: Hierarchical Assessment of Genetic Structure). It is important to recognize the extent of ongoing connectivity between these two nest sites has important ramifications for prioritizing local conservation actions and assessing program successes, given genetic and demographic linkages dictate the overall response to mitigation strategies. Additional complexities arise when considering regional differences in specific and general anthropogenic threats, and intensity of current management initiatives; it may be inappropriate to infer demographic trends, genetic heath, or extinction risk for the broader scarlet macaw population in

LSM based on idiosyncratic site-specific conditions. Closer examination of available data for the

MBR*, Guatemala and CFR, Belize further illustrates the potential disparity between conservation needs and defining management recommendations across local and regional scales.

Despite the recent success of habitat protection efforts championed by WCS-Guatemala and in-country collaborators, land conversion remains a very serious threat to A. m. cyanoptera in the western MBR, as illegal colonization continues to encroach upon focal nest sites. Although

181 anthropogenic activities are generalized across entire ecosystems, not specifically targeting

A. m. cyanoptera, these actions have important implications for the remnant population breeding in Guatemala. Contraction of critical lowland forest corridors limits the ability of individuals to disperse to adjacent breeding sites (i.e. CFR, URDB); altered patterns of regional genetic exchange provide a logical hypothesis to explain the distinct shift in the haplotype distribution observed among historical and contemporary exemplars sampled in the MBR* (Chapter 3:

Genetic Health and Links to Demography). However, population- and individual-based measures of molecular variation (e.g. haplotype diversity, allelic richness, heterozygosity) were comparable between temporal samples, inferring significant retention of genetic diversity despite acute census declines and population fragmentation (Chapter 3: Genetic Health and Links to

Demography). Pairwise indices of relative relatedness recovered little evidence of inbreeding among individuals sampled in the MBR*, as average values across the entire sample are indicative of unrelated dyads (Chapter 3: Genetic Health and Links to Demography). Moreover, males and females are equally represented within each cohort of successful fledglings, thus helping reinforce local demographic stability (Chapter 3: Genetic Health and Links to

Demography). Indices of reproductive success have steadily risen in recent years in response to the highly active field presence of WCS-Guatemala (i.e. nest monitoring, in situ veterinary treatment and husbandry efforts, predator deterrence), with average nest survival rates in the

MBR* consistently exceeding theoretical values simulated for a stable population. In addition, commercial exploitation has largely been abated in the area, a reflection of anti-poaching patrols, environmental education initiatives, and involvement of local communities in conservation management programs. Therefore, while concerns remain regarding the continued deterioration of lowland forest habitats, these data suggest genetic and demographic factors are not an

182 immediate threat to the population remnant currently nesting in the MBR*, Guatemala

(Chapter 3: Genetic Health and Links to Demography).

Molecular analyses suggest a very different situation is unfolding in the CFR, Belize; while results are preliminary (i.e. small sample size, sex ratios and kinship structure remain unevaluated), a notable temporal decrease in haplotype and nucleotide diversity imply recent genetic erosion in response to local anthropogenic pressures (Chapter 3: Genetic Health and

Links to Demography). Humid forest ecosystems along the Macal and Raspaculo Rivers host the smallest estimated census population of scarlet macaws in LSM and is presumed isolated from the MBR*, Guatemala and URDB, Mexico due to loss of migration corridors. Primary threats in the central Maya Mountains specifically target A. m. cyanoptera; intense poaching pressures and continual destruction of nest cavities reduce annual recruitment rates and foster a state of perpetual demographic disequilibrium. The cumulative effects of this nocent triad (i.e. small effective population size, genetic isolation, exploitation) serve as a reasonable mechanism to explain the possible accelerated manifestation of bottleneck effects in the CFR, relative to the

MBR*. Moreover, minimal infrastructure and rugged terrain limit scarlet macaw monitoring efforts to the Chiquibul Reservoir and navigable sections of the upper Macal and Raspaculo

Rivers, with field staff working under threat of violent encounters with Guatemalan xateros.

Although further investigation is necessary to more fully assess the paucity of molecular diversity and extent of demographic instability among scarlet macaw breeding sites in Belize, inferences based on available data raise concerns of potential population collapse (Chapter 3:

Genetic Health and Links to Demography). While conservation initiatives in the CFR have advanced significantly in recent years, the current management scheme may be inadequate to offset demographic declines and mitigate loss of molecular variation, highlighting a need to

183 reinforce monitoring and habitat protection efforts, and discuss the feasibility of more intensive strategies to bolster local survival and recruitment rates.

The differential demographic trends observed in the MBR*, Guatemala and CFR, Belize

(i.e. recovery vs. continued decline), coupled with the idiosyncratic nature of local anthropogenic threats (i.e. general vs. specific), advocate treatment of remnant scarlet macaw populations as independent FMTs. Autonomous management of nesting areas under a scenario of complete genetic and demographic isolation provides a straightforward approach to programmatic development; conservation practitioners define management priorities based on site-specific conditions, with demographic trends observed within the MBR* or CFR reflecting overall efficacy of local mitigation actions. However as discussed above, genetic analyses suggest scarlet macaws ranging across the lowland forests of Mexico, Guatemala and Belize constitute a single panmictic population (Chapter 3: Hierarchical Assessment of Genetic Structure), therefore the absence of gene flow between focal nest sites represents a significant departure from historical equilibrium. Subsequent shifts in underlying population dynamics would have significant ramifications for long-term viability of scarlet macaws throughout LSM. Introduction of human-mediated philopatry within the MBR* and CFR would increase the risk of inbreeding due to a restricted pool of potential mates (Chapter 3: Genetic Health and Links to Demography).

Moreover, small, isolated FMTs are more susceptible to extinction via stochastic forces (i.e. demographic, genetic, environmental, catastrophic); robust populations are able to withstand mild to moderate fluctuations in critical biological parameters (e.g. skewed offspring sex ratio or brief reduction in reproductive success), whereas these same perturbations may produce disproportionately greater effects following a significant reduction in effective population size

(Chapter 3: Genetic Health and Links to Demography).

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Additional concerns arise with autonomous management of remnant populations if scarlet macaws are able to successfully traverse patches of denuded landscapes, maintaining historical genetic and demographic connectivity between focal nest sites (Chapter 3:

Hierarchical Assessment of Genetic Structure). While regional gene flow increases overall effective population size, helping counter the influence of stochastic events, variations in reproductive success may generate source/sink dynamics across LSM, with important ramifications for local conservation outcomes (Chapter 3: Conservation Implications). As discussed above, annual nest survival rates in Guatemala exceed critical theoretical values necessary for population stability, suggesting the MBR* has entered a period of recovery.

However, on-going demographic linkages with the CFR, Belize, an area experiencing negative growth rates, may siphon away individuals and eventually deplete the MBR* subpopulation; these effects may be greater if scarlet macaw fledglings are also drawn away from breeding sites in Guatemala to the lowland forests along the Usumacinta River. Documented seasonal movements between the MBR* and URDB, coupled with an observed lack of nest fidelity, strongly support the hypothesis of contemporary genetic and demographic connectivity across the Mexico/Guatemala border (Chapter 3: Site Fidelity and Nest Use). While the conservation status of A. m. cyanoptera along these riparian habitats is currently unknown, the intensity of reported general and specific anthropogenic threats raise doubts as to whether fecundity and survival rates in the URDB are sufficient to maintain population equilibrium. The presence of one or more demographic sinks within LSM would place tremendous pressure on management activities performed by WCS-Guatemala, and ultimately imperil the long-term viability of scarlet macaws across upper Central America (Chapter 3: Conservation Implications). Although high fledging rates in the MBR* may abate regional population declines, it is unlikely these local

185 efforts completely offset losses in the CFR and URDB, resulting in a continued, albeit more gradual, reduction in effective population size throughout LSM. Therefore, while acknowledging the vital importance of site-specific conservation actions, managers must be conscious of broader population dynamics and human-mediated threats across political boundaries to avoid overconfidence when evaluating the efficacy of local management programs (Chapter 3:

Conservation Implications).

In summary, the work presented here emphasizes the evolutionary significance of A. m. cyanoptera and, notwithstanding the role the MBR plays as a crucial population stronghold, strongly encourages designation of breeding areas throughout the lowlands of Mexico,

Guatemala and Belize as a single FMT (LSM*). A collaborative transnational approach to scarlet macaw conservation management more accurately reflects natural population dynamics and, when coupled with monitoring efforts to track temporal trends in critical biological parameters

(e.g. fecundity, survival, genetic diversity), provides an improved platform to identify deficiencies in local and regional mitigation strategies (Chapter 3: Conservation Implications).

Genetic analyses to-date reveal no immediate need for population reinforcement efforts, instead advocate management actions aimed at promoting intrinsic population growth (e.g. anti- poaching, habitat protection, veterinary assistance, supplemental feeding). Increasing reproductive success in LSM* reduces nadir duration and maximizes retention of high genomic diversity reported across the region (Chapter 3: Genetic Health and Links to Demography), whereas equalizing fledging rates across the MBR, CFR, and URDB diminishes the influence of regional source/sink dynamics and bolsters overall population viability (Chapter 3: Conservation

Implications). Although in situ interventions require fewer logistical and financial resources, relative to population augmentation programs, and minimize incidence of negative secondary

186 effects (e.g. disease transmission), future human-mediated wildlife relocation efforts may be warranted for A. m. cyanoptera in LSM*. Uncertainties regarding skew in the current age distribution, turnover rate for reproductive pairs, and strength of demographic declines highlight the potential for a rapid, and marked, reduction in molecular variation within population remnants. Therefore conservation practitioners should take preemptive measures to establish a programmatic foundation (e.g. identify suitable source population, develop captive breeding program) in the event a genetic recovery program is deemed necessary.

SPECIFIC MANAGEMENT RECOMMENDATIONS

Continue Population Monitoring

Longitudinal studies are necessary to track long-term trends in demography and reproductive success, refine estimates of gene flow and genomic variation, and resolve outstanding uncertainties regarding age structure, annual and generational turnover in breeding pairs, and recruitment rates for scarlet macaws in LSM*. Continuous monitoring efforts, integrating field- and laboratory-based techniques, also provide an important feedback mechanism to minimize response times, prioritize conservation actions, and evaluate the efficacy of mitigation strategies in real time. Furthermore, an active and continuous field presence by local conservation practitioners indirectly reduces the intensity of anthropogenic threats.

Expand Habitat Protection Efforts

While local conservation managers have made significant strides to secure critical habitats across portions of A. m. cyanoptera’s range in LSM*, substantial tracts of humid lowland forests remain

187 susceptible to large-scale land conversion and/or selective destruction of nest cavities.

Additional efforts are needed to provide adequate protection to focal nest sites, communal foraging areas, and migration corridors throughout the region.

Investigate Biological, Ecological, and Environmental Factors Influencing Reproductive Success

Given the potential of rapid growth rates to offset demographic declines and minimize genetic bottlenecks, comprehensive multidisciplinary research efforts [e.g. integrating behavior, health parameters (e.g. growth curves, vital rates, hematology), molecular genetics, nutrition, predation, parasite load, habitat quality, and environmental variables] are needed to elucidate the primary factors affecting nestling survival, thus creating a valuable contextual framework to evaluate existing mitigation strategies and guide the development of innovative approaches to bolster annual fledging rates for scarlet macaws in LSM*.

Characterize Commercial Exploitation Pressures

Efforts to mitigate exploitation of A. m. cyanoptera in LSM* should extend beyond broadened anti-poaching patrols, and include determining whether scarlet macaw chicks represent primary or secondary (i.e. opportunistic attempts to maximize financial gains per-unit-effort) commercial targets, identifying key local and international markets, and plotting major trade routes.

Understanding underlying socioeconomic and logistic factors provides a vital context to reinforce law enforcement activities, support local community development projects, and guide environmental education programs.

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Strengthen International Collaborations

While hands-on conservation actions have the most direct impact on fecundity and survival rates, resulting in noteworthy local successes, effective long-term management of scarlet macaws in

LSM requires concerted efforts between international governmental and non-governmental organizations in Mexico, Guatemala and Belize. Solid transnational partnerships are imperative given the species’ spatial requirements and primary anthropogenic threats traverse political boundaries. In addition, international collaborations provide opportunities to broaden the scope of research efforts, disseminate accrued knowledge and technical expertise between well- established and nascent programs, and establish a unified platform to advocate local, national, and regional environmental policy reform.

Develop Local ex situ Breeding Program

Although available data suggest short-term conservation priorities focus on in situ management activities, long-term strategies should include establishing a demographically and genetically stable ex situ breeding program, within one or more range countries, as a safeguard against possible unabated declines and/or catastrophic events threatening the survival of wild scarlet macaws in LSM. Moreover, captive-bred individuals would serve as a ready source of release candidates if future research advocates implementation of reintroduction or population augmentation efforts.