Biology and Conservation of the Endangered Bahama Swallow (Tachycineta Cyaneoviridis)

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Biology and conservation of the endangered Bahama Swallow (Tachycineta cyaneoviridis)

Maya Wilson

Dissertation submitted to the faculty of the Virginia Polytechnic Institute and State University in partial fulfillment of the requirements for the degree of

Doctor of Philosophy In Biological Sciences

Jeffrey R. Walters Daniel H. Catlin Sarah M. Karpanty Joel W. McGlothlin Meryl C. Mims

November 22, 2019 Blacksburg, VA

Keywords: endangered species, population distribution, population differentiation, cavity-nesting, nest-web, The Bahamas

Biology and conservation of the endangered Bahama Swallow

(Tachycineta cyaneoviridis)

Maya Wilson

Abstract

In order to prevent species extinctions, conservation strategies need to incorporate the identification and mitigation of the root causes of population decline with an assessment of vulnerability to genetic and stochastic factors affecting small populations.

Species or populations with small ranges, such as those on islands, are particularly vulnerable to extinction, and deficient knowledge of these species often impedes conservation efforts. The Bahama Swallow (Tachycineta cyaneoviridis) is an endangered secondary cavity-nester that only breeds on three islands in the northern Bahamas:

Abaco, Grand Bahama, and Andros. I investigated questions related to population size and distribution, genetic diversity and population structure, breeding biology, and ecological interactions of the swallow, with the goal of informing the conservation and management of the species. Using several population survey methods on Abaco, I found that swallow site occupancy and density is higher in southern Abaco, especially near roads and pine snags. Future research should prioritize identifying the causes of variable and low population densities in parts of the swallow’s range. I used microsatellite markers and morphometrics to assess differences between populations on Abaco and

Andros. We found a lack of genetic differentiation (G'ST = 0.03) between populations, but

differences in morphology suggest that gene flow might be low enough to enable traits under selection to diverge. By locating and monitoring nests, I found that swallows rely on woodpecker-excavated cavities in pine snags and utility poles, and that swallows nesting in pine snags had higher fledging success (92%) than those nesting in utility poles

(50-62%). Using a cavity nest-web approach, I assessed how swallows interact with cavity-nesting birds and resources on Abaco. Hairy Woodpeckers (Dryobates villosus) primarily excavated pine snags, while West Indian Woodpeckers (Melanerpes superciliaris) excavated utility poles in non-pine habitat. Only swallows and La Sagra’s

Flycatchers (Myiarchus sagrae) used nest sites in the pine forest. Swallows in non-pine habitat face competition for cavities with American Kestrels (Falco sparverius), and non- native House Sparrows (Passer domesticus) and European Starlings (Sturnus vulgaris).

These results highlight the importance of pine forest and the Hairy Woodpecker for the persistence of the swallow.

Biology and conservation of the endangered Bahama Swallow

(Tachycineta cyaneoviridis)

Maya Wilson

General Audience Abstract

In order to prevent species extinctions, conservation strategies need to identify and resolve the problems that cause species to decline, as well as address issues characteristic of small populations. Species or populations with small ranges, such as those on islands, are particularly vulnerable to extinction, and lack of knowledge of these species often impedes conservation efforts. The Bahama Swallow is an endangered species that only breeds on three “pine islands” in the northern Bahamas. The swallow is a secondary cavity-nester, which means that it nests in a cavity, usually either a natural tree hole or a hole created by another bird species. In this study, I investigated where swallows are found on the islands, the genetics and body sizes of populations, nesting biology, and connections with other species, with the goal of providing information for the conservation and management of the species. On Abaco, I found that there are more swallows in the southern part of the island, especially near roads and the dead standing pine trees (pine snags) used for nesting. Future research should assess why there are fewer birds in other parts of the swallow’s range. I tested whether the genetics and body sizes of populations on Abaco and Andros are different from each other. Populations were genetically similar, but may be separated enough to result in body size differences. I

located swallow nests on Abaco, and found that swallows rely on cavities made by woodpeckers in pine snags and utility poles. By visiting some nests repeatedly, I found that swallows nesting in pine snags were more successful than those nesting in utility poles. I also assessed how swallows interact with the other bird species that create and use nesting cavities. Hairy Woodpeckers usually create cavities in pine snags, while West

Indian Woodpeckers use utility poles outside of the pine forest. Swallows nesting in the pine forest compete with fewer bird species for cavities than swallows nesting in other habitats. These results show that managing the forest to retain pine snags and Hairy

Woodpeckers is important for the conservation of the swallow.

Acknowledgements

I would like to sincerely thank my advisor and mentor, Dr. Jeff Walters, for taking a chance on me and this project. His support and guidance have meant a great deal to me.

I would also like to thank the rest of my graduate committee: Drs. Dan Catlin, Sarah

Karpanty, Joel McGlothlin, and Meryl Mims. They have all been extremely supportive, providing guidance and feedback throughout my time at Virginia Tech. Special thanks to Joel for allowing me to conduct my genetics lab work in the McGlothlin lab, and to Dr. Angela Hornsby for her instruction and advice throughout the process.

I am thankful to the Harold H. Bailey fund at Virginia Tech, the Rufford Foundation,

BirdsCaribbean, IDEA WILD, and the Virginia Tech Graduate Research Development Program for providing funding that made this research possible.

Thank you to my friends and colleagues in the Department of Biological Sciences and the Interfaces of Global Change program for being such a supportive community.

Thank you to the Bahamas National Trust for their support of the project, particularly the

Abaco Bahamas National Trust office staff, Kadie Mills and David Knowles, for their logistical support. Special thanks to Marcus Davis for sharing his unique knowledge of the island and for his unwavering support of our field teams.

Thanks to Nicole Acosta, Tivonia Potts, Melanie Wells, Shannan Yates, Ann-Marie

Carroll, and Alix Rincón for their hard work and dedication as research field assistants. This work, and the adventures in the pine forest, would not have been the same without them.

Thanks to my family, especially Mom, Owen, Leah, Davin, and Hope, for their encouragement and support. Thank you to my other family - Anna, Sonia, and Josh - for always being there.

I could not have done this without my dear friend and work-wife, Angie. I will be forever grateful to her for being by my side through every step.

I cannot adequately express my gratitude to my partner and best friend, Sam. I am so thankful for his support, moving our lives to Virginia and spending a cumulative year apart from each other to allow me to complete this work. His constant love and encouragement have meant the world to me.

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Table of Contents Abstract……………………………………………………………………………………………ii General Audience Abstract……………………………………………………………………….iv Acknowledgements……………………………………….………………………………………vi Table of Contents………………………………………………………………..………………viii List of Figures………………………………………………………………..……………………x List of Tables………………...………………………………………………………………..…xii Attribution………………………………………………………………………..…….………xvii Chapter 1: Introduction and Background………………………………………………………….1 Literature Cited…………………...……………………………………………………….4 Chapter 2: Bahama Swallow population distribution on the Abaco Islands……………………...7 Abstract……………………………………………………………………………………7 Introduction………………………………………………………………………..………8 Methods……………………………………………………………………………..……10 Results………………………………………………..……………………………..……16 Discussion…………………………………………………………………………..……18 Literature Cited……………………………………………………………………..……23 Figures……………………………………………………….……………………...……26 Tables…………………………………………………….………………..………..……30 Chapter 3: Genetic diversity, population structure, and morphology of the endangered Bahama Swallow (Tachycineta cyaneoviridis)……..…….…...….…….…….…….………....…….…….44 Abstract……………………………………………………..……………………………44 Introduction…………………………………………………..……………………..……44 Methods……………………………………………………………………………..……46 Results……………………………………………………..………………………..……49 Discussion…………………………………………………………………………..……52 Literature Cited……………………………………………………………………..……56 Figures………………………………………………………………………..……..……63 Tables…………………………………………………………………...…………..……69 Supplementary Materials….……………………………………..………………...…….74

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Chapter 4: Cavity use and breeding biology of the endangered Bahama Swallow (Tachycineta cyaneoviridis): implications for conservation……………………………………………….….. 83 Abstract………………………………..…………………………………………………83 Introduction……………………………………..…………………………………..……84 Methods……………………………………………………………………………..……86 Results…………………………………………………………………..…………..……93 Discussion…………………………………………………………………………..……98 Acknowledgements……………………………………………………………………..104 Literature Cited…………………………………………………………………………106 Figures……………………………………………………………………………..……114 Tables………………………………………………………………….…………..……118 Supplementary Materials………………………...……………………………………..123 Chapter 5: Using the cavity nest-web to inform conservation of the endangered Bahama swallow (Tachycineta cyaneoviridis)………………………………………………………………….....127 Abstract…………………………………………………………………………………127 Introduction…………………………………………………………………………..…128 Methods…………………………………………………………………………....……130 Results……………………………………………………………………………..……137 Discussion………………………………………………………………………………141 Literature Cited……………………………………………………………..……..……150 Figures……………………………………………………………………………..……157 Tables………………………………………………………………………….…..……166 Chapter 6: Summary and Conclusion…………………………………………………………..173 Literature Cited…………………………………………………………………………181

List of Figures

Chapter 2

Figure 1: Map of the Abaco Islands in the northern Bahamas………...……………………..… 26

Figure 2: Maps showing the locations of driving transects (A), walking transects (B) and point count stations (C) on the Abaco Islands ………………………………………………...………27

Figure 3: Occupancy probability (Ψ) by the scaled latitudinal coordinate, based on the top ranking model for driving transect surveys ………………...……………………………...……28

Figure 4: Occupancy probability (Ψ) predicted by the scaled latitudinal coordinate, whether the survey was on a road, and whether snags were present, based on the top ranking model for point count surveys …………………………………………...………………………………….……29

Chapter 3

Figure 1: Map of Grand Bahama, Abaco, and Andros Islands in the northern Bahamas………..63

Figure 2: Bahama Swallow capture sites on Abaco Island and Andros Island………………….64

Figure 3: Ordination plot of the first (PC1) and second (PC2) principal components of scaled allele frequencies of Bahama Swallows on Abaco Island and Andros Island …………………..65

Figure 4: Body mass of female (F) and male (M) Bahama Swallows on Abaco Island and Andros

Island ………………………………………………………………………………………….…66

Figure 5: Head-bill length of female (F) and male (M) Bahama Swallows on Abaco Island and

Andros Island …..……………………………………………………………………………..…67

Figure 6: Wing length of female (F) and male (M) Bahama Swallows on Abaco Island and

Andros Island ………………………………………………………………………..………..…68

Chapter 4

Figure 1: Map of the Abaco Islands in the northern Bahamas……………………………..…..114

Figure 2: Nesting structure classifications including (A) pine snags, (B), utility poles, and (C) cell phone towers, (D) buildings, and (E) other (e.g., a boat trailer)…………………….……..115

Figure 3: (A) Lay dates of Bahama Swallow nests by year. (B) Average daily temperature for each year during the 2015 laying period (119-131, left) and the 2016/17 laying period (128-143, right). …………………………………………………………………………………………...116

Figure 4. Map of the distribution of the nine swallow species in the Tachycineta genus……...117

Figure S1: Examples of corresponding natural color images (A, C) and habitat classification results (B, D). …………………………..……………………...……………………………….124

Chapter 5

Figure 1: Map of the Abaco Islands in the northern Bahamas…………………………………157

Figure 2: The number of cavities in utility poles as a function of habitat (% pine)……………158

Figure 3: Structure height of available snags from surveys (All), snags from surveys with cavities

(W/Cav), and snags with nests of the Hairy Woodpecker (HAWO) and West Indian Woodpecker

(WIWO)……………………………………………………………………………………...... 159

Figure 4: Diameter at breast height (DBH) of available snags from surveys (All), snags from surveys with cavities (W/Cav), and snags with nests of the Hairy Woodpecker (HAWO) and

West Indian Woodpecker (WIWO). …………………………………………………………...160

Figure 5: Habitat (% pine) surrounding nest sites of Hairy Woodpeckers (HAWO) and West

Indian Woodpeckers (WIWO) in pine snags (S) and utility poles (UP)…..……………………161

Figure 6: Occupancy probability (Ψ) predicted by percent pine and the presence of snags, based on the top ranking occupancy model for Hairy Woodpeckers ………………………..…….…162

Figure 7: Occupancy probability (Ψ) predicted by percent pine, based on the top ranking occupancy model for West Indian Woodpeckers …………………………………...…………163

Figure 8: Cavity nest-web diagram on Great Abaco Island…………………………………….164

Figure 9: Habitat (% pine) within 400 m of nests of European Starlings (EUST; n = 8), House

Sparrows (HOSP; n = 7), American Kestrels (AMKE; n = 12), Bahama Swallows (BAHS; n =

173), and La Sagra’s Flycatchers (LAFL; n = 7). ……………………………………………...165

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List of Tables

Chapter 2

Table 1: Transect covariates used for occupancy analysis in program Presence………………..30

Table 2: Point count covariates used for occupancy analysis in program Presence……………..31

Table 3: Model results for hazard-rate (HR) and half-normal (HN) functions with simple polynomial (SP), hermite polynomial (HP), and cosine (COS) adjustments for point count surveys in program Distance …………………………………………………………………….32

Table 4: Point count covariates used for density estimates in program Distance………………..33

Table 5: Model ranking results from program Presence testing the effects of covariates on detection probability (p) in driving surveys……………………………………………………...34

Table 6: Model ranking results from program Presence testing the effects of covariates on occupancy probability (Ψ) in driving surveys………………………...…………………………35

Table 7: Model ranking results from program Presence testing the effects of covariates on detection probability (p) in walking surveys………………………….…………………………36

Table 8: Model ranking results from program Presence testing the effects of covariates on occupancy probability (Ψ) in walking surveys………………………..…………………………37

Table 9: Model ranking results from program Presence testing the effects of covariates on detection probability (p) in point counts…………………………………………………………38

Table 10: Model ranking results from program Presence testing the effects of covariates on occupancy probability (Ψ) in point counts………………………………………………………39

Table 11: Model ranking results from program Distance testing the effects of covariates on the detection function in point counts ………………………….……………………………………40

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Table 12: Model ranking results from program Distance testing the effects of stratification on density estimates in point counts ……………………………………..…………………………41

Table 13: Encounter rate (ER), detection probability (p), and density (birds/km2; D) for competing stratification models from program Distance …...……………………...……………42

Table 14: Results for the relative abundance index differences between covariate groups..……43

Chapter 3

Table 1: Mean diversity estimates of expected heterozygosity (HE), observed heterozygosity

(HO), inbreeding coefficients (FIS), and allelic richness (AR) for each population and all samples combined ….………………………………...……..…………………………………………….69

Table 2: Model ranking results for competing (∆AICc < 2) linear models testing the effects of sex, island, and date of capture (DOC) on body mass ……………….…….……………………70

Table 3: Means and standard errors (SE) of morphological measurements (body mass, head-bill length, wing length) of Bahama Swallows on Abaco Island and Andros Island …..……………71

Table 4: Model ranking results for linear models testing the effects of sex and island on head-bill length…………………………………………………………………………..…………………72

Table 5: Model ranking results for linear models testing the effects of sex and island on wing length………………………………………………………………………………………..……73

Table S1: Results for linkage disequilibrium for each population and for all samples combined, using the log-likelihood ratio statistic for each pair of loci……………. ………….……………75

Table S2: Loci-specific p-values for Hardy-Weinberg exact test based on Monte Carlo permutations of alleles for each population and all samples combined, and bootstrapped null allele frequency estimates …….…………………………………………………………………78

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Table S3: Allele characteristics (number of alleles (N); minimum allele size (Min); mean allele size (Mean); maximum allele size (Max)), diversity estimates (expected heterozygosity (HE); observed heterozygosity (HO); inbreeding coefficients (FIS); allelic richness (AR)) and genetic differentiation (G'ST) for each locus……………………………………….………..……………79

Table S4: Allele characteristics (number of alleles (N); minimum allele size (Min); mean allele size (Mean); maximum allele size (Max)) and diversity estimates (expected heterozygosity (HE); observed heterozygosity (HO); inbreeding coefficients (FIS); allelic richness (AR)) for each locus in each population…………………………………………………………………………..……80

Table S5: Model ranking results for linear models testing the effects of sex, island, and day of capture (DOC) on body mass…………………………………………….………………………81

Chapter 4

Table 1: Mean, standard error (SE), and sample size (n) for cavity measurements and % pine surrounding nests in pine snags and utility poles. ………………………….………………….118

Table 2: Model selection results using Akaike’s Information Criterion (AICC) for competing logistic exposure models testing the effect of covariates on daily survival rate. ………...…….119

Table 3: Proportional measures of nest success across nests and within nests for each season and for all seasons combined.… ……………………………………...…………………………….120

Table 4: Breeding biology parameters for the nine species of swallows in the Tachycineta genus. …………………………………………………………………………………...…..….121

Table S1: Model selection results using Akaike’s Information Criterion (AICC) for logistic exposure models testing the effect of covariates on daily survival rate. …..…………………. 125

Chapter 5

Table 1: Point count covariates used for occupancy analysis in program Presence………...….166

Table 2: Characteristics of all utility poles from surveys (All poles), poles with cavities from surveys (With Cavities), and utility poles containing nests of Hairy Woodpeckers (HAWO) and

West Indian Woodpeckers (WIWO)………………………………………………...………….167

Table 3: Characteristics of all pine snags from surveys (All), snags with cavities from surveys

(With Cavities), and snags containing nests of Hairy Woodpeckers (HAWO) and West Indian

Woodpeckers (WIWO)…………………………………………………………….…………...168

Table 4: Model ranking results for models testing the effects of covariates on Hairy Woodpecker detection probability (p) from program Presence……………………...……………………….169

Table 5: Model ranking results for models testing the effects of covariates on Hairy Woodpecker occupancy probability (Ψ) from program Presence….…………………………………………170

Table 6: Model ranking results for models testing the effects of covariates on West Indian

Woodpecker detection probability (p) from program Presence…………………..…………….171

Table 7: Model ranking results for models testing the effects of covariates on West Indian

Woodpecker occupancy probability (Ψ) from program Presence……………………..……….172

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Attribution

Dr. Jeffrey R. Walters, Harold Bailey Professor, Department of Biological Sciences, 926 West

Campus Drive, 2125 Derring Hall, Virginia Tech, Blacksburg, Virginia 24061. Dr. Walters was my advisor and committee chair and he is a coauthor on all manuscripts in this dissertation

(Chapters 2, 3, 4 and 5).

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CHAPTER 1

Introduction and Background

Declines and extinctions of wildlife populations occur globally as a result of overhunting, habitat loss, degradation and fragmentation, and the effects of non-native species (Diamond

1989, Hoffmann et al. 2010). There is growing evidence that these losses can be exacerbated by synergies among these forces and climate change (Jetz et al. 2007, Brook et al. 2008, Mantyka-

Pringle et al. 2015, Urban 2015, Segan et al. 2016). The gravity of the impact that these threats can have on populations has been particularly well documented in birds (Szabo et al. 2012,

Ducatez and Shine 2017). For example, North America has lost an estimated 3 billion birds since

1970 (Rosenberg et al. 2019). Species or populations with small ranges, such as those on islands, are particularly vulnerable to these adverse pressures, and in fact, most documented recent extinctions of bird species have been on islands (Johnson and Stattersfield 1990, Manne et al.

1999, Simberloff 2000, Sodhi et al. 2004, Fordham and Brook 2010, Szabo et al.. 2012, Wood et al. 2017).

The Bahama Swallow (Tachycineta cyaneoviridis) is an endangered secondary cavity- nester that breeds only in the northern Bahamas. The breeding range of the swallow previously included the islands of Great Abaco, Grand Bahama, Andros, and New Providence (Raffaele et al. 1998), but it appears that the breeding population on New Providence has been extirpated

(Birdlife International 2016). The causes of extirpation have not been identified. These “pine islands” are the only islands within the Bahamian archipelago that contain large areas of

Caribbean pine (Pinus caribaea bahamensis), which is the primary breeding habitat of the

swallow (Smith and Smith 1989, Allen 1996).

The swallow is listed as Endangered by the International Union for the Conservation of

Nature (IUCN) (Birdlife International 2016) because populations appear to have declined sharply in recent decades (Emlen 1977, Smith and Smith 1989, Allen 1996). The Bahamian pine forest was extensively logged for lumber and pulpwood ~1900-1970 (Henry 1974), although population declines have not been linked to this resource extraction. Other possible causes of population decline include recent loss of pine forest due to renewed logging efforts, development and saltwater intrusion, as well as the impacts of invasive species (Allen 1996, Birdlife

International 2016). The most recent research on this species took place on Grand Bahama Island during one breeding season in 1995 (Allen 1996). The lack of current and more detailed information on this endangered species limits the capacity to develop effective conservation strategies.

The declining-population and small-population paradigms of Caughley (1994) provide a useful approach to conservation of endangered species. Typically, species become endangered because changes in the environment alter their population dynamics in ways that cause populations to decline (declining-population paradigm). These changes may reduce carrying capacity or alter vital rates such as mortality and fecundity. Once small, populations are subject to additional threats inherent to small populations (small-population paradigm). These include vulnerability to demographic and environmental stochasticity, and loss of genetic variability due to genetic drift and inbreeding.

The first step in developing a conservation strategy for an endangered species is to identify and address the factors responsible for population decline to prevent further losses.

Identifying agents of population decline requires studying the natural history of the species to

generate a list of conceivable agents of decline, and then testing whether the decline of the species is causally linked with those agents (Caughley 1994). Components of a species’ natural history such as (1) population distribution and density, (2) life history traits and strategies, and

(3) ecological interactions (Herman 2002) can contribute to this identification process.

Estimating the genetic diversity and connectivity of populations can help inform whether a species is vulnerable to the factors associated with small populations.

The central goal of my dissertation research was to assess possible agents of decline for the Bahama Swallow. My research addressed several questions:

1) How does the distribution of the Bahama Swallow vary across a pine island?

2) What is the level of genetic diversity within swallow populations, and are there

genetic and morphological differences among populations?

3) Which cavity-nesting resources are Bahama Swallows using, and what is their

reproductive success in different resources?

4) How does the Bahama Swallow interact with other cavity-nesting birds and cavity

resources?

In Chapter 2 of this dissertation, I addressed question (1) by using population survey data to estimate Bahama Swallow occupancy and density across the Abaco Islands. In Chapter 3, I addressed question (2) by using microsatellite markers and morphometrics to assess genetic and morphological differences between populations on Great Abaco Island and Andros Island. In

Chapter 4, I addressed question (3) by locating and monitoring Bahama Swallow nests. In

Chapter 5, I addressed question (4) by investigating the components of the cavity nest-web on

Great Abaco.

LITERATURE CITED

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Bulletin 108:480–495.

BirdLife International (2016). Tachycineta cyaneoviridis. The IUCN Red List of Threatened

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Brook, B. W., N. S. Sodhi, and C. J. A. Bradshaw (2008). Synergies among extinction drivers

under global change. Trends in Ecology and Evolution 23:453–460.

Caughley, G. (1994). Directions in conservation biology. Journal of Animal Ecology 63:215–

244.

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Ducatez, S., and R. Shine (2017). Drivers of extinction risk in terrestrial vertebrates.

Conservation Letters 10:186–194.

Emlen, J. T. (1977). Land bird communities of Grand Bahama Island: The structure and

dynamics of an avifauna. Ornithological Monographs 24:iii–xi, 1–129.

Fordham, D. A., and B. W. Brook (2010). Why tropical island endemics are acutely susceptible

to global change. Biodiversity and Conservation 19:329–342.

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conservation on the status of the world’s vertebrates. Science 330:1503–1509.

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change on the global diversity of birds. PLoS Biology 5:1211–1219.

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132:167–180.

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(2015). Climate change modifies risk of global biodiversity loss due to land-cover change.

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of the West Indies. Princeton University Press, Princeton, New Jersey, USA.

Rosenberg, K. V, A. M. Dokter, P. J. Blancher, J. R. Sauer, A. C. Smith, A. Paul, J. C. Stanton,

A. Panjabi, L. Helft, M. Parr, and P. P. Marra (2019). Decline of the North American

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implications. Raffles Bulletin of Zoology 48:1–9.

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summary. Bulletin of the British Ornithologists’ Club 109:170–180.

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subtropical forests. Annual Review of Ecology, Evolution, and Systematics 35:323–345.

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Chapter 2

Bahama Swallow population distribution on the Abaco Islands

ABSTRACT

Understanding the distribution of populations is essential to the management and conservation of a species and its habitat. The Bahama Swallow (Tachycineta cyaneoviridis) is an endangered species that breeds only on three islands in the northern Bahamas. The few available estimates of swallow population size indicate that numbers have declined severely, but current population size and distribution across the landscape are unclear. In this study, we used several survey methods to estimate swallow distribution and density across the Abaco Islands. We found that swallows are most prevalent in southern Abaco, especially near roads and in the presence of pine snags. The density estimates produced by distance analysis were inflated, so we did not use them to estimate population size but rather as a measure of relative density, along with a relative density index. Similar to our occupancy results, we found that density was higher in southern

Abaco, near roads, and where snags were present. These results suggest that conservation efforts should include managing pine forest in southern Abaco to maintain the presence of pine snags.

Our findings also indicate that roads are used as foraging areas for swallows, although future work should address the use of other areas for foraging, and how foraging ecology relates to nest site selection. Additional research is needed to identify the causes of decreased swallow occupancy and density in northern Abaco, and to assess the factors determining the distribution of populations across the rest of the breeding range.

INTRODUCTION

A thorough understanding of a species’ biology, including the distribution and abundance of populations, is essential for effective management and conservation (Herman 2002,

Tewksbury et al. 2014). The Bahama Swallow (Tachycineta cyaneoviridis) is a secondary cavity- nester that only breeds in the northern Bahamas. The breeding population on New Providence apparently has been extirpated (Birdlife International 2016), and thus its current breeding range is limited to the islands of Abaco, Grand Bahama, and Andros (Raffaele et al. 1998). These “pine islands” are the only islands within the Bahamian archipelago that contain large areas of

Caribbean pine (Pinus caribaea bahamensis), which is an important breeding habitat of the swallow (Smith and Smith 1989, Allen 1996). The swallow is listed as Endangered by the IUCN

(Birdlife International 2016) because populations appear to have declined sharply (Emlen 1977,

Smith and Smith 1989, Allen 1996) and remaining populations are believed to be threatened by invasive species and a potential loss of pine forest (Allen 1996, Birdlife International 2016).

Historical estimates of Bahama Swallow population density and abundance are limited to three published studies. Emlen (1977) assessed the density and habitat distribution of all the bird species present on Grand Bahama Island by walking transects in plots representing a variety of habitat types. He calculated local detectability coefficients (Emlen 1971) for each species and then translated those values to density, arriving at an estimated 11 bird/km2 within pine forest for

Bahama Swallows. Henry (1974) estimated 1782 km2 of pine forest on Grand Bahama, Abaco and Andros. Using this estimate, one could infer that there were approximately 19,600 Bahama

Swallows at the time of Emlen’s study (Allen 1996).

Roughly a decade later, Smith and Smith (1989) conducted driving surveys along accessible roadways on Andros Island and estimated that there were approximately 2.6 birds/km2

in the pine forest. Using Henry’s (1974) estimate of 1782 km2 of pine forest, they calculated an estimated population size of 4800 Bahama Swallows, a 76% decline compared to Emlen’s

(1977) estimate. They acknowledge that theirs was a rather crude estimate, but also felt that their estimate was within the correct order of magnitude, between 1000 and 10,000 individuals.

Allen (1996) conducted repeated driving surveys along three routes on Grand Bahama

Island. He also conducted driving surveys on approximately 70% of the roads covered by Smith and Smith (1989) on Andros Island. He concluded that the techniques he was able to use were not suitable for density estimates, and instead simply estimated a sighting rate. He estimated that there were 0.10 birds/km, which would represent a 25% decrease from Smith and Smith (1989).

However, he advised using caution in assuming that these numbers represent a true decrease in population abundance due to differences in survey locations and techniques.

Regardless of the precision of the available abundance estimates, Bahama Swallow populations appear to have declined over a period of less than thirty years. The current Bahama

Swallow population size, based on anecdotal reports, is estimated to be roughly 1500-4000 individuals (Birds International 2016). Bahama Swallows are present only on the pine islands and are known to build nests in abandoned woodpecker cavities in pine snags (Smith and Smith

1989, Allen 1996, Chapter 4), which is the justification for using the extent of pine forest (Henry

1974) as a parameter in estimates of population abundance. However, previous studies have not examined the distribution of swallow populations across habitats or in relation to other landscape variables.

In this study, we assessed the distribution of Bahama Swallows across the Abaco Islands.

We used several survey methods (driving transects, walking transects, point counts) to estimate site occupancy probability. Using point count survey data, we estimated density using distance

analysis (Buckland et al. 2001) and a relative density index (Johnson 2008, Nalwanga et al.

2012). The goal of this study was to assess the current distribution and abundance of swallows, and determine what factors influence these attributes, to inform management and conservation of this endangered species.

METHODS

Data Collection

This study took place on the Abaco Islands in the northern Bahamas. The Abaco Islands include many small islands (i.e., cays), but for the purposes of this study we only refer to the main islands of Great Abaco and Little Abaco (Figure 1). The data described were collected during three field seasons: 25 March – 15 July 2015, 4 April – 12 July 2016, and 9 April – 12

July 2017. These time periods were selected to encompass a majority of the swallow breeding season (Allen 1996).

Abaco transect surveys

During all field seasons (2015-17), we conducted driving surveys on transects consisting of 3 km sections of roads that were at least 6 km long, which included the main highway and an assortment of side roads to settlements (Figure 2A). The number of transects on each road represented the proportion of the total number of available transects that were located on that road, and transects were randomly selected within each road using the Excel random number generator (Excel, Microsoft Corp, Redmond, Washington). In 2015, we selected 20 transects, and sampled each transect 9-10 times. However, sampling the transects north of Marsh Harbour

required substantial travel time and produced few detections. Therefore, we used the 2015 data to estimate regional variation in detection of swallows to design an optimal allocation stratified sampling technique for subsequent seasons, which minimized survey effort by avoiding unnecessary replication where swallows were likely to be absent (Stillman and Brown 1995). In

2016, we selected 25 transects; 15 transects south of Marsh Harbour were sampled 9-10 times, while 10 transects north of Marsh Harbour were sampled twice. Due to the random selection process, 10 of these transects had also been sampled in 2015. In 2017, we sampled all previously sampled transects; 19 transects in the south were sampled 4 times, and 16 transects in the north were sampled once. Within each year, half of the samples for each transect were collected in the morning and half were collected in the evening, with at least two days separating samples of the same transect. Surveys were conducted during the four hours after and before local sunrise and sunset, respectively. While driving at a speed of approximately 15 kph, two observers searched for swallows and recorded all individuals detected.

During 2015 and 2016, we conducted walking surveys on 1 km sections of abandoned logging roads (Figure 2B). We used the KML Tools Project (http://extension.unh.edu/kmltools/) to randomly select points from the polygon representing the large area of accessible pine forest in southern Abaco. Transects were designated as the 1 km sections of road closest to each point.

In 2015, we selected 20 transects, and sampled each transect 4 times. In 2016, we selected 30 transects, but later determined that one transect was inaccessible. Due to the random selection process, two transects in the 2016 sample had also been sampled in 2015. We sampled each transect 3-6 times. Surveys were conducted during the four hours after local sunrise, with at least three days separating samples of the same transect. While walking at a speed of approximately 3 kph, an observer searched for swallows and recorded all individuals detected.

Point count surveys

We conducted point count surveys throughout much of the island in 2017 (Figure 2C).

We used the “create random points” tool in ArcMap (10.5.1) to generate points in polygons of accessible off-road areas (n = 105) and along lines of main roadways (n = 128), with a minimum of 800 m between each point. Although most points were sampled twice (n = 168), points that were difficult to access were sampled once (n = 65). Point counts were conducted for six minutes between 6 and 10 AM, during which time a single observer recorded all swallows seen within distance bins (<5 m, 5-15 m, 15-30 m, 30-50 m, 50-100 m). We also recorded the wind conditions (calm vs noticeable) and cloud cover (more or less than 50%) during the survey.

At each point, we recorded several habitat variables for the area within 100 m. We used

Bahama Swallow nest records to categorize the size of the pine forest (Chapter 4). We found many swallow nests in a large area of southern Abaco, where the height of the pine forest was

~10-15 m tall. Swallows will nest in snags with a diameter at breast height (DBH) as small as ~

10 cm and in utility poles, all of which have a DBH of ~30 cm. Therefore, we estimated the percent (to the nearest 25%) of pine trees that fell into categories of height (< 10 m, 10 – 15 m, >

15 m) and DBH (< 10 cm, 10-30 cm, > 30 cm). We used these categories to set one size threshold (T1) for the percent of trees with a height of at least 10 m (10-15 and >15m) and a

DBH of at least 10 cm (10-30 cm and > 30 cm), representing “available pine forest” for swallows. We set another size threshold (T2) for the percent of trees with a height of at least 15 m and a DBH of at least 30 cm, representing “large pine forest.” We also estimated the percent of the area (to the nearest 10%) that was pine forest and recorded the presence of any snags with a DBH of at least 10 cm.

Occupancy analysis

Occupancy analysis for all surveys was conducted in program Presence (2.12.31) using simple single-season occupancy models. For each analysis, we formulated a list of a priori models testing the effect of covariates on occupancy probability (Ψ) and detection probability (p) and followed a two-step model selection process. While holding the covariates likely to influence

Ψ constant, we compared models with varying p covariates. We used the p covariates from the top ranking model as constants to compare a second set of models with varying Ψ covariates. We selected the model with the lowest Akaike’s Information Criterion (AIC), and models with ∆

AIC < 2 were considered competing models (Akaike 1998). Covariates that are associated with each site can be used to estimate Ψ and p, while covariates that are associated with sampling occasions can only be used to estimate p (MacKenzie 2012). Accordingly, we assigned each covariate to the appropriate component(s) of occupancy models (Table 1, Table 2).

Transects

Many of the covariates for occupancy analysis of transect surveys were included in models for both driving surveys and walking surveys, though some were unique to only one survey type (Table 1). We estimated the breeding stage of the population at the time of each survey from nest monitoring data of that year (Chapter 4). Breeding stage was categorized as before the average lay date, between the average lay date and the average fledge date, and after the average fledge date. To account for potential behavioral changes due to time of day, we included the hour of the survey. For driving surveys, we also included whether the survey was conducted in the morning or the evening.

Covariates related to habitat were determined post-hoc via spatial analysis in ArcMap

(10.5.1) using the procedure outlined in Chapter 4. Briefly, habitat types on Great Abaco were classified from Landsat 8 satellite images (U.S. Geological Survey; Figure 1). Based on informal observations that swallows will forage at least 400 m from nests, we extracted the habitat classifications within 400 m buffers of each transect and determined the percent of habitat that was classified as pine forest. Since swallows often forage over roadways, the presence or absence of a paved road within 400 m was also included as a covariate for walking transects. To account for changes in occupancy along the length of the island, we also included the scaled latitude of the midpoint of driving transects.

Point counts

Covariates for point counts (Table 2) included hour of the survey, observer, breeding stage, wind condition, and cloud cover. Habitat covariates were determined from the habitat characteristics within 100 m of each point and included the percent pine and the presence/absence of snags. We also conducted two principal component analyses to capture the relationship between pine tree height and DBH, one for each pine size threshold (T1 and T2 – see “Point count surveys”). We included the first principal component for T1 (T1PC, 93% of the variation) and the first principal component for T2 (T2PC, 69% of the variation) as covariates for pine forest size. To account for changes in occupancy along the length of the island, we included the scaled latitudinal coordinates of points. Since swallows often use roads as foraging areas, we also included whether the point was on or off a road.

Density estimation

Density was estimated from the point count data using program Distance (7.3). Since there were so few detections (n = 5) north of Marsh Harbour (Figure 1), we analyzed only the points in the southern portion of Great Abaco. We compared models using the recommended functions for point transect data (Buckland et al. 2001). Although the models using hazard-rate function ranked highest (Table 3), this function relies on precise distance estimates of individuals, and is therefore not appropriate for our binned distance data. The model half-normal with a cosine adjustment had a large confidence limit and the detection function plot indicated an overfit of the data. Therefore, the half-normal function with no adjustment terms was used for all subsequent models.

We incorporated covariates from point counts into models to estimate density (Table 4).

We formulated a set of models that included covariates that could potentially affect the shape of the detection function. We then included the detection covariates from competing (∆ AIC < 2) models into models that estimated density by region or habitat groups (a.k.a. strata).

Relative density index

We used the number of birds detected within the survey area at each point as an index for density, which is expected to correlate with the true population density (Johnson 2008, Nalwanga et al. 2012). For each point count site, we adjusted the number of detections by effort to assume that all points were sampled twice, and then calculated the number of detections per hectare. We used generalized linear models with a negative binomial distribution to test for differences in density among multiple covariate groups. First, we assessed differences between sites North and

South of Marsh Harbour (Figure 1). Second, we compared densities among the same covariate

groups used for stratification of density estimates in southern Abaco: northern and southern portion of southern Abaco, the presence/absence of snags, points on or off the road, and habitat dominated by pine or not. These estimates provided a comparison for density estimates from distance analysis, and allowed us to test statistically whether groups differed from one another.

We also tested whether the density index varied by latitude, across the island, across southern

Abaco, and by percent pine.

RESULTS

Occupancy estimates

Transects

For driving surveys, the occupancy model that included the effects of breeding stage, hour, and time of day on detection probability (p) emerged as the top model (Table 5). While holding these p covariates constant, we compared models assessing covariate effects on occupancy probability (Ψ). Based on this comparison, the top ranking occupancy model included only latitude (Table 6). The probability that a site was occupied decreased with increasing latitude (ß = -3.45 ± 2.60, Figure 3).

For walking surveys, the null occupancy model emerged as the top model among those testing the effect of covariates on detection probability (p) (Table 7). Therefore, we did not include any covariates in the models assessing effects on occupancy probability (Ψ). The top ranking occupancy model included both percent pine and the presence of a road (Table 8). The probability that a site was occupied decreased if the percent pine was extremely high (>80%) (ß

= -0.23 ± 0.04) and was higher if there was a road within 400 m (ß = 2.19 ± 1.29). However, the model including only road was almost equally weighted (∆AIC = 0.20; Table 8). The probability that a site was occupied was higher if there was a road within 400 m (ß = 3.18 ± 1.58; Ψ = 0.96 ±

0.12) than if there was no road (ß = -0.21 ± 0.49; Ψ = 0.45 ± 0.11).

Point counts

The global model emerged as the top model testing the effects of covariates on detection probability (p) in point counts. However, the estimates and standard errors indicated that the model was overfitted. When we excluded the global model, the top model included breeding stage as a covariate (Table 9). Therefore, we held breeding stage constant for p and compared models testing the effects of covariates on occupancy probability (Ψ). The top ranking occupancy model included the effects of latitude (ß = -1.36 ± 0.62), whether the survey point was on or off the road (ß = 2.99 ± 1.32), and the presence of snags (ß = 2.58 ± 1.12; Table 10;

Figure 4). The other competing (∆AIC < 2) models included these same covariates, plus one of the principal component covariates or percent pine. However, the absence of these additional covariates in the top model suggest that they did not inform swallow occupancy.

Density estimates

The competing models (∆AIC < 2) among those testing the effects of covariates on the detection function included hour of the survey and/or breeding stage (Table 11). Therefore, we included these covariates in models that stratified the density estimates (Table 12). Models that produced warnings that the number of samples was limited (<10) or that parameters were correlated were eliminated. All of the models that included North/South stratification of southern

Abaco were competing and produced similar density estimates, so we report only the estimates of the top-ranking model. Density was higher in the southern portion of southern Abaco (Table

13). When hour was included as a detection covariate, density was higher when there were snags present, and when the survey site was on the road.

We believe that the density estimates from these models (~25-85 birds/km2) are highly inflated. The strong positive bias is likely due to birds entering the survey area during the six- minute count period, resulting in a violation of the assumption that birds were detected at their initial location (Buckland 2006). Therefore, we view these as estimates of relative rather than absolute abundance, and did not use them to estimate population size on Abaco.

Relative density index

The relative density index (detections/ha) was higher in southern Abaco than in northern

Abaco, and was higher for points on a road than off a road (Table 14). However, density did not differ significantly between the North/South portions of southern Abaco, by the presence/absence of snags, or by whether or not the habitat was pine dominant (Table 14).

Latitude did not predict density across the whole island (ß = -0.12 ± 0.12, residual deviance231 =

2 187.2; ! 1, LR = 1.0, p = 0.32) or across southern Abaco (ß = -0.30± 0.19, residual deviance197 =

2 166.0; ! 1, LR = 2.4, p = 0.12). Density also did not vary by percent pine (ß = -0.003± 0.003,

2 residual deviance197 = 167.6; ! 1, LR = 0.74, p = 0.38).

DISCUSSION

Understanding the distribution and abundance of a species is fundamental to effective management and conservation (Herman 2002, Tewksbury et al. 2014). The breeding range of the

Bahama Swallow is limited to the islands of Abaco, Grand Bahama, and Andros (Raffaele et al.

1998, Birdlife International 2016). In this study, we investigated the distribution of the swallow population on the Abaco Islands. The variation in occupancy and density across this portion of the breeding range has implications for species management and future research efforts.

The probability that a site is occupied by swallows is determined by several factors. Our point count analysis showed that occupancy was higher in southern Abaco, especially near roads and where pine snags were present. The results from our transect survey analyses reinforced these findings. Estimates from our driving transect surveys, which were conducted on roads across the island, showed that occupancy was higher in southern Abaco. Estimates from our walking transects in the pine forest of southern Abaco indicated that occupancy was higher if there was a road nearby.

In support of our occupancy estimates, we saw similar variation in our density estimates derived from program Distance and our analysis using a relative density index. Density was higher on a road and in the presence of snags, although the effect of snags was not significant.

Swallows likely occur at higher densities near roads because they forage where there is a concentration of aerial insects, often in open areas where it is easier to maneuver (Turner and

Rose 1989). We also observed swallows foraging over fields, landfills, and other open areas on the island. Cavities in pine snags are an important resource for breeding swallows (Chapter 4,

Chapter 5). Although we did not survey for the presence of cavities or excavators in this study, we would expect swallows to occur at higher densities in areas that contain this critical nesting resource.

Our relative density index estimates showed that there were significantly fewer birds

North of Marsh Harbour, and there were so few detections in northern Abaco that we could not

estimate density using distance analysis. Within southern Abaco, results were mixed. Although the distance analysis estimated more birds in the southern portion of South Abaco, the relative density index estimate showed the opposite pattern, and did not vary significantly with latitude in this region or across the island as a whole, unlike occupancy. Latitudinal variation in density may be influenced by site characteristics that were not considered in our analysis. For example, a high number of detections at a few northern sites, such as those near cell phone towers (Chapter

4), could affect the model estimates of the relative density across the island.

Since our density estimates were highly inflated, we did not use them to estimate population size. An important assumption of point count sampling is that birds are detected at their initial location. If birds enter the survey area during the count period, this assumption is violated, a violation that can create a strong positive bias in the density estimates (Buckland

2006). I recommend as a next step to analyze a subset of these point count data (e.g., the first minute) to determine if this subsetting would reduce the bias in the estimates. Regardless, it would be valuable to test a variety of distance sampling methods to estimate density of swallows and other highly mobile birds (Cassey et al. 2007).

Interestingly, the percent of pine forest generally did not predict swallow site occupancy or density. The one exception was from our walking transects, which showed that swallow occupancy was lower when the percent pine forest was extremely high, likely because swallows use open, non-pine areas for foraging. These findings and the large differences in swallow numbers between northern and southern Abaco indicate that there is considerable variation in the use of pine forest by swallows. This has important implications for the historical estimates of swallow population abundance. Previous estimates of swallow density were applied uniformly to pine forest across The Bahamas (Henry 1974) to infer population abundance (Smith and Smith

1989, Allen 1996). The pine forest clearly is the most important breeding habitat for swallows

(Chapter 4, Chapter 5), and we do not intend to minimize its importance. However, given that swallow presence and abundance varies considerably among pine forest locations and that swallows will also nest in cavities in non-pine habitat (Chapter 4), it is not appropriate to rely on the extent of pine forest to estimate population size.

The results of this study suggest that the pine forests of southern Abaco are critical habitat for Bahama Swallows, and that conservation efforts should include managing the pine forest in this area for the presence of snags. We cannot assume that the increased occupancy and density near roads reflect a preference for nesting sites near these foraging areas. Although many swallows will occupy cavities in very open areas (e.g., nest boxes), predation and competitive interactions can vary with the distance to a forest edge (Rendell and Robertson 1990). Bahama

Swallows will nest in structures adjacent to roads, but will also use pine snag cavities deep in the forest (Chapter 4), and competition for nesting cavities and the presence of nest predators are higher in non-pine habitat (Chapter 5). Future research should evaluate the relationship between nest site selection and use of roads and other open areas for foraging. While mortality from collisions with vehicles is a concern for swallows foraging over roadways (Orlowski 2005), swallows are extremely agile flyers (Norberg 1986), allowing them to turn away from vehicles at the last moment (personal observation). Also, since there are very few settlements in southern

Abaco, there is generally little traffic on the roads where swallow density is high. However, additional development in this region of Abaco could alter traffic patterns and stakeholders should consider the potential for increased risk of road mortality for swallows and other wildlife.

Additional research is needed to determine the cause(s) of lower swallow occupancy and density in northern Abaco. Differences in the population dynamics of swallows breeding in

northern and southern Abaco may reveal the factors responsible for the decline of this species over the last 30 years. Although nest predation does not appear to be a threat to swallows in southern Abaco (Chapter 4), a higher density of nest predators like cats (Felis catus), rats (Rattus rattus), and racoons (Procyon lotor) in northern Abaco could decrease reproductive success and drive birds to breed elsewhere. Monitoring nests of swallows and other birds, along with surveys for potential nest predators, could determine whether this is the case. Another possibility is that there may be fewer swallows in northern Abaco because the density of snags and/or cavities in snags is lower there (Chapter 5). Future research efforts should include systematic surveys of these nesting resources in northern Abaco. Similar comparisons between Abaco and Grand

Bahama Island might be informative as well. Although we conducted point counts on Grand

Bahama, we were unable to analyze these data because we detected no swallows during the surveys. In fact, in two weeks of intensive searching for swallows and their nests, we located only 3-4 pairs. A significant breeding population of swallows still occurs on Andros Island (K.

Omland, personal communication, personal observation), but additional information is needed to estimate population occupancy and density on this island. Ultimately, ensuring the persistence of this endangered species will require a thorough assessment of the factors determining population distribution across the entire breeding range. The considerable variation in the abundance across and within the pine islands provides an opportunity to assess causes of population decline and develop effective conservation strategies in response.

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FIGURES

Figure 1: Map of the Abaco Islands in the northern Bahamas. The black line indicates the separation of the two main islands, Great Abaco and Little Abaco. The star indicates the location of Marsh Harbour (26°32'2"N, 77°3'42"W). Habitat types were classified from Landsat 8 satellite images (U.S. Geological Survey).

Figure 2: Maps showing the locations of driving transects (A), walking transects (B) and point count stations (C) on the Abaco

Islands. For (A) and (C), the black line indicates the separation of the two main islands, Great Abaco and Little Abaco, and the star indicates the location of Marsh Harbour (26°32'2"N, 77°3'42"W).

Figure 3: Occupancy probability (Ψ) by the scaled latitudinal coordinate, based on the top ranking model for driving transect surveys. The scaled latitude value of zero is approximately

26°29'17"N. MH indicates the latitude of Marsh Harbour (26°32'2"N).

Figure 4: Occupancy probability (Ψ) predicted by the scaled latitudinal coordinate, whether the survey was on a road, and whether snags were present, based on the top ranking model for point count surveys. The scaled latitude value of zero is approximately 26°13'12"N. MH indicates the latitude of Marsh Harbour (26°32'2"N).

TABLES

Table 1: Transect covariates used for occupancy analysis in program Presence. Covariates were used in analysis of driving surveys only, walking surveys only, or both survey types. For each covariate, we provide a descriptive meaning, whether the covariate was categorical or continuous, and whether the covariate was incorporated into detection probability (p) and/or occupancy probability (Ψ) components of occupancy models.

Survey Continuous or Type Covariate Meaning Categorical p Ψ Both Hour Number of hours after or before local sunrise Continuous X and sunset, respectively Both Stage Breeding stage (before incubation, between Categorical X incubation and fledge, after fledge) Driving Time Survey conducted in the morning or evening Categorical X Both Year Year of survey Categorical X Driving Latitude Scaled latitude of transect midpoint Continuous X Both Pine Percent of pine forest within 400m Continuous X X Walking Road Presence/absence of a road within 400m Categorical X

Table 2: Point count covariates used for occupancy analysis in program Presence. Each categorical or continuous covariate was incorporated into detection probability (p) and/or occupancy probability (Ψ) components of occupancy models.

Continuous or Covariate Meaning Categorical p Ψ Hour Hour of the start of survey (between 6 and 10 AM) Continuous X Observer Observer that conducted the survey (MW or not MW) Categorical X Stage Swallow breeding stage (before incubation, between Categorical X incubation and fledge, after fledge) Wind Calm or some noticeable wind Categorical X Clouds Cloud cover (above or below 50%) Categorical X Latitude Scaled latitudinal coordinates of points Continuous X Road Survey point on or off the road Categorical X X Pine Percent of pine forest within 100m Continuous X X T1PC First principal component of the size threshold for Continuous X available pine forest (T1) T2PC First principal component of the size threshold for large Continuous X pine forest (T2) Snags Presence/absence of snags > 10 cm DBH within 100m Categorical X

Table 3: Model results for hazard-rate (HR) and half-normal (HN) functions with simple polynomial (SP), hermite polynomial (HP), and cosine (COS) adjustments for point count surveys in program Distance. For each model, we report the number of parameters (K), ∆ AIC,

AIC, density estimate (birds/km2) with the lower confidence limit (LCL) and upper confidence limit (UCL), model likelihood, and model weight (wi).

Model K ∆ AIC AIC Density LCL UCL CV Likelihood wi HR SP 3 0.00 455.89 266 75 939 0.71 1.00 0.32 HR COS 3 0.30 456.19 225 92 548 0.48 0.86 0.28 HR 2 0.37 456.26 164 81 334 0.37 0.83 0.27 HN COS 3 1.69 457.58 146 90 238 0.25 0.43 0.14 HN HP 1 14.18 470.07 65 46 90 0.17 0.00 0.00 HN 1 14.18 470.07 65 46 90 0.17 0.00 0.00

Table 4: Point count covariates used for density estimates in program Distance. For each covariate, we provide a descriptive meaning, whether the covariate was categorical or continuous, and whether the covariate was incorporated into the detection function (p) and/or used for stratification (Strat).

Continuous or Covariate Meaning Categorical p Strat Hour Hour of the start of survey (between 6 and 10 Continuous X AM) Observer Observer that conducted the survey (MW or not Categorical X MW) Stage Swallow breeding stage (before incubation, Categorical X between incubation and fledge, after fledge) Wind Calm or some noticeable wind Categorical X Clouds Cloud cover (above or below 50%) Categorical X Road Survey point on or off the road Categorical X X Pine Percent of pine forest within 100m Continuous X Snags Presence/absence of snags > 10 cm DBH within Factor X 100m North/South Northern or southern portion of southern Abaco Categorical X Pine dominant Area within 100 m is < or ≥ 50% pine Categorical X

Table 5: Model ranking results from program Presence testing the effects of covariates on detection probability (p) in driving surveys. For each model, we report the AIC, ∆AIC, model weight (wi), model likelihood, number of parameters (K), and deviance. Covariates for Ψ included latitude (L) and percent pine (PP). Covariates for p included percent pine (PP), year

(Y), breeding stage (S), hour of the survey (H), and whether the survey was in the morning or evening (T).

Model AIC ∆AIC wi Likelihood K Deviance Ψ(L + PP),p(S + H + T) 604.87 0.00 1.00 1.00 8 588.9 Ψ(L + PP),p(S + H) 616.87 12.00 0.00 0.00 7 602.9 Ψ(L + PP),p(S + T) 620.55 15.68 0.00 0.00 7 606.6 Ψ(L + PP),p(PP) 620.83 15.96 0.00 0.00 5 610.8 Ψ(L + PP),p(H) 625.73 20.86 0.00 0.00 5 615.7 Ψ(L + PP),p(S) 626.78 21.91 0.00 0.00 6 614.8 Ψ(L + PP),p(H + T) 627.63 22.76 0.00 0.00 6 615.6 Ψ(L + PP),p(T) 631.39 26.52 0.00 0.00 5 621.4 Ψ(L + PP),p(Y) 632.32 27.45 0.00 0.00 6 620.3 Ψ(.),p(.) 633.29 28.42 0.00 0.00 2 629.3 Ψ(L + PP),p(.) 637.48 32.61 0.00 0.00 4 629.5

Table 6: Model ranking results from program Presence testing the effects of covariates on occupancy probability (Ψ) in driving surveys. For each model, we report the AIC, ∆AIC, model weight (wi), model likelihood, number of parameters (K), and deviance. Covariates for Ψ included latitude (L) and percent pine (PP). Covariates for p included breeding stage (S), hour of the survey (H), and whether the survey was in the morning or evening (T).

Model AIC ∆AIC wi Likelihood K Deviance Ψ(L),p(S + H + T) 598.63 0.00 0.67 1.00 7 584.6 Ψ(.),p(S + H + T) 600.87 2.24 0.22 0.33 6 588.9 Ψ(PP),p(S + H + T) 602.87 4.24 0.08 0.12 7 588.9 Ψ(L + PP),p(S + H + T) 604.87 6.24 0.03 0.04 8 588.9 Ψ(.),p(.) 633.29 34.66 0.00 0.00 2 629.3

Table 7: Model ranking results from program Presence testing the effects of covariates on detection probability (p) in walking surveys. For each model, we report the AIC, ∆AIC, model weight (wi), model likelihood, number of parameters (K), and deviance. Covariates for Ψ included road (R) and percent pine (PP). Covariates for p included percent pine (PP), year (Y), breeding stage (S), and the hour of the survey (H).

Model AIC ∆AIC wi Likelihood K Deviance Ψ(R + PP),p(.) 253.15 0.00 0.50 1.00 4 245.2 Ψ(R + PP),p(H) 255.15 2.00 0.18 0.37 5 245.2 Ψ(R + PP),p(PP) 255.81 2.66 0.13 0.26 5 245.8 Ψ(R + PP),p(S) 256.65 3.50 0.09 0.17 6 244.7 Ψ(R + PP),p(Y) 257.15 4.00 0.07 0.14 6 245.2 Ψ(R + PP),p(H + S) 258.65 5.50 0.03 0.06 7 244.7 Ψ(.),p(.) 263.13 9.98 0.00 0.01 2 259.1

Table 8: Model ranking results from program Presence testing the effects of covariates on occupancy probability (Ψ) in walking surveys. For each model, we report the AIC, ∆AIC, model weight (wi), model likelihood, number of parameters (K), and deviance. Covariates for Ψ included road (R) and percent pine (PP). No covariates were included for p (.).

Model AIC ∆AIC wi Likelihood K Deviance Ψ(PP + R),p(.) 253.15 0.00 0.48 1.00 4 245.2 Ψ(R),p(.) 253.35 0.20 0.43 0.90 3 247.4 Ψ(PP),p(.) 256.59 3.44 0.09 0.18 3 250.6 Ψ(.),p(.) 263.13 9.98 0.00 0.01 2 259.1

Table 9: Model ranking results from program Presence testing the effects of covariates on detection probability (p) in point counts. For each model, we report the AIC, ∆AIC, model weight (wi), model likelihood, number of parameters (K), and deviance (Dev). Covariates for Ψ included latitude (L), road (R), percent pine (PP), the first principal components for pine size

(T1PC and T2PC), and the presence of snags (SN). Covariates for p included percent pine (PP), road (R), observer (O), breeding stage (S), hour of the survey (H), wind condition (W), and cloud cover (C).

Model AIC ∆AIC wi Likelihood K Dev Ψ(L + R + PP + T1PC + T2PC + SN),p(S) 469.89 0 0.67 1.00 10 449.9 Ψ(L + R + PP + T1PC + T2PC + SN),p(S + H) 471.46 1.57 0.31 0.46 11 449.5 Ψ(L + R + PP + T1PC + T2PC + SN),p(W + C) 479.02 9.13 0.01 0.01 10 459.0 Ψ(L + R + PP + T1PC + T2PC + SN),p(W) 479.45 9.56 0.01 0.01 9 461.5 Ψ(L + R + PP + T1PC + T2PC + SN),p(PP) 480.72 10.83 0.00 0.00 9 462.7 Ψ(L + R + PP + T1PC + T2PC + SN),p(PP + O) 482.63 12.74 0.00 0.00 10 462.6 Ψ(L + R + PP + T1PC + T2PC + SN), p(PP + R + O + H +S + W + C) 485.44 15.55 0.00 0.00 16 453.4 Ψ(L + R + PP + T1PC + T2PC + SN),p(.) 486.1 16.21 0.00 0.00 8 470.1 Ψ(L + R + PP + T1PC + T2PC + SN),p(R) 486.95 17.06 0.00 0.00 9 469.0 Ψ(L + R + PP + T1PC + T2PC + SN),p(C) 487.63 17.74 0.00 0.00 9 469.6 Ψ(L + R + PP + T1PC + T2PC + SN),p(H) 487.66 17.77 0.00 0.00 9 469.7 Ψ(L + R + PP + T1PC + T2PC + SN),p(O) 487.74 17.85 0.00 0.00 9 469.7 Ψ(L + R + PP + T1PC + T2PC + SN),p(R + O) 488.49 18.6 0.00 0.00 10 468.5 Ψ(L + R + PP + T1PC + T2PC + SN),p(H + C) 488.94 19.05 0.00 0.00 10 468.9 Ψ(.),p(.) 496.11 26.22 0.00 0.00 2 492.1

Table 10: Model ranking results from program Presence testing the effects of covariates on occupancy probability (Ψ) in point counts. For each model, we report the AIC, ∆AIC, model weight (wi), model likelihood, number of parameters (K), and deviance. Covariates for Ψ included latitude (L), road (R), percent pine (PP), the first principal components for pine size

(T1PC and T2PC), and the presence of snags (SN). Breeding stage (S) was included as a covariate for p.

Model AIC ∆AIC wi Likelihood K Deviance Ψ(L + R + SN),p(S) 476.78 0 0.32 1.00 7 462.8 Ψ(L + R + T2PC + SN),p(S) 476.85 0.07 0.31 0.97 8 460.9 Ψ(L + R + T1PC + SN),p(S) 477.53 0.75 0.22 0.69 8 461.5 Ψ(L + R + PP + SN),p(S) 478.78 2 0.12 0.37 8 462.8 Ψ(L + R + T1PC),p(S) 484.91 8.13 0.01 0.02 7 470.9 Ψ(SN),p(S) 485.17 8.39 0.00 0.02 5 475.2 Ψ(L + R + T2PC),p(S) 485.49 8.71 0.00 0.01 7 471.5 Ψ(L + SN),p(S) 486.1 9.32 0.00 0.01 6 474.1 Ψ(L + R),p(S) 486.65 9.87 0.00 0.01 6 474.7 Ψ(PP + SN),p(S) 486.82 10.04 0.00 0.01 6 474.8 Ψ(L + PP + SN),p(S) 486.91 10.13 0.00 0.01 7 472.9 Ψ(L + R + PP),p(S) 487.07 10.29 0.00 0.01 7 473.1 Ψ(T1PC + SN),p(S) 487.17 10.39 0.00 0.01 6 475.2 Ψ(T2PC + SN),p(S) 487.57 10.79 0.00 0.00 6 475.6 Ψ(L + T1PC + SN),p(S) 488.02 11.24 0.00 0.00 7 474.0 Ψ(L + T2PC + SN),p(S) 488.09 11.31 0.00 0.00 7 474.1 Ψ(.),p(S) 488.65 11.87 0.00 0.00 4 480.7 Ψ(L),p(S) 488.72 11.94 0.00 0.00 5 478.7 Ψ(T1PC),p(S) 489.32 12.54 0.00 0.00 5 479.3 Ψ(R),p(S) 489.56 12.78 0.00 0.00 5 479.6 Ψ(PP),p(S) 489.81 13.03 0.00 0.00 5 479.8 Ψ(T2PC),p(S) 489.93 13.15 0.00 0.00 5 479.9 Ψ(L + T1PC),p(S) 490.02 13.24 0.00 0.00 6 478.0 Ψ(L + T2PC),p(S) 490.36 13.58 0.00 0.00 6 478.4 Ψ(L + PP),p(S) 490.51 13.73 0.00 0.00 6 478.5 Ψ(.),p(.) 496.11 19.33 0.00 0.00 2 492.1

Table 11: Model ranking results from program Distance testing the effects of covariates on the detection function in point counts. For each model, we report the number of parameters (K),

AIC, ∆AIC, estimated density with the lower (LCL) and upper (UCL) confidence limits, the percent coefficient of variance (%CV), model likelihood, and model weight (wi). Covariates included percent pine (PP), road (R), observer (O), breeding stage (S), hour of the survey (H), wind condition (W), and cloud cover (C).

Model K ∆ AIC AIC Density LCL UCL %CV Likelihood wi H + S 4 0.00 465.91 68 50 93 0.16 1.00 0.32 H 2 1.39 467.30 66 49 91 0.16 0.50 0.16 S 3 1.91 467.82 66 49 91 0.16 0.39 0.12 O 2 2.32 468.23 66 48 90 0.16 0.31 0.10 H + C 3 2.75 468.66 67 49 91 0.16 0.25 0.08 O + R 3 3.56 469.47 66 48 90 0.16 0.17 0.05 1 4.16 470.07 65 46 90 0.17 0.12 0.04 O + PP 3 4.28 470.19 66 48 90 0.16 0.12 0.04 R 2 4.75 470.65 65 48 89 0.16 0.09 0.03 PP 2 6.02 471.93 65 47 88 0.16 0.05 0.02 C 2 6.05 471.96 65 47 88 0.16 0.05 0.02 W 2 6.12 472.03 65 47 88 0.16 0.05 0.02 W + C 3 8.05 473.96 65 47 88 0.16 0.02 0.01

Table 12: Model ranking results from program Distance testing the effects of stratification on density estimates in point counts. For each model, we report the number of parameters (K), AIC,

∆AIC, estimated density (birds/km2) with the lower (LCL) and upper (UCL) confidence limits, the percent coefficient of variance (%CV), model likelihood, and model weight (wi). Strata included the north/south portions of southern Abaco (NS), whether the survey was on a road (R), the presence/absence of snags (SN), and whether the habitat was pine dominant (PD). Covariates for the detection function included breeding stage (S) and hour of the survey (H).

Model K ∆ AIC AIC Density LCL UCL CV Likelihood wi NS strata, S 5 0.00 463.55 52 38 71 0.16 1.00 0.19 NS strata, H 4 0.21 463.76 52 38 71 0.16 0.90 0.17 SN strata, H 4 1.55 465.10 53 39 72 0.16 0.46 0.09 R strata, H 4 1.57 465.12 68 50 93 0.16 0.46 0.09 NS strata, H + S 7 1.67 465.22 53 38 73 0.16 0.43 0.08 SN strata, H + S 7 2.09 465.64 55 40 75 0.16 0.35 0.07 PD strata, H + S 7 2.14 465.70 60 42 84 0.17 0.34 0.06 SN strata, S 5 2.30 465.85 53 39 73 0.16 0.32 0.06 H + S 4 2.36 465.91 68 50 93 0.16 0.31 0.06 H 2 3.75 467.30 66 49 91 0.16 0.15 0.03 R strata, H + S 8 3.85 467.40 78 7 895 1.88 0.15 0.03 S 3 4.27 467.82 66 49 91 0.16 0.12 0.02 PD strata, S 5 4.91 468.46 57 41 79 0.17 0.09 0.02 R strata, S 6 5.46 469.02 76 7 839 1.83 0.07 0.01 PD strata, H 4 7.07 470.62 56 40 79 0.17 0.03 0.01

Table 13: Encounter rate (ER), detection probability (p), and density (birds/km2; D) for competing stratification models from program Distance. The North/South stratification included breeding stage as a covariate for the detection function. The No snags/snags and Off road/on road stratifications included hour as a covariate for the detection function. For each stratum, we report the corresponding estimate, percent coefficient of variance (%CV), degrees of freedom

(df), and lower (LCL) and upper confidence limits (UCL).

Stratum 1 Stratum 2 Estimate %CV df LCL UCL Estimate %CV df LCL UCL North (1) ER 0.41 25 67 0.25 0.66 0.39 17 130 0.28 0.55 vs. p 0.29 13 52 0.22 0.37 0.22 10 139 0.18 0.27 South (2) D 45 28 98 26 76.4 56 20 206 38.08 82.31 No snags (1) ER 0.25 40 52 0.12 0.54 0.45 15 145 0.4 0.6 vs. p 0.31 19 25 0.21 0.46 0.23 8 163 19 0.27 snags (2) D 25 44 71 11 59 64 17 234 46 89 Off road (1) ER 0.28 19 104 0.19 0.41 0.52 19 93 0.36 0.76 vs. p 0.17 13 54 0.13 0.21 0.20 10 94 0.16 0.24 on road (2) D 53 23 157 34 83 85 22 142 56 130

Table 14: Results for the relative abundance index differences between covariate groups: North and South of Marsh Harbor; the northern and southern portions of southern Abaco (S); the presence/absence of snags; points on or off a road; habitat that is not pine dominant (not Pdom) or is pine dominant (Pdom). For each group, we report the number of detections (Det), the detections adjusted for effort (Adj Det), and detections per hectare (mean + SE) with the number of points (n). For each GLM, we report the estimate (ß ± SE) for each group, and the residual deviance (RDev) with degrees of freedom (df). We also report the likelihood ratio statistic (LR), degrees of freedom (df), and p-value for the chi-square likelihood ratio test with one degree of

2 freedom (χ 1).

2 Detections/ ha GLM χ 1 Group Det ADet Mean SE n ß SE RDev df LR p-value North 5 10 0.09 0.05 34 -2.37 0.56 184.1 231 4.1 0.04 South 149 160 0.26 0.04 199 1.01 0.58 S North 54 60 0.28 0.06 68 -1.27 0.23 168.1 197 0.2 0.62 S South 95 100 0.24 0.04 131 -0.14 0.29 No Snags 26 28 0.17 0.06 53 -1.78 0.33 166.0 197 2.4 0.12 Snags 123 132 0.29 0.04 146 0.54 0.37 No Road 54 56 0.17 0.03 105 -1.78 0.24 161.9 197 6.5 0.01 Road 95 104 0.35 0.06 94 0.73 0.29 Not Pdom 42 45 0.33 0.11 43 -1.1 0.26 167.2 197 1.2 0.27 Pdom 107 115 0.23 0.03 156 -0.35 0.31

Chapter 3

Genetic diversity, population structure, and morphology of the endangered

Bahama Swallow (Tachycineta cyaneoviridis)

ABSTRACT

Evaluating the extinction risk of an endangered species should incorporate an understanding of the genetic diversity and structure of remaining populations. The Bahama

Swallow breeds only on three islands in the northern Bahamas, and the movement of breeding populations between islands has important implications for the persistence of this endangered species. In this study, we used microsatellite markers to estimate measures of genetic diversity, effective population size (Ne), and population differentiation (G'ST) on Abaco Island and Andros

Island. We also assessed variation in several morphological measures (mass, head-bill length, wing length). Although there is uncertainty in our effective population estimates, breeding populations may be small enough (~250-800 individuals) to warrant concern for adverse effects of loss of genetic diversity such as long-term evolutionary potential. Our results indicate that populations are not genetically differentiated (G'ST = 0.03), but variation in wing length suggests that gene flow might be low enough to enable traits under selection to diverge.

INTRODUCTION

Populations that have declined substantially are more vulnerable to extinction due to demographic and environmental stochasticity, and loss of genetic variability from genetic drift and inbreeding (Frankel and Soule 1981, Caughley 1994). Maintaining genetic diversity and

gene flow between populations is critical to the persistence of endangered species (Allendorf and

Luikart 2007, Frankham et al. 2014). This is particularly relevant for island species, which are more vulnerable to extinction due to inbreeding depression (Frankham 1998). Therefore, conservation of island species requires a fundamental understanding of the genetic diversity and structure of populations.

The Bahama swallow is an endangered species that breeds only on the islands of Grand

Bahama, Abaco, and Andros in the northern Bahamas (Raffaele et al. 1998, Birdlife International

2016; Figure 1). The species’ distribution during the non-breeding season is very poorly understood, although swallows have been recorded in the southern Bahamas, Cuba and the

Florida Keys (Birdlife International 2016). It is also unknown whether swallows disperse from their natal islands or whether they have high site fidelity between breeding seasons. The frequency of movement between breeding populations on different islands has important implications for genetic population structure and maintenance of genetic diversity in this species, and thus its conservation.

In this study, we examined the genetic diversity, effective population size (Ne), and differentiation of swallow populations on Great Abaco Island and Andros Island using microsatellite markers (Makarewich et al. 2009). Microsatellites are neutral markers that can be used to infer gene flow (Holderegger et al. 2005, Waits and Storfer 2015). We also examined several morphological measures (body mass, head-bill length, wing length) to describe population variation of these traits. The goal of this work was to assess the extent to which genetics might impact extinction risk of this endangered species.

METHODS

Morphological measurements and blood sampling

We captured swallows on Abaco Island and Andros Island during three consecutive breeding seasons (2015-17; Chapter 4). We set up mist nets in areas where swallows were actively foraging (Figure 2), with a total sampling effort of approximately 20 hours. Each captured individual was banded with a USGS aluminum band, and its body mass was measured with a spring balance (Pesola 30 g), head-bill length with a dial caliper (SPI 150 mm), and flattened wing length with a ruler with a wing stop (Avinet 15 cm). When possible, we determined sex at the time of capture by the presence of a brood patch or cloacal protuberance.

We took a blood sample (75 µl maximum) from most birds captured during mist netting, and nestlings in two accessible nests, via brachial venipuncture and stored the sample in lysis buffer.

DNA extraction and amplification tests

We extracted genomic DNA from blood samples using a DNeasy Blood and Tissue kit

(Qaigen). Genetic testing was used to confirm the sex identified in the field or determine sex when it was unknown. Primers P2 and P8 were used in polymerase chain reaction (PCR) to amplify the CHD1 gene on the avian sex chromosomes (Griffiths et al. 1998). PCR reactions consisted of 5 µl Master mix, 3.6 µl molecular grade water, 2 µM of each primer, and 1 µl DNA.

The PCR cycling profile included a 2 min activation step at 95 °C, 35 cycles of 30 s at 95 °C, 45 s at 47 °C, 45 s at 72 °C, and a final extension step of 5 mins at 72 °C. PCR products were run

through a 3% agarose gel to identify individuals as male or female by the presence of one or two bands, respectively.

Type-it Microsatellite Kits (Qiagen) were used to complete PCRs using fluorescently labeled primers for the seventeen loci developed by Makarewich et al. (2009) that amplified in the Bahama swallow (see Supplementary Materials). PCR reactions consisted of 6.25 µl 2x

Type-it Multiplex PCR Master Mix, 4 µl RNase-free water, 2uM of each primer, and 1 µl DNA.

The PCR cycling profile included a 5 min activation step at 95 °C, 28 cycles of 30 s at 95 °C, 90 s at the locus-specific annealing temperature (Makarewich et al. 2009), 30 s at 72 °C, and a final extension step of 30 min at 72 °C. PCR products were run through a 1% agarose gel, and DNA amplification at each locus was determined by the presence of at least one band. Amplification at four loci failed during this step. The final thirteen loci were assigned to one of four multiplex mixes for final amplification in all individuals. PCR products were diluted with molecular grade water (1:10) and genotyped with a 3730xl 96-Capillary Genetic Analyzer at the Yale DNA

Analysis Facility (New Haven, CT).

Genotyping and marker screening

Fragment lengths were scored and binned using standard protocols in Geneious (10.2.2).

Linkage disequilibrium was estimated for each population and for all samples combined, using the log-likelihood ratio statistic for each pair of loci, as implemented in Genepop (Web version

4.2, option 2; Raymond and Rousset 1995, Rousset 2008; Supplemental Table S1). Loci were also screened for deviations from Hardy-Weinberg equilibrium (HWE) based on Monte Carlo permutations of alleles (pegas, Paradis 2010), and the presence of null alleles (PopGenReport,

Adamack and Gruber 2014).

Genetic diversity, effective population size, and population structure

We estimated expected heterozygosity (HE), observed heterozygosity (HO), inbreeding coefficients (FIS) (hierfstat, Goudet and Jombart 2015), and allelic richness (AR)

(PopGenReport, Adamack and Gruber 2014). The effective population size (Ne) for each island and for both islands combined was estimated using the linkage disequilibrium method of Waples and Do (2008), as implemented in NeEstimator V2 (Do et al. 2014) with allele frequency critical values that exclude rare (0.05) and very rare (0.01) alleles (Hale et al. 2012). We also calculated

G'ST (mmod, Winter in press), a measure of genetic differentiation that is appropriate for microsatellite loci (Hedrick 2005). We estimated population structure using a principal component analysis (PCA) of scaled allele frequencies (adegenet, Jombart 2008, Jombart and

Ahmed 2011; ade4, Chessel et al. 2004, Dray and Dufour 2007). We used permutational multivariate analysis of variance (PERMANOVA) and multivariate homogeneity of dispersion tests to determine if the structure (location) and variability (dispersion) of principal components differed by population (vegan, Oksanen et al. 2019). We retained all samples, including potential full siblings, since removing such individuals would have decreased the statistical power of our already limited sample size and potentially removed important evolutionary information (Waples and Anderson 2017).

Morphometric analysis

All analyses of morphological measurements were conducted using the statistical package R (3.4.3). Data or model residuals were plotted and tested for normality (Shapiro-Wilk test p < 0.05) and non-parametric statistics were applied if appropriate. Means are reported with

standard errors. Least squares means were calculated when appropriate to account for covariates and unbalanced sample sizes. Results were considered statistically significant at p < 0.05.

We used general linear models to estimate the effects of sex and island on each measurement (mass, head-bill length, wing length). Since captures occurred outside and within the breeding period, and we expected body mass to change across the breeding cycle (Milenkaya et al. 2013), we also estimated the effect of day of capture (DOC) relative to the mean lay date of that year on mass. To examine the effects of nesting stage on mass, we performed additional, separate analyses to test the effect of DOC on mass before and after lay date. We compared models for independent, additive and interactive effects of all factors, as well as an intercept-only model, and selected competing models based on Akaike Information Criterion (AIC) (Akaike

1998). We did not use residual body mass as a measure of condition (Ardia 2005), as has been done in other Tachycineta swallows (Ardia and Clotfelter 2006, Liljesthröm et al. 2012), because the regression of body mass to head-bill was not significant.

RESULTS

Genetic diversity, effective population size, and population structure

We obtained genetic samples from 90 birds on Abaco (71 adults, 13 fledglings, 6 nestlings) and 22 birds on Andros (14 adults, 8 fledglings). Two microsatellite loci were excluded from analysis due to deviations from HWE and high null allele frequencies

(Supplemental Table S2). We used the remaining eleven loci to estimate expected heterozygosity

(HE), observed heterozygosity (HO), inbreeding coefficients (FIS), and allelic richness (AR)

(Table 1; loci-specific values: Supplemental Tables S3 and S4). These estimates of genetic

diversity were similar for both populations. Excluding rare alleles from analysis impacts the estimate of effective population size (Ne) for each population and for all samples combined

(Table 1).

Although the estimate of G'ST varied across loci (Supplemental Table S3), the global G'ST estimate of 0.03 did not provide strong evidence of population differentiation. This result is supported by the principal component analysis of scaled allele frequencies, which indicated very weak but significant differences in the location (r2 = 0.02, p = 0.0001), but no differences in dispersion (F1 = 1.89, p = 0.17; Figure 3) between populations.

Morphological variation

We obtained morphological measurements and determined the sex of 90 adult birds on

Great Abaco (32 males, 39 females) and Andros (9 males, 7 females). Over the entire capture period, the top model for mass (Table 2) showed that females had a larger body mass than males

2 (ß = -0.03 ± 0.01, F = 4.87(1, 87), r = 0.04, p = 0.03; Table 3). The second ranking model (F =

2 2.83(2, 86), r = 0.04 p = 0.06) showed that although mass did not change significantly by date of capture (ß = -0.0003± 0.0003, p = 0.37), males had a smaller body mass than females (ß = -0.03

2 ± 0.02, p = 0.06). The third ranking model (F = 2.81(2, 86), r = 0.04, p = 0.07; Figure 4) showed that although mass did not vary significantly by island (ß = -0.02 ± 0.02, p = 0.38), males had a smaller body mass than females (ß = -0.03± 0.01, p = 0.04). Although the top ranking models for

2 mass before incubation included the effect of sex (F = 3.51(1, 45), r = 0.05 p = 0.07) and the

2 effects of DOC and sex (F = 2.62(2, 44), r = 0.07, p = 0.08), the competing models included the null model, indicating that these effects were not informative (Table 2).

Two of the females captured on Great Abaco had a mass of 20 g or more (Figure 4).

These values were not the result of observer error. It is possible that these females were gravid, but we did not determine this at the time of capture. Therefore, we did not eliminate them as outliers in the ranked models (Table 2). However, when these females were excluded, the smaller least square mean body mass of Abaco females (15.50 ± 0.13 g) resulted in a weaker

2 effect of sex over the entire capture period (F = 3.38(1, 86), r = 0.03, p = 0.07) and before

2 incubation (F = 2.48(1, 45), r = 0.03, p = 0.12). We also found that body mass increased with

2 DOC before incubation (ß = 0.003 ± 0.001, F = 5.83(1, 45), r = 0.10, p = 0.02).

2 The top model for head-bill length included additive effects of sex and island (F = 7.62, 87, r =

0.13, p = 0.0009; Table 4). Males had larger head-bills than females (ß = 0.011± 0.004, p =

0.0035; Table 3), and birds on Andros had larger head-bills than those on Abaco (ß = 0.010 ±

0.005, p = 0.0321; Table 3). The second-ranked model was almost competitive (∆AICc = 2.19),

2 and included interactive effects of sex and island (F = 5.03, 86, r = 0.12, p = 0.003; Table 4).

Although the interaction was not significant (ß = -0.002 ± 0.009, p = 0.82), males had significantly larger head-bills than females on Abaco (t = -2.8, p = 0.03), but not on Andros (t = -

1.1, p = 0.72; Figure 5)

2 The top model for wing length included an interaction of island and sex (F3,86 = 24.6, r =

0.44, p < 0.0001; Table 5). The interaction was significant (ß = -0.03± 0.02, p = 0.04); although males had longer wings than females on both islands (Table 3, Figure 6), the difference was significant on Abaco (t = -8.4, p < 0.0001), but not on Andros (t = -1.6, p = 0.40). The other

2 competing model included the effect of sex (F3,88 = 66.8, r = 0.43, p < 0.0001), and showed that males had longer wings than females (ß = -0.05± 0.01, p <0.0001; Table 3).

DISCUSSION

In this study, we assessed the genetic diversity and population structure of the Bahama

Swallow in order to advance knowledge of the extinction risk of this endangered species. Our estimates of effective population size (Ne) all included confidence intervals with an infinite upper bound, indicating that results be interpreted with caution. It is difficult to obtain reliable Ne estimates, although the linkage disequilibrium method (Waples and Do) that we implemented is considered to be a reliable single-sample method (Gilbert and Whitlock 2015). Much of this uncertainty could be due to our small sample size, particularly on Andros, where the sample size

(n = 22) was lower than ideal to accurately estimate allele frequencies (Hale et al. 2012). The Ne estimate on Andros increased when rare alleles were excluded, whereas the Abaco estimate and the combined estimate decreased. However, while eliminating rare alleles did alter the estimates in all cases, the degree of change was not substantial (~270-520 individuals).

Despite this uncertainty, the mean Ne estimates and the lower bounds of the confidence intervals suggest that breeding populations might be small enough to warrant concern for this species over the long-term. Many of the estimates fall within the range of values proposed as indicative of risk of inbreeding depression and loss of genetic diversity compromising long-term evolutionary potential, such as Franklin’s (1980) 50/500 rule and the 100/1000 thresholds suggested by Frankham et al. (2014). All of the mean Ne estimates were fewer than 1000 individuals, and many have a lower confidence limit of fewer than 100 individuals. Additional genetic sampling effort, and perhaps testing of additional Ne estimation methods, are needed to determine if breeding population sizes expose this species to genetic extinction risk factors.

Our global estimate of G'ST (0.03) and the principal component analysis of scaled allele frequencies suggest that the populations on Abaco and Andros are not differentiated genetically.

The similarity in estimates of genetic diversity on each island supports this conclusion. Overall, our analyses of neutral markers suggest that there is recent gene flow among the populations that breed on these two islands, although we did not directly measure gene flow in this study.

Interestingly, we found evidence that birds on the two islands, despite their lack of genetic differentiation, differed in measures of structural body size. Birds of both sexes had larger head-bills on Andros than on Abaco. Following the same pattern, Andros females had longer wings than Abaco females. Yet the males on Abaco had longer wings than any other group, including Andros males. It is possible that these differences are an artifact of the small sample size on Andros. However, in Tree Swallows (Tachycineta bicolor), longer wings correlated with male reproductive success (number of extra pair young) when population densities were high, suggesting density-dependent sexual selection of this trait (Lessard et al.

2014). This could also be the case for Bahama Swallows. Although we were unable to estimate true population density on either island (Chapter 1), we did observe more birds breeding on southern Abaco than on Andros. On both islands, males had larger head-bills and longer wings than females, although the differences were not significant on Andros. A similar pattern of sexual dimorphism has been seen in Tree Swallows (Johnson et al. 2003) and Golden Swallows

(Tachycineta euchrysea; Proctor 2016).

We saw no differences in mean body mass of birds on the two islands. Females had a larger body mass than males, but this difference was driven by the two females captured on

Abaco, whose body masses (~ 4.5-5 g above the mean) suggest that they were gravid at the time of capture (Winkler et al. 2011). When these females were excluded, we saw an increase in mass leading up to the start of laying. Although this pattern might indicate a response to the energetic demands of breeding (Winkler and Allen 1996, Nooker et al. 2005, Ardia and Clotfelter 2006,

Perez et al. 2008, Ardia et al. 2009, Ardia et al. 2010), we cannot assume this is the case without repeated measures of individual birds or known nesting stage of captured individuals. Also, the adult body mass in this study (mean 15.48 ± 0.12 g, n = 87) was considerably less than those reported by Allen (1996; mean = 16.3 g, n = 4), and Turner and Rose (1989; 16.3 - 19.5).

However, it is unclear if this difference represents variation in morphology across years and/or islands, or is merely a reflection of sample sizes.

In this study, we found weak evidence of genetic differentiation in populations that varied morphologically, which is a pattern that has been seen in other studies of birds (Ball et al. 1988,

Grapputo et al. 1998, Chan and Arcese 2003) and other taxa (Adams et al. 2006, Gruber et al.

2013). Neutral markers can evolve at different rates than loci that are under selection

(Holderegger et al. 2006, Waits and Storfer 2015) and can lead to different interpretations of population differentiation (Landguth and Balkenhol 2012, Whitlock 2014). Therefore, it is possible to see population differences in heritable traits like bill size and wing length (Smith and

Dhondt 1980, Lessells and Ovenden 1989, Merilä and Gustafsson 1993, Tarka et al. 2010, Cava et al. 2019) while seeing no indication of population differentiation in neutral markers such as microsatellites. Additional research is needed to measure heritability and selection on these traits, and to determine whether variation across populations reveals differentiation that was not detected through neutral genetic markers or differences in developmental environmental conditions.

Our results indicate that more information is needed to determine whether Bahama

Swallow populations are isolated and vulnerable to the extinction risks associated with small populations. Our sample size on southern Abaco was sufficient, but future assessments of genetic and morphological variation would benefit from additional samples on Andros and other parts of

the breeding range. Although considerable effort would be required, it is particularly important to assess the populations in northern Abaco and Grand Bahama, where swallow density is lower

(Chapter 2).

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FIGURES

Figure 1: Map of Grand Bahama, Abaco, and Andros Islands in the northern Bahamas.

Figure 2: Bahama Swallow capture sites on Abaco Island and Andros Island. Each black square indicates a site where foraging swallows were captured using mist nets.

Figure 3: Ordination plot of the first (PC1) and second (PC2) principal components of scaled allele frequencies of Bahama Swallows on Abaco Island and Andros Island. Ellipses indicate a

95% confidence level for each island.

Figure 4: Body mass of female (F) and male (M) Bahama Swallows on Abaco Island and Andros

Island. Mass is presented on the log scale with linear labels.

Figure 5: Head-bill length of female (F) and male (M) Bahama Swallows on Abaco Island and

Andros Island. Head-bill is presented on the log scale with linear labels.

Figure 6: Wing length of female (F) and male (M) Bahama Swallows on Abaco Island and

Andros Island. Wing length is presented on the log scale with linear labels.

TABLES

Table 1: Mean diversity estimates of expected heterozygosity (HE), observed heterozygosity

(HO), inbreeding coefficients (FIS), and allelic richness (AR) for each population and all samples combined. Effective population sizes (Ne) with jackknifed 95% confidence intervals (upper and lower) are reported for each population and all samples combined using allele frequency critical values that exclude rare (0.05) and very rare (0.01) alleles.

Diversity estimates Ne (critical value 0.05) Ne (critical value 0.01) Samples AR HO HE FIS Estimate Lower Upper Estimate Lower Upper Abaco 6.4 0.64 0.66 0.04 294 94 Infinite 567 192 Infinite Andros 6.2 0.67 0.68 0.01 775 30 Infinite 317 68 Infinite Combined 8.5 0.65 0.68 0.04 263 91 Infinite 779 237 Infinite

Table 2: Model ranking results for competing (∆AICc < 2) linear models testing the effects of sex, island, and date of capture (DOC) on body mass. See Supplemental Table S5 for full model results. For each model, we report the number of parameters (k), the Akaike Information

Criterion corrected for small sample sizes (AICc), the difference in AICc (∆AICc ), the likelihood, and model weight (wi).

Model K AICc ∆ AICc Likelihood wi sex 3 -218.36 0.00 1.00 0.27 Entire sex + DOC 4 -217.00 1.36 0.51 0.14 island + sex 4 -216.96 1.40 0.50 0.14 sex 3 -111.59 0.00 1.00 0.26 Before DOC + sex 4 -110.97 0.63 0.73 0.19 incubation null 2 -110.35 1.24 0.54 0.14 DOC 3 -109.94 1.65 0.44 0.11 null 2 -101.73 0.00 1.00 0.25 island 3 -101.48 0.25 0.88 0.22 After incubation sex 3 -100.42 1.31 0.52 0.13 DOC 3 -100.24 1.49 0.47 0.12

Table 3: Means and standard errors (SE) of morphological measurements (body mass, head-bill length, wing length) of Bahama Swallows on Abaco Island and Andros Island. Arithmetic means are reported for all birds combined (n = 90). Least square means are reported for birds by sex and island: Abaco males (n = 32) and females (n = 42), and Andros males (n = 9) and females (n =7).

Both sexes Male Female Measurement Island Mean SE Mean SE Mean SE Both 15.5 0.1 15.2 0.2 15.7 0.2 Body Mass (g) Abaco 15.5 0.1 15.3 0.2 15.8 0.2 Andros 15.2 0.3 15.0 0.3 15.5 0.3 Both 29.0 0.1 29.2 0.1 28.9 0.1 Head-bill (mm) Abaco 29.0 0.1 29.2 0.1 28.9 0.1 Andros 29.3 0.1 29.5 0.1 29.2 0.1 Both 112.8 0.4 116.0 0.5 110.0 0.4 Wing (mm) Abaco 113.0 0.5 116.0 0.5 110.0 0.5 Andros 113.0 1.0 115.0 0.9 110.0 0.9

Table 4: Model ranking results for linear models testing the effects of sex and island on head-bill length. For each model, we report the number of parameters (k), the Akaike Information

Criterion corrected for small sample sizes (AICc), the difference in AICc (∆ AICc ), the likelihood, and model weight (wi).

Model K AICc ∆ AICc Likelihood wi island + sex 4 -471.22 0.00 1.00 0.61 island * sex 5 -469.04 2.19 0.33 0.20 sex 3 -468.64 2.59 0.27 0.17 island 3 -464.53 6.69 0.04 0.02 null 2 -461.04 10.18 0.01 0.00

Table 5: Model ranking results for linear models testing the effects of sex and island on wing length. For each model, we report the number of parameters (k), the Akaike Information

Criterion corrected for small sample sizes (AICc), the difference in AICc (∆ AICc ), the likelihood, and model weight (wi).

Model K AICc ∆ AICc Likelihood wi island * sex 5 -384.29 0.00 1.00 0.66 sex 3 -382.32 1.97 0.37 0.25 island + sex 4 -380.28 4.01 0.13 0.09 null 2 -326.75 57.54 0.00 0.00 island 3 -324.68 59.61 0.00 0.00

SUPPLEMENTARY MATERIALS

Bahama Swallow microsatellite amplification tests

Makarewich et al. (2009) developed microsatellite markers and tested for cross amplification in three species of Tachycineta swallows. We tested the 21 markers that amplified in the Tree Swallow (T. bicolor), the most closely related to the Bahama swallow of the three species, to test for amplification in one Bahama Swallow from each island. PCR reactions consisted of 5 µl Master mix, 3.6 µl molecular grade water, 4 µM primer, and 1 µl DNA. The

PCR cycling profile included a 2 min activation step at 95 °C, 35 cycles of 30 s at 95 °C, 45 s at the optimized locus-specific annealing temperature (Makarewich et al 2009), 30 s at 72 °C, and a final extension step of 5 mins at 72 °C. PCR products were run through a 1% agarose gel, and

DNA amplification at each locus was determined by the presence of at least one band.

Table S1: Results for linkage disequilibrium for each population and for all samples combined, using the log-likelihood ratio statistic for each pair of loci, as implemented in Genepop on the

Web. Significant p-values with a Bonferroni correction for multiple comparisons (Rice 1989)

(critical p-value = 0.0006) are shown in bold. The cases of significant linkage occurred only with all samples combined, but not when samples were separated by population.

Abaco Andros Combined Locus 1 Locus 2 P-Value S.E. P-Value S.E. P-Value S.E. TaBi1 TaBi25 0.17 0.02 0.31 0.01 0.30 0.02 TaBi1 TaBi34 0.76 0.03 0.59 0.01 0.37 0.04 TaBi1 TaBi8 0.45 0.03 0.32 0.01 0.36 0.03 TaBi1 Tal11 0.17 0.01 0.62 0.01 0.40 0.01 TaBi1 Tal6 0.82 0.01 0.285 0.005 0.68 0.01 TaBi1 Tal8 0.27 0.03 0.25 0.02 0.32 0.03 TaBi1 Tle14 0.08 0.01 0.38 0.01 0.07 0.01 TaBi10 TaBi1 0.15 0.02 0.67 0.01 0.03 0.01 TaBi10 TaBi25 0.88 0.03 1.00 <0.0001 0.88 0.03 TaBi10 TaBi34 0.97 0.02 0.08 0.02 0.78 0.04 TaBi10 TaBi4 0.76 0.02 0.34 0.01 0.79 0.02 TaBi10 TaBi8 0.78 0.04 0.43 0.04 0.42 0.04 TaBi10 Tal11 0.84 0.02 0.65 0.02 0.90 0.02 TaBi10 Tal6 0.34 0.02 0.89 0.01 0.67 0.02 TaBi10 Tal8 0.005 0.005 1.00 <0.0001 0.20 0.04 TaBi10 Tle14 0.59 0.03 0.54 0.02 0.56 0.03 TaBi25 Tal8 0.48 0.05 0.30 0.04 0.66 0.04 TaBi25 Tle14 0.79 0.01 0.04 0.01 0.66 0.02 TaBi34 TaBi25 0.009 0.005 1.00 <0.0001 <0.0001 <0.0001 TaBi34 Tal11 0.80 0.03 0.21 0.02 0.31 0.04 TaBi34 Tal6 0.31 0.03 0.70 0.01 0.28 0.03 TaBi34 Tal8 0.44 0.05 1.00 <0.0001 0.93 0.03 TaBi34 Tle14 0.14 0.02 0.84 0.01 0.23 0.03 TaBi4 TaBi1 0.15 0.01 0.064 0.002 0.32 0.02 TaBi4 TaBi25 0.49 0.02 0.70 0.01 0.17 0.02 TaBi4 TaBi34 0.57 0.03 0.09 0.01 0.22 0.03 TaBi4 TaBi8 0.96 0.01 1.00 <0.0001 0.991 0.003 TaBi4 Tal11 0.73 0.01 0.53 0.01 0.77 0.01 75

Table S1 continued Abaco Andros Combined Locus 1 Locus 2 P-Value S.E. P-Value S.E. P-Value S.E. TaBi4 Tal6 0.017 0.003 0.548 0.005 0.06 0.01 TaBi4 Tal8 0.61 0.04 0.81 0.01 0.24 0.03 TaBi4 Tle14 0.44 0.02 0.788 0.004 0.40 0.02 TaBi8 TaBi25 0.46 0.04 0.08 0.02 0.27 0.04 TaBi8 TaBi34 0.61 0.05 1.00 <0.0001 0.79 0.04 TaBi8 Tal11 0.51 0.03 0.989 0.003 0.24 0.03 TaBi8 Tal6 0.49 0.03 0.93 0.01 0.52 0.02 TaBi8 Tal8 0.16 0.03 1.00 <0.0001 0.35 0.05 TaBi8 Tle14 0.64 0.02 0.09 0.01 0.48 0.03 Tal11 TaBi25 0.16 0.02 0.51 0.02 0.09 0.01 Tal11 Tal8 0.57 0.03 0.64 0.03 0.77 0.03 Tal11 Tle14 0.62 0.01 0.84 0.01 0.62 0.01 Tal6 TaBi25 0.48 0.02 0.37 0.01 0.27 0.02 Tal6 Tal11 0.51 0.01 0.36 0.01 0.61 0.01 Tal6 Tal8 0.77 0.02 0.56 0.02 0.94 0.01 Tal6 Tle14 0.003 0.001 0.844 0.004 0.022 0.003 Tal8 Tle14 0.51 0.03 1.00 <0.0001 0.25 0.03 Tle16 TaBi1 0.32 0.03 0.65 0.01 0.31 0.03 Tle16 TaBi10 0.21 0.04 0.32 0.04 0.24 0.04 Tle16 TaBi25 0.45 0.04 1.00 <0.0001 0.20 0.03 Tle16 TaBi34 0.93 0.02 1.00 <0.0001 0.81 0.04 Tle16 TaBi4 0.67 0.03 0.34 0.01 0.50 0.04 Tle16 TaBi8 0.002 0.002 0.38 0.04 0.14 0.03 Tle16 Tal11 0.006 0.002 0.45 0.03 0.010 0.006 Tle16 Tal6 0.36 0.02 0.32 0.01 0.44 0.03 Tle16 Tal8 0.04 0.02 1.00 <0.0001 0.06 0.02 Tle16 Tle14 0.16 0.02 0.06 0.01 0.03 0.01 Tle19 TaBi1 0.60 0.02 0.22 0.01 0.23 0.02 Tle19 TaBi10 0.86 0.02 0.94 0.01 0.90 0.02 Tle19 TaBi25 0.80 0.02 0.45 0.03 0.53 0.03 Tle19 TaBi34 0.54 0.04 1.00 <0.0001 0.48 0.04 Tle19 TaBi4 0.37 0.02 0.31 0.01 0.24 0.02 Tle19 TaBi8 0.54 0.03 0.96 0.01 0.50 0.04 Tle19 Tal11 0.32 0.01 0.71 0.02 0.31 0.01 Tle19 Tal6 0.81 0.01 0.03 0.00 0.67 0.01 Tle19 Tal8 0.64 0.04 1.00 <0.0001 0.59 0.04

Table S1 continued Abaco Andros Combined Locus 1 Locus 1 P-Value S.E. P-Value S.E. P-Value S.E. Tle19 Tle14 0.57 0.01 0.27 0.01 0.36 0.02 Tle19 Tle16 0.59 0.03 0.86 0.02 0.03 0.01 Tle19 Tle4 0.20 0.03 1.00 <0.0001 0.80 0.03 Tle4 TaBi1 0.01 0.01 0.48 0.02 0.001 0.001 Tle4 TaBi10 0.85 0.04 1.00 <0.0001 0.69 0.05 Tle4 TaBi25 0.22 0.04 1.00 <0.0001 0.11 0.03 Tle4 TaBi34 1.00 <0.0001 1.00 <0.0001 1.00 <0.0001 Tle4 TaBi4 0.76 0.03 0.30 0.01 0.23 0.03 Tle4 TaBi8 0.86 0.03 0.12 0.03 0.29 0.04 Tle4 Tal11 0.50 0.03 1.00 <0.0001 0.52 0.04 Tle4 Tal6 0.60 0.03 1.00 <0.0001 0.67 0.03 Tle4 Tal8 0.06 0.02 1.00 <0.0001 0.82 0.04 Tle4 Tle14 0.79 0.03 1.00 <0.0001 0.58 0.04 Tle4 Tle16 0.07 0.03 1.00 <0.0001 <0.0001 <0.0001

Table S2: Loci-specific p-values for Hardy-Weinberg exact test based on Monte Carlo permutations of alleles for each population and all samples combined, and bootstrapped null allele frequency estimates. Loci with consistently significant p-values and high null allele frequencies are shown in bold.

P-value Null allele frequency Locus Combined Abaco Andros Estimate Lower CI Upper CI TaBi1 <0.0001 0.04 0.002 0.20 0.04 0.37 TaBi10 0.006 0.001 0.76 0.02 -0.03 0.08 TaBi25 0.009 0.05 0.19 0.06 0.007 0.12 TaBi34 <0.0001 <0.0001 0.02 0.18 0.12 0.25 TaBi4 0.02 0.01 0.22 0.01 -0.08 0.11 TaBi8 0.40 0.07 0.61 0.04 -0.02 0.10 Tal11 0.86 0.64 0.51 0.01 -0.04 0.07 Tal6 0.51 0.82 1.00 -0.02 -0.05 0.00 Tal8 0.55 0.78 0.52 0.01 -0.03 0.04 Tle14 0.90 0.65 1.00 -0.02 -0.07 0.04 Tle16 0.18 0.31 0.79 0.003 -0.03 0.04 Tle19 <0.0001 <0.0001 0.69 0.08 0.02 0.14 Tle4 0.37 0.51 0.44 0.009 -0.02 0.04

Alleles Diversity estimates

Locus N Min Mean Max AR HO HE FIS G'ST TaBi10 11 333 359 381 10.7 0.77 0.80 0.05 0.02 TaBi25 7 196 205 217 6.5 0.59 0.71 0.17 0.00 TaBi4 4 243 249 255 3.9 0.45 0.51 0.11 0.04 TaBi8 10 297 309 319 9.5 0.77 0.79 0.03 -0.04 Tal11 3 207 210 213 3.0 0.70 0.66 -0.06 0.04 Tal6 3 359 361 363 3.0 0.34 0.30 -0.12 -0.02 Tal8 19 291 329 357 17.7 0.83 0.88 0.05 0.03 Tle14 3 207 209 211 3.0 0.52 0.52 -0.01 -0.03 Tle16 9 243 245 256 9.0 0.84 0.81 -0.03 0.29 Tle19 4 144 147 150 4.0 0.48 0.57 0.16 0.01 Tle4 24 242 284 320 23.2 0.91 0.94 0.03 0.09

Table S4: Allele characteristics (number of alleles (N); minimum allele size (Min); mean allele size (Mean); maximum allele size

(Max)) and diversity estimates (expected heterozygosity (HE); observed heterozygosity (HO); inbreeding coefficients (FIS); allelic richness (AR)) for each locus in each population.

Abaco Andros Alleles Diversity estimates Alleles Diversity estimates

Locus N Min Mean Max AR HO HE FIS N Min Mean Max AR HO HE FIS TaBi10 10 333 360 381 8.0 0.77 0.80 0.04 7 333 360 381 6.7 0.76 0.78 0.03 TaBi25 7 196 205 217 5.1 0.59 0.68 0.13 5 196 202 208 4.9 0.59 0.72 0.18 TaBi4 4 243 249 255 2.8 0.54 0.51 -0.06 2 243 245 247 2.0 0.36 0.50 0.28 TaBi8 10 297 309 319 7.4 0.67 0.77 0.13 7 299 310 319 6.9 0.86 0.79 -0.09 Tal11 3 207 210 213 3.0 0.59 0.64 0.08 3 207 210 213 3.0 0.82 0.66 -0.24 Tal6 3 359 361 363 2.6 0.31 0.28 -0.11 3 359 361 363 2.9 0.36 0.31 -0.16 Tal8 18 291 329 357 12.1 0.89 0.88 -0.01 11 291 321 335 10.1 0.77 0.85 0.09 Tle14 3 207 209 211 2.5 0.54 0.51 -0.06 3 207 209 211 2.7 0.50 0.51 0.02 Tle16 9 243 245 256 7.4 0.77 0.77 0.00 9 243 245 256 4.0 0.91 0.84 -0.08 Tle19 4 144 147 150 3.9 0.41 0.54 0.24 4 144 147 150 4.0 0.55 0.57 0.04 Tle4 22 242 284 320 15.6 0.91 0.92 0.01 18 242 283 317 16.2 0.91 0.92 0.01

Table S5: Model ranking results for linear models testing the effects of sex, island, and day of capture (DOC) on body mass. For each model, we report the number of parameters (k), the

Akaike Information Criterion corrected for small sample sizes (AICc), the difference in AICc (∆

AICc ), the likelihood, and model weight (wi).

Model K AICc ∆ AICc Likelihood wi sex 3 -218.36 0.00 1.00 0.27 sex + DOC 4 -217.00 1.36 0.51 0.14 island + sex 4 -216.96 1.40 0.50 0.14 island + sex + DOC 5 -216.10 2.25 0.32 0.09 null 2 -215.65 2.71 0.26 0.07 DOC 3 -215.53 2.83 0.24 0.07 Entire island + DOC 4 -215.39 2.97 0.23 0.06 sex * DOC 5 -215.20 3.16 0.21 0.06 island *sex 5 -214.75 3.61 0.16 0.04 island 3 -214.66 3.70 0.16 0.04 island *DOC 5 -213.16 5.20 0.07 0.02 island * sex * DOC 9 -208.23 10.13 0.01 0.00 sex 3 -111.59 0.00 1.00 0.26 DOC + sex 4 -110.97 0.63 0.73 0.19 null 2 -110.35 1.24 0.54 0.14 DOC 3 -109.94 1.65 0.44 0.11 island + sex 4 -109.31 2.28 0.32 0.08 Before island + sex + DOC 5 -108.50 3.09 0.21 0.05 incubation sex * DOC 5 -108.47 3.12 0.21 0.05 island 3 -108.26 3.33 0.19 0.05 island + DOC 4 -107.64 3.95 0.14 0.04 island * sex 5 -106.80 4.79 0.09 0.02 island * DOC 5 -105.13 6.46 0.04 0.01 island * sex * DOC 8 -100.21 11.38 0.00 0.00 null 2 -101.73 0.00 1.00 0.25 island 3 -101.48 0.25 0.88 0.22 sex 3 -100.42 1.31 0.52 0.13 After incubation DOC 3 -100.24 1.49 0.47 0.12 island + sex 4 -99.49 2.24 0.33 0.08 island + DOC 4 -99.46 2.27 0.32 0.08

Table S5 continued Model K AICc ∆ AICc Likelihood wi sex + DOC 4 -98.40 3.33 0.19 0.05 island *sex 5 -97.42 4.31 0.12 0.03 After incubation island +sex + DOC 5 -97.16 4.57 0.10 0.02 island * DOC 5 -97.03 4.70 0.10 0.02 sex * DOC 5 -95.81 5.92 0.05 0.01 island * sex * DOC 9 -86.92 14.81 0.00 0.00

CHAPTER 4

Cavity use and breeding biology of the endangered Bahama Swallow

(Tachycineta cyaneoviridis): implications for conservation

Maya Wilson and Jeffrey R. Walters

In revision for the Journal of Field Ornithology

ABSTRACT

Bird populations, especially on islands, have declined or gone extinct due to overhunting, habitat loss and fragmentation, and adverse effects of the introduction of non-native species. The

Bahama Swallow (Tachycineta cyaneoviridis) is an endangered secondary cavity-nester that only breeds on three islands in the northern Bahamas, but the causes of its population decline are unknown. On Great Abaco Island, we identified the cavity-nesting resources used by breeding swallows in the native pine forest and other habitats, and estimated the phenology and breeding parameters from a subset of nests. In order to determine whether poor productivity was a likely cause of population declines, we compared Bahama Swallow breeding parameter estimates with congeners. The Bahama Swallow will nest in cavities in a variety of structures, but relies on woodpecker-excavated cavities in pine snags and utility poles. Swallows nesting in pine snag cavities had higher fledging success than those nesting in utility pole cavities, which were concentrated in non-pine habitat that may expose swallows to increased competition for nesting cavities and/or predation. However, the high reproductive success of swallows nesting in the pine forest, relative to other Tachycineta species, indicated that population declines cannot be

attributed to poor productivity on southern Great Abaco. Our results suggest that the dependence of the Bahama Swallow on the excavations of a single species of woodpecker for nesting sites may be a factor in its decline and highlight the potential importance of pine forest protection and management to future efforts to conserve the Bahama Swallow.

Keywords: Bahamas, cavity-nesting, pine forest, reproductive success, woodpecker-excavated cavities

INTRODUCTION

Globally, birds have declined or gone extinct as a result of overhunting, habitat loss and fragmentation, and adverse effects of the introduction of non-native species (Diamond 1989,

Szabo et al. 2012). Species or populations with small ranges, such as those on islands, are particularly vulnerable to these pressures (Johnson and Stattersfield 1990, Manne et al. 1999,

Simberloff 2000, Sodhi et al. 2004, Fordham and Brook 2010, Szabo et al.. 2012, Wood et al.

2017). Many of the birds on Caribbean islands are endemic and threatened, but deficient knowledge of these species often impedes conservation efforts (Johnson and Stattersfield 1990,

Wege and Anadón-Irizarry 2005).

The Bahama Swallow (Tachycineta cyaneoviridis) is a secondary cavity-nester that breeds only in the northern Bahamas. Since seemingly being extirpated as a breeding bird on

New Providence (Birdlife International 2016), due to unidentified causes, the breeding range of this species is limited to the islands of Great Abaco, Grand Bahama, and Andros (Raffaele et al.

1998). These “pine islands” are the only islands within the Bahamian archipelago that contain large areas of Caribbean pine (Pinus caribaea bahamensis), which was heavily logged for

lumber and pulpwood (Henry 1974) and is an important breeding habitat of the swallow (Smith and Smith 1989, Allen 1996). The swallow is listed as Endangered by the IUCN (Birdlife

International 2016) because populations appear to have declined sharply (Emlen 1977, Smith and

Smith 1989, Allen 1996) and are threatened by invasive species and a potential loss of pine forest due to renewed logging efforts, development and saltwater intrusion (Allen 1996, Birdlife

International 2016).

In a study on Grand Bahama in 1995, Allen (1996) described swallow breeding behavior, including nest locations and composition, and estimated breeding parameters from a small number of nests in artificial cavities. Allen’s work provided the first quantitative data on the breeding biology and natural history of the Bahama Swallow. However, an absence of research in the decades since, during which the species status changed from Near-threatened to

Endangered (Birdlife International 2016), has limited the knowledge of this species, including causes of population decline.

The goal of this study was to provide information that can be used to develop effective strategies to manage and conserve the Bahama Swallow. On Great Abaco Island, we identified the cavity-nesting resources used by breeding Bahama Swallows in the pine forest and other habitats, and compared the reproductive success of nests in two commonly used resources. We estimated phenology and breeding parameters (clutch size, incubation period, nestling period, and nest success), and assessed these estimates within a phylogenetic context in order to detect potential discrepancies in life history characteristics. In particular, we aimed to assess the possibility that poor productivity might be responsible for population declines.

METHODS

Study Area

This study took place on Great Abaco Island in the northern Bahamas. Most research was conducted in the southern portion of Great Abaco between the settlements of Marsh Harbour

(26.5328°N, 77.0692°W), Little Harbour (26.3256°N, 76.9992°W), and Sandy Point

(26.0078°N, 77.4044°W) to Hole-in-the-Wall (25.8589°N, 77.1828°W) (Fig. 1), particularly within and surrounding Abaco National Park (ANP). ANP is a protected area of 9,105 hectares, including 2,023 hectares of pine forest. In addition to pine forest, the island contains areas of

“coppice” (dry broadleaf forest), wetlands, grassy fields, agricultural lands, and human habitation.

Data Collection

The data described were collected during four field seasons: 12 May – 7 July 2014, 25 March –

15 July 2015, 4 April – 12 July 2016, and 9 April – 12 July 2017. These periods were selected to encompass a majority of the swallow breeding season (Allen 1996). Data collection during 2014 was limited to locating nest sites that provided information for the subsequent seasons.

Nest locations

Active nests were located on Great Abaco opportunistically by observing adult swallow behavior. A nesting cavity was identified as active if at least one adult entered the cavity fully.

Nesting structures were classified into one of five types (Fig. 2). Swallows built nests in woodpecker-excavated cavities in dead standing pine trees (pine snags) and in utility poles.

Telecommunication towers were the third type of structure, where swallows nested in the hollow pipes near the top of the tower. The fourth category included any nest located in a house or other building. A fifth category was reserved for miscellaneous “other” nesting structures in which nests were found. The structure type and GPS location (Etrex 10 or 20, ± 3m) were recorded for all active nests.

Excavated cavity measurements.

If possible, the contents of excavated cavities were examined using a camera mounted on a telescoping pole (Sandpiper Technologies Treetop Peeper or IBWO Wireless Cavity Inspection

Camera). For safety reasons, the camera was not used when utility cables had the potential to obstruct the movement of the pole. For camera-accessible nests, the height of the nesting structure and the cavity were measured using the telescoping pole. Otherwise, heights were measured using a range finder (Nikon Forestry Pro, ± 0.5 ft). The diameter at breast height

(DBH) of the structure was determined by measuring the circumference to the nearest cm with a measuring tape at a height of ~1.3 meters from the ground and converting the measurement to diameter.

Pine snag monitoring

Detailed breeding biology data were collected from a subset of pine snag nests using the camera. We visited these nests every 2-5 days to determine the stage of the nest (building, laying, incubation, nestling, fledged). In keeping with the protocols used for other Tachycineta breeding biology studies (Massoni et al. 2007, Lilijeström 2011, Lorenzón et al. 2012, Stager et al. 2012, Proctor 2016), a nesting attempt began on the date the first egg was laid (lay date), and

we assumed that the female began incubating the day that the clutch was complete. Although eggs can hatch within a 48-hour period (Allen 1996), hatch date was considered to be the day that the first egg hatched. If the nest was not visited on the day of hatching, hatch date was determined by visually estimating the age of nestlings the first time that nestlings were found in a nest. Allen (1996) found that Bahama Swallows fledged after 22-25 days, but most Tree

Swallow nestlings leave the nest between days 18 and 22 (Winkler et al. 2011). Therefore, if nestlings were estimated to be 17 days or older on a nest check day, nest activity was first determined by observing the nest from a distance to avoid causing nestlings to fledge prematurely. If no activity was detected, the nest was cautiously approached and checked with the camera. Unless additional information was available (e.g., nest depredation observed), the date of nest fate (fledged or failed) was the midpoint between the date of last known activity and the date of the subsequent (final) nest check. The only case of partial brood loss was recorded early in the nestling stage. Therefore, a nest was classified as successfully fledged when all nestlings were gone from the nest with no evidence of predation, and the age of the nestlings on the fate date was at least 18 days.

Utility pole monitoring

During the 2017 field season, we conducted behavioral observations on a subset of active nests in utility poles that we were unable to access with the camera in order to estimate reproductive success. We conducted a twenty-minute observation every 2-7 days within the first four hours after sunrise. If adult swallows were perched nearby, circling, perched outside and/or entered the cavity, we classified the nest as active. If no swallows were seen near the cavity during two consecutive observations, we classified the nest as inactive. Only females incubate,

but both adults feed nestlings (Allen 1996). Therefore, we determined nesting stage by observing parental care behavior. We recorded the nesting stage as “building” if a bird was seen bringing nesting material (e.g., grass, pine bark) into the cavity. If only one adult entered the cavity and remained inside for an extended period (>1 minute), we recorded the stage of the nest as

“incubation.” If both adults were observed entering the cavity for a brief period (<1 minute), we recorded the nest as in the “nestling” stage. If nest records indicated that a nest was likely late in the nestling stage and then classified as inactive during the next observation, we classified the nest as successful. In all cases, the presence of family groups (adults with fledglings) near the nest confirmed this classification. If a nest was inactive earlier than the expected time of fledging

(during the incubation stage or early in the nestling period), then we classified the nest as failed.

Tower occupation

During the 2014 and 2015 field seasons, occupation of towers by swallows was determined opportunistically. Beginning in 2016, a 20-minute observation was conducted at each tower in southern Great Abaco during the nesting period (May-June) to determine whether it was occupied. In 2017, additional towers that were erected by telecommunication companies on the island were included in the survey. A tower was identified as occupied if a swallow fully entered any of the pipes.

Buildings and “other” nesting structures

Many of the nests in buildings and “other” nesting structures were limited to human settlements and located on private property. Additionally, the type and dimensions of the structure varied considerably, and the nests often were difficult to reach or view with the peeper

camera. Consequently, although these nests were recorded to provide a thorough understanding of the nesting structures that this species uses, they were not monitored and were not included in statistical analysis.

GIS Spatial Analysis

Spatial analysis was conducted in ArcMap (10.5.1). Habitat types on Great Abaco were identified using maximum likelihood classification of Landsat 8 satellite images with 30 m spatial resolution, courtesy of the U.S. Geological Survey (Fig. 1, Supplemental Fig. S1). The

Tree Swallow (T. bicolor) has been shown to spend a majority of time foraging within ~200 m of nests (McCarty and Winkler 1999). However, informal observations of swallows on Great Abaco indicate that they will forage at least 400 m from nests. Therefore, habitat classifications were extracted within buffers of 200 m and 400 m from each nest location. The % of total cells within buffers that were classified as pine forest was included in nest records and nest monitoring data sets for statistical analysis.

Statistical Analysis

All analyses were conducted using the statistical package R (3.4.3). Data or model residuals were tested for normality (Shapiro-Wilk test p < 0.05) and non-parametric statistics were applied if appropriate. Means are reported with standard errors. Least squares means were calculated when appropriate to account for covariates and unbalanced sample sizes. Results were considered statistically significant at p < 0.05.

Excavated cavity measurements

We used Mann-Whitney U-tests to determine whether pine snags and utility poles differed from one another in structure height, cavity height, DBH, and % pine within 200 m and

400 m of the nest. If nests were located in the same structure (n = 28) or the same cavity (n = 26) during more than one year, then the associated measurements were considered one sample for these comparative analyses.

Pine snag monitoring

Three pine snag nests that were initiated late in the season, two of which were known to be second attempts in the same cavity, were eliminated from analysis because in Tachcyineta species, late nests can differ from early nests in aspects of breeding biology (Winkler and Allen

1996, Massoni et al. 2007, Ardia et al. 2006, Stager et al. 2012). A lack of known lay dates and camera failure during most of the nestling stage in 2015 excluded those nests from estimates of incubation period and nestling period, and some measures of nest success.

If a monitored nest was found with a complete clutch and the hatch date was known, then the lay date was estimated by subtracting the clutch size and the average incubation period

(Allen 1996) from the hatch date. To test the validity of including estimated lay dates, a paired t- test was used to compare known lay dates with estimated lay dates. Analysis of variance tests were used to determine whether lay date, clutch size, incubation period and nestling period varied by year. Aerial insect abundance is affected by temperature (Ashdown and McKechnie

2008, Jenni-Eiermann et al. 2008, Winkler et al. 2013), and higher insect abundance earlier in the season allows Tree Swallows to lay earlier (Nooker et al. 2005, Dunn et al. 2011). Therefore, we determined whether daily average temperature varied in Nassau, New Providence (~90-150 km

from nest sites) during observed laying periods using the Global Historical Climatology Network

(GHCN) Daily data set from National Centers for Environmental Information (Menne et al.

2012a, Menne et al. 2012b). After testing for overdispersion, we used a generalized linear model with a Poisson distribution to determine if clutch size varied with lay date (Winkler et al. 2014).

We estimated daily survival rate (DSR) using a logistic exposure model (Shaffer 2004), which is a generalized linear model with a binomial distribution and a link function that incorporates the time that a nest was exposed to failure during the interval between visits. Year

(2016 and 2017) was included as a covariate to test for variation in nest survival across breeding seasons. We tested several other covariates that could affect the risk of nest depredation. Adult activity at the nest can vary with nesting stage and clutch/brood size and increased activity can attract predators (Skutch 1949, Martin et al. 2000). Therefore, we included the number of eggs and chicks at the beginning of each interval as a covariate. We included cavity height because mammalian predators (e.g. feral cats, rats, raccoons) might be less likely to depredate nests that are higher off the ground (Nilsson 1984, Peterson and Gauthier 1985, Rendell and Robertson

1989, Li & Martin 1991, Politi et al. 2009). Avian predators such as West Indian Woodpeckers

(Melanerpes superciliaris) and American Kestrels (Falco sparverius) are associated with habitats other than the pine forest (Raffaele et al. 1998). Therefore, we included % pine within

200m and 400m of the nest as additional covariates. Thirty-six models were generated and compared using the approach of Kwon et al. (2018). The probabilities of survival during nesting stages were estimated by DSRt, where t = average number of days of the period of interest (Allen

1996).

To allow comparisons with reported estimates for other Tachycineta species, we calculated several proportional measures of nest success in the population. Egg survival is the

proportion of eggs that survived the entire incubation period, and hatching success is the proportion of surviving eggs that hatched. Nestling survival is the proportion of nestlings that fledged. Fledging success is the total number of initiated nests that successfully fledged at least one young. We also calculated within-nest egg survival, hatching success, and nestling survival and used analysis of variance tests to determine if these measures varied by year.

Towers

We used Mann-Whitney U-tests to determine if the habitat (% pine at 200 m and 400 m) surrounding swallow-occupied towers differed from unoccupied towers.

Tachycineta Literature Review

We compiled breeding parameter estimates (clutch size, incubation period, nestling period, nest success) from the published literature for the other eight Tachycineta species. If multiple sources presented an estimate for the same parameter, we selected the estimate from the source with the highest sample size. If published estimates for species were not available, we obtained data from species experts.

RESULTS

Excavated Cavity Measurements

We compared measurements from all nests found in pine snags (n = 124) to those found in utility poles (n = 56) (Table 1). Structure height and cavity height were lower, and the structure DBH

smaller, for nests in pine snags compared to those in utility poles. Nests in pine snags also had significantly more pine forest within 200 m and within 400 m than nests in utility poles.

Pine Snag Monitoring

We examined breeding biology in a sample of 54 pine snag nests. Clutches contained a mean of

2.92 ± 0.07 eggs (n = 36 nests, range = 2 – 4 eggs) that were incubated for a mean of 15.50 ±

0.51 days (n = 14 nests, range 11-19 days). Nestlings were in the nest for a mean of 22.64 ± 0.64 days (n = 22, range = 18 - 30 days). These measurements did not vary across years (clutch size

2 Kruskal Wallis: c2 = 2.70; incubation period Mann-Whitney U-test: U = 13; nestling period

Mann-Whitney U-test: U = 60; all P > 0.20). Clutch size did not vary by lay date (GLM Poisson with log link function: Dev = 2.07, df = 32, ß = 0.0015, z = 0.094, P = 0.93).

Estimated lay dates did not differ from known lay dates (paired t-test, n = 16, t13 = 0.979,

P = 0.35). Therefore, estimated lay dates (n = 16) were pooled with known lay dates (n = 17) to estimate means. Mean lay date varied by year (ANOVA, F2, 31 =15.6, P < 0.001; Fig. 3A).

Clutches in 2015 (mean = 125.00 ± 1.53, n = 9) were laid earlier than those in 2016 (mean =

135.90 ± 1.46, n = 10; P < 0.001) and 2017 (mean = 135.07 ± 1.23, n = 14; P < 0.001), which did not differ from each other (P < 0.99).

Daily average temperature varied significantly during both the 2015 laying period (F5, 27

2 2 = 3.4, r = 0.27, P = 0.02) and the 2016/17 laying period (F5, 27 = 11.94, r = 0.57, P < 0.001)

(Fig. 3B). In both cases laying occurred during a period of increasing temperatures. During the period of increasing temperatures when laying occurred in 2015 (ß = 0.32, P = 0.04), temperatures were decreasing in 2016 (ß = -0.58, P = 0.01) and 2017 (ß = -0.70, P = 0.003).

Conversely, during the period of increasing temperatures when laying occurred in 2016 (ß =

0.48, P < 0.001) and 2017 (ß = -0.70, P < 0.001), temperatures decreased in 2015 (ß = -0.18, P =

0.004).

In 2015, two nests failed during the incubation period due to predation and one failed when the pine snag fell. Both nests that failed in 2016 were abandoned with nestlings. Both nests that failed in 2017 were depredated, one during incubation and one during the nestling stage.

Another nest lost two of three nestlings, likely to predation, which was the only recorded case of partial nest loss.

2 Our global logistic exposure model fit the data (c8 = 12.11, P = 0.15). However, the null model emerged as the top model above those that tested the effects of covariates (Table 2;

Supplemental Table S1). This model estimated a daily survival rate of 99.6 ± 0.4%, which produced estimates of 94.1 ± 5.5% survival during incubation, 91.6 ± 7.6% survival during the nestling stage, and 86.2 ± 12.2% survival over the entire nesting period.

Across-nest and within-nest measures of nest success were similar at all nesting stages

(Table 3). Within-nest measures did not vary significantly across years (hatch success, Kruskal

2 2 Wallis: c8 = 2.61, P = 0.27; egg survival, Kruskal Wallis: c8 = 4.38, P = 0.11; nestling survival,

Mann-Whitney U-test: U = 169, P = 0.93), although estimates of hatch success and egg survival were lower in 2015 than in the other two years (Table 3).

Utility Pole Monitoring

Nests in thirteen utility poles were monitored via behavioral observations to estimate reproductive success. Three nests were still active at the conclusion of the field season, so final fate could not be determined. Five nests were determined to have successfully fledged young.

Four nests were identified as inactive at the first behavioral observation subsequent to

discovering the nest. It is possible that these cavities did not actually contain an active nest when first observed. However, misidentifying the presence of a nest is unlikely, given that all nests in a pine snag identified by adults entering the cavity and examined with the camera were confirmed to be active (at least contained nesting material), and breeding attempts were recorded in 90%

(64/71) of the nests that were revisited. If the four inactive nests are included as failed nesting attempts, along with one nest determined to have failed during the incubation stage by consecutive behavior observations, then the fledging success rate in utility poles was 50%. If we assume that the three still-active nests successfully fledged, then the fledging success rate was

62%.

Towers

Observations were conducted on twelve towers in 2016, and six additional towers were observed in 2017. In some cases, swallows entering pipes indicated that several (up to four) pairs were nesting in the same tower.

Six towers were occupied by swallows in both years, two only in 2016, and four only in

2017 (three of which were new). Therefore, twelve towers were occupied by swallows in at least one of two observation years. Of the six towers that were not occupied by swallows during either

2016 or 2017, two were occupied by swallows during a previous field season (2014 or 2015), and three were occupied by House Sparrows (Passer domesticus). No towers were simultaneously occupied by swallows and sparrows, although one was occupied by the two species in different years. The swallow-occupied towers differed from unoccupied towers in the

% of pine within 200 m (mean occupied: 52.39 ± 5.79; mean unoccupied: 8.32 ± 1.98; Mann-

Whitney U-test: U = 3, P < 0.001) and within 400 m (mean occupied: 58.65 ± 5.79; mean unoccupied: 13.80 ± 1.98; Mann-Whitney U-test: U = 5, P = 0.002) of the tower.

Buildings and Other Nesting Structures

Nests were located in buildings within and near the settlement of Sandy Point (n = 12). “Other” structures in which swallows nested included a boat trailer (n = 1), an inactive boat electricity station (n = 1) and nest boxes (n = 2).

Tachycineta Literature Review

Breeding parameter estimates were available in the published literature for seven of nine

Tachycineta species, and we obtained unpublished data and calculated estimates for the

Mangrove Swallow (T. albilinea) and the Violet-green Swallow (T. thalassi). Data for the

Mangrove Swallow were collected at Hill Bank Research Station, Belize, by the Golondrinas de las Américas project (D. Ardia, unpublished data), and included 227 nesting attempts from four seasons (2009-2012). Data for the Violet-green Swallow were collected near Corvallis, Oregon by Rivers et al. (J. Rivers, unpublished data), and included 278 nesting attempts from five seasons (2010-2014). Although several species have large breeding ranges, our estimates were limited to one field site per species (Fig. 4).

We organized the species by absolute latitude (Table 4) to determine if traits followed expected latitudinal trends, and to examine how the traits of the Bahama Swallow fit within this context.

Clutch size generally increased with latitude, despite some species with noticeably higher (T. albilinea) or lower (T. thalassina, T. meyeni) clutch size than expected from latitude. The

Bahama Swallow had a slightly smaller clutch size than would be expected given the latitude at

which it breeds. We did not observe a clear latitudinal trend for incubation period or nestling period. All species had high rates of egg survival (78-94%) and hatching success (84-99%).

However, the rates of nestling survival and fledging success of the Bahama Swallow were considerably higher than those of all its congeners (Table 4).

DISCUSSION

In this study, we examined cavity use and breeding biology of the Bahama Swallow, aiming to provide information that can be used to develop effective management and conservation strategies for this endangered species. Bahama Swallows will nest in cavities in a variety of structures, but mostly use woodpecker-excavated cavities in pine snags and utility poles. There are consequences for swallows selecting between these two structures, which differ in their dimensions and surrounding habitat, since the survival of nests in pine snags (92%) was much higher than survival of those in utility poles (50-62%).

Utility companies erect poles that are standardized to provide the best support for cables and other attached hardware. Pine snags are shorter and smaller than utility poles, reflecting the dimensions of live trees, and fracturinging and decay over time. The differences in height of the cavity and the surrounding habitat are the outcomes of nest site selection by the two woodpecker species that breed on Abaco. The West Indian Woodpecker (Melanerpes superciliaris) is associated with coppice forest and developed areas, while the Hairy Woodpecker (Dryobates villosus) is associated with pine forest (Raffaele et al. 1998). With very few exceptions, West

Indian Woodpeckers were responsible for cavities excavated in utility poles and Hairy

Woodpeckers for cavities excavated in pine snags (Wilson unpublished data). We monitored fewer utility pole nests, and difficulty accessing these nests with the camera limited our ability to

thoroughly monitor the contents of the cavity. Nevertheless, we documented several instances in which a nest was lost very quickly after initiation, a phenomenon we rarely observed in a much larger sample of nests in pine snags. Furthermore, several utility pole nests were still active at the conclusion of the field season, while late-season nests in pine snags were extremely rare. In our study, with one exception, late-season nests in pine snags were second attempts that were initiated after the first attempt failed. If the late-season nests in utility poles were also second attempts, this would support the conclusion that nest survival in utility poles is lower.

A higher probability of nest failure in utility poles could be due to increased exposure to predators in non-pine habitat. We witnessed likely depredation of several swallow nests by West

Indian Woodpeckers, which create and reuse cavities that are distributed in clusters (Wilson unpublished data). This species has a polyandrous breeding strategy where a female establishes nests with multiple males (Willimont et al. 1991), possibly sustaining cavity exploration that would expose swallows to predation. We also observed swallow nest depredation behavior by

American Kestrels (Falco sparverius) that also nest in cavities in utility poles (Wilson unpublished data) and prefer to hunt near roads and other open habitats (Raffaele et al. 1998) where utility pole cavities are concentrated.

Swallows in non-pine habitat may also face more competition for nesting cavities, particularly with two non-native species. The European Starling (Sturnus vulgaris) is considered rare in The Bahamas (Raffaele et al. 1998) but will usurp cavities from other species in buildings

(Willimont 1990) and utility poles (Wilson unpublished data) on Abaco. House Sparrows are common in developments and agricultural areas in the northern Bahamas (Raffaele et al. 1998) and are known to aggressively exclude other birds from nesting cavities (Jackson and Tate 1974,

Gowaty 1984, Winkler 1992). House Sparrows will usurp utility pole cavities from swallows

(Wilson unpublished data), and this antagonistic relationship may extend to cavities in towers.

The towers that were not occupied by swallows had less pine forest in the surrounding area, and several were occupied by House Sparrows, indicating that interactions between these species is dependent on habitat. We did not find any evidence that swallows face high rates of predation or competition for cavities in pine snags. Like Allen (1996), we observed antagonistic interactions of swallows with Hairy Woodpeckers and La Sagra’s Flycatchers (Myiarchus sagrae) near pine snags, but there is no indication that either of these species depredates nests or excludes swallows from nesting sites. Overall, the productivity of the Bahama Swallow population on

Great Abaco is highly dependent on cavities excavated in pine snags by Hairy Woodpeckers.

Contrary to Allen’s (1996) findings, we saw no evidence of double brooding.

Documentation of double broods on Grand Bahama included one case of two successful nesting attempts by the same female in different cavities, one case of two successful nesting attempts in the same cavity, and another nest near a recently active cavity that was initiated within days of the other two cases (Allen 1996). In our study, we recorded two cases of a second nesting attempt in the same cavity, but only after the first attempt failed, and another late-season nest that was initiated two days before one of the second attempts. Although he saw more instances of double brooding on Grand Bahama than we observed in a much larger sample on Abaco, Allen’s

(1996) sample is too small to establish a change in the frequency of double brooding between the two studies. Although this is the only aspect of reproduction for which there is some evidence of a decline in productivity, that evidence is not conclusive and double brooding is unlikely to contribute sufficiently to productivity to account for population declines.

Bahama Swallow clutch size did not decrease with lay date, which would otherwise be expected for a subtropical Tachycineta species (Winkler et al. 2014), suggesting that later

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breeders within the population do not suffer a fitness cost in the form of fewer offspring.

However, the onset of breeding can vary by year, as the mean lay date of nests was earlier in

2015 than either of the following two years and was similar to the mean lay date of nests monitored in 1995 (Allen 1996; 125 + 2.2 SE days). In each year, temperatures were increasing during the laying period, but were decreasing outside of the laying period. Aerial insect abundance is affected by temperature (Ashdown and McKechnie 2008, Jenni-Eiermann et al..

2008, Winkler et al. 2013), and higher insect abundance earlier in the breeding season might allow Bahama Swallows to lay earlier, as has been seen in Tree Swallows (T. bicolor) (Nooker et al. 2005, Dunn et al. 2011). However, this pattern is purely correlative, and the temperature data used in our analysis were collected on a different island. Additional research is needed to test the relationship between environmental conditions, aerial insect abundance, and Bahama Swallow phenology.

Latitudinal patterns in life history traits predict that bird species breeding at higher latitudes will show a suite of traits associated with greater investment in each breeding attempt.

Higher latitude breeders have larger clutch sizes (Moreau 1944, Martin 1996, Böhning-Gaese et al. 2000, Martin et al. 2000, Winkler et al. 2014), and shorter incubation (Ricklefs 1968,

Robinson et al.. 2008) and nestling periods (Skutch 1949, Ricklefs 1968, Böhning-Gaese et al..

2000) than their tropical relatives. We compared the breeding parameters of the Bahama

Swallow with other Tachycineta species to identify potential deviations from expected values that could suggest possible mechanisms responsible for population declines. Clutch size increases with latitude across populations of Tachycineta species, at least among early-laying birds (Winkler et al. 2014). When clutch size is summarized by species, this trend generally holds, despite some species with noticeably higher (T. albilinea) or lower (T. thalassina, T.

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meyeni) clutch size than expected (Table 4). There also were several deviations from the expected decrease in incubation and nestling period with increasing latitude (Table 4). For example, the traits of the Chilean Swallow (T. meyeni), which breeds at the highest latitude in the southern hemisphere, are more similar to the traits of the tropical and subtropical species. It is important to keep in mind that our summary includes one site per species, and does not account for other environmental variables such as elevation (Proctor 2016), aridity (Stager et al. 2012), and extreme cold (Liljesthröm et al. 2012) that could affect the traits of these species. Although it is clear that more in-depth analyses of these traits across the Tachycineta genus are needed, our goal was to provide a phylogenetic context in which to evaluate the trait values of the Bahama

Swallow we observed.

The clutch size of the Bahama Swallow is slightly smaller than expected for a subtropical species, and is similar to several tropical Tachycineta species (Table 4). One explanation for this is that we measured Bahama Swallow clutch size in natural cavities, while the clutch sizes of the other species were measured in nest boxes, which tend to have more space available.

The clutch size of the Tree Swallow (T. bicolor) is smaller for nests in snags than nests in boxes

(Robertson and Rendell 1990), and the same may be true for the Bahama Swallow. However,

Allen (1996) reported a clutch size nearly identical to that we observed both in a variety of artificial cavities (3.0 + 0.2 eggs, n = 11) and in pine snags (3 eggs, n = 2). Determining whether small clutch sizes represent life history differences vs. nesting cavity characteristics would require strategic redesign and placement of nest boxes on the pine islands, since swallows generally do not use the standard nest boxes that are currently available on Abaco (M. Wilson personal observation). Regardless, there is no evidence that clutch size has decreased (Allen

1996), so population declines cannot be attributed to changes in this parameter.

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The Bahama Swallows at our study site have higher rates of nestling survival and fledging success than all of its congeners (Table 4). This conclusion is supported by our logistic exposure model, which provided a more robust estimate of nest survival (86.2 ± 12.2%). Two common sources of nest failure in other Tachycineta species are nest depredation and food scarcity. Since aerial insect abundance is largely temperature dependent (Ashdown and

McKechnie 2008, Jenni-Eiermann et al. 2008, Winkler et al. 2013), the species that breed in temperate climates (Fig. 5) are prone to nest failure if there is a severe decrease in temperature

(Hess et al. 2008, Liljesthröm et al. 2012, Winkler et al. 2013). Bahama swallows are not likely to experience extreme fluctuations in food availability while breeding in the subtropics.

Although rates of nest depredation are lower in cavities than in open nests (Martin and Li 1992), a high density of nest boxes (Krebs 1971, Dunn 1977) and the absence or malfunction of guards against terrestrial predators can result in high rates of nest loss in species nesting in nest boxes

(Robertson and Rendell 1990, Stager et al. 2012, Proctor 2016, D. Ardia unpublished data). Feral cats (Felis catus), rats (Rattus rattus), racoons (Procyon lotor) and boas (Epicrates exsul) are present on Great Abaco and will depredate nests of other species (Stahala 2016). However,

Bahama Swallow nests, especially those in pine snags, are spatially dispersed (Wilson unpublished data), and do not appear to be at high risk from these terrestrial predators.

Overall, the results of this study indicate that Bahama Swallow population declines cannot be attributed to poor productivity, at least not in southern Abaco. Predators or competitors, especially invasive ones, are responsible for population declines of many threatened and endangered island birds (Johnson and Stattersfield 1990, Manne et al. 1999, Simberloff

2000, Sodhi et al. 2004, Fordham and Brook 2010, Szabo et al. 2012, Wood et al. 2017).

Although these threats may be responsible for the lower nest success in non-pine habitat,

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swallows that nest in the pine forest have a very high success rate. These findings highlight the potential importance of the pine forest to the persistence of this endemic species. Although some pine forest is protected within the national park system, we cannot assume that all pine forests are suitable for breeding swallows, or for the Hairy Woodpeckers on whose excavations they depend, since the structure of these forests was altered by extensive logging (Henry 1974) and subsequent fire regimes (Myers et al. 2004). Structural changes to the pine forest may be of particular concern in northern Abaco and the other “pine islands,” where swallow density is lower than in southern Abaco (Wilson unpublished data). Our breeding parameter estimates in pine snags, including high nest success, are similar to Allen’s (1996) estimates in artificial cavities on Grand Bahama. However, just over two decades later, there are very few swallows breeding on Grand Bahama (Wilson unpublished data). Limited pine habitat and/or fewer Hairy

Woodpeckers could push swallows into areas where they are exposed to predation and competition. Research on the cavity use and breeding biology of swallows on the other pine islands is needed in order to determine whether poor productivity in other parts of the breeding range is contributing to population declines. Regardless, it is likely that strategies to conserve the

Bahama Swallow should include targeted protection and management of the pine forest.

ACKNOWLEDGEMENTS

We are grateful to Nicole Acosta, Tivonia Potts, Melanie Wells, Shannan Yates, Ann-

Marie Carroll, and Alix Rincón for their hard work and dedication as research field assistants.

Special thanks to Marcus Davis, the Abaco Bahamas National Trust Park Warden, for sharing his unique knowledge of the island and for his unwavering support of our field teams. We would also like to thank the Abaco Bahamas National Trust office staff, Kadie Mills and David

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Knowles, for their logistical support. Thanks to Dr. Dan Ardia for providing unpublished data and helpful feedback on this manuscript, and to Dr. Jim Rivers for providing unpublished data.

Funding for this study was provided by the Bailey Fund at Virginia Tech, the Rufford

Foundation Small Grants for Nature Conservation, the BirdsCaribbean David S. Lee Fund, IDEA

WILD, and Virginia Tech Graduate Research Development Program. This research was conducted in compliance with the Virginia Tech Institutional Animal Care and Use Committee

(permit 15-022 BIOL), the Bahamas Environment, Science and Technology Commission, and the Bahamas National Trust.

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FIGURES

Figure 1: Map of the Abaco Islands in the northern Bahamas. The black line indicates the separation of the two main islands, Great Abaco and Little Abaco. From North to South, stars indicate the location of Marsh Harbour, Little Harbour, Sandy Point, and Hole-in-the-Wall.

Habitat types were classified from Landsat 8 satellite images (U.S. Geological Survey). 114

Figure 2: Nesting structure classifications including (A) pine snags, (B), utility poles, and (C) cell phone towers, (D) buildings, and (E) other (e.g., a boat trailer).

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Figure 3: (A) Lay dates of Bahama Swallow nests by year. (B) Average daily temperature for each year during the 2015 laying period (119-131, left) and the 2016/17 laying period (128-143, right). Dates in both figures are standardized to ordinal date.

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Figure 4. Map of the distribution of the nine swallow species in the Tachycineta genus (Birdlife

International and Handbook of the Birds of the World 2018). Species are listed in the legend in order of increasing absolute latitude of the field site representing each species and correspond with Table 4. Color-coded species distributions were matched to those in Cerasale et al. 2012 and Stager et al. 2014.

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TABLES

Table 1: Mean, standard error (SE), and sample size (n) for cavity measurements and % pine surrounding nests in pine snags and utility poles. Results are reported for Mann-Whitney U-tests for differences between the two structure types.

Pine snags Utility poles Mann-Whitney U-test

Measurement Mean SE n Mean SE n U P-value

Cavity height (m) 7.27 0.28 89 10.47 0.29 39 523.5 <0.001

Structure height (m) 8.53 0.30 92 11.16 0.26 37 714 <0.001

DBH (cm) 21.61 0.61 88 29.15 0.44 24 250 <0.001

Pine 200m (%) 71 3 95 34 3 40 3249 <0.001

Pine 400m (%) 73 3 95 37 3 40 3241 <0.001

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Table 2: Model selection results using Akaike’s Information Criterion (AICC) for competing

logistic exposure models testing the effect of covariates on daily survival rate. Competing

models (∆ AICC < 2) are shown with the number of parameters (k), log likelihood (logLik), and

weight (wi) of each model.

Model K logLik ∆ AICC wi null1 1 -21.96 0.00 0.12 brood size 2 -21.65 1.39 0.06 cavity height 2 -21.68 1.46 0.06

% pine 400m 2 -21.77 1.64 0.05

% pine 200m 2 -21.92 1.95 0.04 year 2 -21.92 1.95 0.04

1 AICC value of top model = 45.94.

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Table 3: Proportional measures of nest success across nests and within nests for each season and for all seasons combined.

All seasons 2015 2016 2017

Across-nest1 Mean n Mean n Mean n Mean n egg survival 90 106 74 31 100 33 93 42 hatch success 90 95 83 23 94 33 90 39 nestling survival 92 101 NA NA 92 52 92 49 fledge success 92 37 NA NA 89 18 95 19

Within-nest2 Mean ± SE n Mean ± SE n Mean ± SE n Mean ± SE n egg survival 89 ± 5 36 73 ± 14 11 100 11 93 ± 7 14 hatch success 89 ± 3 36 81 ± 7 11 94 ± 4 11 91 ± 5 14 nestling survival 90 ± 5 37 NA NA 89 ± 8 18 91 ± 6 19

1 Across-nest sample sizes are the number of eggs or nestlings

2 Within-nest sample sizes are the number of nests.

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Table 4: Breeding biology parameters for the nine species of swallows in the Tachycineta genus. Species are listed in order of increasing absolute latitude, and each estimate was taken from the source with highest sample size a-l.

Clutch size (eggs) Incubation period (days) Nestling period (days) Egg Hatching Nestling Fledging

survival success survival success

Species Mean Range Mean Range Mean Range (%) (%) (%) (%)

T. stolzmanni a 2.68 ± 0.10 2-4 16.11 ± 0.14 15-18 28.27 ± 0.42 26-32 84 92 50 50

T. albiventer b 3.11 ± 0.11 2-4 14.42 ± 0.18 13-16 24.7 22-28 93 99 81 84

T. albilinea c 4.6 ± 0.1 3-6 14.72 ± 0.07 10-18 25.93 ± 0.26 18-36 91 86 80 85

T. euchrysea d 3.0 ± 0.4 2-4 17.8 ± 0.8 17-20 25.7 ± 0.9 24-27 94 94 56 50 e

T. cyaneoviridis* 2.92 ± 0.07 2-4 15.5 ± 0.5 11-19 22.6 ± 0.6 18-30 90 90 92 92

T. leucorrhoa f 4.92 ± 0.05 4-6 14.8 ± 0.2 23.3 ± 0.2 21-27 78 84 86 54

T. bicolor g 5.4 ± 0.9 3-8 14.5 ± 1.1 11-20 h 20.6 ± 1.6 15-25 h 86 h 88 h 88 h 72 h

T. thalassina i 4.77 ± 0.05 3-7 15.2 ± 0.1 13-21 26.02 ± 0.21 23-30 87 93 66 53

T. meyeni j 3.8 ± 0.1 3-5 16.3 ± 0.1 15-21 26 ± 2 k 21-34 k 81 l 86 l 72 k 73 l

*Bahama Swallow estimates from pine snag nests in this study a Stager et al. 2012

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b Struve et al. 2012 c D. Ardia unpublished data d Proctor 2016 e Townsend et al. 2008 f Massoni et al. 2007 (from information provided in text, nestling survival was calculated to include loss of full broods) g Allen 1996 h Winkler et al. 2011 i J. Rivers unpublished data j Lilijeström 2011 k Lilijeström et al. 2012 l Lilieström et al. 2009

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SUPPLEMENTARY MATERIALS

Habitat classification methods

Landsat 8 bands 2, 3, and 4 with 30 m spatial resolution (U.S. Geological Survey) were combined in ArcMap (10.5.1) to create a natural color image (Supplemental Fig. S1A, S1C). The maximum likelihood classification tool was trained to classify image cells into habitat types

(Supplemental Fig. S1B, S1D). Classification of pine forest targeted areas that are dominated by

Caribbean Pine (Pinus caribaea bahamensis). Coppice is a dense, mixed-species, dry broadleaf forest. Another vegetation category included fields dominated by grasses, and areas currently or recently used for agriculture. Open areas that were paved (e.g., roads) or dominated by sand/rock

(e.g., beaches) were classified together. The wetland areas encompassed a variety of habitats that are inundated with water, including mangrove creeks, tidal flats, salt marshes, and inland ponds.

These classifications were intended to capture all of the land area of Great Abaco. Some cells were unclassified or incorrectly classified if cells contained multiple habitat types or categories overlapped in their classification values (e.g., wetland and paved/sand). However, we are confident in the classification of pine forest, the target habitat for this study, due to our familiarity with the location of pine forest tracks.

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Figure S1: Examples of corresponding natural color images (A, C) and habitat classification results (B, D). Box 1 (A, B) and Box 2 (C, D) each represent a different area of Great Abaco.

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Table S1: Model selection results using Akaike’s Information Criterion (AICC) for logistic exposure models testing the effect of covariates on daily survival rate. Models are shown with the number of parameters (K), log likelihood (logLik), and weight (wi) of each model.

Model K logLik ∆ AICC wi null 1 -21.96 0.00 0.14 brood size 2 -21.65 1.39 0.07 cavity height 2 -21.68 1.46 0.07 pine (400m) 2 -21.77 1.64 0.06 pine (200m) 2 -21.92 1.95 0.05 year 2 -21.92 1.95 0.05 clutch size 2 -21.96 2.02 0.05 cavity height + pine (400m) 3 -21.32 2.78 0.03 brood size + cavity height 3 -21.33 2.81 0.03 cavity height + pine (200m) 3 -21.39 2.92 0.03 brood size + pine (400m) 3 -21.46 3.05 0.03 year + brood size 3 -21.58 3.29 0.03 brood size + pine (200m) 3 -21.61 3.35 0.03 year + cavity height 3 -21.66 3.46 0.02 cavity height + clutch size 3 -21.68 3.50 0.02 year + pine (400m) 3 -21.73 3.60 0.02 clutch size + pine (400m) 3 -21.77 3.67 0.02 year + pine (200m) 3 -21.87 3.88 0.02 clutch size + pine (200m) 3 -21.92 3.98 0.02 year + clutch size 3 -21.92 3.98 0.02 brood size + cavity height + pine (400m) 4 -20.99 4.17 0.02 brood size + cavity height + pine (200m) 4 -21.04 4.27 0.02 year + brood size + cavity height 4 -21.28 4.76 0.01 year + cavity height + pine (400m) 4 -21.31 4.81 0.01 cavity height + clutch size + pine (400m) 4 -21.32 4.83 0.01 year + cavity height + pine (200m) 4 -21.37 4.92 0.01 year + brood size + pine (400m) 4 -21.39 4.96 0.01 cavity height + clutch size + pine (200m) 4 -21.39 4.97 0.01 year + brood size + pine (200m) 4 -21.52 5.23 0.01 year + cavity height + clutch size 4 -21.66 5.51 0.01 year + clutch size + pine (400m) 4 -21.73 5.65 0.01 year + clutch size + pine (200m) 4 -21.87 5.93 0.01 year + brood size + cavity height + pine (400m) 5 -20.96 6.16 0.01

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Table S1 continued

Model K logLik ∆ AICC wi year + brood size + cavity height + pine (200m) 5 -20.98 6.21 0.01 year + cavity height + clutch size + pine (400m) 5 -21.31 6.88 0.00 year + cavity height + clutch size + pine (200m) 5 -21.37 6.98 0.00 AICC value of top model = 45.94.

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Chapter 5

Using the cavity nest-web to inform conservation of the endangered

Bahama swallow (Tachycineta cyaneoviridis)

ABSTRACT

The cavity nest-web is a theoretical framework that illustrates the hierarchical relationships between cavity nesting resources, primary cavity excavators, and secondary cavity nesters. We used the cavity nest-web approach to investigate the interactions of cavity-nesting species and cavity resources on Great Abaco Island in The Bahamas, with the goal of providing information relevant to the conservation and management of the endangered Bahama Swallow

(Tachycineta cyaneoviridis). We conducted systematic surveys to assess the density of available and excavated cavity-nesting resources, evaluated the distribution of woodpeckers and their nests, and assessed the variation in competition between secondary cavity-nesters for nest sites across habitat types. Hairy Woodpeckers (Dryobates villosus) primarily excavated snags of

Caribbean Pine (Pinus caribaea bahamensis), while West Indian Woodpeckers (Melanerpes superciliaris) excavated utility poles in non-pine habitat. Only swallows and La Sagra’s

Flycatchers (Myiarchus sagrae) used nest sites in the pine forest. Swallows nesting in non-pine habitat face competition for cavities with American Kestrels (Falco sparverius), and non-native

House Sparrows (Passer domesticus) and European Starlings (Sturnus vulgaris). These findings indicate that managing for pine snags and the presence of Hairy Woodpeckers in the pine forest is a potential mechanism for increasing the Bahama Swallow population.

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INTRODUCTION

Globally, cavity-nesting bird species interact with each other through the creation and use of cavities, and maintaining links within cavity-nesting communities is critical to their persistence (van der Hoek et al. 2017). Primary excavators are species that make their nesting cavities in trees or other resources, while secondary cavity-nesters rely on primary excavators or other processes to produce nesting cavities. The cavity nest-web is a theoretical framework that illustrates the hierarchical relationships between cavity nesting resources, primary excavators, and secondary cavity nesters (Martin and Eadie 1999). The use of nest-webs has expanded in recent years and has yielded important insights into species interactions and habitat requirements. For example, depicting the nest-web in a longleaf pine system in Florida identified the excavating species that created the majority of large cavities, the resource used by the largest range of species, and the most abundant secondary cavity-nesting species (Blanc and Walters

2008).

We used the cavity nest-web model to assess how the Bahama Swallow (Tachycineta cyaneoviridis) interacts with other cavity-nesting bird species and cavity resources. The Bahama

Swallow is an endangered, obligate secondary cavity-nester that only breeds in the northern

Bahamas (Birdlife International 2016). Since the apparent extirpation of the breeding population on New Providence Island due to unidentified causes, the breeding range of the swallow includes only the islands of Great Abaco, Grand Bahama, and Andros (Raffaele et al. 1998, Birdlife

International 2016). These “pine islands” are the only islands within the Bahamian archipelago that contain large areas of Caribbean pine (Pinus caribaea bahamensis), which is an important breeding habitat of the swallow (Smith and Smith 1989, Allen 1996). However, these islands are also comprised of non-pine habitats such as “coppice” (dry broadleaf forest) and settlements. On

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Great Abaco Island, we investigated how the density of cavity-nesting resources varies across habitats, and how cavity-nesting species interact over these resources.

There are only two primary cavity excavators that breed on Great Abaco. The Hairy

Woodpecker (Dryobates villosus) is a medium-sized woodpecker with a range that extends over most of North and Central America, and the habitat preferences of this species vary geographically (Jackson et al. 2018). In the Caribbean, it is found only on the pine islands of The

Bahamas, where it is associated with the pine forest (Raffaele et al. 1998). The West Indian

Woodpecker (Melanerpes superciliaris) is a poorly known species that is considerably larger than the Hairy Woodpecker and is found only in The Bahamas, Cuba and the Cayman Islands. In

The Bahamas, this species is believed to be extirpated on Grand Bahama, but still occurs on

Great Abaco and San Salvador, where it is found in a variety of habitats including coppice and human settlements (Cruz and Johnston 1984, Raffaele et al. 1998). In order to understand interactions within the cavity-nesting community, it is essential to assess how cavity excavation by these two woodpeckers varies across resources and habitats.

Five secondary cavity-nesting species breed on Great Abaco. In addition to the swallow, the native secondary cavity-nesting bird species include the La Sagra’s Flycatcher (Myiarchus sagrae) and the American Kestrel (Falco sparverius). The other two secondary cavity-nesters, the House Sparrow (Passer domesticus) and the European Starling (Sturnus vulgaris), are non- native and invasive species (Raffaele et al. 1998). Non-native species can change “natural” competitive interactions, but habitat alterations and the interactions of competition and predation

(Chase et al. 2002) could produce unanticipated dynamics even within the native community.

For the swallow, increased competition for nesting cavities could reduce carrying capacity

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(fewer absolute number of cavities available) and could affect vital rates by restricting them to cavities of lower quality.

In this study, we examined each level of the cavity nest-web on Great Abaco, with the goal of providing information relevant to the conservation and management of the Bahama

Swallow. We assessed the distribution and density of cavity nesting resources through systematic surveys, and determined the characteristics of structures that were being selected by woodpeckers. We used point count surveys to estimate the presence of primary cavity excavators across the island, and assessed the habitat distribution of their nests. We also examined the nest sites of secondary cavity-nesters to assess variation in competition for nesting cavities across habitat types.

METHODS

Study Area

This study took place on the Abaco Islands in the northern Bahamas (Figure 1). Although surveys extended into Little Abaco, most research was conducted in the southern portion of

Great Abaco between the settlements of Marsh Harbour (26°31'58N, 77°04'09W), Little Harbour

(26°19'32N, 76°59'57W), and Sandy Point (26°00'28N, 77°24'16W) to Hole-in-the-Wall

(25°51'32N, 77°10'58W).

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Data Collection

The data described were collected during four field seasons: 12 May – 7 July 2014, 25

March – 15 July 2015, 4 April – 12 July 2016, and 9 April – 12 July 2017. These time periods were selected to encompass a majority of the swallow breeding season (Allen 1996).

Nest records

Active nests of all cavity-nesting bird species were located on Great Abaco opportunistically during population surveys (Chapter 2), cavity resource surveys, and movement between locations for all research protocols. These activities involved traversing much of southern Abaco for ~6-10 hours per day for an average of 6.3 days per week during all field seasons. Although nest sites in settlements (i.e., buildings) are likely underrepresented, we are otherwise confident that our nest records accurately reflect the cavity-nesting community.

A nesting cavity was identified as active if at least one adult entered the cavity fully.

Since West Indian Woodpeckers were frequently observed investigating cavities, including those that contained nests of other species, we did not confirm nest locations of this species until an adult was seen entering a cavity on two separate visits. We recorded the GPS location (Etrex 10 or 20, ± 3m) of all active nests observed.

We used Bahama Swallow nest records from 2014 to classify cavity-nesting resources

(see Chapter 4 for detailed descriptions). Excavated structures included dead standing pine trees

(pine snags) and utility poles. If possible, we examined the contents of the cavity using a camera mounted on a telescoping pole (Sandpiper Technologies Treetop Peeper or IBWO Wireless

Cavity Inspection Camera) to confirm nesting activity. We measured the height of the nesting structure and the cavity using the telescoping pole or a range finder (Nikon Forestry Pro, ± 0.5

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ft). The diameter at breast height (DBH) of the structure was determined by measuring the circumference to the nearest cm with a measuring tape at a height of approximately 1.3 meters from the ground and converting the measurement to diameter.

Additional anthropogenic structures included telecommunication towers and buildings.

Miscellaneous other nesting sites, such as hollow pipe in a boat trailer, comprised a fifth category; these were not included in analyses (n = 4). The cell phone towers, which are the property of the Bahamas Telecommunications Company LLC, are of a standard design, and are located along roadways throughout the pine islands. Nesting activity in towers was determined opportunistically (2014 and 2015) or via a 20-minute observation (2016 and 2017) during the swallow breeding season (see Chapter 4 for full methods).

Cavity resource surveys

We assessed the characteristics and density of woodpecker-excavated cavity resources

(pine snags and utility poles) through systematic habitat surveys. In 2015 and 2016, we conducted pine snag surveys within plots of pine forest. We used the KML Tools Project

(http://extension.unh.edu/kmltools/) to randomly select points from polygons representing accessible pine forest in southern Abaco, and each point was used as the northwest corner of a plot. In 2015, we surveyed eleven 200 x 200 m plots. Substantial time was required to traverse these plots due to the topography and vegetation. Therefore, to increase the spatial dispersal of plots given these logistics, we reduced plot size to 100 m x 100 m (1 ha) and surveyed 30 plots in

2016. Within each sampling plot, we walked parallel transects that were 50 m apart and measured all pine snags larger than 10 cm diameter at breast height (DBH) within 25 m of the transect. We used this DBH threshold because Bahama Swallow nests have been found in snags

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with a DBH as small as ~10 cm (Chapter 4). We recorded the total number of fully excavated cavities within each snag. Using an extendable measuring pole or a range finder (Nikon Forestry

Pro, ± 0.5 ft), we measured the height to the top of the snag, and the height of the lowest and highest cavity. Using a measuring tape, we measured the circumference of the snag at ~1.3 m from the ground, and converted the measurement to DBH.

Utility pole surveys were conducted along forty-seven 1 km transects on main roadways, which included the main highway and an assortment of side roads to settlements. The number of transects on each road represented the proportion of the total number of available transects, and were randomly selected within each road using the Excel random number generator (Excel,

Microsoft Corp, Redmond, Washington). We recorded the total number of fully excavated cavities within each pole. Using a range finder (Nikon Forestry Pro, ± 0.5 ft), we measured the height to the top of each pole, and, if applicable, the height of the lowest and the highest cavity.

Due to the standard design of the utility poles, we did not measure the DBH of each pole.

Woodpecker point count surveys

In 2017, we conducted point count surveys throughout much of the island using the procedure outlined in Chapter 2. Briefly, we conducted point counts on randomly selected points located in accessible off-road areas (n = 105) and along lines of main roadways (n = 128).

Although most points were sampled twice (168), points that were difficult to access were sampled once (n = 65). During a six-minute point count, a single observer recorded all Hairy

Woodpeckers and West Indian Woodpeckers detected within 100 m. We applied a subset of the recorded sample and site covariates (see Chapter 2 for all covariates) to woodpecker surveys.

Woodpeckers were often detected by sound before their location was confirmed by sight. Since

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wind could interfere with sound detection, we recorded wind condition (calm vs noticeable wind) during the surveys. Within 100 m of each point, we estimated the percent of the area (to the nearest 10%) that was pine forest, and noted the presence or absence of any snags with a DBH of at least 10 cm.

GIS Analysis

Spatial analysis was conducted in ArcMap (10.5.1) for all nest site locations and utility pole transects using the procedure detailed in Chapter 4. Briefly, habitat types on Great Abaco were classified from Landsat 8 satellite images (U.S. Geological Survey). Based on informal observations that swallows will forage at least 400 m from nests, we then extracted the habitat classifications within 400 m buffers of each transect and determined the percent of habitat that was classified as pine forest. Although we acknowledge that the home ranges of the other cavity- nesting species are likely to differ from those of swallows, we maintained the buffer size in all analyses to allow for species comparisons. The percent of the area within 400 m that was classified as pine forest was used as a proxy for “habitat” in statistical analyses.

Statistical Analyses

Unless otherwise noted, all analyses were conducted using the statistical package R

(3.6.1). Means are reported with standard errors. Results were considered statistically significant at p < 0.05. For all regression analyses and analysis of variance tests, we ran generalized linear models with appropriate distributions for continuous response variables (guassian and Gamma) and count response variables (poisson and negative binomial), and selected the model with the lowest Akaike’s Information Criterion (AIC) value (Akaike 1998). If appropriate, Tukey post-

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hoc analysis was conducted to determine how groups differed from one another using either the lsmeans (Russell 2016) or multcomp (Torsten et al 2008) package.

Cavity-nesting resource density

From habitat surveys, we determined the structure and cavity density of utility poles (per km) and pine snags (per ha). We used regression analysis to determine whether cavity density varied with structure density. For both structure types, we determined the proportion of structures in a transect/plot that contained cavities, and the density of cavities within a structure.

The random placement of the utility pole transects along the entire length of the island enabled us to test whether the density of poles and cavities within poles varied with latitude and habitat.

Comparison of available resources to woodpecker excavations

In utility poles and in pine snags, we compared the characteristics of cavity-nesting resources from habitat surveys to woodpecker nest sites. For each structure type, resource groups included available structures from surveys (All), and the subset of structures that contained cavities (W/Cav). Woodpecker nest sites were grouped into nests of the Hairy Woodpecker

(HAWO) and West Indian Woodpecker (WIWO). If nests were located in the same structure (n =

11) or the same cavity (n = 7) during more than one year, then the associated measurement was considered one sample point for analyses. Using analysis of variance tests, we tested whether structure height, DBH, cavity height, and the distance of the cavity from the top (CFT) of the structure varied by group (All, W/Cav, HAWO, WIWO).

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Woodpecker nest sites and habitat selection

Using analysis of variance tests, we determined whether the habitat surrounding nest sites varied by woodpecker species or by structure type. We also tested whether there were interactive or additive effects of species and structure type on nest site habitat.

We also estimated occupancy of the two woodpecker species in program Presence

(2.12.31) using simple single-season occupancy models. For each analysis, we formulated a list of a priori models testing the effect of covariates (Table 1) on occupancy probability (Ψ) and detection probability (p) and followed a two-step model selection process. While holding the covariates likely to influence Ψ constant, we compared models with varying p covariates. We then included the p covariates from the top ranking model as constants and compared models with varying Ψ covariates. We employed covariates that were associated with site in analyzing Ψ and covariates that were associated with sample in analyzing p. However, if we thought site covariates could influence detection, we also included them in analyzing p.

Cavity nest-web and nesting habitat of secondary cavity-nesters

A cavity nest-web was created using the nest records of primary excavators (Hairy

Woodpecker and West Indian Woodpecker) and secondary cavity-nesters (Bahama Swallow, La

Sagra’s Flycatcher, American Kestrel, European Starling, and House Sparrow). To assess whether secondary cavity-nesters were selecting cavities based on habitat, we used an analysis of variance test to determine whether the habitat surrounding nest sites varied by species.

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RESULTS

Cavity-nesting resource density

The mean density of utility poles was 13.8 ± 0.7 poles/km (range 9-27, n = 47) and the mean density of cavities in utility poles was 1.8 ± 0.4 cavities/km (range 0-12, n = 47). The density of cavities was not correlated to the number of poles (GLM (family = negative binomial),

2 ß = -0.02 ± 0.05, residual deviance45 = 43.4; ! 1, LR = 0.08, p = 0.75). Within each transect, a mean proportion of 0.11 poles contained cavities (range = 0 – 0.5, n = 47). Within poles with cavities, there was a mean of 1.3 ± 0.07 cavities/pole (range = 1-3, n = 66). The density of cavities in utility poles decreased as percent pine increased (GLM (family = negative binomial),

2 ß = -0.03± 0.01, residual deviance45 = 44.1; ! 1, LR = 5.4, p = 0.02; Figure 2). There was also a marginally significant decrease in pole density as percent pine increased (GLM (family =

2 negative binomial), ß = -0.004 ± 0.002, residual deviance45 = 43.5; ! 1, LR = 3.4, p = 0.06).

Neither the density of poles (GLM (family = negative binomial), ß = 0.05± 0.17, residual

2 deviance45 = 44.2; ! 1, LR = 0.07, p = 0.79) nor the density of cavities in poles (GLM (family =

2 negative binomial), ß = 0.31± 0.90, residual deviance45 = 43.4; ! 1, LR = 0.13, p = 0.72) varied with latitude.

The mean density of snags was 6.6 ± 1.1 snags/ha (range 0-41.2, n = 41) and the mean density of cavities in snags was 0.8 ± 0.2 cavities/ha (range 0-3.8, n = 41). The number of cavities increased with the number of snags in a plot (GLM (family = negative binomial), ß =

2 0.05± 0.01, residual deviance39 = 44.6; ! 7 , LR = 73.4, p <0.0001). Within each plot, a mean proportion of 0.12 snags contained cavities (range = 0 – 1, n = 40). Within snags with cavities, there was a mean of 1.4 ± 0.1 cavities/snag (range = 1-3, n = 51).

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Comparison of available resources to woodpecker excavations

In utility poles, none of the measured characteristics varied between available cavity resources (All), resources with cavities (W/Cav), and the nest sites of Hairy Woodpeckers

(HAWO) and West Indian Woodpeckers (WIWO) (Table 2). In pine snags, cavity height and

CFT did not vary among these four categories, but structure height and DBH did (Table 3). On average, the snags that contained Hairy Woodpecker nests were taller than available snags as estimated from the surveys (z = 2.83, p = 0.02; Figure 3). The structure height did not differ between any other categories (With Cavities/HAWO, z = 0.78; WIWO/HAWO, z = 1.02; All

Snags/With Cavities, z = 2.14; WIWO/With Cavities, z = 0.70; WIWO/All Snags, z = 0.09; all p > 0.72). The mean DBH of available snags was smaller than the means of snags with cavities

(z =5.45, p < 0.0001), snags with HAWO nests (z = 6.75, p < 0.0001), and snags with WIWO nests (z = -3.61, p = 0.002; Figure 4). However, snag DBH did not differ between the latter three groups (With Cavities/HAWO, z = 1.64; WIWO/HAWO, z = -0.46; WIWO/With Cavities, z = -

1.37; all p > 0.33).

Woodpecker nest sites and habitat selection

Hairy Woodpecker nest sites were surrounded by more pine forest than West Indian

Woodpecker nest sites (GLM (family = Gamma), ß = 0.028 ± 0.005, residual deviance67 = 32.4;

2 !1 , deviance = 20.6, p< 0.0001; Figure 5A). Nests in pine snags were also surrounded by more pine forest than nests in utility poles (GLM (family = Gamma), ß = -0.237 ± 0.005, residual

2 deviance67 = 32.6; ! 1, deviance = 14.4, p< 0.0001; Figure 5B). The models that examined

2 additive (GLM (family = Gamma), residual deviance66 = 32.0; ! 2, deviance = 21.0, p< 0.0001)

2 and interactive (GLM (family = Gamma), residual deviance65 = 31.6; ! 3, deviance = 21.4, p<

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0.0001) effects of woodpecker species and structure were both significant. However, this significance was driven by differences in species (z = -3.95, p = 0.0004) and not structure (z = -

0.91, p = 0.80; Figure 5C).

For Hairy Woodpeckers, the null occupancy model emerged as the top model among those testing the effects of covariates on detection probability (p) (Table 4). Therefore, we did not include any covariates in the p component of the models assessing covariate effects on occupancy probability (Ψ). The top ranking occupancy model included percent pine and the presence of snags (Table 5). The probability that a site was occupied increased as percent pine increased (ß = 0.02 ± 0.01) and was higher when snags were present (ß = 0.80 ± 0.87; Figure 6).

The other competing model included the effects of latitude (ß = 0.08 ± 0.58) and percent pine (ß

= 0.03 ± 0.2), but produced almost identical occupancy estimates, indicating that the top model was the most appropriate.

For West Indian Woodpeckers, the model including observer as a covariate emerged as the top model testing the effects of covariates on detection probability (p) (Table 6). While holding observer constant for p, we compared models assessing covariate effects on occupancy probability (Ψ). Based on this comparison, the top ranking occupancy model included the effect of percent pine (Table 7). The probability that a site was occupied decreased as percent pine increased (ß = -0.06 ± 0.05; Figure 7). The other competing models included percent pine and other covariates, indicating that the additional covariates were not informative for estimating occupancy.

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Cavity nest-web and nesting habitat of secondary cavity nesters

Using nest records, we created a cavity nest-web diagram illustrating the interactions between cavity-nesting species on Great Abaco (Figure 8). Each primary cavity excavator was linked to cavity-nesting resources based on the percentage of nests found in that resource.

Although we were not able to directly establish which woodpecker excavated cavities used by secondary cavity-nesters, we assigned the excavator based on the proportion of woodpecker nests in each structure, and consistent patterns in the habitat associations of each woodpecker species, their nests, and the cavities in each resource. A majority of Hairy Woodpecker nests were found in pine snags, while a majority of West Indian Woodpecker nests were found in utility poles (Figure 8). Hairy Woodpecker occupancy (Figure 6), nests (Figure 5A, 5C), and cavities in snags (Figure 5B) were highly associated with pine forest. West Indian Woodpecker occupancy (Figure 7), nests (Figure 5A, 5C), and cavities in utility poles (Figure 2, Figure 5B) were highly associated with non-pine habitat. Therefore, we assumed nests of secondary cavity- nesters in pine snags and utility poles were excavated by Hairy Woodpeckers and West Indian

Woodpeckers, respectively. We are confident that any violations of this assumption would be negligible and unlikely to influence our conclusions about species interactions. Each secondary cavity-nester was linked to primary excavators and resources based on the percentage of nests found in each resource.

The habitat surrounding nest sites of secondary cavity-nesters varied by species (GLM

2 (family = Gamma), residual deviance205 = 114.4; ! 4, deviance = 28.2, p < 0.0001; Figure 9).

There was more pine surrounding Bahama Swallow nests (BAHS; 59 ± 2%, n = 176, ß = -0.036

± 0.008) than nests of European Starlings (EUST; 12 ± 1%, n = 8, ß = 0.03 ± 0.02; z = 4.03, p

<0.001), House Sparrows (HOSP; 11 ± 5%, n = 7, ß = -0.005 ± 0.013; z = 3.08, p =0.015), and

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American Kestrels (AMKE; 19 ± 4%, n = 12, ß = 0.053 ± 0.008; Tukey, z = -4.22, p <0.001).

The nests of European Starlings, House Sparrows, and American Kestrels did not differ from one another (EUST/HOSP, z = -1.77, p = 0.36; EUST/AMKE, z = 1.59, p =0.47; HOSP/AMKE z =-

0.34, p = 0.99). There was more pine surrounding nests of La Sagra’s Flycatchers (LAFL; 33 ±

10%, n = 7, ß = -0.02 ± 0.01) than nests of European Starlings (z = -3.00, p = 0.02), but not than those of the other three species (LAFL/HOSP, z = -1.53; LAFL/AMKE, z = -2.16,; LAFL/BAHS z = 2.06; all p > 0.17).

DISCUSSION

Cavity nest-webs are a useful approach to investigate the hierarchical relationships between cavity-nesting resources, excavators, and secondary cavity-nesters. The cavity nest-web on Great Abaco Island provided insights into the ways that Bahama Swallows interact with other cavity-nesting species and cavity resources. Secondary cavity-nesters will nest in anthropogenic structures like buildings and cell phone towers, but these resources are of limited value for native species. Although natural tree holes (i.e., cavities created by decay) are an important cavity resource in many cavity-nest webs (Bai et al. 2005, Wesolowski 2007, Kahler and Anderson

2006, Blakely et al. 2008, Camprodon et al. 2008, Koch et al. 2008, Remm et al. 2008, Politi et al. 2009, Cockle et al. 2011, Robles et al. 2012, Lindenmayer et al. 2012, Ruggera et al. 2016,

Altamirano 2017, Cockle et al. 2019), they are essentially non-existent in the habitats in which

Bahama Swallows nest. This is likely because natural cavities that are suitable for cavity-nesters tend to be limited to hardwood trees, particularly large, mature hardwoods (Cockle et al. 2019,

Wesolowski and Martin 2018), since conifers release a resin that protects them from decay

(Schmidt 2005). It is possible that we missed natural cavities in hardwood trees in the coppice, as

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we did not conduct surveys or other protocols in this dense and largely inaccessible forest.

However, in the coppice that we were able to access, we saw no evidence of nesting swallows or other secondary cavity-nesters. Therefore, on Abaco the native secondary cavity-nesters depend on primary excavators to supply cavities.

Excavators are the primary source of cavities in many other conifer-dominated and mixed conifer forests (Stauffer and Best 1982, Raphael and White 1984, Martin et al. 2004, Blanc and

Walters 2008, Ouellet-Lapointe et al. 2012). In contrast, primary excavators are a minor source of cavities in many temperate hardwood (Kahler and Anderson 2006, Wesolowski 2007, Blakely et al. 2008, Camprodon et al. 2008, Koch et al. 2008, Cockle et al. 2011, Lindenmayer et al.

2012, Altamirano 2017) and tropical forests (Ruggera et al. 2016). In addition to being characterized by the dependency of secondary cavity-nesters on primary excavators, the nest- web in The Bahamas is strikingly simple, simpler than any nest web previously described. Taken together, these features create high potential for strong interactions between avian species, and between cavity-nesters and cavity resources. In the case of the Bahama Swallow, these interactions take the form of the dependence of the swallow on Hairy Woodpeckers for nesting sites, and of the woodpeckers on pine snags. It is conceivable that these interactions are sufficiently strong to be a factor in the decline of the Bahama Swallow, and that increasing the presence of pine snags suitable for excavation by Hairy Woodpeckers and thereby increasing the population of this primary excavator may be critical to the recovery of this secondary cavity- nester.

In pine snags and utility poles, only ~11-12% of structures contained cavities, suggesting that woodpeckers are using additional characteristics to select nest sites. Our assessment of pine snags indicates that woodpeckers are selecting snags based on their size. Nest records indicated

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the size threshold for excavation was~10 cm DBH, and Hairy Woodpeckers nested in snags near this threshold. However, both woodpecker species excavated snags that were, on average, larger than what was available above this threshold. Although the difference was not significant, the average diameter of the few snags used by West Indian Woodpeckers was larger than those used by the Hairy Woodpecker. This is not surprising, given that the West Indian Woodpecker (26 cm) is considerably larger than the Hairy Woodpecker (20-23 cm; Raffaele et al. 1998). In addition to providing more space for excavation, larger-diameter snags create a more stable microclimate for nesting cavities (Weibe 2001) and are less likely to fall (Morrison and Raphael

1993). The excavation of larger snags has similar positive effects for secondary cavity-nesters, since larger snags are likely to persist longer (Cockle et al. 2017; 2019). Both woodpeckers also selected snags that were taller than available snags, although the effect was only significant for the Hairy Woodpecker. While nest success did not vary significantly by cavity height in my sample of Bahama Swallow nests (Chapter 4), other studies have shown that nests in cavities higher off the ground are less likely to be depredated (Nilsson 1984, Peterson and Gauthier 1985,

Rendell and Robertson 1989, Li & Martin 1991, Politi et al. 2009). Additional information is needed to determine whether woodpeckers are selecting nest sites based on the surrounding habitat. Although we did not measure snag density in relation to nest sites, the number of cavities increased with the number of snags, suggesting that woodpeckers are nesting in areas where they can select among snags. Bahamian pine forests are relatively young due to previous extensive logging (Henry 1974). As the forest matures, it is possible that there will also be a shift in the snags that woodpeckers select.

Woodpecker excavation of utility poles, on the other hand, is driven by habitat, and not by the availability or the size characteristics of poles. The density of utility pole cavities

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increased in non-pine habitat. Pole density was also higher in non-pine habitat, likely because additional poles are required to support cables leading to settlements and other infrastructure, which are located outside of the pine forest. However, all of the utility pole transects contained at least 9 poles/km, and there was no correlation between the density of cavities and the density of poles. Also, excavated poles did not differ in height from available utility poles, and there was no difference in the diameter of poles excavated by the two woodpecker species. The standard design of the poles erected by utility companies creates very little variation in size of the poles that are available.

Although both woodpecker species will nest in utility poles and snags, we found extensive evidence that each species is the principal creator of cavities in one structure type, and that this specialization is linked to habitat use. The Hairy Woodpecker specializes in excavating pine snags. Unsurprisingly, pine snags are generally located in pine forest habitat, although excavated pine snags were occasionally found in areas that were not pine-dominant. The association of Hairy Woodpeckers with pine forest (Raffaele et al. 1998) was supported by our results showing that sites with more pine are more likely to be occupied by Hairy Woodpeckers, especially if there are snags present. Hairy Woodpecker nests were also mostly found in pine habitat, regardless of the structure type. In the Caribbean, this species is only present on the pine islands of The Bahamas, but its full range extends over much of North America and Central

America (Jackson et al. 2018). It is an important cavity excavator in other systems (Saab et al.

2004, Robles and Martin 2014), although the types of trees in which it excavates varies geographically. For example, Hairy Woodpeckers almost exclusively excavate cavities in live quaking aspen (Populus tremuloides) in British Colombia, Canada (Martin et al 2004, Blanc and

Martin 2012).

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The West Indian Woodpecker specializes in excavating utility poles. Although poles are available in all habitat types, cavities in poles and West Indian Woodpecker nests are found primarily in non-pine habitat. The association of this species with coppice (dry broadleaf) forest and human habitation (Cruz and Johnston 1984, Raffaele et al. 1998) was evidenced by increased occupancy in non-pine habitats. Although our survey efforts were focused on utility poles and pine snags, we are aware that this species also nests in snags of coconut palms (Willimont et al.

1991) and sabal palms (Miller et al. 2018). It is also possible that this species nests in other hardwood trees in the coppice forest. Interestingly, West Indian Woodpeckers on San Salvador

Island do not excavate the available utility poles (M. Akresh, personal communication), suggesting that the structure specialization of this species also varies geographically.

The lack of latitudinal variation in woodpecker occupancy and utility pole cavity density indicate that the lower swallow occupancy in northern Abaco (Chapter 1) cannot be attributed to a lack of potential nesting cavities. However, we did not survey the density of snags or snag cavities in northern Abaco. We did observe areas with extensive pine forest die-off due to saltwater inundation in northern Abaco, but it is unknown whether woodpeckers are excavating the snags in these highly exposed areas, or that the swallows would use them if they did.

Additional surveys are needed to evaluate the density of woodpeckers, pine snags, and cavities in the northern pine forest.

The distribution of secondary cavity-nester nests indicates that competition for nesting cavities varies by habitat. Bahama Swallows will nest in cavities in all habitats, but a majority of nests were found in pine-dominated habitat. The only other secondary cavity-nester with a nesting habitat preference similar to the swallow was the La Sagra’s Flycatcher. Despite the widespread presence of flycatchers in the pine forest (Lloyd and Slater 2011, personal

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observation), we were only able to locate three nests in pine snags due to the behavior of this species. Flycatchers would often approach and follow us through the pine forest, but, unlike the other cavity-nesting species, would never go near nesting cavities in our presence. Therefore, it is likely that flycatcher nests are underrepresented in the pine forest. Like Allen (1996), we observed antagonistic interactions of swallows with Hairy Woodpeckers and La Sagra’s

Flycatchers near pine snags, but did not see any evidence that these species usurp swallow cavities or depredate swallow nests.

Nests of the two non-native cavity-nesters – the House Sparrow and the European

Starling – were restricted to non-pine habitat, which was expected given that both species are associated with human habitation (Raffaele et al. 1998). We recognize that these species are likely underrepresented in our nest records because our nest searching effort was lower in settlements relative to other areas on the island. However, we believe that additional nest records would only strengthen our conclusion that these species are using nesting cavities, perhaps exclusively, in anthropogenic structures. House Sparrows (Jackson and Tate 1974, Gowaty 1984,

Winkler 1992) and European Starlings (Willimont 1990) are known to aggressively exclude other birds from nesting cavities. We did not observe any direct interactions between swallows and these species, but our nest records show that House Sparrows will usurp utility pole cavities from swallows. Similarly, sparrows excluded swallows from nesting cavities in developed areas on Grand Bahama (Allen 1996). This antagonistic relationship may extend to cavities in towers, since the towers that were occupied by House Sparrows had less pine forest in the surrounding area (Chapter 4). We also observed one case of European Starlings usurping a utility pole cavity from a pair of American Kestrels.

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American Kestrel nests were also found in non-pine habitat. This may be because this small raptor prefers to hunt near roads and other open areas (Raffaele et al. 1998, Smallwood and

Bird 2002). However, it nests in pine habitat elsewhere, notably in the case of the Southeastern

American Kestrel nesting in longleaf pine (Pinus palustris) forest in the southeastern United

States (Gault et al. 2004, Blanc and Walters 2008). Yet the larger body size of the this species

(23-30 cm; Raffaele et al. 1998) restricts it to cavities excavated by large excavators (Saab et al.

2004, Martin et al. 2004, Blanc and Walters 2008), such as those excavated by the West Indian

Woodpecker and those in buildings, which are located in non-pine habitat. Thus, the lack of a large excavating species and natural holes in pine habitat may limit the kestrel’s nesting distribution on Abaco. In this case, using the cavity nest-web approach revealed nesting habitat limitation for one of the secondary cavity-nesters in this community.

For Bahama Swallows, the concentration of cavity-nesters in non-pine habitat not only increases competition for nesting cavities, but probably increases predation pressure as well. We witnessed likely depredation of several swallow nests by West Indian Woodpeckers and

American Kestrels. The presence of both species usually provokes anti-predator mobbing behavior (Curio 1978) by swallows and other birds. The West Indian Woodpecker also has a polyandrous breeding system, whereby a female establishes nests with multiple males

(Willimont et al. 1991), possibly sustaining cavity exploration that would expose swallows to nest predation. Invasive mammalian predators such feral cats (Felis catus), rats (Rattus rattus), racoons (Procyon lotor) are also present on the pine islands, especially in developed areas.

Although we saw no direct evidence that these species will depredate swallow nests, rats and cats will depredate nests of the endemic Bahama Parrot (Amazona leucocephala bahamensis)

(Stahala 2016, C. Stahala personal communication). Although these predators might be more

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likely to target this ground-nesting parrot, they could still pose a threat to Bahama Swallows and other cavity-nesting species.

Conclusion

The cavity nest-web on Great Abaco Island revealed that secondary-cavity nesters are dependent on excavated cavities, with some use of holes in anthropogenic structures but no contribution of natural holes. Interactions between cavity-nesting species and cavity resources vary by habitat. Bahama Swallows nesting in the pine forest only compete with one other species for pine snag cavities that are excavated by the Hairy Woodpecker. However, swallows nesting in non-pine habitat compete with several other species for nesting cavities excavated by West

Indian Woodpeckers in utility poles, including aggressive non-native species and nest predators.

It is possible that increased competition for nesting cavities and predation pressure in non-pine habitat is responsible for the lower rate of nest success for nests in utility poles (~50-62%) relative to nests in pine snags (~92%) (Chapter 4).

This research highlights the importance of the native pine forest and the presence of

Hairy Woodpeckers to the survival of the endemic Bahama Swallow. The pine forest that regenerated after logging (Henry 1974) is young and generally uniformly structured (Allen 1996,

Myers et al. 2004, personal observation), likely due to suboptimal fire and hurricane disturbance regimes immediately after logging (Henry 1974), and in the decades since (Myers et al. 2004).

Large areas of pine forest are protected within the national park system in The Bahamas.

However, many swallows nest outside of the park boundaries, where the pine forest is vulnerable to development. Also, the Bahamas Forestry Unit is currently managing portions of the pine forest by issuing renewable timber harvesting licenses. Tree harvesting can remove trees that

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maintain cavity-nesting communities (Cockle et al. 2010, Edworthy and Martin 2013,

Vaillancourt et al. 2008). As these forests age and management strategies progress, it is essential that they incorporate the habitat requirements of the wildlife that depend on it. The results of this study indicate that allowing large pine trees to remain in the forest and become snags is critical for Hairy Woodpeckers and the swallows that rely on excavated cavities. Additional research is needed to assess the structural dynamics of the pine forest, and the subsequent effects on the

Hairy Woodpecker and the rest of the cavity-nesting community.

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FIGURES

Habitat types were classified from Landsat 8 satellite images (U.S. Geological Survey).

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Figure 2: The number of cavities in utility poles as a function of habitat (% pine). The line indicates the GLM prediction.

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Figure 3: Structure height of available snags from surveys (All), snags from surveys with cavities

(W/Cav), and snags with nests of the Hairy Woodpecker (HAWO) and West Indian Woodpecker

(WIWO).

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Figure 4: Diameter at breast height (DBH) of available snags from surveys (All), snags from surveys with cavities (W/Cav), and snags with nests of the Hairy Woodpecker (HAWO) and

West Indian Woodpecker (WIWO).

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Figure 5: Habitat (% pine) surrounding nest sites of Hairy Woodpeckers (HAWO) and West

Indian Woodpeckers (WIWO) in pine snags (S) and utility poles (UP). Analysis of variance was conducted for nests by species (A), structure (B), and species and structure (C).

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Figure 6: Occupancy probability (Ψ) predicted by percent pine and the presence of snags, based on the top ranking occupancy model for Hairy Woodpeckers.

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Figure 7: Occupancy probability (Ψ) predicted by percent pine, based on the top ranking occupancy model for West Indian Woodpeckers.

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Figure 8: Cavity nest-web diagram on Great Abaco Island. For each primary cavity excavator

(PE) and secondary cavity-nester (SCN), n is the number of nest records for that species. For each PE, E is the total number of cavities that were excavated by that species. Links between PEs and cavity-nesting resources (CNR) indicate the percentage of nests that were found in that resource. Links between SCNs and PE indicate the percentage of SCN nests found in a cavity that was excavated by that PE. Links between SCNs and CNRs indicate the percentage of SCN nests found in a cavity in that resource.

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Figure 9: Habitat (% pine) within 400 m of nests of European Starlings (EUST; n = 8), House

Sparrows (HOSP; n = 7), American Kestrels (AMKE; n = 12), Bahama Swallows (BAHS; n =

173), and La Sagra’s Flycatchers (LAFL; n = 7). Statistically significant differences between species are indicated by unshared letters (a-c).

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TABLES

Table 1: Point count covariates used for occupancy analysis in program Presence. Whether each covariate was incorporated into detection probability (p) and/or occupancy probability (Ψ) components of occupancy models is indicated.

Covariate Meaning p Ψ Hour Time of survey (between 6 and 10 AM) X Observer Survey observer MW or not MW X Wind Wind condition calm or some noticeable wind X Latitude Scaled latitudinal coordinate X Road Survey on or off the road X X Pine Percent of pine within 100m X X Snags Presence/absence of snags > 10 cm DBH within X 100m

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Table 2: Characteristics of all utility poles from surveys (All poles), poles with cavities from surveys (With Cavities), and utility poles containing nests of Hairy Woodpeckers (HAWO) and West Indian Woodpeckers (WIWO). For each GLM, we report the estimate (ß ±

SE) for each group, and the residual deviance (RDev) with degrees of freedom (df). We also report the deviance (Dev), degrees of freedom (df), and p-value for the chi-square analysis of deviance test (χ 2).

Summary statistic GLM χ 2 Characteristic Group Mean SE n ß SE RDev df Dev df p-value All poles 11.5 0.1 650 -0.23 1.26 Structure Height With Cavities 11.4 0.2 66 -0.33 1.28 2355.4 746 0.8 3 0.9670 (m)1 HAWO 11.7 0.2 2 11.74 1.26 WIWO 11.6 0.2 32 -0.18 1.30 DBH HAWO 30.9 0.6 2 30.88 1.45 126.3 30 3.8 1 0.3430 (cm)1 WIWO 29.5 0.4 30 -1.42 1.50 With Cavities 10.7 0.2 66 -0.70 1.18 Cavity Height 2 274.2 101 4.8 2 0.4164 (m)1 HAWO 11.4 0.5 11.43 1.17 WIWO 10.4 0.3 36 -1.08 1.20 With Cavities 0.7 0.1 66 -1.71 3.24 CFT 2 242.5 101 9.7 2 0.0955 (m)2 HAWO 0.3 0.3 3.18 3.23 WIWO 1.2 0.2 36 -2.36 3.23 1GLM family = gaussian 2GLM family = Gamma

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Table 3 Characteristics of all pine snags from surveys (All), snags with cavities from surveys (With Cavities), and snags containing nests of Hairy Woodpeckers (HAWO) and West Indian Woodpeckers (WIWO). For each group, we report the mean measurement ±

SE, sample size (n). For each GLM, we report the estimate (ß ± SE) for each group, and the residual deviance (RDev) with degrees of freedom (df). We also report the deviance (Dev), degrees of freedom (df), and p-value for the chi-square analysis of deviance test (χ 2).

Summary statistic GLM (family = Gamma) χ 2 Characteristic Group Mean SE n ß SE RDev df Dev df p-value All Snags 7.4 0.2 649 0.032 0.011 Structure Height With Cavities 8.7 0.6 51 0.011 0.014 248.3 732 3.4 3 0.0157 (m) HAWO 9.7 0.8 30 0.103 0.011 WIWO 7.3 1.2 6 0.035 0.034 All Snags 15.5 0.2 649 0.019 0.003 DBH With Cavities 19.4 0.7 51 0.006 0.003 62.3 730 6.6 3 <0.0001 (cm) HAWO 21.8 1.0 29 0.046 0.003 WIWO 23.4 1.8 5 -0.003 0.007 With Cavities 6.9 0.5 51 0.008 0.016 Cavity Height 7.3 0.6 30 22.2 84 0.1 2 0.8076 (m) HAWO 0.138 0.012 WIWO 6.4 1.2 6 0.018 0.033 With Cavities 1.9 0.3 51 0.122 0.122 CFT 2.4 0.5 30 99.5 84 4.8 2 0.1575 (m) HAWO 0.416 0.087 WIWO 0.8 0.2 6 0.782 0.565

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Table 4: Model ranking results for models testing the effects of covariates on Hairy Woodpecker detection probability (p) from program Presence. For each model, we report the AIC, ∆AIC, model weight (wi), model likelihood, number of parameters (K), and deviance. Covariates for Ψ included the latitudinal coordinates of points (L), whether the point was on a road (R), percent pine (PP), and the presence of snags (SN). Covariates for p included whether the point was on a road (R), percent pine (PP), observer (O), wind condition (W), and hour of the survey (H).

Model AIC ∆AIC wi Likelihood K Deviance Ψ(L + R + PP + SN),p(.) 440.92 0.00 0.34 1.00 6 428.9 Ψ(L + R + PP + SN),p(PP) 442.03 1.11 0.19 0.57 7 428.0 Ψ(L + R + PP + SN),p(O) 442.17 1.25 0.18 0.54 7 428.2 Ψ(L + R + PP + SN),p(W) 442.49 1.57 0.15 0.46 7 428.5 Ψ(L + R + PP + SN),p(PP + O) 443.24 2.32 0.11 0.31 8 427.2 Ψ(L + R + PP +SN),p(PP+R+O+W+H) 447.91 6.99 0.01 0.03 11 425.9 Ψ(L + R + PP + SN),p(R) 448.00 7.08 0.01 0.03 7 434.0 Ψ(L + R + PP + SN),p(O + R) 448.97 8.05 0.01 0.02 8 433.0 Ψ(L + R + PP + SN),p(H) 454.26 13.34 0.00 0.00 7 440.3 Ψ(.),p(.) 460.18 19.26 0.00 0.00 2 456.2

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Table 5: Model ranking results for models testing the effects of covariates on Hairy Woodpecker occupancy probability (Ψ) from program Presence. For each model, we report the AIC, ∆AIC, model weight (wi), model likelihood, number of parameters (K), and deviance. Covariates for Ψ included the latitudinal coordinates of points (L), whether the point was on a road (R), percent pine (PP), and the presence of snags (SN). No covariates were included for p (.).

Model AIC ∆AIC wi Likelihood K Deviance Ψ(PP + SN),p(.) 438.35 0.00 0.37 1.00 4 430.4 Ψ(L + PP),p(.) 439.79 1.44 0.18 0.49 4 431.8 Ψ(L + PP + R),p(.) 440.06 1.71 0.16 0.43 5 430.1 Ψ(L + PP + SN),p(.) 440.34 1.99 0.14 0.37 5 430.3 Ψ(L + R + PP + SN),p(.) 440.92 2.57 0.10 0.28 6 428.9 Ψ(R + SN),p(.) 443.12 4.77 0.03 0.09 4 435.1 Ψ(L + R + SN),p(.) 444.64 6.29 0.02 0.04 5 434.6 Ψ(L + SN),p(.) 446.89 8.54 0.01 0.01 4 438.9 Ψ(PP),p(.) 448.36 10.01 0.00 0.01 3 442.4 Ψ(R),p(.) 448.78 10.43 0.00 0.01 3 442.8 Ψ(SN),p(.) 449.63 11.28 0.00 0.00 3 443.6 Ψ(L + R),p(.) 450.59 12.24 0.00 0.00 4 442.6 Ψ(L),p(.) 456.91 18.56 0.00 0.00 3 450.9 Ψ(.),p(.) 460.18 21.83 0.00 0.00 2 456.2

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Table 6: Model ranking results for models testing the effects of covariates on West Indian

Woodpecker detection probability (p) from program Presence. For each model, we report the

AIC, ∆AIC, model weight (wi), model likelihood, number of parameters (K), and deviance.

Covariates for Ψ included the latitudinal coordinates of points (L), whether the point was on a road (R), percent pine (PP), and the presence of snags (SN). Covariates for p included whether the point was on a road (R), observer (O), wind condition (W), and hour of the survey (H).

Model AIC ∆AIC wi Likelihood K Deviance Ψ(L + R + PP + SN),p(O) 226.14 0.00 0.41 1.00 7 212.1 Ψ(L + R + PP + SN),p(O + R) 227.55 1.41 0.20 0.49 8 211.6 Ψ(L + R + PP + SN),p(.) 228.70 2.56 0.11 0.28 6 216.7 Ψ(L + R + PP + SN),p(H) 229.31 3.17 0.08 0.20 7 215.3 Ψ(L + R + PP + SN),p(R) 229.57 3.43 0.07 0.18 7 215.6 Ψ(L + R + PP + SN),p(R + O + H + W) 229.80 3.66 0.07 0.16 10 209.8 Ψ(L + R + PP + SN),p(W) 230.69 4.55 0.04 0.10 7 216.7 Ψ(.),p(.) 278.49 52.35 0.00 0.00 2 274.5

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Table 7: Model ranking results for models testing the effects of covariates on West Indian

Woodpecker occupancy probability (Ψ) from program Presence. For each model, we report the

AIC, ∆AIC, model weight (wi), model likelihood, number of parameters (K), and deviance.

Covariates for Ψ included the latitudinal coordinates of points (L), whether the point was on a road (R), percent pine (PP), and the presence of snags (SN). We included observer (O) as the covariate for p.

Model AIC ∆AIC wi Likelihood K Deviance Ψ(PP),p(O) 223.43 0.00 0.22 1.00 4 215.4 Ψ(PP + L),p(O) 223.71 0.28 0.19 0.87 5 213.7 Ψ(PP + SN),p(O) 223.79 0.36 0.18 0.84 5 213.8 Ψ(L + PP + SN),p(O) 224.18 0.75 0.15 0.69 6 212.2 Ψ(PP + R),p(O) 224.69 1.26 0.12 0.53 5 214.7 Ψ(L + PP + R),p(O) 225.53 2.10 0.08 0.35 6 213.5 Ψ(PP + L + R + SN),p(O) GOF 226.14 2.71 0.06 0.26 7 212.1 Ψ(R + SN),p(O) 244.56 21.13 0.00 0.00 5 234.6 Ψ(SN),p(O) 248.07 24.64 0.00 0.00 4 240.1 Ψ(R),p(O) 260.60 37.17 0.00 0.00 4 252.6 Ψ(R + L),p(O) 261.74 38.31 0.00 0.00 5 251.7 Ψ(L),p(O) 268.57 45.14 0.00 0.00 4 260.6 Ψ(.),p(O) 277.23 53.80 0.00 0.00 3 271.2 Ψ(.),p(.) 278.49 55.06 0.00 0.00 2 274.5

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Chapter 6

Summary and Conclusion

In order to prevent species extinctions, conservation strategies need to incorporate the identification and mitigation of the root causes of population decline (the declining population paradigm) with the assessment of genetic vulnerability of small populations (the small population paradigm) (Caughley 1994). The goal of this research was to assess the endangered

Bahama Swallow within these paradigms in order to inform the conservation and management of the species. In this dissertation, I investigated questions related to the swallow’s population size and distribution, genetic diversity and genetic population structure, breeding biology, and ecological interactions.

How does the distribution of the Bahama Swallow vary across a pine island?

In Chapter 2, I used several survey methods to assess the distribution of the swallow population on the Abaco Islands. I found that swallow site occupancy and population density is higher in southern Abaco, especially near roads and in the presence of pine snags. These results suggest that conservation efforts should include managing pine forest in southern Abaco to maintain the presence of pine snags. Our findings also indicate that roads are used as foraging areas for swallows, although future work should address the use of other areas for foraging, and how foraging ecology relates to nest site selection. Additional research is needed to identify the causes of decreased swallow occupancy and density in northern Abaco, and to assess the factors determining the distribution of populations across the rest of the breeding range.

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What is the level of genetic diversity within swallow populations, and are there genetic and morphological differences among populations?

In Chapter 3, I used microsatellite markers to estimate genetic diversity, effective population size (Ne), and population differentiation (G'ST) on Abaco Island and Andros Island.

We also assessed variation in several morphological measures (mass, head-bill length, wing length). Although there is uncertainty in our effective population estimates, breeding populations may be small enough (~250-800 individuals) to warrant concern for adverse effects of loss of genetic diversity such as long-term evolutionary potential. Our results indicate that populations are not genetically differentiated (G'ST = 0.03), but variation in wing length suggests that gene flow might be low enough to enable traits under selection to diverge.

Which cavity-nesting resources are Bahama Swallows using, and what is their reproductive success in different resources?

In Chapter 4, I identified the cavity-nesting resources used by breeding swallows in the native pine forest and other habitats on Abaco, and estimated breeding parameters and phenology from a subset of nests. The Bahama Swallow nests in cavities in a variety of structures, but relies on woodpecker-excavated cavities in pine snags and utility poles. Swallows nesting in pine snag cavities had higher fledging success (92%) than those nesting in utility pole cavities (50-62%), which were concentrated in non-pine habitat that may expose swallows to increased competition for nesting cavities and/or nest predation. However, the high reproductive success of swallows nesting in the pine forest, relative to other Tachycineta species, indicated that population declines cannot be attributed to poor productivity on southern Abaco. These results highlight the potential

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importance of pine forest protection and management to future efforts to conserve the Bahama

Swallow.

How does the Bahama Swallow interact with cavity-nesting birds and resources?

In Chapter 5, I used a cavity nest-web approach to investigate the interactions of the

Bahama Swallow with other cavity-nesting species and cavity resources on Abaco. I conducted systematic surveys to assess the density of available and excavated cavity-nesting resources, evaluated the distribution of cavity-excavating woodpeckers and their nests, and assessed variation in competition between secondary cavity-nesters for nest sites across habitat types.

Hairy Woodpeckers (Dryobates villosus) primarily excavated snags of Caribbean Pine (Pinus caribaea bahamensis), while West Indian Woodpeckers (Melanerpes superciliaris) excavated utility poles in non-pine habitat. Among secondary cavity-nesters, only swallows and native La

Sagra’s Flycatchers (Myiarchus sagrae) used nest sites in the pine forest. Swallows nesting in non-pine habitat face competition for cavities with American Kestrels (Falco sparverius), and non-native House Sparrows (Passer domesticus) and European Starlings (Sturnus vulgaris).

These findings indicate that managing for pine snags and the presence of Hairy Woodpeckers in the pine forest is a potential mechanism for increasing the Bahama Swallow population.

Conclusion

Historically, there has been very little research conducted on the Bahama Swallow. Allen

(1996) provided unprecedented information regarding the natural history and breeding biology of this species. However, in the nearly two decades that passed before I initiated the research presented in this dissertation, the species status changed from Near-threatened to Endangered

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(Birdlife International 2016). Designing management and conservation strategies for this species is imperative, and the results of this project provide information that can be used to inform these strategies. However, there are still critical gaps in our knowledge of this species that need to be addressed in future research.

In order to assess the extinction risk of the swallow, it is necessary to estimate the absolute and breeding population sizes, and determine the extent to which populations are isolated both demographically and genetically. Using distance-sampling methods to estimate swallow density is difficult, given their high mobility (Chapter 2). Identifying an appropriate survey method will likely require testing a variety of methods (Buckland 2006, Cassey et al.

2007). I intended to use capture-recapture methods (Williams et al. 2002) to estimate population size in this study. However, it was extremely difficult to capture swallows off of the nest, and I was able to recapture only two birds, precluding use of this method. I did not use color-bands for mark-resight methods because I assumed that the short tarsus and posture of this species would prevent identification of individuals. However, future research could explore the possibility of using these methods and others. A study that included more substantial and effective capture effort within the entire range, along with genetic sampling and morphological measurements, could provide better estimates of effective population size and differences between populations

(Chapter 3).

The results of this study indicate that the swallow is likely limited by habitat, and highlight the importance of the native pine forest and the Hairy Woodpecker for the persistence of the species. Swallows are more likely to occupy sites at higher densities in southern Abaco, particularly where there are pine snags present (Chapter 2). The swallows nesting in this region have high rates of nest success, especially in cavities in pine snags (Chapter 4). Pine snag

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cavities are excavated primarily by the Hairy Woodpecker, and are only used by one other secondary cavity-nesting species (Chapter 5). In order to effectively conserve the swallow, future research needs to investigate how the structural dynamics of the pine forest impact the Hairy

Woodpecker and, consequently, the swallow.

The Bahamian pine forest is relatively young, since virtually all of it was logged for timber and pulpwood between 1905 and 1969. To allow for regeneration after logging, young saplings were left standing in cut areas (Henry 1974). However, the remnant pine trees do not appear to have matured as expected, as large areas of forest are uniformly structured (Allen

1996, Myers et al. 2004, personal observation). This state is likely due to suboptimal fire and hurricane disturbance regimes immediately after logging (Henry 1974), and in the decades since

(Myers et al. 2004). Bahamian pine forest is fire-dependent, and should burn every 3-10 years, as this frequency promotes seedling recruitment and prevents conversion to hardwood forest.

However, most of the forest experiences a higher frequency (every ~1-2 years) of fires, most of them human-caused, which prevents recruitment of seedlings and subsequent regeneration

(Myers et al. 2004). Fire regimes can also influence the components of cavity nest-webs (Saab et al. 2004, Saab et al. 2007). Additional research, including maintaining precise records of fires, could provide insight into the structural responses of the forest to fire regimes and the resulting impacts on cavity-nesting birds that could be vital to forest management strategies.

The Forestry Unit of The Bahamas was established by the Forestry Act of 2010 to sustainably manage the forests through various measures, including the regulation of a renewable timber harvesting industry. However, tree harvesting can remove mature trees that maintain cavity-nesting communities (Cockle et al. 2010, Edworthy and Martin 2013, Vaillancourt et al.

2008). As these forests age and management strategies progress, it is essential that they

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incorporate the habitat requirements of the wildlife that depend on the forest. The results of this study indicate that allowing large trees to remain in the forest and become snags is critical for

Hairy Woodpeckers and the swallows that rely on excavated cavities (Chapter 5). Promoting mature trees in more uneven-aged forests could also reveal cavity-nesting resource preferences of woodpeckers and swallows that cannot be detected within the current forest structure.

Due to their geographical location, the islands of the Bahamas experience hurricanes every 10-15 years (Fraza and Elsner 2014), but changes in climate are projected to produce more frequent and intense hurricanes (Dale et al. 2001). Hurricanes have the potential to increase mortality of birds, and to have indirect effects on populations by destroying habitat (Wiley and

Wunderle 1993, Dale et al. 2001, Bonilla-Moheno 2012). For example, storm surges from a series of hurricanes have killed trees across large areas of pine forest on Grand Bahama. In

September 2019, Hurricane Dorian hit the Bahamas as a Category 5 storm, and devastated northern Abaco and Grand Bahama. It is likely that the pine forest and other habitats were heavily impacted by this hurricane. I hope that my research can provide a baseline understanding of the swallow in order to determine how this species is affected by strong hurricanes and other disturbances. More research is needed to understand how hurricanes affect the age structure of the pine forest and the use of these altered forests by cavity-nesting species.

In the Caribbean, the Hairy Woodpecker is found only on the pine islands of The

Bahamas (Raffaele et al. 1998), yet very little is known about the species in this part of its range.

This woodpecker species is highly associated with the pine forest and is the primary excavator of cavities in pine snags (Chapter 5). Future research should assess the fine-scale distribution and abundance of this important excavator throughout the pine islands, the characteristics of snags that they select for nest sites, and their reproductive success.

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The stark absence of swallows on northern Abaco and Grand Bahama (Chapter 2) is concerning. Identifying the cause(s) of the decline in swallow populations in these locations could prevent extirpation of the swallow in this part of its range, and potentially provide insight into recent declines of other cavity-nesting birds in The Bahamas. Despite being common on

Abaco, the West Indian Woodpecker appears to have been recently (~1990s) extirpated on Grand

Bahama. The Bahama Nuthatch (Sitta pusilla insularis) is present only on Grand Bahama, and is

Critically Endangered because populations have plummeted from ~1800 (Hayes et al. 2004) to less than 50 individuals in the last couple of decades (Birdlife International 2018). This species is feared to be extinct after Hurricane Dorian. Nest predators, especially invasive mammalian predators like cats (Felis catus), rats (Rattus rattus), and racoons (Procyon lotor) can have significant impacts on island birds (Johnson and Stattersfield 1990, Simberloff 2000, Fordham and Brook 2010, Szabo et al. 2012, Wood et al. 2017). Monitoring nests of swallows and other cavity-nesting birds, in addition to surveying for potential nest predators, could determine whether population declines are linked to nest predation.

For secondary cavity-nesters like the swallow and the nuthatch, a lower density of available nesting cavities could limit populations. A thorough assessment of cavity-nesting resources could determine if this is the case. If cavities are limited, initiating a nest box program has the potential to aid in conservation of these species. However, while all of its congeners will readily nest in standard nest boxes, the Bahama Swallow generally does not use them. Allen

(1996) found two nests in nest boxes on Grand Bahama. Although there are currently ~80 nest boxes placed in several locations on Abaco, I observed only two breeding attempts in a nest box, of which only one was successful. A pine snag in southern Abaco used by swallows for three consecutive breeding seasons (2014-2016) fell due to a forest fire. In 2017, I placed a nest box

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on an adjacent tree, and a pair of swallows nested and successfully fledged three young.

Although it is purely anecdotal, this case reveals that swallows will use nest boxes, but initiating a program on a larger scale would likely require strategic redesign and/or placement of nest boxes.

Initiating scientifically sound management and conservation strategies for the Bahama

Swallow increases the potential that this unique bird will endure. Although I have not definitively identified the causes of the decline of this species, my work has revealed several possibilities that merit further investigation. My hope is that this dissertation provides relevant information and sparks additional research that contributes to these efforts and will ultimately result in effective conservation.

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