Philopatry And Population Genetics Across Seabird Taxa

PHILOPATRY AND POPULATION GENETICS ACROSS SEABIRD TAXA A THESIS SUBMITTED TO THE GRADUATE DIVISION OF THE UNIVERSITY OF HAWAI‘I AT MĀNOA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN NATURAL RESOURCES AND ENVIRONMENTAL MANAGEMENT (ECOLOGY, EVOLUTION, AND CONSERVATION BIOLOGY) MAY 2018 By Carmen C. Antaky Thesis Committee: Melissa Price, Chairperson Tomoaki Miura Robert Toonen Lindsay Young Keywords: seabirds, dispersal, philopatry, endangered species, population genetics Copyright 2018 Carmen C. Antaky All Rights Reserved ii ACKNOWLEDGEMENTS This thesis, my Master’s work, would not have been possible without the help and valuable assistance of many others. Firstly, thank you to my advisor Melissa Price for guiding me through my Master’s degree. Her support has made me a better researcher, scientific writer, and conservationist. I would like to thank my committee members Rob Toonen, Lindsay Young, and Tomoaki Miura for their feedback, support, and encouragement. Thank you Lindsay for your expert advice on all things seabirds and being an incredible role model. Thank you Rob for welcoming me into your lab and special thanks to Ingrid Knapp for guidance throughout my lab work. I would like to thank Tobo lab members Emily Conklin, Derek Kraft, Evan Barba, and Zac Forsman for support with genetic data analysis. I would also like to thank all the members of the Hawaiʻi Wildlife Ecology Lab, especially to Jeremy Ringma and Kristen Harmon for their support and friendship. I would like to thank my major collaborators on my project who supplied samples, a wealth of information, and for helping me in the field, especially Andre Raine of the Kauaʻi Endangered Seabird Recovery Project, Nicole Galase at Pōhakuloa Training Area, and Tracy Anderson at Save Our Shearwaters. Thank you to the British Trust of Ornithology and Bird Banding Laboratory at USGS for preparing and providing access to seabird banding data. Funding for this project came from US Fish & Wildlife Service. Thanks also to my funding sources: Hawaiʻi Audubon Society, Pacific Seabird Group, and of course U.S. Fish and Wildlife Service. Thank you especially to Beth Flint, Sheldon Plentovich, and Michelle Bogardus at USFWS for the support. iii Last but not least, thank you to my family and friends for supporting me in every way, shape, and form. In particular, I dedicate this thesis to my Mom, Dad, and Philip for their enduring support, patience, and love. iv ABSTRACT Successful conservation depends on an understanding of dispersal patterns for spatially complex species. Among seabirds there are opposing pressures to either disperse or return to natal colonies. We explored philopatry across 36 species, based on banding and census data. Philopatry correlated with foraging strategy, taxonomy, and region, suggesting that translocation will be more successful for Procellariiformes, those in tropical regions and with non-central foraging strategies, as they are more likely to return to translocation sites. Additionally, we compared genetic diversity between two orders of seabirds with differing philopatry and explored population genetics of a species in the order Procellariiformes, the Band-rumped Storm Petrel (BSTP; Oceanodroma castro). Findings indicated no difference in genetic diversity between orders and high genetic diversity within BSTP. Although this study suggests that BSTP are not at risk genetically, they remain vulnerable to threats. Management efforts to ensure successful nesting is crucial to recover the endangered BSTP. v TABLE OF CONTENTS Acknowledgements ...................................................................................................................... ii Abstract ........................................................................................................................................ iv List of Tables ................................................................................................................................ v List of Figures .............................................................................................................................. vi Introduction .................................................................................................................................. 1 Chapter 1. Dispersal under the seabird paradox: Probability, foraging strategy, or spatial attributes? ..................................................................................................................................... 3 Abstract ............................................................................................................................. 4 Introduction ........................................................................................................................ 5 Methods .............................................................................................................................. 8 Results .............................................................................................................................. 12 Discussion ........................................................................................................................ 14 Chapter 2. Population genetics of the Band-rumped Storm Petrel (Oceanodroma castro), an endangered and elusive Hawaiian seabird .............................................................................. 31 Abstract ............................................................................................................................ 32 Introduction ...................................................................................................................... 34 Methods ............................................................................................................................ 36 Results .............................................................................................................................. 41 Discussion ........................................................................................................................ 43 Summary …................................................................................................................................. 57 Appendix 1: List of identified O. castro microsatellites from Hawaiian populations .......... 58 Literature Cited ......................................................................................................................... 63 vi LIST OF TABLES Table Page 1.1 List of seabird taxa included with colony and species associated attributes …………… 19 1.2 Linear mixed-effects model summary of all seabird taxa ……………………….……… 21 1.3 Linear mixed-effects model summary of seabird taxa in tropical colonies ……..……… 22 1.4 Linear mixed-effects model summary of seabird taxa in temperate colonies …..……… 23 2.1 Population genetic diversity statistics for O. castro in Hawaiian island populations .….. 49 2.2 Comparison of population nucleotide diversity within species in the order Procellariiformes ……………………………………………………………………………….. 50 2.3 Comparison of population nucleotide diversity within species in the order Charadriiformes …………........………………………………………………………….…..… 52 vii LIST OF FIGURES Figure Page 1.1 Graphs of hypotheses of predicted philopatry for seabird taxa ………………..……..… 24 1.2 Map of locations of all seabird colonies surveyed ……………...…………………….… 25 1.3 Phylogenetic tree of all seabird species surveyed …………………...…………..……… 26 1.4 Graph of colony size percentage compared to philopatry for all seabird colonies ……... 27 1.5 Graph of colony size percentage compared to percent philopatry to colonies by taxonomic seabird order …………………………………………………………………………..… 28 1.6 Graphs of colony size percentage compared to percent philopatry to colonies by region and foraging strategy ………………………………………………………………….… 29 1.7 Principle component analysis of seabird colonies grouped by order …………………… 30 2.1 Collection sites of O. castro across the main Hawaiian Islands …………………...…… 54 2.3 Global distribution of O. castro populations …………………………….....………...… 55 2.2 Phylogenetic tree of global populations of O. castro ……………...………………...… 56 viii INTRODUCTION Knowledge on the dispersal and connectivity of species is a key part of conservation biology and biogeography. Seabirds, spatially complex taxa, are crippled with one of the highest ongoing rates of extinction, and many taxa are under federal protection (Spatz et al., 2014). Nevertheless, seabirds continue to be threatened by commercial fisheries, introduced predators, habitat loss, light pollution, marine debris, and climate change (Croxall et al., 2012). This thesis examines behavioral philopatry and genetic diversity within seabird populations, and how this information can be used to inform best management practices for seabird conservation. Within long-lived colonial seabird species, there are opposing pressures to either disperse to new colonies or return to natal colonies. The behavior of returning to the natal breeding site, natal philopatry, usually guarantees resources and mates but also increases the potential for inbreeding, competition, and ecological traps. There are two types of philopatry, genetic and behavioral. Philopatry, as measured by genetic means, is defined as less than one migrant per generation; anything beyond this indicates dispersal (Spieth, 1974; Lewontin, 1974). Behavioral philopatry is measured by the number of individuals that return to their natal site to breed (Weatherhead & Forbes, 1994). Chapter 1 documents dispersal patterns across 36 different seabird species, based on long-term banding, along with census data. The goal of this chapter is to identify underlying mechanisms driving behavioral philopatry with seabirds. Chapter 2 examines the effects of genetic philopatry between taxonomic seabird orders and within a federally endangered species, the Band-rumped Storm Petrel (BSTP; Oceanodroma castro). This is the first study to look at the population genetics of the BSTP across the Hawaiian Islands. The evaluation of genetic structure 1 in the present O. castro colonies in the Hawaiian Islands can help prioritize management efforts toward strategies that will be successful in increasing population size and conserving the current genetic diversity. 2 CHAPTER 1 DISPERSAL UNDER THE SEABIRD PARADOX: PROBABILITY, FORAGING STRATEGY, OR SPATIAL ATTRIBUTES? 3 ABSTRACT An understanding of natal dispersal in spatially structured populations is necessary for successful conservation. Within long-lived colonial seabird species, there are competing pressures to either disperse to novel colonies or return to natal colonies. The behavior of returning to the natal breeding sire, philopatry, usually guarantees resources and mates but also increases the potential for inbreeding, competition, and ecological traps. Thus, the high degree of philopatry among seabird species, coined the seabird paradox, is surprising, given their dispersal capabilities. We evaluated whether seabirds returned to their natal colony at rates greater than those predicted by potential dispersal variables including colony demographics, life history, or geography. We compiled long-term banding and census data from 36 seabird species across 465 colonies. A linear mixed-effects model was employed to determine how dispersal related to colony demographics wing load, foraging strategy, and spatial variables. Our results suggest that philopatric rates significantly differ from those expected based on colony size and demographics, and instead predicted by region, taxonomic order, and foraging strategy. The success of seabird translocation efforts depends on philopatric rates, as seabirds must return to the colony to benefit from predator control and habitat restoration. Our study suggests that conservation translocation programs will be more successful for species in the order Procellariformes, as well as for species in tropical regions, and those with non-central foraging strategies, as these seabird species will be more likely to return to translocation sites. 4 INTRODUCTION Seabirds exhibit high rates of colonial philopatry despite high dispersive potential, a phenomenon coined “the seabird paradox” (Milot et al., 2008). Within long-lived colonial species, there are competing pressures to either disperse to a novel colony to breed or return to the natal colony. Individual colonies are not always discrete populations and dispersal to other colonies is usually made by young seabirds before breeding for the first time (Schreiber & Burger, 2002). Advantages of dispersal, recruitment to breed at a new site, include the potential to minimize inbreeding and competition, but dispersal also comes with a high risk of mortality. Philopatric behavior, where individuals return and recruit to their natal breeding ground, usually guarantees resources and mates but also increases the potential for inbreeding, competition, and ecological traps under climate and land use change. Thus, the high degree of philopatry among seabird species is surprising, given their dispersal capabilities (Milot et al., 2008; Weimerskirch et al., 1984; Fisher, 1976; Huyvaert & Anderson, 2004; Frederiksen & Peterson, 1999). A recent review (Coulson, 2016), suggests that previous estimates of philopatry may be inflated within seabird species due to the failure to consider factors that may influence coloniality. Colonial breeding, a complex behavior exhibited by the majority of seabirds, is based on an evaluation of site-based factors. The “commodity selection” theory (Danchin & Wagner 1997) suggests that colonial animals assess environmental conditions to choose where to breed, considering environmental conditions such as nesting habitat, shelter, predator avoidance, food, and mates. Likewise, colonial nesting areas such as steep cliffs or isolated islands may be selected due to a lack of predators. Hence, when assessing philopatric rates to seabird colonies, one should consider the quality and condition of the natal colony. 5 Variation in philopatric rates across taxa may also be due to other dispersal-associated variables (Coulson, 2016). For example, wing morphology, linked to the ability to fly, may influence bird migration (Berthold, 1996). By comparing body mass over wing surface area, or wing load, to dispersal distance among species, we can determine what portion of dispersal within a species is explained by wing load. Additionally, foraging mode, central foraging within near shore waters versus non-central foraging in pelagic offshore waters, may influence dispersal. Central-place foragers (i.e. Sterna, Pelecaniformes, Sula, etc.) may have lower rates of philopatry because they must follow the food immediately around their nesting sites (Jovani et al., 2016; Elliot et al., 2009; Wakefield et al., 2017). Seabirds that are dietary generalists, switching from terrestrial to at-sea foraging, feed in areas adjacent to the colony (Isaksson et al., 2016). In contrast, other seabirds, including Procellarid species, forage hundreds, or even thousands of kilometers away from their colonies and are thus less dependent upon local food shifts (Freeman et al., 2010; Young et al., 2009; Froy et al., 2015). Region, associated with particular environmental conditions, may be correlated with philopatry due to environmentally-influenced factors. For example, tropical waters generally have a lower productivity and more patchy distribution of resources than polar or temperate regions (Weimerskirch, 2007), leading to differences in foraging and breeding behaviors. Lower resource availability in the tropics lengthens breeding cycles (Reynolds et al., 2014; Nisbet & Ratcliffe, 2008) and many tropical species also exhibit asynchronous breeding cycles which are thought to be tied to resource availability. Tropical seabirds forage greater distances than temperate seabirds, due to lower resource availability (Nelson, 1983). For example, Procellariiformes that breed in the tropics feed in productive high latitude regions during the nonbreeding season (Conners et al., 2015). Seabirds breeding in the tropics may even shift 6 foraging activities outside of the breeding season, due to low resource availability, or may shift their breeding season entirely to track resources (Nelson, 2005). In contrast, temperate seabirds are synchronous due to the short availability of food for their offspring in the temperate summer. Temperate seabirds tend to breed within or close to colder, more productive waters and forage less widely than tropical seabirds (Nelson, 1983). Inter-colony dispersal rates are likely to be affected by factors of colony demographics such as colony size, number of colonies, and space between breeding colonies (Lebreton et al., 1992). We expect a higher probability of dispersal from small to large colonies than from large to small based on the theory of habitat selection shaped by fitness maximization and thus provides access to more mates (Serrano et al., 2005). In support of this, single species social attraction studies and mark-recapture modeling have shown that colony size is a driver in post-fledging dispersal (Fernández-Chacón et al., 2013; Podolsky & Hess, 1992). In this study, we determined whether seabirds return to their natal colony at rates greater than expected based on demographic colony structure and dispersal factors, including distance, number of colonies, size of each colony, wing load, foraging strategy, taxonomy by order, and region (temperate, tropical). If rates of observed philopatry within seabirds were proportional to colony demographics, we expected a linear relationship of philopatry to relative colony size. If philopatry was consistent with the seabird paradox, we expected consistently high philopatric rates regardless of relative colony size. In contrast, if natal dispersal were random, we expected philopatry to average 50 % in respect to relative colony size (Figure 1.1). 7 METHODS Seabird banding and census data Species were selected based on the availability of data from both long-term census and banding studies. Data were obtained from comprehensive nesting databases as well as long-term banding databases. Nesting databases indicating colony size and location included Hawaiian seabird census data from the Bishop Museum Hawaiʻi Biological Survey (Pyle & Pyle, 2017) and British Isles seabird census data from the Seabird 2000 colony census (Mitchell et al., 2004). Banding databases of the associated seabird species were provided by the Bird Banding Laboratory (BBL) for tropical species within the Hawaiian region and by the British Trust for Ornithology (BTO) for temperate species in the British Isles. Banding data from BBL was retrieved May 25th, 2017 (USGS Bird Banding Laboratory, 2017) and banding data from BTO was retrieved July 25th, 2017. Banding recapture data spanned over a century representing a total of 36 seabird species from the orders Phaethontiformes, Procellariiformes, Suliformes, and Charadriiformes, including 19 tropical species and 17 temperate species (Table 1.1) within the British Isles and the Hawaiian Islands (Figure 1.2). The BBL and BTO banding data were filtered to include all recapture data from individual birds that were banded as fledglings, including dead or alive recaptures. We assumed that if an adult individual was observed at the natal site during the nesting season, it had returned to breed. For BBL data, at-sea captures and those found outside of the Hawaiian colonies were excluded from the final dataset. BTO data excluded at sea captures. Natal philopatry for each species was determined by dividing the number of recaptures at the natal site by the total number of recaptures for the species. For this study, individuals returning to sites under 20 km from their 8 natal banding location were considered to have returned to the natal site, similar to previous studies (Coulson, 2016). Seabird colonies with less than five recaptures for a given species were removed from the final analysis due to small sample sizes. Spatial data For this study, the term “colony” varied by study system due to differences in data collection of colony size. For the tropical seabird dataset, each island was treated as a single colony. For the temperate seabird dataset, each county was defined as a single colony. Distances between colonies were found by calculating the Euclidean distance between center GPS points of the focal colonies (i.e. island or county). Although there was high variability in size of the colonies in both tropical and temperate datasets, there were no outliers within the colony dataset. Species characteristics: wing load and foraging strategy data Wing load and foraging strategy for each species were collected from online databases: Cornell Lab of Ornithology – Birds of North America, The Cornell Lab - All About Birds, USFWS Hawaiian Island National Wildlife Refuge – Seabirds, and Wildscreen Arkive – Species (Poole, 2005; The Cornell Lab, 2017; U.S. Fish & Wildlife Service, 2017; Arkive, 2017). Noncentral foraging strategy was determined based on taxonomy and published studies using GPS trackers indicating a foraging range over 200 km without consistent trips back to the colony during non-breeding seasons (Adams et al., 2016; Shoji et al., 2016). All Charadriiformes and Phaethontiformes were classified as central foragers while all Procellariiformes were classified as non-central foragers (Guilford, et al., 2008; McDuie et al., 2015; Le Corre et al., 2012). Most Suliformes were central foragers, with the exception of the Northern Gannet (Morus bassanus) 9 and Great Frigatebird (Fregata minor); Thaxter et al., 2012; Gilmour et al., 2012). Wing load was calculated as average mass (g) over average wingspan (cm) since wing surface area was not available for most species. Genetic data A cladogram for all 36 species of seabirds in this study was built using concatenated nucleotide sequences of mitochondrial genes cytochrome b (cyt b), 12S ribosomal RNA (12S), cyclooxygenase (COX), ATP synthase subunit 6 (ATP6), and NADH dehydrogenase subunit 2 (ND2), and nuclear gene recombination activating gene 1 (RAG1). Sequence data from 35 species were obtained from a previously published seabird study (Hughes & Page, 2007). Concatenated nucleotide sequences of the Hawaiian Petrel (Pterodroma sandwichensis) were also added to the initially published alignment (NCBI accession numbers: HQ420351-HQ42080, HQ918211-HQ918230, JF264905-JF26972, and JN015536-JN016231). Individual seabird species were then grouped by taxonomic order and aligned to produce a cladogram (Figure 1.3). The tree was constructed using the software program Geneious (Kearse et al., 2012) with a Tamura-Nei model under neighbor-joining tree build method, creating a consensus bootstrap tree with 1000 replicates and a 50% support threshold. Statistical analysis We applied principle components analysis and a linear mixed-effects model to the data, depending on the nature of the test variable, to understand each dispersal variable’s relationship to philopatric rates among seabird species. We also computed Pearson’s correlation coefficient, to measure the strength of variables to philopatry, and effect sizes (Cohen’s d) to facilitate 10 comparison across other studies and datasets. All statistical analyses were conducted in the statistical environment of R version 3.3.3 (R Core Team, 2013). The relationships of variables to rates of philopatry were measured using a linear mixedeffects model. All quantitative variables were transformed to meet the assumption of normality under linear regression using the skewness test for normality (Shapiro et al., 1968). A linear mixed-effects model was developed for the entire dataset and split by region, tropical and temperate, using R package ‘lme4’. The linear mixed-effect models allowed for the analysis of philopatry at multiple colonies across species. We designated each species as an individual group to account for potential bias due to the uneven sampling across seabird colonies. Random effects across colonies were grouped by species while explanatory variables were designated as fixed effects. The linear mixed-effects models allowed for variance-weighted averaging by species when determining significance between philopatry and explanatory variables. Multiple iterations of the model were performed, removing non-significant variables one at a time, to produce the model with the best fit. 11 RESULTS We evaluated whether extreme cases of behavioral philopatry in seabirds were simply in line with predictions based on colony demographics, or were indeed a paradox. Based on our study, data from banding and census studies suggested that philopatric rates are higher in the tropical region, the Procellariiformes order, and for non-central foragers. Our results indicated the seabird paradox holds true for some seabird taxonomic groups, but not others. When compared to the relative proportion of individuals in each colony, seabirds returned to their natal colony at higher rates than expected, with Procellariiformes, non-central foragers, and tropical region having a significant effect on philopatry (Table 1.2). Philopatry had a weak slope toward colony size (Figure 1.4). Under the model with the best fit, the significant independent variables explained 49.2% of the variance (R2=0.492) within philopatry rates. Philopatry was significantly higher in the Procellariiformes order than in the other seabird taxonomic orders (t464= 2.44, P = 0.0211; Figure 1.5). Additionally, seabird philopatry had a significant relationship with region (t464 = 2.766, P = 0.0100) and non-central foraging strategy (t464 = -2.40, P = 0.0229) (Figure 1.6). When split by region, the same dispersal factors, as well as colony size, were significantly associated with philopatry in the tropical seabird colonies (Table 1.3). For temperate seabird colonies, the only significant relationship to philopatry was that with distance to the closest colony (Table 1.4). The principle components analysis showed the variation found within the seabird dataset. We saw clustering by taxonomic order based on colony-level quantitative variables (wing load, philopatric rate, colony size, number of colonies, and distance from the natal colony) (Figure 12 1.7). The first principle component explained 41.8% of the variation within the dataset with colony size and distance having the highest correlation coefficients. 13 DISCUSSION We investigated whether behavioral philopatry in seabirds was based on colony demographics or a product of other mechanisms of dispersal. Although not directly proportional, colony size was positively correlated with philopatry rates. This suggested that relative colony size is an important consideration when making inferences about philopatric rates from single study locations, especially large colonies where people might be more inclined to conduct research. This may lead to the conclusion that high philopatric rates are more common in seabirds than actually true (Coulson, 2016). Both seabirds under the order of Procellariiformes and those exhibiting a non-central foraging strategy returned to their natal colony at higher rates than other seabirds orders (Charadriiformes, Suliformes, and Phaethontiformes) than those with a central foraging strategy. These variables explained 49.4% of variation within philopatric rates of seabirds, which was noteworthy considering the complex behavior of dispersal. As all Procellariiformes in the study were identified as non-central foragers, the results indicated a linked dispersal pattern to taxonomy and foraging behavior. Furthermore, our study validated previously published trends within the Procellariiformes and non-central foragers. Extensive banding records of Albatross families, in the Procellariiformes order, have exemplified the ‘seabird paradox’, showing over 99% return rates to some colonies (Weimerskirch et al., 1984; Fisher, 1976). Additionally, seabird restoration programs have had the highest success within the Procellaridae family, with translocated chicks returning at high rates to the new colony (Jones & Kress, 2011). The same relationship between philopatry, foraging strategy, and taxonomic order was found in tropical seabird colonies but not in the temperate seabird colonies, potentially due to the 14 low sample size of Procellariiformes within the temperate region (N=2). Furthermore, the Northern Fulmer (Fulmarus glacialis), one of the two species of Procellariiformes in our study surveyed in the British Isles, had a much lower philopatric rate (43%) than other species of Procellariiformes in the study. The Northern Fulmar underwent a recent expansion in the British Isles, suggesting other factors may have driven dispersal, such as a change in food availability, a genotype favoring range-expansion and colonization, or the gradual warming of the eastern Atlantic during the last century (Lloyd et al., 2010). Overall Procellariiformes had high philopatric tendencies, but there were exceptions within the order. Within temperate seabird colonies, philopatry was positively correlated with the distance to the closest colony. Most seabirds surveyed in the British Isles were central foragers with smaller home ranges, thus having less potential to disperse to colonies at far distances. Potentially, seabirds were more likely to return to their natal colony due to lack of available surrounding colonies within dispersal range. Additionally, other studies looking at seabird dispersal concluded a significant driver was the distance from the source colony, indicating distance may be an important factor for some species, but not all seabirds (Buxton et al., 2014; Oro et al., 2011; Hénaux et al., 2007). The higher philopatric rates in the tropical region than in temperate colonies may be slightly biased due to higher sampling of Procellariiformes in the tropical region. However, conditions in this region may favor Procellariiformes and philopatric behavior. The difference in predator composition of Hawai‘i, comprised of isolated oceanic islands, and the British Isles, made up of continentally-adjacent islands, is explained by the theory of island biogeography (MacArthur & Wilson, 2016). Seabird colonies in the Hawaiian Islands, hosting a chain of islands remotely isolated in the Pacific Ocean, formed by undersea volcanos, evolved without 15 connection to a continent and without mammalian predators (Stearns, 1946). In contrast, the British Isles were connected to Europe during the last glacial period by a plateau called Doggerland, allowing for the dispersal of mammalian predators (Montgomery et al., 2014). Hawaiian colonies may have a greater rate of return because they have historically provided a consistently predator-free or minimum-predation nesting habitat. Tropical waters surrounding Hawai‘i have fewer resources than those at high latitudes, but the benefit of nesting in Hawai‘i’s minimum-predation atolls may outweigh the risk, following the foraging/predation risk trade-off behavior (Verdolin, 2006). The risk of predation has been previously shown to influence population movement in terns and skimmers (Coulson, 2016). Additionally, breeding success has been negatively correlated with the abundance of predators (Oro et al., 2011; Smith et al., 2002; McChesney & Tershy, 1998). Other environmental drivers may also account for the difference in regional philopatry. Many of the seabirds observed in Great Britain and Ireland were coastal, inland, and roof nesters that breed in many locations within a county, allowing more options for dispersal in this region. Coastal seabird colonies tend to be more stable in occupancy than in inland colonies, most likely due to higher predictability regarding access to food in coastal colonies than inland colonies (Schreiber & Burger, 2002). In England and Wales, seabirds readily moved between inland sites, depending on factors such as disturbance and water levels, which may be responsible for the fluctuation in numbers at many colonies (Gribble, 1979). The British dataset contained a high number of inland colonies, while the Hawai‘i dataset had primarily coastal colonies, which may account for some differences in philopatry. Primary productivity near foraging grounds may also have driven dispersal patterns. In the tropical regions, adults invest less time in intensive chick guarding than temperate seabirds 16 due to longer foraging trips (Nelson, 1983). For example, Masked Boobies (Sula dactylatra) leave their young to forage for food as soon as they can thermoregulate. In comparison, temperate species like the Northern Gannet (Morus bassanus) guard offspring continuously until fledging. Also, tropical pelagic Pelecaniformes return to their chick with food less frequently than inshore temperate seabirds (Nelson, 1983). Due to the need to forage for longer durations in the tropics, there may be more pressure for seabirds to leave their young in predator-free colonies, where needs for parental protection are minimal. Similarly, migrating terrestrial mammals in the temperate regions shift migrate during warmer months to high altitude and latitude grazing grounds to give birth in areas of higher quality habitat and lower predation (Fryxell & Sinclair 1988, Sawyer et al. 2009). Foraging and predation risk trade-off behaviors found in herbivorous cervids may similarly be found within tropical seabird colonies (Geist, 1998). Thus, tropical colonies, for example, predator-free remote atolls, may hold higher quality seabird habitat, leading to increased population stability and higher rates of natal philopatry. We addressed some potential dispersal variables across seabird species in this study, but other confounding variables were likely at play. For example, the presence of other species of seabirds may have influenced philopatry. Congeneric species may act as a social attraction as observed within Short-tailed Albatross (Phoebastria albatrus) (Deguchi et al., 2012). In contrast, other seabird species may serve as competitors reducing philopatry. For examples, Wedge-tailed Shearwaters (Ardenna pacifica) outcompete Newell’s Shearwater (Puffinus newelli) for preferred burrowing space (Raine & Vynne, 2016) and Black-legged Kittiwakes (Rissa tridactyla) outcompete Common Guillemots (Uria aalge) for space during population booms (Durant et al., 2011). Additionally, sex may influence dispersal as seen in other long-lived species (Chang et al., 2014; Bowen et al., 2005). Studies have shown no sex difference (Milot et 17 al., 2008; Munilla et al., 2016) as well as male-mediated (Greenwood, 1980; Young, 2010) and female-mediated (Steeves et al., 2005; González-Jaramillo & Rocha-Olivares, 2011) within seabirds suggesting species-level variation. Furthermore, changes in food availability highly influence dispersal. For example, young Long-tailed Jaegers (Stercorarius longicaudus) have been known to move to new areas where there was higher food availability (Barraquand et al., 2014). Seabird populations are decreasing globally with many colonies vulnerable to climate change and land-use change, leading to potential ecological traps under continued philopatric behavior (Mitchell et al., 2004). Although philopatry in seabirds is complex, this study indicated that Procellariiformes, non-central foraging behavior, and tropical region may be responsible some of the underlying mechanisms driving natal site fidelity. Continued research on philopatry across multiple spatial scales within more seabird species is required to support these findings. Seabirds return to their natal colony at higher rates than expected based colony size, making seabirds fit candidates for successful translocation, especially those in threatened colonies. Our study suggests that translocation programs will be more successful for species in the order Procellariformes, as well as for species in tropical regions, and those with non-central foraging strategies, as these species are more likely to return to translocation sites. 18 Table 1.1. Summary of taxa analyzed (N=36), with colony and species associated attributes as well as average philopatric rates for species and range at colonies Species Sooty Tern (Onychoprion fuscatus) Gray-backed Tern (Onychoprion lunatus) Arctic Tern (Sterna paradisaea) Little Tern (Sternula albifrons) Common Tern (Sterna hirundo) Sandwich Tern (Thalasseus sandvicensis) White Tern (Gygis alba) Black-Headed Gull (Chroicocephalus ridibundus) Common Gull (Larus canus) Herring Gull (Larus argentatus) Lesser Black-backed Gull (Larus fuscus) Great Black-backed Gull (Larus marinus) Black-legged Kittiwake (Rissa tridactyla) Brown Noddy (Anous stolidus) Black Noddy (Anous minutus) Razorbill (Alca torda) Atlantic Puffin (Fratercula arctica) Black-footed Albatross (Phoebastria nigripes) Laysan Albatross (Phoebastria immutabilis) Tristram’s Storm Petrel (Oceanodroma tristrami) Wedge-tailed Shearwater (Ardenna pacificus) Newell’s Shearwater (Puffinus newelli) Christmas Shearwater (Puffinus nativitatis) Wing Load Number of Colonies Observed Percent Philopatric Range of Philopatry at colonies Order Region Foraging Strategy Charadriiformes Tropical Central 2.27 11 0.82+/- 0.69 – .96 Charadriiformes Tropical Central 4.66 9 0.91+/- 0.88 – 0.97 Charadriiformes Tropical Central 2.81 92 0.72+/- 0–1 Charadriiformes Temperate Central 2.27 15 0.42+/- 0–1 Charadriiformes Temperate Central 16.38 21 0.18+/- 0 – 0.88 Charadriiformes Temperate Central 1.61 21 0.20+/- 0–1 Charadriiformes Tropical Central 1.49 11 0.93+/- 0.79 – .99 Charadriiformes Temperate Central 4.34 58 0.75+/- 0–1 Charadriiformes Temperate Central 6.84 66 0.63+/- 0.33 – 0.76 Charadriiformes Temperate Central 5.42 100 0.64+/- 0–1 Charadriiformes Temperate Central 1 31 0.50+/- 0–1 Charadriiformes Temperate Central 10.78 65 0.55+/- 0.08 – 0.82 Charadriiformes Temperate Central 1.89 90 0.72+/- 0 – .95 Charadriiformes Tropical Central 9.29 12 0.88+/- 0.57 – .91 Charadriiformes Tropical Central 16.33 12 0.77+/- 0 – .91 Charadriiformes Temperate Central 10.65 51 0.84+/- 0.14 – 0.96 Charadriiformes Tropical 1.5 37 0.92+/- 0.38 - 1 Procellariiformes Tropical 15.89 10 0.98+/- 0.35 - .99 Procellariiformes Tropical 4.43 6 0.98+/- 0.05 – .99 Procellariiformes Tropical 1.55 6 0.97+/- 0.8 – 1 Procellariiformes Tropical 3.88 18 0.96+/- 0.83 – 1 Procellariiformes Tropical 4.74 4 0.88+/- 0.87 – 0.87 Procellariiformes Tropical Central NonCentral NonCentral NonCentral NonCentral NonCentral NonCentral 1.49 18 0.91+/- 0.89 – 1 19 Bulwer’s Petrel (Bulweria bulwerii) Manx Shearwater (Puffinus puffinus) Northern Fulmar (Fulmarus glacialis) Bonin Petrel (Pterodroma hypoleuca) Hawaiian Petrel (Pterodroma sandwichensis) European Shag (Phalacrocorax aristotelis) Great Cormorant (Phalacrocorax carbo) Brown Booby (Sula leucogaster) Masked Booby (Sula dactylatra) Red-footed Booby (Sula sula) Northern Gannet (Morus bassanus) Great Frigatebird (Fregata minor) Red-tailed Tropicbird (Phaethon rubricauda) Procellariiformes Tropical Procellariiformes Temperate Procellariiformes Temperate Procellariiformes Tropical NonCentral NonCentral NonCentral NonCentral Procellariiformes Tropical Suliformes 2.18 11 0.95+/- 0.89 – 1 2.27 15 0.88+/- 0.64 – 1 10.78 65 0.43+/- 0 – 0.61 1.61 18 0.99+/- 0.8 – 1 NonCentral 6.44 8 0.79+/- 0.82 – .82 Temperate Central 19.51 52 0.72+/- 0.09 – .82 Suliformes Temperate Central 4.41 75 0.25+/- 0 – .83 Suliformes Tropical Central 3.04 6 0.89+/- 0–1 Suliformes Tropical Central 11.59 11 0.95+/- 0.67 – 0.98 Suliformes Tropical 6.41 12 0.83+/- 0.62 – .92 Suliformes Temperate 21.72 69 0.15+/- 0 – .70 Suliformes Tropical Central NonCentral NonCentral 1.37 11 0.42+/- 0 – .6 Phaethontiformes Tropical Central 6.88 17 0.94+/- 0.89 – 1 20 Table 1.2. Dispersal variables from all seabird taxa (N=36) with comparison to rate of philopatry using a linear mixed-effects model Dispersal variable Seabird Species from Temperate and Tropical Colonies x̄ ± SE t-value P Effect Size Number of Colonies 4667 ± 1.394e-05 t464=-0.843 0.4082 d=1.737 r=-0.311 Colony Size Distance to Closest Colony 0.204 ± 6.159e-03 t464=1.715 0.0870 d=-0.221 r=0.162 -0.5342 ± 2.446e-01 t464=1.511 0.1314 d=-4.445 r=0.325 1.639 ± 4.795e-02 t464=0.372 0.7126 d=1.754 r=-0.021 1 ± 1.569e-01 t464=-2.402 0.0229* d=1.897 - Wing Load Foraging Strategy(1) a b Pearson’s r Taxonomy(2) 2 ± 2.142e-01 t464=0.712 0.4816 d=1.461 b Taxonomy(3) 3 ± 1.224e-01 t464=-0.528 0.6023 d=1.461 Taxonomy(4)b 4 ± 1.824e-01 t464=2.438 0.0211* d=1.461 c Region(1) 1 ± 8.809e-02 t464=2.766 0.0100* d=1.926 a 1 = Non-Central Foraging Strategy, 2 = Central Foraging Strategy b Taxonomy: 1= Charadriiformes, 2= Phaethontiformes, 3=Suliformes, 4=Procellariiformes c 1 = Tropical, 2 = Temperate *number of colonies, wing load, distance to closest colony, and colony size percentage were transformed to meet assumptions of normality. 21 Table 1.3. Dispersal variables from seabird taxa in tropical colonies (N=19) with comparison to rate of philopatry using a linear mixed-effects model Dispersal variable Seabird Species for Tropical Colonies x̄ ± SE t-value P Number of Colonies 121 ± 0.0004 Colony Size Distance to Closest Colony Wing Load Foraging Strategy(1) a Effect Size Pearson’s r t102=-1.540 0.1488 d=2.394 r=0.016 1.047 ± 0.0108 t102=2.078 0.0405* d=0.170 r=0.140 -0.474 ± 0.5069 t102=-1.344 0.1823 d=-6.467 r=-0.056 1.570 ± 0.0614 t102=-2.148 0.0557 d=1.244 r=-0.118 1 ± 0.1752 t102=-3.288 0.0076* d=1.704 - Taxonomy(2) b 2 ± 0.1965 t102=2.057 0.0636 d=2.315 - Taxonomy(3) b 3 ± 0.2111 t102=1.627 0.1368 d=2.315 - b Taxonomy(4) 4 ± 0.1421 t102=3.826 0.0026* d=2.315 1 = Non-Central Foraging Strategy, 2 = Central Foraging Strategy b Taxonomy: 1= Charadriiformes, 2= Phaethontiformes, 3=Suliformes, 4=Procellariiformes *number of colonies, wing load, distance to closest colony, and colony size percentage were transformed to meet assumptions of normality a 22 Table 1.4. Dispersal variables from seabird taxa in temperate colonies (N=17) with comparison to rate of philopatry using a linear mixed-effects model Dispersal variable Seabird Species from Temperate Colonies x̄ ± SE t-value P Effect Size Pearson’s r Number of Colonies 5950 ± 0.00002 t361=-1.010 0.3365 d=2.526 r=-0.0231 Colony Size Distance to Closest Colony -0.036 ± 0.11016 t361=0.865 0.3877 d=-0.359 r=0.0389 -0.551 ± 0.27943 t361=2.161 0.0313* d=-4.998 r=0.1269 1.658 ± 0.07219 t361=1.365 0.2011 d=1.929 r=0.0314 1 ± 0.24711 t361=-1.044 0.3189 d=2.396 - 3 ± 0.28667 t361=-1.458 0.1761 d=1.370 - Wing Load Foraging Strategy(1) Taxonomy(3) b a b Taxonomy(4) 4 ± 0.19321 t361=1.037 0.3216 d=1.370 1 = Non-Central Foraging Strategy, 2 = Central Foraging Strategy b Taxonomy: 1= Charadriiformes, 2= Phaethontiformes, 3=Suliformes, 4=Procellariiformes *number of colonies, wing load, distance to closest colony, and colony size percentage were transformed to meet assumptions of normality a 23 Percent Philopatry (a) Colony Size Percentage Percent Philopatry (b) Colony Size Percentage Percent Philopatry (c) Colony Size Percentage Figure 1.1: Graphs of hypotheses of predicted seabird philopatry as (a) proportional to colony size as percentage, (b) consistent with seabird paradox, and (c) following random distribution 24 Figure 1.2: Map of locations of all seabird colonies surveyed (N=465), with extent indicators on the Hawaiian archipelago (HI) and British Isles (BI) 25 Figure 1.3: Cladogram of all seabird species surveyed (N=36) by taxonomic order 26 Figure 1.4: Graph of colony size as a percentage of total population size (P=0.0870) compared to percent philopatry to all seabird colonies (N=465). Colony size percentage was log transformed to meet assumptions of normality. 27 Figure 1.5: Graph of colony size as a percentage of total population size compared to percent philopatry to colonies by taxonomic seabird order. Colony size percentage was log transformed to meet assumptions of normality. Procellariiformes were consistent with the seabird paradox (P=0.0211); However, colony size had a small positive effect on philopatry. The Suliformes and Charadriiformes (P=0.6023) showed no relationship between colony size and philopatry. Phaethontiformes were removed from final analysis because there was only a single species represented. 28 (b) Percent Philopatry Percent Philopatry (a) log (Colony Size Percentage) log (Colony Size Percentage) Figure 1.6: Graphs of colony size as a percentage of total population size compared to percent philopatry to seabird colonies by (a) region (P=0.0100) and (b) foraging strategy (P=0.0229). Colony size percentage was log transformed to meet assumptions of normality. 29 Figure 1.7: Principle component analysis using quantitative variables (wing load, colony size, number of colonies, and distance from the natal colony) from all seabird colonies (N=465) grouped by order 30 CHAPTER 2 POPULATION GENETICS OF THE BAND-RUMPED STORM PETREL (OCEANODROMA CASTRO), AN ENDANGERED AND ELUSIVE HAWAIIAN SEABIRD 31 ABSTRACT Philopatry, the behavior of returning to the natal site to breed, limits dispersal and presents a conundrum for genetic diversity. A higher degree of philopatry is found within Procellariiformes, a seabird order that comprises of albatross, shearwaters and petrels, storm petrels, and diving petrels. As a high rate of philopatry implies limited dispersal among populations, lower genetic diversity may be expected assuming equilibrium, within Procellariiformes compared to Charadriiformes, a seabird order that includes terns, gulls, and auks that are not considered to be philopatric. Furthermore, species within Procellariiformes that have experienced bottlenecks and thus lowered effective population size would be expected to have even lower genetic diversity. In this study, we evaluated genetic diversity measures in the only known populations of the endangered Band-rumped Storm Petrel (BSTP; Oceanodroma castro), a species in the highly philopatric Procellariiformes order. We then compared published genetic diversity measures between species in two orders with divergent rates of philopatry, the Procellariiformes and Charadriiformes, to determine whether genetic diversity is significantly different between these groups. We utilized next-generation sequencing to evaluate patterns in genetic diversity in BSTP populations on the only two confirmed islands to host breeding populations. Results indicated moderate genetic differences between populations and higher genetic diversity than expected. Furthermore, we found no difference in genetic diversity between species in the order Procellariiformes and those in Charadriiformes, contrary to predictions based on significant differences in philopatric rates. Although species may be behaviorally philopatric, the variation within genetic diversity within Procellariiformes indicated that they are not genetically philopatric. This may be explained by sex-biased gene flow or other 32 mechanisms of complex population structure. Findings from this study may be used to inform seabird conservation efforts, especially those managing genetic diversity and connectivity of threatened colonies. 33 INTRODUCTION Philopatry, the behavior of returning to the natal site to breed, may provide predictable resources, mates, and safety, but also increases the potential for inbreeding, competition, and ecological traps if habitat has since been degraded. Highly philopatric species, which limits migration, may thus have lower genetic diversity than related species with higher rates of gene flow (Mitton, 2001). A significantly higher rate of philopatry is found in species in the order Procellariiformes, compared with other seabirds (Chapter 1). Philopatry has been found to influence genetic patterns in some Procellariiformes (Ovenden et al., 1991; Levin & Parker, 2012; Welch, 2011; Milot et al., 2008). As philopatry limits dispersal between populations, lower genetic diversity would be expected within Procellariiformes compared to Charadriiformes (Chapter 1). Species that have undergone historical bottlenecks, significantly decreasing population size, are more likely to have decreased effective population and have even lower genetic diversity (Avise, 2012). Effective population size is the number of breeding individuals in a population while population size the number of individuals in a population. Given a high rate of philopatry, combined with a historic bottleneck, we would predict that endangered species in the order Procellariiformes would have lower measures of genetic diversity than other species without these risk factors (Mitton, 2001; O’Brien, 1994). The Band-rumped Storm Petrel (BSTP; Oceanodroma castro), an endangered seabird in the order Procellariiformes that nests on the Main Hawaiian Islands, is predicted to have low genetic diversity based on its Endangered status, an assumed low population number due to low rates of detection, presumed historical population loss based on previous records and presence in midden sites across the Main Hawaiian Islands 34 (Pyle & Pyle), and an expected high rate of philopatry similar to closely related species (Friesen, 2015). The endangered BSTP is among the least commonly observed nesting seabirds in Hawai‘i, and therefore difficult to study, with only two confirmed burrows known in the Hawaiian Archipelago (Galase et al., 2017). One of the only tools currently available to assess the remaining individuals is those of genetics. With potentially only a limited number of individuals remaining (Pyle & Pyle, 2017), the populations may have problems normally associated with small numbers, including demographic stochasticity and inbreeding (Kennedy, 2009). In this study, we sampled 10 of the remaining individuals (five per island) to evaluate patterns in genetic diversity and connectivity between the two confirmed island populations in the Hawaiian Archipelago. The objectives of this study were to: (1) evaluate genetic diversity in an endangered species, the BSTP, with a high rate of philopatry and potentially recent decline (Pyle & Pyle, 2017); (2) develop microsatellite markers useful for population-level studies, to complement nuclear and mitochondrial markers developed for a global study of BSTP (Smith, 2007); (3) compare genetic structure of BSTP populations in the Hawaiian Archipelago to global populations of O. castro; and (4) compare genetic diversity between species in the orders Procellariiformes with Charadriiformes, to evaluate predictions based on differing rates of philopatry between these groups. 35 METHODS Sample collection Source populations of BSTP included slot canyons along the Honopu Valley and Waimea Canyon on the island of Kaua‘i and breeding areas on the Pōhakuloa Training Area (PTA) on the island of Hawai‘i, the only two confirmed breeding populations in the Hawaiian islands (Figure 2.1). Kaua‘i and Hawai‘i islands represent the northern and southern extent of the main Hawaiian Islands and are approximately 300 miles apart (Figure 2.1). Blood samples from the metatarsal vein from individuals on Kaua‘i were collected by the Kaua‘i Endangered Seabird Recovery Project, a Hawai‘i Department of Land and Natural Resources, Division of Forestry and Wildlife project, from birds captured using conspecific playback and mist-netting techniques. Blood samples from one BSTP at Porters Landing Zone (LZ), Waimea Canyon and four individuals from Honopu, Kaua‘i were stored on filter paper. One other sample, supplied by the Kaua‘i Endangered Seabird Recovery Project, was a blood sample collected from a downed fledgling from Kaua‘i. Samples from individuals on Hawai‘i island were collected by the PTA Natural Resources Office, using dog and personnel searches. Samples included flight feathers from five individuals, collected from individual carcasses or found near nest sites, from the southeast portion of PTA. Laboratory analyses DNA was individually extracted from the blood and feather samples using the DNeasy Blood and Tissue Kit (Qiagen, Valencia, CA) according to the manufacturer’s protocol. The extracted DNA was quantified with the AccuClear™ Ultra High Sensitivity dsDNA Quantitation 36 Kit (Biotium, Hayward, CA) using two rows of eight standards. Due to low DNA yield, whole genome amplification was performed on individual samples with the REPLI-g UltraFast Mini-kit (Qiagen, Valencia, CA) which effectively increases yields of high-fidelity DNA (Ahsanuddin et al., 2017). Equimolar amounts of whole genomic DNA extracted from five blood samples from Kaua‘i and five feather samples from Hawai‘i island were pooled by their respective population (Kaua‘i, Hawai‘i) using the ezRAD protocol (Toonen et al., 2013) version 2.0 (Knapp et al., 2016). The two pooled libraries were then digested with the frequent cutter restriction enzyme DpnII from New England Biolabs® (Ipswich, MA) and fragments between 300 and 700 bp in length were prepared for sequencing on the Illumina®MiSeq using the Kapa Biosystems (Wilmington, MA) Hyper Prep kit. Laboratory work was conducted at the Hawai‘i Institute of Marine Biology (HIMB) in the ToBo Lab at Coconut island in Kāne‘ohe Bay, O‘ahu, Hawai‘i. Libraries were sent to Genetics Core Facility at HIMB and sequenced on the Ilumina®MiSeq platform. Genetic data analyses Next-generation sequencing resulted in a total number of 9,266,904 paired-end reads. The dDocent pipeline (Puritz, et al., 2014) was applied using the command-line environment in a Unix-based system to assemble loci and detect single nucleotide polymorphisms (SNPs) within the aligned sequences. Trimmed alignments were transformed to be run in Popoolation1 (Institute of Population Genetics, Vienna, Austria) and Popoolation2 (Institute of Population Genetics, Vienna, Austria), using a sliding window analysis for measures of diversity including fixation indices (FST), nucleotide diversity (π), Watterson estimator of diversity (θ), and Tajima’s D (DT). 37 The number of SNPs shared between populations ranged from 4,672 at a minimum coverage depth of 4x to 75 at a minimum coverage of 60x and maximum coverage of 200x. For the subsequent analyses, we used a minimum coverage cutoff of 10 reads per nucleotide position, which resulted in a total of 1,431 shared SNPs with 10 – 200x read coverage. We then calculated genetic diversity indices based on 1,970 SNPs within the Hawaiʻi Island population and 36,572 SNPs within the Kauaʻi population, utilizing Popoolation1 with a 10 – 200x read coverage. We calculated fixation indices based on all 1,431 SNPs shared SNPs utilizing Popoolation2. The discrepancy between the number of SNPs identified in each population, Hawaiʻi Island (1,970 SNPs) and Kauaʻi (36,572 SNPs) may be due to the state of initial sampling material or population demographics. Samples from Hawaiʻi Island (N=5) included slightly degraded feather samples that were found near nests in the colony. Samples from Kaua‘i Island (N=5) included fresh blood samples collected from live birds. DNA extracted from feathers has been found to be more questionable in quality than DNA extracted from blood samples, which may account for the difference in observed SNPs (McDonald & Griffith, 2012). Another explanation is that Kaua’i’s population is expected to have more individuals (Pyle & Pyle, 2017) and thus a higher effective population size which is more likely to have a higher number of SNPs within the population, causing the discrepancy. Microsatellite identification was carried out using the pal_finder Perl script on the RADseq reads which were filtered for several parameters: include loci with designed primers; exclude loci where the primer sequences occurred more than once in the reads; only include loci with ‘perfect’ motifs; and rank by motif size (Castoe et al., 2012). Default parameters included optimal 50% GC percent for primers, 18 bp as the minimum length of primer, and an optimal melting temperature of 60 degrees Celsius to identify at minimum six perfect dinucleotide repeat 38 units. PANDAseq was also utilized to filter out the best quality microsatellite in the reads. PANDAseq identifies ‘perfect’ microsatellite markers as those with highest-quality bases, checked for sequencing errors, and that contain suitable overlap (Masella et al., 2012). Mircosatellites were not scored in this study but instead identified for the use in future studies. To compare relationships among global populations with populations in this study, NGS sequences for populations in the Hawaiian Archipelago were aligned to sequences from global populations of O. castro accessible in GenBank (accession numbers: KU217330-KU217328, KU863867-KU863946, KU863779-KU863846, KU863964-KU863985) using Geneious 6.0 (Biomatters, Newark, NJ). Construction of phylogenetic trees was carried out with MEGA7 (Kumar et al., 2016) using the Hawaiian Islands population along with those available in GenBank (Azores, Galapagos, and Japan) (Fig. 2.2). To isolate sequences from mitochondrial regions cytochrome b (cyt b) and control region (CR) for comparison to other population genetics studies, our next-generation reads were aligned to previously published cytochrome b and control region I & II sequences (accession numbers: KU217339 & AY600297) in Geneious 6.0 (Biomatters, Newark, NJ). Overlap included a fragment of cytochrome b (177 bp) and a fragment of control region I & II (236 bp). Mitochondrial DNA fragments of cytochrome b and control region I & II were concatenated, forming a 416 base pair region that was analyzed for population genetic diversity statistics using TASSEL (Bradbury et al., 2007). TASSEL was implemented as it was designed to analyze isolated genetic sequences, similar to other mitochondrial marker studies. Other seabird mitochondrial studies 39 Population diversity statistics were compared between species in the seabird orders Charadriiformes and Procellariiformes. Following a literature search of Web of Science, Google Scholar, and OneSearch Mānoa, 44 published papers were identified that contained genetic diversity measures for species in the seabird orders Charadriiformes and Procellariiformes. Population genetics studies were included if they reported nucleotide diversity (π) population statistics and surveyed a minimum of two populations. Majority of seabird population genetics studies were based on mitochondrial genes. Only studies that included the mitochondrial control region and cytochrome b were chosen to limit mutation rate bias. Some studies only supplied a combined average nucleotide diversity for sequences from both the mitochondrial control region and cytochrome b. In studies that gave estimates individually, the average diversity estimate was used to keep with consistency. We employed an independent samples t-test in the R computational environment version 3.3.3 (R Core Team, 2013) to compare the means of nucleotide diversity between two seabird orders, Charadriiformes and Procellariiformes. 40 RESULTS O. castro population genetics We identified an FST of 0.108 (P=0.943) between the two extant Hawaiian island populations, based on 1,431 SNPs. Tajima’s D (DT) was negative for both Kauaʻi (-0.112 ± 0.015) and Hawaiʻi Island (-0.103 ± 0.022). A negative Tajima’s D (DT) indicated an excess of rare alleles within a population. Kaua‘i island population had a slightly lower nucleotide diversity (π) (0.003 ± .001) and Watterson estimator (0.004 ± .001) than the Hawaiʻi island population nucleotide diversity (π) (0.004 ± .001) and Watterson estimator (θ) (0.005 ± .001; Table 2.1). The concatenated mitochondrial fragments of cytochrome b and control region I & II (416 bp) aligned with 13 sequences from this study. Based on these sequences, nucleotide diversity (π) was found to be higher in the mitochondrial regions (0.079), than in the regions across the entire genome as sampled in the next-generation sequencing alignments (Table 2.1). The Watterson estimator (θ) was also higher in the mitochondrial regions (0.132) than across the entire genome (Table 2.1). Tajima’s D (DT) was found to be negative in the mitochondrial region (-0.456) (Table 2.1). This strong of a negative Tajima’s D (DT) indicated an excess of rare polymorphisms, that may be generated by population expansion, selection, or most likely high mutation rate in the species (Wares, 2010). Microsatellite marker identification 41 Microsatellite discovery through pal_finder resulted in 6,451 microsatellite loci with generated primers. Those loci filtered through PANDASeq resulted in 153 microsatellite markers with identified primers (S1). Global O. castro phylogenetic relationships among groups The phylogenetic reconstruction of global populations of O. castro (Azores, Japan, Galapagos, and Hawai‘i) was constructed with concatenated alignments of two nuclear markers and one mitochondrial marker using a bootstrap Neighbor-joining Tajima-Nei model with 1000 replicates. The method created a genetic distance matrix based on the number of nucleotide substitutions between each group: Azores, Japan, Galapagos, and Hawai‘i. The phylogenetic tree resulted in clustering of Japanese and Galapagos populations with a bootstrap value of 72. Samples from Japanese and Galapagos populations were more genetically similar to each other than to populations from the Hawaiian Archipelago (Figure 2.3). Populations from the Azores did not cluster to any other population with a significant bootstrap value. Comparison of genetic diversity between seabird orders We found 44 seabird population genetics studies that published nucleotide diversities statistics within the control region and cytochrome b, resulting in a comparison of 23 species of Charadriiformes and 21 species of Procellariiformes in this study (Table 2.2 & 2.3). Due to the skewness of the data, π values were log transformed to produce a normal distribution assessed with a Shapiro-Wilk normality test. Mean nucleotide diversity (π) was not significantly different between Charadriiformes (0.007 ± 0.005) and Procellariiformes (0.010 ± 0.012) (P = 0.811, t43 = 0.241). 42 DISCUSSION Despite their Endangered status with a potentially low population size and philopatric tendencies, we found BSTP in the Hawaiian Archipelago had relatively high genetic diversity and structure given the assumed life history. Furthermore, we did not find behavioral philopatry to have a significant effect on genetic diversity, suggesting other mechanisms were driving genetic diversity within seabirds. We note that caution should be used when interpreting population genetics statistics for only two populations and especially when inferring biology without demographic history, as FST is best interpreted in a comparative, or relative, context (Marko & Hart, 2011). However, an FST greater than 0.05 between these two islands, located at the northern and southern reaches of the main Hawaiian Islands, suggested moderate differences among populations (Hartl & Clark, 1997). Previous RADseq analysis on these species globally indicated distinct population genetic groups with having at least an FST of 0.14 (Taylor, 2017). Nevertheless, an FST of 0.108 was higher than expected between two Hawaiian islands 300 miles apart compared to an FST of 0.14 between Japan and Hawai‘i approximately 4,000 miles apart (Taylor, 2017). Under equilibrium Mills and Alendorf (1996) argue it only requires up to 10 migrants to have an FST of 0, thus an FST of 0.108 indicated fewer than 10 migrants a generation within BSTP in the Hawaiian Islands. Although results were based on a small sample size (N=10), other studies have shown even with a small sample size (i.e. two individuals), accurate estimates of FST can be obtained with a large number of SNPs (≈ 1,500) from RADseq data (Nazareno et al., 2017). Nucleotide diversity estimates within the BSTP Hawaiian island populations were higher than expected based on nucleotide diversity estimates from global populations of BSTP mtDNA, 43 other Procellariiformes mtDNA, and other seabirds gDNA. Nucleotide diversity of mtDNA for populations of O. castro in the Hawaiian Archipelago (π=0.079) was higher than the average for the global species (π=0.013; Smith, 2007), as well as higher than other Procellariiformes (mean π=0.010; this study). Although we note caution when interrupting mtDNA genetic diversity measurements because the low coverage generated from the NGS data overlap. Nucleotide diversities based on gDNA of O. castro in the Hawaiian Archipelago were higher than those found in some studies of other seabird species (Tigano et al., 2017; Dierickx et al., 2015) but not all (Clark, 2017). Moderate rates of nucleotide diversity appeared to be present despite their Endangered status and presumed low population size of the BSTP in the Hawaiian Islands. Other recent studies found lower genetic differentiation metrics based on a NGS SNPs approach (FST or ΦST range: 0.009 - 0.33) compared to those calculated based on only mitochondrial sequences (FST or ΦST range: 0.44 – 0.91) for the same species, which was consistent with our findings (Taylor, 2017; Clark, 2017; Tigano et al., 2017; Dierickx et al., 2015). Most RADseq studies find lower values due to sampling across the whole genome which includes both areas of high and low polymorphism, while marker approach studies focus on a few chosen isolated sequences, like the mitochondrial genes, which are often more polymorphic (Cariou et al, 2016). Although we found a difference in magnitude between gDNA and mtDNA for O. castro, both produced similar trends in genetic diversity (π, DT, θ) (Table 2.1). Although there was moderate genetic differentiation in the populations of BSTP in the Hawaiian Archipelago, a higher genetic structure was found between island archipelagos of this species, as evidenced mitochondrial, microsatellite, and ddRAD analysis (Taylor, 2017). Furthermore, the constructed phylogenetic tree placed samples of the Hawaiian Archipelago 44 separately from other previously sampled global populations (Galapagos and Japan), which supports the finding that populations from the Hawaiian Islands are genetically less similar to those populations of O. castro found globally. Our results supported previous findings identifying O. castro in the Hawaiian Islands as a distinct genetic group (Taylor, 2017), but we note caution when interrupting the phylogentic tree (Figure 2.3) as it only consisted of three markers with low coverage due to the broadened use of NGS data in comparison to published markers. Past research has shown genetic structure within seabird species was less a result of philopatric tendencies, but instead linked to foraging range and non-breeding distribution (Friesen et al., 2007). Genetic differentiation between global populations may be driven by ocean currents and at-sea food abundance (Friesen et al., 2007; Taylor, 2017). Foraging strategy has been correlated to population genetic structure within other marine organisms that rely on cold-water upwelling systems (Cassens et al., 2005, Schlosser et al., 2009, Jeyasingham et al., 2013). Ocean patterns influence foraging strategies, and seabirds exhibiting a local foraging strategy may be more likely to return to breed within their natal archipelago. Thus, low genetic differentiation within Procellariiformes within a focal archipelago may be due to complex population structure (Bowen et al., 2005). Spatially complex marine animals, like seabirds, mandate management to survey and conserve different units critical for population structure. Although species in the order Procellariiformes are more philopatric on average than Charadriiformes (Chapter 1) nucleotide diversity estimates were not significantly different between seabird orders, in contrast to what has been predicted for highly philopatric species (Ovenden et al., 1991; Levin & Parker, 2012; Welch, 2011; Milot et al., 2008). Nucleotide diversity is driven by dispersal rate as well as the combined impact of effective population size, 45 mutation rates, and history, all of which are expected to vary across avian species and may be driving the variation within the nucleotide diversity estimates (Nabholz et al., 2009). Currently, of these three (dispersal rate, effective population size, mutation rate), mutation rates are debated to be the best predictor of within-species mitochondrial diversity (Nabholz et al., 2009). Although we only included nucleotide diversity estimates from two different genes in the mitochondrial genomes, these regions have different mutation rates (Friesen et al., 2007). Furthermore, in our comparison of seabirds orders we assumed species were at equilibrium, but if demographic impacts persisted, history may be a stronger force driving the observed patterns (Chan et al., 2014). Although seabird orders had significantly different philopatric trends (Chapter 1), other factors of migration may be greater drivers of genetic diversity. Banding records showed that no single Procellariiformes was completely philopatric, with some dispersal between colonies in all species (Chapter 1). This study’s results suggested only a small amount of gene flow is necessary, less than ten migrants per generation, to homogenize genetic structure throughout the metapopulation (Mills and Allendorf, 1996). Another possible explanation is that seabirds may mate while on visiting forays at neighboring colonies, increasing gene flow, as evidenced by female Laysan Albatross (Phoebastria immutabilis) in the Main Hawaiian Islands (Young et al, 2010). Within other philopatric species, it is known that they breed offsite, mating with males from other colonies, prior to returning to a natal site to lay eggs (Bowen et al., 2005). Thus, complex population structure for a species must be taken into account to interpret population genetics. There are many potential reasons for the high genetic diversity observed within populations of O. castro in the Hawaiʻi Archipelago. A population bottleneck likely occurred 46 since the arrival of humans to the Hawaiian Islands 1100 years ago, due to habitat destruction and introduced mammalian predators such as rats (Rattus sp.), cats (Felis castus) and mongoose (Herpestes javanicus) (Pyle & Pyle, 2017). In some cases, relatively high genetic diversity in a rare species can indicate that decline in population numbers was recent (Mortiz, 1994). A second possible explanation may be lengthy generation times, coupled with delayed sexual maturity, that can postpone the loss of genetic variation (Kuo & Janzen, 2004). The Band-rumped Storm Petrel is long-lived and has a generation time of 12 years, leading to overlapping generations (Harrison, 1990). In addition, demography can contribute to the retention of genetic diversity (Goossens et al., 2005). Relatively high genetic diversity despite population decline has been observed in other long-lived seabird species (e.g. the Balearic Shearwater Puffinus mauretanicus, Genovart et al., 2007; the Magenta Petrel Pterodroma magenta, Lawrence et al., 2017; Buller’s Albatross Thalassarche bulleri, Van Bekkum, 2006). Furthermore, even though the Band-rumped Storm Petrel most likely experienced population decline in Hawai‘i, there may be more individuals than currently estimated (Pyle & Pyle, 2017), as only a few hundred individuals are needed to capture all of the genetic diversity in population (Gaither et al., 2010). Further research examining these populations of O. castro is recommended to understand the connectivity between islands across the Hawaiian Archipelago. Assessment of genetic diversity on other islands and gene flow among islands, as well as in comparison with extinct colonies using historical samples, will facilitate understanding of the historical genetic context and allow for the creation of haplotype networks and effective population size estimates of the endangered populations. Furthermore, fixed SNPs identified between island populations in this study (N=23) may create valuable diagnostic genetic markers to address evolutionary history and 47 assign unknown or bycatch individuals to a known extant population. Research adding more individuals from multiple years of sampling will add to and support current findings. Despite their Endangered status and indication of population loss, this study suggests genetics of O. castro in the Hawaiian Islands currently do not warrant management concern. Although BSTP do not appear to be in current danger of a genetically induced extinction vortex, they remain vulnerable to other extinction vortexes (Gilpin & Soulé, 1986). Predator control and related management efforts to ensure successful nesting will be crucial to recover endangered populations of the Band-rumped Storm Petrel. 48 Table 2.1 Population genetic diversities for O. castro in Hawaiian island populations. gDNA refers to the RADseq alignments and mtDNA refers to the concatenated mitochondrial sequence of the control region and cytochrome b. Population DNA Region Nucleotide diversity (π ± sd) Watterson estimator (θ ± sd) Tajima’s D (DT± sd) FST Kaua‘i gDNA 0.004 ± 0.001 0.003 ± 0.001 -0.112 ± 0.015 Island 0.108 P=0.943 Hawai‘i gDNA 0.005 ± 0.001 0.004 ± 0.001 -0.103 ± 0.022 Island Kaua‘i & Hawai‘i mtDNA 0.079 0.132 -0.456 Island * standard deviation of mtDNA was not included because the software used to produce the genetics diversities, TASSEL, does not report them 49 Table 2.2 Comparison of mean nucleotide diversity within populations (π) from mitochondrial studies of species in the order Procellariiformes (N=21) Seabird Black-footed Albatross (Phoesbatris nigripes) Black-browed Albatross (Thalassarche melanophrys) Grey headed-Albatross (Thalassarche chrysostoma) Laysan Albatross (Phoebastria immutabilis) Family Marker Cyt b (609bp) π (range) 0.0005 (0.000.0012) Diomedeidae CRI (219bp) 0.02 (0.008-0.042) Diomedeidae CRI (220bp) 0.03 (.0022-0.040) Diomedeidae CRI (189bp) 0.045 (.030-.059) Northern Fulmar (Fulmarus glacialis) Procellariidae CRI (299bp) 0.0107 (0.0079.0139) Burg et al., 2003 Cory’s Shearwater (Calonectris borealis) Procellariidae 0.0132 (0.00230.024) Gómez-Díaz, 2007 Scopoli Shearwater (Calonectris diomedea) Procellariidae 0.0139 (0.00370.024) Gómez-Díaz, 2007 0.010 (0.0030.016) Gómez-Díaz, 2007 ≤ 0.004 Friesen et al., 2007 0.0144 (0.0015 – .0272) Genovart et al., 2007 0.0036 (.00080.00471) Lombal et al., 2017a 0.0005 (.0003.0007) Gangloff et al., 2013 0.0013 (.0011.0015) Gangloff et al., 2013 Diomedeidae Cape Verde Shearwater Procellariidae (Calonectris edwardsii) Sooty Shearwater (Puffinus griseus) Balearic Shearwater (Puffinus mauretanicus) Flesh-footed Shearwater (Ardenna carneipes) Procellariidae Procellariidae Procellariidae Deserta Petrel (Pterodroma deserta) Procellariidae Cape Verde Petrel (Pterodroma feae) Procellariidae CRI, Cyt b (1393bp) CRI, Cyt b (1393bp) CRI, Cyt b (1393bp) Cyt b, CRII (695bp) Cyt b, CR (1180bp) Cyt b (858 bp) Cyt b, CO1 (1604bp) Cyt b, CO1 (1604bp) 50 Paper Walsh & Edwards, 2005 Burg & Croxall, 2001 Burg & Croxall, 2001 Young et al., 2010 Zino’s Petrel (Pterodroma madeira) Procellariidae Providence Petrel (Pterodroma solandri) Procellariidae Magenta Petrel (Pterodroma magenta) Procellariidae Hawaiian Petrel (Pterodroma sandwichensis) Gould’s Petrel (Pterodroma leucoptera) Leach’s Storm Petrel (Oceanodroma leucorhoa) European Storm Petrel (Hydrobates pelagicus) White-faced Storm Petrel (Pelagodroma marina) Cyt b, CO1 (1604bp) Cyt b (872bp) Cyt b, CRI & II (1458bp) 0.0016 (.0009.0023) Gangloff et al., 2013 0.0093 (0.00690.0117) Lombal et al, 2017b 0.01387 (0.0013.0223) Lawrence et al., 2007 0.00397 (0.003820.00409) Procellariidae Cyt b (524bp) Procellariidae CRI, Cyt b (1327bp) 0.00115 (0.000630.0016) Hydrobatidae CRI, II (357bp) 0.0055 (0.0050.007) Bicknell et al., 2012 Hydrobatidae Cyt b (970bp) 0.0005 (0.00.0011) Cagnon et al, 2004 Hydrobatidae CRI, II (522 bp) 0.0075 (.004-.011) Silva, 2015 51 Welch, 2011 Iglesias-Vasquez et al., 2017 Table 2.3. Comparison of mean nucleotide diversity within populations (π) from mitochondrial studies of species in the order Charadriiformes (N=23) Seabird Ivory Gull (Pagophila eburnea) Lesser Black-backed Gull (Larus fuscus) European Herring Gull (Larus argentatus) Common Gull (Larus canus) Glaucous-winged Gull (Larus glaucescens) Glaucous Gull (Larus hyperboreus) Black-legged Kittiwake (Rissa tridactyla) Red-legged kittiwake (Rissa brevirostris) Family Marker π (range) 0.0016 (.0005.0027) 0.0028 (0.00180.0042) 0.0110 (0.0010.018) 0.0146 (0.010.024) 0.0137 (0.010.021) 0.0070 (0.0040.013) 0.0048 (0.0030.009) 0.0150 (.011.016) Paper Laridae CR (264bp) Laridae CRI, Cyt b (1573bp) Laridae CRI (430bp) Laridae CRI (430bp) Laridae CRI (430bp) Laridae CRI (430bp) Laridae CR I, II, III (773bp) Laridae CRI (445bp) Sooty Tern (Sterna fuscata) Laridae RFLP, CRI, II, III (1399bp) 0.0210 (.018.026) Peck & Congdon 2004; Avise et al., 2000 South American Tern (Sterna hirundinacea) Laridae Cyt b, ND2 (790bp) 0.0012 (0.00090.0017) Faria et al., 2010 Least Tern (Sternula antillarum) Laridae CR (840bp) 0.0050 (0.0010.007) Draheim et al., 2010 Common Murre (Uria aalge) Alcidae Cyt b (204bp) 0.0040 (0.00260.0053) Friesen et al., 1996a Thick-bulled Murre (Uria lomvia) Alcidae Cyt b (253bp) 0.0076 (0.0036.0156) Friesen et al., 1996a Razorbill (Alca torda) Alcidae CRI (300bp) Black Guillemot (Cepphus grylle) Alcidae CRII, III (504bp) 0.0130 (0.0093.0198) 0.0030 (0.000.0058) Pigeon Guillemot (Cepphus columba) Alcidae CRII, III (504bp) 0.0087 (0.0047.0170) 52 Royston & Carr, 2016 Liebers et al., 2004 Sonsthagen et al., 2012 Sonsthagen et al., 2012 Sonsthagen et al., 2012 Sonsthagen et al., 2012 Patirana, 2000 Patirana et al., 2002 Moum & Arnason, 2001 Kidd & Friesen, 1998 Kidd & Friesen, 1998 Xantus’s Murrelet (Synthliboramphus hypoleucus) Ancient Murrelet (Synthliboramphus antiquus) Marbled Murrelet (Brachyramphus marmoratus) Kittlitz’s Murrelet (Brachyramphus brevirostris) Crested Auklet (Aethia cristatella) Whiskered Auklet (Aethia pygmaea) Cassin’s Auklet (Ptychoramphus aleuticus) 0.0058 (0.0020.0094) Alcidae Cyt b (1045bp) Alcidae CRI, II, III, Cyt b (1132bp) 0.0042 (0.0040.0044) Pearce et al., 2002 Alcidae Cyt b, CRI (1592bp) 0.0070 (0.0028.0104) Friesen et al., 1996b; Friesen et al., 2005 Alcidae Cyt b (1045bp) 0.0024 (0.00170.0030) Friesen et al., 1996b Alcidae CR (408bp) Alcidae CR (670bp) 0.0140 (0.0120.019) 0.0052 (.002.007) Pshenichnikova et al., 2015 Pshenichnikova et. al., 2017 Alcidae CR (706bp) 0.0061 (.004.008) Wallace et al., 2015 53 Friesen et al., 2007 Main Hawaiian Islands Figure 2.1 Collection sites of the Band-rumped Storm Petrel (Oceanodroma castro) across the Main Hawaiian Islands 54 Figure 2.2 Global distribution of Band-rumped Storm Petrel (Oceanodroma castro) colonies (AS – Ascension, AZ – Azores, CV – Cape Verde, FR – Farilhoes, GA – Galapagos, HI – Hawai‘i Island, JP – Japan, KI – Kaua‘i Island, MD – Madeira) 55 Figure 2.3 Phylogenetic tree of global populations of Oceanodroma castro (nuclear and mitochondrial markers) bootstrap Neighbor-Joining Tajima-Nei model constructed using MEGA7. The bootstrap value of 72, indicates the percentage of replicate trees which the associated taxa clustered together in the bootstrap test (1000 replicates). The other values are the branch lengths in the same units as those of the evolutionary distances to infer the phylogenetic tree (number of base substitutions per site). 56 Summary This thesis represents a comprehensive examination of behavioral philopatry and patterns of population genetics across seabird taxa. Our research goal was to further understand behavioral and genetic philopatry within seabirds to inform conservation efforts. While some of the hypotheses presented in this research were supported, others contradicted our predictions. Seabirds provide an interesting challenge when studying population dynamics: many are highly philopatric but also highly migratory, which influence dispersal and gene flow in opposing directions. Within the exploration of behavioral philopatry through use of long-term banding studies, we found dispersal to be less correlated to colony size and population structure and instead linked to with foraging strategy, taxonomic order, and geographic region. Across seabird orders, Procellariiformes, a historically philopatric order, showed no difference in genetic diversity to those in the Charadriiformes, a less philopatric order. The Band-rumped Storm Petrel, an endangered and philopatric seabird, had higher genetic diversity and population structure than hypothesized in the Hawaiian Islands. Population genetics of the endangered O. castro suggested moderate gene flow possibly due past population size, high mutation rates, and complex population structure. Although BSTP populations in the Hawaiian Islands had relatively high genetic diversity, there are still a multitude of threats to BSTP, from introduced predators to climate change. This study suggests BSTP in the Hawaiian Islands do not appear to be in danger of a genetics induced vortex but remain vulnerable to other threats (Gilpin & Soulé, 1986). Consequently, management efforts should continue conservation actions at existing colonies and seek to expand potential predator-free refuges, like Lehua Islet (Raine et al., 2017), to increase population size within the Hawaiian Islands. 57 Appendix 1: Microsatellite markers (N=153) identified by PANDASeq using next-generation ezRAD reads from Oceanodroma castro Id 1 2 3 4 5 6 7 8 9 Forward Primer TGCCTCAGCTGTTTGTTTCC ACCAGGAAAATACCGAGGTCC GATCCCTGCATAGAGCCTGC CTCATCGTTTGATGAACCCG GATCTGAGGGGTTAGTGGCG CTTTCCATCCTCGCAATCG ACAGCCTGTCCTGAGGATGC TGTTCAGTTTATTGGCTTTTGCC CTTCACTTAAGGCCCATGCC 10 11 12 13 14 15 16 CAACACGCACTTGAGACAGC GATCCACCAAGAACCACACG TCGACGATTTGTGTGTGTGC ATGCCATTATCTTCACCGGG TAAGATGCTGGTCGGTCGG ATGGCCTTGCCAGAGATACC AGTCATGAAAAGGAAGGCCC 17 18 GACCCACGCAGTCAAATCC AACATTTACACATCGAGTAAGCA GC ATGCAGTACGGTGGCAAGG TCACGTTTGGAGGAAAAGGG GGCAATCCAACTCCAACTCC CAAAGCCTGTGTTGCTGAGG CCTAAGGGTAGGGATGTGGG AGTGAGTGACAGCCTGGACG GATGGTGGTGGATATGGACG CAAACACTTCCCACCCAACC CCAATGATAGACTAGACCGGCG TTGTTGGACACTGTCGAGGC GTGTGTTGCTGTGTTGCACG GGTCTGAACTCTCAAAATATACA GGC 19 20 21 22 23 24 25 26 27 28 29 30 Reverse Primer GCAGCATGGAGGGAGATACC ACACCCGTTATTGCTCAAAGG CCTGTAAACTGTCCCCTGGC AATTCATCCCATGTTTCACGC ACGTGAACATGAATGACGGG GAGGAAACGTGGAAAGGTGG GATCATCCCTTTGTCCTGGG GGCTGGATTTGAAGTGTGGG GGTGGTACCAGTAGTGGTTTAGTG G CAGATGTAGCTGACGTACGGG TGCTACCCTGTTTCAGCAGC AATATCAATCTGCCGAGCCC AAAACTGACTCCGCATTTTGC AAATCAACATGTGGCCTCCC CTATAAGACAGCACCCGCCC GCACAAGGAAAGAAAGTGTAGTA GG CAGAGTCTGCTTAGGATTCTCCC ACACTTCCAGAGGGGACCG ATATGGCAGTCACGGTCACG GATCCATGCTTGCTCCAGG ACCTGAAGAAAACGCTTGGG CTCCTATGTGTTGGGTCCGC GAAGACACTGGAGCTGTTGACC GTCTTCGGCGATACGAGCC ACATTCCGGAGTTGGTTTCC TGTGTGCATTTGTTTCTGCG TGAGGGTCTCACATGTTCGC TCAACACTGCCGTCTTCTCG CAGCTTGATGGACGTTCTGC TGGTGCATAAGTGTTCTCAATGG 58 Motifs (bases) TC(12) TC(12) TC(32) TC(14) AC(18) TC(22) AC(12) AC(12) AT(12) AC(20) AT(14) AC(14) TC(12) AC(26) AC(12) TC(16) TC(14) AT(14) AC(14) AC(12) TC(12) AC(12) AC(14) AC(14) AT(12) AC(12) AT(12) TC(12) AC(14) AT(12) 31 32 33 34 35 36 37 38 39 40 TGTACAGTTCGTTCGTCCCC CTGGCAGGGAGAATTATGGG TGTACTAGCCATTGCCTGCC GGTGCCTATAGTTGGCAGGC TGTGTCGGGTCTTGTATGGG CGAGTCTCACATTGGGCTCC CTACAGCCAATGGCAACACC GCAGAAACGAGAATGATGGG TTAACATAAAACCTCCCACCCC CCTTTGGCACAGAATGAAAGG 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 ACTGCAGCAGACACACAGGC GGTCGAGGATATGCCTTCCC TTTACCGATTCCGATGTGGC CACAATTTATCTTGCCCGCC CGGGGAACTAGTCATCGAGC GGGTAGATCGAGCAAATCCG ACCGCCCTTCTTCTACCTCC TGCTTGCCAGTTCAATTTCG ATGACACAGACTCGATGGGC GCATGGCCTTTTCACTGTCC TGCCACCAAGACTTCTTCCC ATGTTTGAGCCCCATGTTGG AGCTCTGATCTGCCCCTCC CACCATGTCACCACCAAAGC CCAGTCCTGTGACCATCAGC CAAATCCCTGCAATTTTCGG CAGTCGTGTAGCTTGCAGCC TTTTGGGGCAATTTCTATGG AATCCCAGTTGCAGACACCC GCTCACCGGGAACTATTTGG TCCTAAGGAATCTCGAGGCG CTCTGCGGAGTTGTGACTCG AGCCGAAGCCACAGTCTACC GAAAAGCTGCTCAACGCTCC GATCATGTGGTGAGATAAGCG CTTCTGATTGTCACGAGCGG GGAGACAGGAATGGGTGATGG CTCTCCCATCTTCTCCCAGG TGACTGCAGCAATCTTTGCC CGGGTGAGCTGAGATTAGGG ACTTTGCCTGCTGCATTTCC CACATTGCCTTTACAGTTACTGCC TCTGTCTCTTTGCAGACGGG CGTCCAGAGTTGTCCCTTCC CCTCGGAAGGAATCACTGACC TCCAACTGGCAGTCAGAACG TCTCTCTCTGCTGTGCCTGC GAGACCAAAATAGACAGGAGAAG AGC ATAGGATGTGTGGGCAAGGG TCACAGGAGAAGTCAAAGGGC CTCATGGATAAAGGACCGGG GAGATAGTCAAAGAGAGCCCTGC CGACGTTGTTCGAGCAGG CAAAGGATTGATTCCCTCCG GTTGGTCGTCGTACTGGTCG GGACTTTCCAGAAGTCGGGC AAATACGTCGGACAGTCGGG GTCTTCAAGGAGCCACTGGG GCTAGATGTCGTGCTCGTGG CTCCCTTGCTGTGTCATTGC AGCCTGCTTCTCTCTTTGCC ATGCCAACTCCTTGACCTCC CAGATGGACCAAACAGCAGC AAGAACTCACTTGGTGGCCC TTTGTTGGAGGTTGCTGTGG CTAATCCCTCCAAACCCTGG AATGAAATGGCACAGCATGG TAGCAAGTGCTCGTTCTCCG CTTCCCCTTCTTCCATCCC ACAAACCGACATACCCTGGC ATTCGGTATAGCGACCGGG TTCACCTCTGAGAGCATCCG ATGTGTGAGGGAACAGGAGC GCGAAACGGTTCTTCAAAGG GCTATAGGCTGTCTGTCAGTGGC GTCCAAGGGGAGAAGGAACC 59 AC(12) TC(12) AC(12) AC(14) TC(14) TC(24) AT(20) AC(12) AC(12) TC(28) AC(12) TC(12) AC(16) AC(12) TC(20) AC(12) AC(16) TC(12) TC(14) TC(20) TC(14) AC(30) TC(14) TC(12) TC(14) AC(12) AC(12) AC(12) TC(24) TC(12) TC(18) AC(14) AC(12) AT(12) AC(12) TC(14) AC(26) TC(12) 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 GCAGCCAAGAAGACACATGG GGAGGTCTGGGTTTCTGAGC CCGTATCCAAAACAAAACGC TTGCAAATTGCCCTTTATGG GAAGAATTTCCCAGCAATCCC CAACAGAAGCAGCTTGGACC ATCCTGAAGACCCAGGATCG CCCAGAGCTGAAGGGAAGG CACAACCGTGACATCGAACC TAGGTGCTCTGGCATTCTGC AACCTCAGCTTCCTGCTTGC CTTCCTTGTTCCACTTCGCC ATACCCCACACTCCGTCTCG GTACAAAGTGCCGTGCAAGC ACACGCAACGTACCACAAGG CACAGGTGAATTCAAGGGGC CACAAGACCATCGGATAGCG GGGTCTCTACAGTGCAGGGG TAGGAATGTGGCGATGATGG TAAACCCCACCTTCTGCTGG AGACCGTCAATGGGTCAAGG TGCACTAGAGCACACGTCCC GGCATACTCTGCAGCTCACG GACTCCATCAGCACCAAGAGC ACTCCAACACACCCTCTCCC ATCCAGAGAGCAAGCAAGCC CTTGCTTTTCTTCTCCTCTCCC GCCCATGTACATACCAGAAAGG AGAGAGGGGCTGAGCAGAGG GAAGCAATTTCAGGGCATGG TGAGCCCATTCAGAAGAAATCC TCATGCAAAAGAGGAGCTGG ACAAGCCACCCGTAGTCTCG AGCTCGGCATTTTCATTTGC AGACGAGATGAAAGCGGAGG TCGGGTAAGTATCATCCGGC CCAAGAAGAACAAGACCGCC TGCCAGAGATTACATACATGCC GGGCCTAGTTTGGTGAGAGC GGCGTAGGACATCAGTTCCC GATCGAAAAGGTTGCTTAACG TTGGGCTTAACTGGTGATTGG TTAGGTGGTCGAGTGGAGGG GAAAACGTTGTGCCCAATCC AAATTCTTTTAAGCACTGGGAAGC CATTCTTCCTGCCAACACCC GAATACGAATCCTCCAACAGCC AAGTCCAAAGCAGCCCTCC AAGTTGATTGCACGTCCTCG GCGTCCTTGTTAATGTGCGG CTTTTGTCCTGCAAACACCC CAGGAGACTTGGTGCATTGG GACTCGCTTCTCACCGTTCC AGCTCGTCAACCTTCATGGG TTACAGCAAATTCGGCAAGC GAAGACGGCAGCAAAGAACC ATCTCTCCGTGAGTGCCAGC CTGCTGGAGAGGCTTTGAGG GAAGACGACAGTGAAAGGCG AGCCTCTCTCACAAGCCAGC ATTCCGACACTCCTTGTTGC TCAGGCATTAAACAGCCTCG TCAGCAGAGACAAGGCTTCG TGTTTGTGTTTCGGTCTGGC AATGGCTCAGGTTCTGCTGG ACTCGCCAAAATGAATCACG GACAGATCCCTTGGTCCTGC CACAGGTCGTTCGAGTGCC AAGCTGGCTGCTCAAGTTACG CATTGATGGCGACTTTCAGC AACCACGCCAAAAGACAAGG CGTATGGGCTGTGAGAGACG CGGTGTCCTCAAGACCTTCC TCCAAAGATGATGACTGGCG TGGTCAACACAGCACCAGC AGCTGCTCTGATACCTGGGG 60 AC(16) AT(18) TC(18) AT(14) AT(18) AC(26) TC(18) AC(22) TC(14) AT(12) AC(12) AC(12) TC(14) AC(14) AC(12) CG(12) TC(22) AC(12) AC(16) AC(12) TC(14) AC(12) AC(12) TC(18) CG(16) AC(16) TC(14) AC(14) AC(22) AC(12) TC(12) AC(12) TC(14) AC(12) TC(12) TC(12) AC(12) AT(14) 107 CATCGCATGATGTTTTCACG 108 109 AAACCGGGTGTTGGGAGG AAATACTCCCCACACACCCC 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 GCTTCTGGGAAGAAAGCTCC TTGTACGATGGCCTCTCTCG AGACAAGCCATTGTTTGGGG CTCATGCATTGAACAAGCCC GATCCAGTCCTGCATAGGGC GACGCGTTAGCTGGAGAAGG CGTGAACATCACCACCAACC AAAGTTCATTCATGACGGCG TTTGTCTGTGATTACTGCTGCG AAAATCCAATCCCCTGTCCC CTGGAACTCTGACGCGACC GTTGGATTGCATTTGTTCGC GATCCAGTCCTGCATTGGG CTTCTCTCGCTGCGTCTTGG GCATCTGCGAAATGAACAGC TCAGCTGCTGTCTCTTTGCC 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 CCTGAGATCATGACCTGGACC CAACATCCCCAGATCTCTCTCC GGCTGTTAGAGATGAGCCACG TTTGAGGAGCGAGAAATGGC GGTTCCCTTTACCCCTCACC GATCTGTCATCGGTGGATGG GATTAAGTACTGCCTGTGTCTCT GC AACATCAACAACCCAGGAACG ACAGACGAGGACGACACAGG TCCACAATTCGCCAGTTCG TCATGTCCGAACAGCATCG ATGGTGGCATAGACAAAGCG GCTTAGCCGGACAGTTTTGG CGTAGGATGGAGAGAAGGCG TGTATCTCGGCGATTTGTCG 141 TCGTCATGTCTGTCAGCCG AGAGGTTTAAATATTACTCCTCCG C GACAAGGATGAGCGACCAGC TCATACTTGTTTCTTTATTCCTGTT GC CTTGAGATCAGCCTAGGGGC AGAGGGATGAAACAGAGCCG CTGCTTTTCCCACCATAGCC ACGGAACCAACAGATGGACG CTTCCATTGGGCATGTCTGG ATCCCCACGGACAGGAGG TTTGTTCCAGCAGCTTCACG GGAGAAAGTGAGCAGTGGGG TTTTCGAATGAAAGTCGACCC AGCACTTCTCCATTGCTCCC ATCTGGATGCTTTTCCGTGG AAGTAGCGATTGTCCCGAGC TTGAAAGAGACAGGATTTGAGTGC CTCGTCGTACTTGGCACTGG TATCAAGAGCGGTGTGACGG CACACAAAGGAACTTTATTTAGTT TTGC TGCTAATTTTGTGTCCTCTGTGG TGGTGAAGATGTGCAAAGACG TAAAGAAGGGACTGCCACGC CGAACTGCAATGACTCCAGG CGGACTGGAAAGTTGAACTGC CAGACTGGATTCGCTCATGC GATCAGCTGGGAGCCTGC AT(12) TTGTCACCCACGACTCTTCG GAGTTCATCGCAAGCTACGC TGCTTACGCTTGGTCTTTGC ACCTGTTCATCCTGGGTTGG ATGGCTACATCACTGGTGCG TCCGGTTCTTACAATTGGGC GGACGAGTTTTGTCACCTCG CGATGAATAGAGCCATGTAGAATG C TGTAGTCGTTGATGTCGGTGC AC(26) TC(12) CG(12) AC(12) TC(18) TC(12) TC(16) AC(34) 61 TC(12) TC(12) AT(12) TC(16) AT(12) TC(14) TC(12) TC(24) AC(12) TC(12) AT(12) AC(14) TC(18) TC(12) TC(14) CG(12) AT(16) AT(16) TC(18) AC(26) TC(12) AT(18) AT(16) AC(12) TC(32) AC(12) 142 143 144 145 146 ACGGTTTGTAAGTGCGGAGC CACCGAGACTCTGAACTGAGC ATTCCTGCCACATGAAAGCC GACGATGGCGATGTATCTGG CTCGAGTCCAATCTCCTGCC 147 148 149 150 151 TATTCAGGTCCGACACACGC AAGAAAAGCCCCTACCACGC TTGCAGTGAGCACTGTTTGC CATATGCGACATAGGAGGAGC TGAAGAGACTAGATATGCAAGG GC GAACCCAAGGCTCAGAGAGG 152 CAGAGACGACCCATCTGTGG TGCAGCAAGAGAAATCTTCACC GTGTGAGGTCTGGACTCCCC GGTGTTTTGGGCTTGCTAGG GAGAGAAATGGTGGAGAAAAGAC C TGCGAATTGCTATGACAGGG GAGCCGAGCTCTTTGTACCG GAGAGCGGTCTACGCATGG TGTGTTTGTGTGCAAATACTACG GGCACTAAGAAAGCAGTAGGGG TC(12) AC(12) AC(14) TC(12) AC(12) TGCAGGAGTCTGCTTCTCCC TC(16) 62 TC(24) AC(28) TC(20) AC(12) AC(12) LITERATURE CITED Adams, J., Felis J.J., Henry, R.W., VanderWerf, E., Hester, M.H., Young, L., & Raine, A. 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