Genetic Differentiation Among Populations of Bald Eagles, Haliaeetus Leucocephalus

GENETIC DIFFERENTIATION AMONG POPULATIONS OF BALD EAGLES, HALIAEETUS LEUCOCEPHALUS

Ericka Elizabeth Helmick

A Thesis Submitted to the Faculty of

The Charles E. Schmidt College of Science

in Partial Fulfillment of the Requirements for the Degree of

Master of Science

Florida Atlantic University

Boca Raton, Florida

May 2011

ii GENETIC DIFFERE TIAnON AMO G POPULATIO S OF BALD EAGLES, HALIAEETUS LEUCOCEPHALUS

Ericka Elizabeth Helmick

This thesis was prepared under the direction of the candidate's thesis advisor, Dr. Colin R. Hughes, Department of Biological Sciences, and has been approved by the members of her supervisory committee. It was submitted to the faculty of the Charles E. Schmidt College of Science and was accepted in partial fulfillment of the requirements for the degree ofMaster of Science.

SUPE:~---==M=I_TT_E_E_: _

Colin Hughes, Ph.D. Thesis Advisor

ale GawlIk, Ph.D. 4co~

Gary . erry, P .D. Dean, The Charles E. Sc dt College of Science 13~7. ~~ Barry T. Rosson,'--Ph.D. Dean, Graduate College

III

ACKNOWLEDGEMENTS

Special thanks go to my committee members who without their patience I would not have been able to accomplish this study. I am extremely grateful to those who were willing to share their bald eagle samples for this project, without their cooperation, this study definitely would not have been possible: Brian Mealey, Phil Schempf, and Dr.

Daniel Wolf, I owe each of you quite a bit. Thanks to the DIS students that I had for helping me in the lab: M. Creamer and R. Debernardi (special thanks for working into the wee hours of the morning and keeping me company). For my parents who always knew that I would finish ―schooling‖ someday and supported me with lots of love; hours of political conversations, that not only succeeded in getting my mind off my work but also increased my blood pressure exponentially. Thanks to all of my fellow students at FAU and their support in conversations and diversions. I would also like to thank my colleagues and friends at the University of Florida, especially Dr. N. Harrison who gave me a job, even though I was pursuing a graduate degree. To all of my friends, thanks for the support and the ―you can do it‖ attitudes. Finally, thanks to Dr. T. Chouvenc for keeping me motivated, fed, and for always letting me know when I was just too tired to keep going.

ABSTRACT

Author: Ericka Elizabeth Helmick

Title: Genetic Differentiation Among Populations of Bald Eagles, Haliaeetus leucocephalus

Institution: Florida Atlantic University

Thesis Advisor: Dr. Colin Hughes

Degree: Master of Science

Year: 2011

The bald eagle, Haliaeetus leucocephalus, population declined dramatically in the early 20th century reducing the population from tens of thousands of birds within the lower 48 states, to <450 pairs of birds, effectively inducing a population bottleneck. The overall population has recovered and was removed from the endangered species list in

2007. This study investigates whether such overall population statistics are appropriate descriptors for this widespread species. I investigated the genetic differentiation between three populations of bald eagles from Alaska, North Florida and Florida Bay using both mitochondrial and nuclear DNA loci to determine whether discrete subpopulations comprise the broad range. Significant FST values, for both mtDNA and microsatellites, were found between both Florida populations and Alaska, but not within Florida populations. Results indicate that there is strong population structure, rejecting the null hypothesis of a panmictic population. Future conservation efforts should focus on subpopulations rather than the overall population.

GENETIC DIFFERENTIATION AMONG POPULATIONS OF BALD EAGLES, HALIAEETUS LEUCOCEPHALUS

TABLES ...... viii

FIGURES ...... x

INTRODUCTION ...... 1

Overview ...... 1

Objectives ...... 2

Molecular Markers ...... 2

Theoretical Background ...... 6

Study Populations ...... 7

Habitat and Nesting Preferences ...... 8

Dispersal and Migration ...... 9

Breeding and Reproduction ...... 12

Population Structure...... 14

Current Genetic Data of Bald Eagles ...... 15

MATERIALS AND METHODS ...... 17

Sample Collection ...... 17

DNA Extraction ...... 18

PCR Amplification and Sequencing ...... 18

Mitochondrial Control Region ...... 18

Microsatellite Loci ...... 19

DATA ANALYSES ...... 21

Mitochondrial Control Region ...... 21

Microsatellite Loci ...... 22

RESULTS ...... 23

Mitochondrial Control Region Variability – Domain I ...... 23

Haplotypes and polymorphic sites ...... 23

Overall population differentiation...... 23

Differentiation among populations ...... 24

Mitochondrial Control Region Variability – Domain I and II ...... 24

Haplotypes and polymorphic sites ...... 24

Overall population differentiation...... 25

Differentiation among populations ...... 26

Other Outcomes – Assessment of Female Turnover in Florida Bay ...... 26

Microsatellite gene diversity ...... 27

Gene diversity among populations...... 28

Gene diversity within populations ...... 28

Assignment of Individuals to Populations ...... 29

DISCUSSION ...... 31

Genetic diversity ...... 31

Implications for conservation of Florida populations ...... 34

CONCLUSION ...... 36

REFERENCES ...... 50

vii

TABLES

Table 1. Variable sites, numbers and frequency of 22 mtDNA haplotypes (H) based

upon Domain I of mtDNA ...... 38

Table 2. Overall population variation for 22 haplotypes from Domain I ...... 38

Table 3. Matrix of within population FST values (below diagonal) and

corresponding P-values (above diagonal) for Domain I of the mtDNA

control region ...... 39

Table 4. Exact test of differentiation P-values for Domain I ...... 39

Table 5. Molecular diversity estimates for Domain I haplotypes. Sample size (n),

number of haplotypes (H), nucleotide diversity (π), haplotype diversity (h)

and average number of nucleotide differences (k). Standard error is in

parenthesis...... 40

Table 6. Variable sites, numbers and frequency of 24 mtDNA haplotypes (H) based

upon Domains I and II of mtDNA control region...... 41

Table 7. Overall population variation for 24 haplotypes of Domains I and II ...... 42

Table 8. Molecular diversity estimates for Domains I and II. Sample size (n),

number of haplotypes (H), nucleotide diversity (π), haplotype diversity (h)

and average number of nucleotide differences (k)...... 42

Table 9. Matrix of FST values (below diagonal) and corresponding P-values (above

diagonal) for Domains I and II of the mtDNA control region ...... 43

viii

Table 10. Exact test of differentiation P-values for Domains I and II ...... 44

Table 11. Overall population differentiation as weighted average over microsatellite

loci...... 44

Table 12. Observed (HO) and expected (HE) heterozygosity and polymorphic

information content (PIC) values for microsatellite loci ...... 44

Table 13. Population specific FST values of pairs of populations for microsatellite

loci; pairwise FST values (below diagonal) and P-values (above diagonal) ....45

Table 14. Averaged population statistics for microsatellite loci. Sample size (n),

number loci typed (N), expected heterozygosity (HE, ±Standard error),

observed (HO, ±Standard error), unbiased heterozygosity (unbiased HE)

and mean number of alleles (A,±Standard error) averaged over both loci ...... 45

Table 15. Genetic differentiation using locus-by-locus AMOVA; results as

weighted average over all microsatellite loci ...... 45

Table 16. Population specific FIS indices per polymorphic locus...... 46

FIGURES

Figure 1. Median-joining network of 24 mitochondrial DNA haplotypes...... 47

Figure 2. Collapsed median-joining network utilizing 12 active mitochondrial DNA

haplotypes...... 47

Figure 3. Triangle plot from STRUCTURE, K=3. Each individual is represented by

a colored dot and the color corresponds to the population as entered in the

data file...... 48

Figure 4. Bar plot from STRUCTURE, K=3. Each individual is represented by a

colored bar and the color corresponds to the population as entered in the

data file...... 48

Figure 5. Triangle plot, showing number of putative ancestral migrants, from

STRUCTURE, K=3. Each individual is represented by a colored dot and

the color corresponds to the population as entered in the data file...... 49

Figure 6. Bar plot, showing number of putative ancestral migrants, from

STRUCTURE, K=3. Each individual is represented by a colored bar and

the color corresponds to the population as entered in the data file...... 49

INTRODUCTION

Overview

The bald eagle, Haliaeetus leucocephalus, symbolizes the freedom and beauty of our nation. It has been revered and reviled throughout its long history (van Name 1921;

Snyder 1927; Dale 1936). In the late 1800’s and early 1900’s the population, previously thought to be in the tens of thousands (Buehler 2000), started to decline mainly due to human encroachment upon land and industrial development. The advent and wide spread use of the pesticide dichlorodiphenyltrichloroethane (DDT), inadvertently caused the eagle population to quickly decline throughout its North American range (Grier 1982).

By mid-1960, less than 450 pairs of eagles were known to exist within the lower 48 states

(USFWS 2007). The bald eagle was declared endangered and with the implementation of the Endangered Species Act, in 1973, was officially listed as an endangered species. The banning of DDT (Grier 1982), and implementation of nationwide population recovery plans, has succeeded in increasing the bald eagle population to its current estimated size of 9,789 nesting pairs (USFWS 2007) within the lower 48 states.

The bald eagle, removed from the Endangered Species List in 2007, has become a national success story for endangered species. Although the nationwide recovery of bald eagles is an overall population success story, it ignores the fact that there is local differentiation and recognized differences among bald eagles in different regions of the

United States (Buehler 2000). This raises the question of whether the overall success of

the bald eagle recovery also applies to local populations and whether these local populations should be considered separately from the overall population.

Objectives

This study uses both mitochondrial DNA (mtDNA) and microsatellites to address the following questions: 1) Is there any significant genetic differentiation between the

Florida Bay population and populations in North Florida and Alaska? 2) Is the level of genetic differentiation great enough to suggest that the Florida Bay population represents a discrete population?

Molecular Markers

The use of molecular genetic markers to determine genetic variation between and within species has yielded results useful to basic and applied biology. Data from studies using mtDNA, microsatellites and specific gene complexes have been useful in determining conservation status of species (Galbusera et al. 2000; Friesen et al. 2006;

Fallon 2007; Funk et al. 2007; Nims et al. 2007; Hefti-Gautschi et al. 2008). These same markers have been used extensively to study population structure in a variety of species, including highly vagile species such as albatrosses (e.g., Burg and Croxall 2001; Milot et al. 2008), Swainson’s hawk (Hull et al. 2008), marine fishes (e.g., Theisen et al. 2008) and mammals (e.g., Baker et al. 1998; Hoffman et al. 2006).

Both mtDNA and microsatellites are selectively neutral markers that provide evidence of gene flow and drift. Analysis of haplotype and allele frequency differences provide estimates of differentiation among populations (Waser and Strobeck 1998).

MtDNA has the ability to detect more recent (intermediate) population structure because it is particularly sensitive to drift through having a lower Ne (effective population size),

approximately ¼ of that found in microsatellites (Parker et al. 1998; Sunnucks 2000;

Wan et al. 2004). Microsatellites are useful for detecting inbreeding, population bottlenecks, migration, and population structure from the more distant past (Zink and

Barrowclough 2008).

Mitochondrial DNA is maternally inherited, without recombination (reviewed in

Mitton 1994) , thus mtDNA can be readily used to reveal historical relationships between populations and to determine species level population structure. MtDNA has an inefficient repair mechanism that results in high rates of nucleotide substitution. High rates of substitution lead to a higher rate of fixation of neutral or nearly neutral mutations, as selection is not acting to remove these mutations (Kimura 1991). The mtDNA mutation rate (approximately 10-8 substitutions/site/year) is 5-10 times higher than in single-copy nuclear DNA (Brown et al. 1979; Hartl and Clark 1997; Wan et al. 2004).

Since most variation within mtDNA is selectively neutral, the population patterns it reveals are not due to natural selection (Zink 2005; Zink and Barrowclough 2008).

However, this theory has been challenged in several recent papers (Lambert et al. (2002),

Ballard and Whitlock (2004), and Bazin et al. (2006)).

The mitochondrial control region is the most variable region within the mitochondrial genome; the control region nucleotide substitution rate has been estimated to be from 0.4% to 2.5%/my (Parsons et al. 1997; Anne 2006), but a recent study by

Subramanian et al. (2009) indicates that this rate could actually be two to six times greater. This high rate of substitution means that analyses of DNA sequence variation in this region have the ability to detect current population structure (Moum and Árnason

2001). The control region is a non-coding region though it may have a role in

transcription and translation and so may not be entirely neutral (Saunders and Edwards

2000).

The control region is categorized into three domains: I, II and III. Domains I and

II are subject to frequent substitutions and insertion/deletions (indels); therefore, they are useful for determining current population structure and for identifying subspecies, species and recently divergent species or populations. Domain III is a conserved domain, with high GC content, containing the heavy-strand (HO) origin of replication. This domain has a slower rate of evolution than domains I and II and is a good marker for deeper phylogenetic relationships (Saunders and Edwards 2000).

Microsatellites are short, 1-6 base pairs, tandemly repeated DNA sequences mainly found in non-coding regions scattered throughout the eukaryotic genome. The repeat unit may repeat from five to 100 times (e.g. (AAAG)n). These loci show

Mendelian inheritance of co-dominant alleles and individuals are easily genotyped at specific loci. Microsatellite mutation rates are considered to range from 10-2 to 10-6 per locus per generation (Chakraborty et al. 1997; Ellegren 2007; Anmarkrud et al. 2008); however, the mutation rates depend on the length of the microsatellite and differ between and within species (Ellegren 2000; Zhang and Hewitt 2003; Wan et al. 2004).

Several features make microsatellites excellent markers for elucidating population structure. First, they are selectively neutral so differences among the genetic structures of populations are only due to genetic drift, gene flow and demographic processes (Nielsen et al. 1998; Gillet 1999), not to selection. Second, they are highly polymorphic due to the high rate of mutation, so the resulting allelic diversity can be used to detect bottlenecks or decreases in population size (Selkoe and Toonen 2006). Third, because microsatellite

alleles are co-dominant, genotypes can be accurately determined (Hedrick 2005). For overall population structure the allelic frequencies are determined; for within population structure the genotype is determined (reviewed in Parker et al. 1998; Gillet 1999;

Ellegren 2000; Sunnucks 2000; Zhang and Hewitt 2003; Wan et al. 2004; Diniz-Filho et al. 2008).

There are issues in scoring microsatellites that may prevent accurate determination of genotypes. Slipped-strand mispairing occurs during replication of the repeated sequences and results in either gain or loss of a repeat unit. In birds, Primmer et al. (1998), noted that the frequency of mutations caused by slipped-strand mispairing tended to favor longer repeat units rather than shorter units. However, this type of mutation is more common in mono- and di-nucleotide repeats than tetra-nucleotide repeats (Zhang and Hewitt 2003; Wan et al. 2004).

Null alleles result from point mutations located in the primer-binding region flanking the microsatellite. Such mutations reduce primer binding, causing low or no amplification of that allele. Therefore, null heterozygotes appear to be homozygous for the single amplifying allele. Similar to null alleles, allelic dropout occurs when template

DNA is at too low of a concentration, reducing ability to amplify all alleles that are present (Miller and Waits 2003). Under these conditions, the more efficient amplification of short alleles rather than long alleles effectively leads to non-amplification of the longer allele. This also has the effect of reducing the number of heterozygotes recognized, potentially reducing the frequency below that expected under random mating (Wan et al.

2004).

Size homoplasy is a different problem that arises because alleles are distinguished by length. Alleles that arise independently are therefore indistinguishable if they are the same length (Zhang and Hewitt 2003; Wan et al. 2004). For example, an allele containing 12 repeats of the sequence AAAG can arise from slipped-strand mispairing that increases length from 11 repeats, or that decreases it from 13 repeats. These independently derived alleles therefore violate the infinite allele assumption that underlies some population genetic analyses.

Theoretical Background

In a population at stable equilibrium, genetic drift reduces genetic diversity by

1/(2Ne) per generation, where Ne is the effective population size (Hartl and Clark 1997).

Small populations are more susceptible to the loss of genetic diversity via genetic drift than larger populations. Two factors that reduce population size, population bottlenecks and founder effects, increase genetic drift and lead to loss of a large proportion of alleles

(Nei et al. 1975). Populations that have genetic signatures of a bottleneck or founder effect show reduced allelic diversity, and lower heterozygosity, than those not affected by reductions in size. Bottlenecked populations are expected to have reduced average fitness and ability to adapt to changing environments (Hartl and Clark 1997).

In the past century, the bald eagle population of the lower 48 states passed through a bottleneck; it declined from 10’s of thousands or more birds to less than 450 pairs in 1963 (USFWS 2007). The decrease in population size, coupled with extirpation of the species throughout most of its natural range, may have compromised the long-term survival of the species by fragmenting the population into smaller subpopulations each with small Ne and increased genetic drift (Martínez-Cruz et al. 2007). If the rate of gene

flow between adjacent eagle populations is negligible or non-existent, these populations will have lost genetic diversity, especially if isolation has been long enough or Ne has remained small. Repatriation of bald eagles into extirpated areas may also have led to low local genetic diversity, if there were only a few founders for new populations. The evolutionary dynamics of reestablished populations would be the same as for bottlenecked ones: more susceptible to drift due to low Ne.

The evolutionary dynamics of peripheral populations are also of interest since they experience different environments than core populations (e.g. Hampe and Petit

2005; Eckert et al. 2008; Zakharov and Hellmann 2008). Populations, such as the Florida

Bay eagles, which are near or at the edge of the species range, tend to have smaller Ne than those that are in the core range. It has been calculated that such peripheral populations will experience genetic drift 2 – 30 times faster than populations in the core

(Vucetich and Waite 2003).

Study Populations

This study focuses on three populations, two of which are on the periphery of the bald eagles range. There are notable ecological and biological differences between these study populations that indicate a potential for isolation. First, habitat and nesting preferences differ greatly between Florida Bay and Alaska and even between Florida Bay and North Florida (USFWS 1986; Enos 1989; Curnutt and Robertson 1994). Second, migratory routes have indicated that the populations may be separated by preference for certain migratory flyways (Millsap 1986). In addition, the limited available data suggests female-biased dispersal, yet dispersal distances are in the range of a few hundred kilometers as compared to several thousand during migration (Greenwood and Harvey

1982; Harmata et al. 1999). Third, breeding and reproduction schedules of northern and southern populations putatively preclude the populations from reproducing due to the temporal difference in breeding seasons (USFWS 2008).

Habitat and Nesting Preferences

Habitat and nesting site characteristics differ greatly between the Florida Bay bald eagle population and other Florida populations. Florida Bay is an estuarine system located in the most southern region of Florida. It is approximately 2200 km2 , of which approximately 82% is located in the Everglades National Park (reviewed by McIvor et al.

1994 and references therein). Bald eagles in Florida Bay occupy small keys that are scattered throughout the bay. There are four types of keys (Enos 1989) in Florida Bay, bald eagles were found to nest on higher keys containing grasses, hardwoods, and buttonwoods (Curnutt and Robertson 1994). Nesting substrate includes black mangroves

(Avicennia nitida), red mangroves (Rhizophora mangle), strangler fig (Ficus spp.), fishpoison trees (Piscidia piscipula) and ground nests (Curnutt and Robertson 1994).

There are no fresh water sources on keys within Florida Bay, though keys that contain shallow depressions (e.g. Calusa Key) accumulate rainwater on the surface of saturated salt water (BK Mealey pers. comm.). Despite the lack of fresh water, there may be considerable advantage to nesting on keys close to prey. Bald eagles in central and northern Florida nest farther away from water sources (McEwan and Hirth 1979) even compared to those nesting in Alaska and Canada.

North-central Florida habitat is a mosaic of freshwater marshes, pine lands and mixed hardwood forests, large lakes and river systems, wet prairies, and scrub habitat

(McEwan and Hirth 1979; Wood and Collopy 1993). West-central Florida habitat

includes pines, mangrove swamps and freshwater swamps along with hardwoods

(Millsap et al. 2004) and south-western Florida is mainly pine flatwoods (Wood et al.

1989). In all these areas, bald eagles prefer pines for nesting (McEwan and Hirth 1979;

Wood et al. 1989; Millsap et al. 2004), particularly long-leaf pines (Pinus palustris), and may also nest in cypress (Cupressus sp.) and black mangroves (Avicennia nitida) (Broley

1947).

Alaska bald eagles tend to nest near water sources and in the winter gather along the coastline in large numbers. Those that nest along the coastline choose mature or old growth timbers such as Sitka spruce (Picea sitchensis), western hemlock (Tsuga heterophylla), yellow (Chamaecyparis nootkatensis) and red cedar (Thuja plicata). Bald eagles that nest in the interior of Alaska choose cottonwoods (Populus balsamifera) and white spruce (Picea glauca) (USFWS 1986). In either case, the nest sites are close to water, whether the coast or inland lakes and rivers. During winter season, eagles move from the interior to the coastlines, as rivers and lakes freeze over. Bald eagles nesting in the southern regions and along the coastline of Alaska are considered year-round residents (USFWS 1986).

Dispersal and Migration

Though the terms dispersal and migration have partly overlapping meanings, they subsume two distinctly different processes. Dispersal implies that the subject is leaving their natal territory permanently; it is unidirectional movement and implies that the individual(s) will not return to the natal territory for breeding purposes. Dispersal also occurs when a population is increasing its geographic range or individuals establish new breeding territories (e.g., Winkler 2005; Winkler et al. 2005). Dispersal is the main

mechanism for distribution of genes from one population to another. The frequency and distance over which genes are distributed via dispersal determines the genetic consequences. Frequent and long distance dispersal homogenizes genetic variation over the entire population (Winkler 2005), creating a panmictic population structure. Rare and local dispersal allows populations to differentiate genetically, creating a set of diagnosable subpopulations (Winkler 2005; Winkler et al. 2005). The differentiation could be due to genetic drift, mutation or selection (Hedrick 2005). There is little data showing whether bald eagles from Florida Bay are dispersing into populations away from their natal territories.

In many species, one sex disperses farther than the other, resulting in sex-biased dispersal. In birds, including diurnal raptors, dispersal is typically female-biased (e.g.,

Greenwood and Harvey 1982). Observational studies of bald eagles in Yellowstone

National Park (YNP) noted that bald eagles exhibit female biased dispersal and male philopatry (Harmata et al. 1999). This generalization does not hold true for all avian species, for example spotted owls in California (Lahaye et al. 2001) do not show sex- biased dispersal. Female biased dispersal tends to decrease variation in mtDNA between populations and increase variability within populations (e.g., Huck et al. 2007). Sex- biased dispersal can been inferred by comparing population genetic analyses of variation at mtDNA to variation at microsatellites (Gibbs et al. 2000).

Migration implies a bidirectional movement; most often this means leaving an area for a distinct period (e.g., winter) and returning to the same area later. Migration is a physiological (innate) phenomenon; the subject ―must‖ migrate even if environmental conditions and food resources do not dictate the necessity. Thus it is ―obligatory

movement‖ (Winkler 2005). Migration does not typically cause gene flow between populations.

Bald eagles from northern populations migrate south in winter, though how far south they go is dependent on winter severity. Northern bald eagles winter further south during harsh winters and further north during mild winters (Millsap 1986). Millsap

(1986) also noted most eagles (95.8%) winter to the west of a hypothetical line from Lake

Michigan to the Mississippi river delta; this was consistent over the years that surveys were taken and did not fluctuate with weather conditions. Although Millsap studied bald eagles west of the hypothetical line, if bald eagles east of this line migrate southward in the same fashion, then northern and southern bald eagles would overlap on the east coast from Florida into Canada.

Mojica et al. (2008) used platform terminal transmitters (PTTs) to track the migration of nestling bald eagles from south-western Florida. They found that fledglings migrated northwards in April-June, while sub-adults migrated in late March-August.

Migrants were tracked along the Appalachian Mountains, Coastal Plain, and the

Mississippi Valley. The majority of these migrants moved northward along the

Appalachian and Coastal Plain flyways while only a couple moved through the

Mississippi Valley. The study also noted that five of the bald eagles did not migrate out of Florida, inferring that although the majority did migrate, that some bald eagles remain in Florida. This supports the idea that eastern eagles stay in the East, and makes it likely that northern and southern birds would mingle during winter.

A study of bald eagles from west-central Florida showed that juveniles moved northward through Florida into Canada through Nova Scotia, Quebec, and Newfoundland

(Millsap et al. 2004). Their summering range centered on Chesapeake Bay

(Virginia/Maryland) and the North Carolina coastal plains, while midwinter locations included central Florida, Florida panhandle and coast of South Carolina (Millsap et al.

2004). In contrast, (Curnutt 1992) suggests that bald eagles in Florida Bay stay in

Everglades National Park year round. Curnutt (1992) observed a communal roost located in Everglades National Park from March 1990 until February 1991 and found that during breeding season there was an increase in the number of juvenile or sub-adult bald eagles, presumably northern birds wintering. He also noted that the number of juveniles at the roost decreased during the summer, presumably due to the wintering juveniles migrating.

However, there was still a population of juveniles (sub-adults) at the roost during summer months that he interpreted as being bald eagles that had fledged in Florida Bay and remain in the vicinity rather than migrating (Curnutt 1992).

Breeding and Reproduction

The timing of bald eagle breeding differs significantly among populations.

Florida bald eagles start nesting in September and young fledge in May/June.

Chesapeake Bay eagles start nest building in November and young fledge in June/July.

The north-central and western state eagles start nesting in January and young fledge by the end of August; southwestern bald eagles start nesting in November and young fledge in June/July. Alaskan bald eagles start nesting in February and young fledge in

September/October (USFWS 2008). These temporal differences may decrease the likelihood of inter-breeding between northern and southern populations. In addition, bald eagles exhibit mate and site fidelity (e.g., Stalmaster 1987; Jenkins and Jackman 1993;

Buehler 2000), so it is unlikely that individuals or pairs will change breeding territories from season to season.

Reproduction in bald eagles has been studied extensively. There has been a particular focus on the effects of DDT, its derivative dichlorodiphenyldichloroethylene

(DDE), and other organochlorine contaminants which have been implicated as causes egg-shell thinning and reproductive problems (e.g., Grier 1982; Kozie and Anderson

1991; Cesh et al. 2008). In general bald eagles lay one clutch of eggs per nesting season

(Stalmaster 1987); however Florida bald eagles may lay two clutches during the nesting season if one clutch is removed, or not viable, early in the nesting season (Wood and

Collopy 1993). As far as is known, the same is not true for bald eagles nesting in northern regions, such as Alaska or the Aleutian Islands (Hensel and Troyer 1964;

Morrison and Walton 1980; Wood and Collopy 1993). This difference in reproduction may be due to seasonal constraints providing less time for a second clutch to be laid in

Northern regions (Stalmaster 1987), whereas the change between seasons in Florida is gradual and provides leeway for a second clutch to be laid. Typical clutch size is from one to 3, averaging two eggs, with only one brood per season (Buehler 2000).

Bald eagles, along with other raptor species, exhibit sexual size dimorphism

(SSD) with females typically larger than the males (Bortolotti 1984). Sexual size dimorphism may result from a variety of selection pressures including egg production, nestling protection, prey selection and mate selection (e.g., Andersson and Norberg 1981;

Bollmer et al. 2003; Blanckenhorn 2005). There are numerous theories that have been attributed to the differences in size between female and male raptors (reviewed in

Andersson and Norberg 1981; Székely et al. 2007). One factor, which may determine

whether bald eagles from northern and southern populations would reproduce, is whether the female would choose a mate that has a larger (or equal) body size than her own. The theories put forth in the previously mentioned reviews suggest that it is more advantageous for the female to choose a smaller mate. For example, one theory is that smaller males are more agile than larger males, and therefore have an increased advantage of capturing prey. Another theory is that the females increased body size is beneficial to nest and egg protection, a smaller bodied female may not be able to defend the nest from predators while the male is hunting. If southern females reject larger males as mates, this could possibly result in reproductive isolation between northern and southern populations.

Population Structure

Populations of widespread species are rarely panmictic, rather they are a metapopulation, an array of subpopulations connected by gene flow (Levins 1969), although exceptions have been found (e.g. Barnett et al. 2007; Coltman et al. 2007;

Lorenzen et al. 2008; Makowsky et al. 2008; Theisen et al. 2008). Range division can be caused by physical barriers, e.g. mountain ranges prohibiting dispersal (Hull et al. 2008); or cryptic barriers, e.g. inferred obstacles such as ocean currents (Bergek and Björklund

2007). In either case, the reduction in gene flow between subpopulations can lead to genetic differentiation and higher risk of extinction of the subpopulation.

Populations of highly vagile species such as bald eagles may not be separated by physical barriers, but may be separated by cryptic barriers which effectively reduce gene flow (Bergek and Björklund 2007). In the case of Florida Bay bald eagles, these cryptic barriers are unknown but may include adaptation to become sedentary and take advantage

of abundant year round food sources and adaptation to the sub-tropical climate. If the

Florida Bay population is highly philopatric (Curnutt 1992), immigration may be relatively low, and inbreeding more common. Reproductive isolation, or inbreeding, in

Florida bald eagles has been suggested by Vyse (publishing in Hunt et al. 1992a).

Current Genetic Data of Bald Eagles

There are only a few genetic studies on bald eagles. Three of these studies rely on allozymes and two used DNA fingerprinting techniques. Morizot et al. (1985) looked at

50 loci in bald eagles from Alaska, Washington state, Oregon and Arizona. Of these 50 loci, only four were polymorphic. There is a gradual north-south clinal variation at these loci, and Morizot et al. (1985) concluded this could be due to natural selection or gene flow from founder populations. Knight et al. (1995) used allozymes to investigate differentiation between a small bald eagle population in Colorado and a larger population in Ontario. They looked at differentiation at 32 genetic loci, six of which could not be analyzed; of the remaining 26 loci, only peptidase 2 was polymorphic. They found one rare allele in the Ontario population, of which there were eight heterozygotes and no homozygotes; peptidase 2 was monomorphic for all Colorado bald eagles. They concluded that there was an absence of genetic variation both within populations and between populations, which they attributed to gene flow of neutral alleles.

Vyse (publishing in Hunt et al. 1992a), used DNA fingerprinting to examine genetic variability between Arizona, California and Florida bald eagles. Vyse found that bald eagles in Florida were more genetically similar to each other than either the Arizona or California populations. The author suggested that the Florida population was more inbred than the other two populations but cautioned that this result may be due to

sampling error. This study also found that the Florida and California populations were more closely related than either population was to the Arizona population.

A follow-up study, by Zegers and Hostert (publishing in Hunt et al. 1992b), included more extensive sampling of bald eagles and larger sample sizes. They used allozymes to estimate differentiation among several populations and found that the

Arizona population was similar in genetic variation to other populations and that it was genetically closer to the Maryland population than other populations. They also found that the Texas population was the most divergent, thus more genetically distinct, even from the neighboring Arizona population. The mean heterozygosity per locus (MHL) of the Florida population (MHL = 0.135) fell within the range of other populations with the

Arizona population having the highest, 0.211, and the California population with the lowest, 0.033.

Tracey (1994) surveyed genetic differentiation between bald eagles in Florida (n

= 15, Florida Bay; n = 11, Maitland Bird of Prey Center) and Saskatchewan (n = 21).

DNA fingerprinting with probes M13 and pV47-2 (neutral genetic markers), revealed private alleles but FST was low, 0.008, showing little genetic differentiation between the two populations. The lower levels of genetic variation found in the Florida population was attributed to small population size resulting in genetic drift and/or founder effects.

MATERIALS AND METHODS

Sample Collection

All Florida Bay (n = 57, sampled between 1995 – 2008) bald eagle samples were collected by Brian Mealey (BKM) and include blood samples taken over a period of approximately 14 years representing 16 keys and one city (Port St. Lucie, St. Lucie

County, Florida). To limit errors due to re-sampling of the same key over several years, only one eagle from each key was considered as a sample. This eliminated both re- sampling and the inclusion of siblings in data analysis. Samples were taken while in the field by removing the fledgling from the nest, hooding to reduce stress, and using a syringe to take up to 3 cc of blood from the brachial artery located in the wing. Blood samples, 100-500 µL, were immediately added to blood storage/preservation buffer (100 mM Tris, pH 8.0, l00 mM EDTA, 10 mM NaCl, 0.5% SDS) (Longmire et al. 1988).

Alaska tissue samples (n = 68, representing 9 areas, sampled between 1995 –

2008) were supplied by Phil Schempf of the US Fish and Wildlife Services in Anchorage,

Alaska. Tissue samples from North Florida (n = 21, representing 7 counties, sampled between 2002 – 2009) were supplied by Dr. Daniel Wolf of Florida Fish and Wildlife in

Gainesville, Florida. Tissue samples were biopsied from the breast of deceased eagles using a biopsy tool sterilized in 95% ethanol and flamed between samples. These samples were then stored in SED buffer (saturated NaCl; 250 mM EDTA, pH 7.5; 20%

DMSO) until extraction.

DNA Extraction

DNA extraction from blood (15-50 µl) and tissue (~10 mg) was performed using a Qiagen (Valencia, CA) DNeasy extraction kit, per the manufacturers’ protocols for animal tissues and nucleated blood cells. DNA extractions were quantified by running 2-

5 µl of the extract on a 1% agarose gel and determining amount by eye for use in the polymerase chain reaction (PCR).

PCR Amplification and Sequencing

Mitochondrial Control Region

Mitochondrial control region primers were developed from Buteo buteo sequences and amplified approximately 1767 bp of the control region in bald eagles

(Buteo tRNA ThrL – CRL=5’-CATTGGTCTTGTAAACCAAAAACTGA-3’; Buteo tRNA ProH – CRH= 5’-CCAGCTTTGGGAGTTGGTG-3’). Due to limitations of the

ABI 310 genetic analyzer sequencing of the amplified PCR product yielded two partial forward and reverse sequences of approximately 400 bp each. The first sequence (CRL) falls into the area of Domain I and contains most of the sequence polymorphisms; the second sequence (CRH) falls into the area of Domain III, which contains a variable number of tandem repeats (VNTR’s). Two additional internal sequencing primers (CRL2

5’-TGGACTGCGGTGATTTACACCAGATT-3’; CRH2 5’-

CTCCAGTGCCTTGACGTATA-3’) were developed in order to sequence Domains I through III of the control region.

PCR reactions used approximate 2-3 µl total DNA extracted from tissue samples and 3-5 µl of total DNA extracted from blood samples as template. A typical PCR reaction included 33.8 µl dH2O, 5.0 µl 10X Buffer, 1.0 µl of 50X dNTPs, 4.0 µl (100 ng)

each forward and reverse primer, 0.2 µl Taq polymerase at approximately 1U, for a total volume of 50 µl. Amplifications were performed on an Eppendorf thermocycler using the following cycling parameters: initial denaturation 94ºC 2 minutes followed by 34 cycles at 94ºC 1 min, 65ºC for 30s and 72ºC for 30s, with a final extension of 72ºC for 5 minutes. PCR products were cleaned using Millipore (Billerica, MA) PCR Clean-up Kit, modified by using 20-30 µl of 65°C TE buffer (10 mM Tris-HCl, 1 mM EDTA, pH 8.0) as the final elution step. Once cleaned, the PCR product was quantified by running 2-5

µl on a 1% agarose gel and comparing brightness against a known quantity (200 ng/µL)

DNA marker. Depending upon the quality of the cleaned PCR product, between 5 and 10 microliters were used in the sequencing PCR reaction, in a total volume of 20 µl.

Sequencing products were cleaned using Sephadex columns, dried in vacuum and resuspended in 15 µl of formamide and loaded on an ABI Prism 310 Genetic Analyzer

(Applied Biosystems, Carlsbad, CA), per the manufacturers’ protocol for BigDye

Terminator Version 3.1 (Applied Biosystems, Carlsbad, CA).

Sequences were aligned and edited using DNA Baser Version 2.91

(HeracleSoftware). For confirmation that the sequences were of mitochondrial origin rather than nuclear, sequences were compared against the mitochondrial control region sequence for the White-tailed Sea Eagle (H. albicilla, GenBank accession FJ167527). For this study, I analyzed both 390 bp of Domain I and 732 bp containing Domain I and continuing into Domain II (Domain I+II) of the mtDNA control region.

Microsatellite Loci

Two tetra-nucleotide microsatellite loci, IEAAAG05 and IEAAAG12 (Busch et al. 2005), originally developed for the Eastern Imperial Eagle (Aquila heliaca) and tested

against Steppe Eagles (Aquila nipalensis) and White-tailed Sea Eagles (H. albicilla) were used for the microsatellite analysis. IEAAAG05 is a simple tandem repeat of four nucleotides (AAAG)7 and IEAAAG12 is a complex repeat consisting of

(AAAG)10(GAAG)3(AAAG)5.

Microsatellite PCR reactions used approximate 2-3 µl total DNA extracted from tissue samples and 2-4 µl of total DNA extracted from blood samples as template. A typical PCR reaction included 4.3 µl dH2O, 1.0 µl 10X Buffer, 0.5 µl of 50X dNTPs, 1.0

µl (25 ng) each forward and reverse primer, 0.2 µl Taq polymerase at approximately 1U, for a total PCR reaction of 10 µl. Amplifications were performed on an Eppendorf thermocycler using the following cycling parameters: initial denaturation 94ºC 2 minutes followed by 34 cycles at 94ºC 1 min, 58ºC for 30s and 72ºC for 30s, with a final extension of 72ºC for 5 minutes. Resulting PCR products were electrophoresed using 5%

- 8% polyacrylamide (PAGE) gels to determine whether the microsatellites were polymorphic and which individuals amplified. Amplified samples were used in a PCR reaction of the same parameters as above with the exception that the forward primer was fluorescently tagged. One microliter of the resulting PCR was added to 20 µl of formamide containing 0.25 µl of CXR 400 (Promega) internal size standard. These products were then genotyped using an ABI Prism 310 Genetic Analyzer (Applied

Biosystems, Carlsbad, CA) utilizing the GeneScan program. Genotypes were analyzed and alleles were scored using GeneMarker v1.85 (SoftGenetics).

DATA ANALYSES

Mitochondrial Control Region

All mitochondrial control region sequences were aligned using CLUSTAL in

MEGA version 4 (Tamura et al. 2007). DnaSP version 5 (Librado and Rozas 2009) was used to determine mitochondrial haplotypes, polymorphic site data, nucleotide diversity

(π), haplotype diversity (h) and the average number of nucleotide differences (k) for both

Domain I and Domain I+II of sequence data. Tests for neutrality, Tajima’s D (Tajima

1989) statistic, was conducted in DnaSP version 5 (Librado and Rozas 2009). Analysis of molecular variance (AMOVA), to test population differentiation, was conducted in

ARLEQUIN version 3.5 (Excoffier et al. 2005). All fixation indices values, FST, were calculated using haplotype frequencies. The exact test of differentiation was conducted using ARLEQUIN version 3.5 (Excoffier et al. 2005). The exact test is based on differences in haplotype frequencies among populations, whereas AMOVA uses the average over all nucleotide sites within sequences to determine global differences between populations. Derivation of all significance values was conducted using the

Markov chain method (based on the ―random-walk in space‖ for contingency tables) and set at 10000 dememorization steps followed by 100000 steps of the Markov chain.

Significance levels and the allowable level of missing data were set to 0.05.

To investigate haplotype relationships, median-joining networks (Bandelt et al.

1999) were carried out in Network 4.5 (Fluxus Technology Ltd.). The median-joining network was collapsed and cleaned of unnecessary connections and median vectors using the MP calculation (Polzin and Daneschmand 2003).

Microsatellite Loci

Micro-Checker V 2.2.3 (Shipley 2003) was used to check for null alleles and scoring errors that may be due to stuttering and large allele dropout. Micro-Checker is a program specifically designed for use with samples of diploid, panmictic populations.

MICROSATELLITE TOOL-KIT version 3.1 for PC Microsoft Excel (Park 2001) was used to determine the mean number of alleles per locus, observed and expected heterozygosity

(per locus and per population), polymorphic information content (PIC; (Botstein et al.

1980)) values and to format microsatellite raw data into other data formats for further analysis. GenePop 4.0 (Rousset 2008) was used to test for deviations from Hardy-

Weinberg equilibrium (HWE; heterozygote deficit and heterozygote excess) and linkage disequilibrium; no Bonferroni correction for linkage disequilibrium was conducted.

GenePop 4.0 utilizes the Markov chain method for calculation of exact P-values; all parameters were set to 10000 dememorization steps, 30 batches, with 5000 iterations per batch. ARLEQUIN version 3.0 (Excoffier et al. 2005) was used to calculate molecular diversity between populations (FST) using the number of different alleles distance method. The program STRUCTURE v. 2.3 (Pritchard et al. 2000a) was used to determine population division, to assign individuals to specific populations and to test for ancestral gene flow between populations.

RESULTS

Mitochondrial Control Region Variability – Domain I

Haplotypes and polymorphic sites

Analysis of Domain I of the mitochondrial control region from 111 bald eagles samples yielded 22 haplotypes defined by 13 polymorphic sites. Of the polymorphic sites, ten were transitions and 3 were transversions. Eighteen haplotypes were in the

Alaska population, four in the Florida Bay population and seven in the North Florida population. Shared haplotypes include H3 and H4 between Alaska and Florida Bay; H3,

H4, H5 and H8 between Alaska and North Florida; and H3, H4 and H19 between Florida

Bay and North Florida. Haplotypes found to be unique to each population are H1, H2,

H6, H7, and H9 - H18 for Alaska; haplotype H20 for Florida Bay; and haplotypes H21 and H22 for North Florida (Table 1).

Overall population differentiation

Over all populations nucleotide diversity (π) was 0.005, haplotype diversity (h) was 0.865±0.02 and the average number of nucleotide differences (k) was 2.086±1.175.

Tajima’s D (Tajima 1989) statistic was negative, -0.405, and not significant (P > 0.10).

Genetic structure analysis indicated that 90.03% of genetic variation was within populations as compared to 9.97% among populations. Resulting FST value of 0.099 was highly significant with a P-value of 0.000 (Table 2).

Differentiation among populations

Pairwise comparison of populations revealed significant differentiation between

Alaska and Florida Bay (FST =0.118, P=0.000) and between Alaska and North Florida

(FST =0.073, P=0.019). Differentiation between Florida Bay and North Florida was not significant (FST = 0.069, P = 0.059) (Table 3). An exact test of differentiation between all pairs of samples indicated significant differentiation between populations (Table 4).

The exact test tests for global significant differentiation between populations in order to reject the null hypothesis of panmictic population structure.

Florida Bay had the lowest nucleotide diversity (π) of 0.0026±0.0005, haplotype diversity (h) of 0.598±0.082 and average number of nucleotide differences (k) was

1.008±0.699 as compared to Alaska (π = 0.0063±0.0005, h = 0.880±0.026, k =

2.457±1.364) and North Florida (π = 0.0038±0.0005, h = 0.828±0.063, k = 1.485±0.943)

(Table 5).

Mitochondrial Control Region Variability – Domain I and II

Haplotypes and polymorphic sites

Sequencing of mtDNA control region Domains I and II revealed 24 haplotypes defined by 15 polymorphic sites from 94 bald eagle samples. Eighteen haplotypes were in the Alaska population, four in the Florida Bay population and eight in the North

Florida population. Shared haplotypes include H3 between Alaska and Florida Bay; H3,

H4, and H5 between Alaska and North Florida; and H3, H19 and H20 between Florida

Bay and North Florida. Haplotypes that are unique to each population are H1, H2, and

H6 - H18 for Alaska; haplotype H21 for Florida Bay; and haplotypes H22 – H24 for

North Florida (Table 6). Polymorphic sites included 11 transitions, 3 transversions and

one indel site. Nine transitions were in Alaska, 2 in Florida Bay and 4 in North Florida; there was one transversion found in Alaska, 2 in Florida Bay and one in North Florida.

Only Florida Bay and North Florida contained the polymorphism resulting in the indel.

The median-joining network (Bandelt et al. 1999) initially produced a convoluted network map (Figure 1) which included two median vectors. The median vectors (mv1 and mv2) are hypothetical missing sequences that would join H6 to the network. To confirm that these were true vectors, I used the program TCS (Clement et al. 2000) to construct a basic haplotype tree and H6 was not attached to the tree at any point, confirming that there are missing sequences within the haplotypes sampled that would putatively attach H6. The second median-joining network (Figure 2) is the same network but collapsed using the MP calculation (Polzin and Daneschmand 2003). This tree removes 12 of the 24 haplotypes and shows the 12 ―active‖ haplotypes, those that are not

―superfluous‖ to the network (Polzin and Daneschmand 2003).

Overall population differentiation

Over all populations for Domains I + II, nucleotide diversity (π) was 0.003, haplotype diversity (h) was 0.896±0.024 and average number of nucleotide differences

(k) is 2.402. Genetic structure analyses indicated that most of the variation, 87.71%, is within populations as compared to 12.29% between populations. An FST value of 0.123 and P-value of 0.000 indicate significant differentiation overall populations (Table 7).

Tajima’s D (Tajima 1989) was -0.56472, but not significant, P = 0.297, over all populations. The negative value of D can be indicative of population expansion; however, with the non-significant P-value it is more likely that this locus is neutral.

Differentiation among populations

Florida Bay had the lowest nucleotide diversity (π) of 0.0016, haplotype diversity

(h) of 0.692 and average number of nucleotide differences (k) was 1.183 as compared to

Alaska (π = 0.0033, h = 0.873, k = 2.424) and North Florida (π = 0.0021, h = 0.905, k =

1.543) (Table 8). Mean number of pairwise differences within populations ranged from

1.183 in Florida Bay to 2.424 in Alaska.

Pairwise comparison of pairs of populations revealed significant differentiation between Alaska and Florida Bay (FST =0.160, P=0.000) and Alaska and North Florida

(FST =0.101, P=0.009). Between Florida Bay and North Florida the FST was -0.0003 and not significant, P=0.387 (Table 9). Although the fixation indices for Florida Bay and

North Florida was not significant, the exact test of differentiation between all pairs of samples indicates significant differentiation between populations, rejecting the null hypothesis of panmictic population structure (Table 10).

Other Outcomes – Assessment of Female Turnover in Florida Bay

Florida Bay bald eagle samples were taken over consecutive years from each key that had accessible and active nests. Sequences of these samples were analyzed to determine how frequently female turnover occurred. I used sequences for Domain I (390 bp) of the mitochondrial control region to determine if there were haplotype changes on keys. Changes in haplotypes indicate a change of female nesting on the key.

Comparison of sequences between individuals from the same key indicated that there was turnover that occurred on three keys (Rankin, Sandy and Clive). Rankin key sample from the 1994/1995 breeding season was H19, the next two breeding seasons recorded were 1996/1997 and 1997/1998 where the haplotype changed to H3. In the

following breeding season, 1998/1999, the haplotype reverted to H19. The second key was Sandy key from H20 in 1996/1997 to H4 in the 1999/2000 breeding season. Finally,

Clive key changed from H4 in 1996/1997 to H3 in 2005/2006. Dividing, the sum of years between samples by the number of turnovers, the turnover rate is one turnover approximately every 5 years for these three keys.

Microsatellite gene diversity

Micro-Checker (Shipley 2003) did not detect the presence of null alleles or scoring errors in microsatellite loci. The null hypothesis for testing Hardy-Weinberg equilibrium (HWE) is that all populations are in HWE; secondary hypotheses are that all populations have heterozygote deficits or heterozygote excess. HWE tests for heterozygote deficit indicate that the Alaska population is not in HWE (FIS = 0.083, P =

0.007±0.002 for IEAAAG05; FIS = 0.061, P = 0.000 for IEAAAG12). Both Florida Bay

(FIS = 0.031, P = 0.386±0.009 for IEAAAG05; FIS = 0.109, P = 0.249±0.024 for

IEAAAG12) and North Florida (FIS = -0.218, P = 0.998±0.000 for IEAAAG05; FIS = -

0.218, P = 1.0 for IEAAAG12) populations do not show heterozygote deficits. The test for heterozygote excess indicated that the North Florida population is not in HWE for locus IEAAAG05, FIS = -0.2618 (P = 0.0121±0.0021) but conforms to HWE for locus

IEAAAG12 (FIS = -0.2180, P = 0.2901). Both Alaska (FIS = 0.0609, P = 0.9989) and

Florida Bay (FIS = 0.1097, P = 0.7178) populations do not show significant heterozygote excess at either of the microsatellite loci. Tests for linkage disequilibrium did not detect evidence of linkage between the two microsatellites (P = 0.089). Initial data analysis of these microsatellites, using ARLEQUIN (Excoffier et al. 2005), indicated that there was

more than 5% missing data for IEAAAG12, therefore the allowed level of missing data was adjusted to 10% for further analyses.

Gene diversity among populations

Among population gene diversity for both microsatellites was 0.693±0.474. The mean number of alleles was 12.5±7.78, mean observed heterozygosity (HO) was

0.695±0.047, expected heterozygosity (HE) was 0.795±0.114, and the mean allelic range was 27±1.414. Tests for genetic differentiation indicated that most of the variation is within individuals, 87.38%, as compared to among individuals, 12.62%; overall FIS value was 0.126 and statistically significant (P=0.000). Locus specific FIS was 0.169 for

IEAAAG12 and 0.074 for IEAAAG05 (Table 11). Observed and expected heterozygosities and polymorphic information content (PIC) values for all populations, over both loci are in (Table 12). Population pairwise FST and P values were calculated using the number of different alleles option in ARLEQUIN version 3.0 (Excoffier et al.

2005) and were significant for all population combinations (Table 13).

Gene diversity within populations

Nei's unbiased gene diversity (Nei 1987) was calculated for all populations and indicated that the North Florida population was less genetically diverse over both loci

(unbiased HE = 0.539±0.403) as compared to Alaska (unbiased HE = 0.693±0.475) and

Florida Bay (unbiased HE = 0.654±0.563). Within populations, HE was 0.740±0.195 for

Alaska, 0.759±0.059 for Florida Bay, and 0.575±0.260 for North Florida. HO was

0.684±0.186 for Alaska, 0.698±0.022 for Florida Bay, and 0.713±0.338 for North

Florida. Mean number of alleles (A) were 11.5±6.364 for Alaska, 8.0±4.243 for Florida

Bay and 4.5±2.121 for North Florida (Table 14); mean allelic size range was 27 for

Alaska, 19 for Florida Bay and 18 for North Florida. Genetic differentiation results showed that the majority of differentiation was within individuals at 82.88%, among populations at 13.28% and among individuals within populations 3.84%; average FIS was

0.044 (P = 0.061), FST was 0.133 (P = 0.000) and FIT was 0.171 (P = 0.000) (Table 15).

Average (absolute) FIS value for each microsatellite locus was 0.015 for IEAAAG05 and

0.069 for IEAAAG12; population specific FIS values are shown in Table 16.

Assignment of Individuals to Populations

The assignment test for individuals into specific populations was tested in

Structure to find the number of populations that best fit the data. The microsatellite data was initially run as individuals, without population identification designations.

Parameters for this test were as follows: admixture ancestry model, allele frequencies correlated, all other parameters were set to default. Structure indicated that the most likely number of populations for the data set was three. Analysis with population data set at K = 3, and population identification information allowed, indicated a general separation, but obvious admixture between Florida Bay and Alaska populations (Figure 3 and Figure 4). In both populations, most individuals formed a cluster though the number of individuals not assigned to one or the other population is greater than that for North

Florida. This could be due to the greater number of samples for the Alaska and Florida

Bay populations. Although separation is not as clear for Alaska and Florida Bay populations, North Florida individuals clustered together quite clearly, differentiating the

North Florida population from Florida Bay and Alaska. When the migration model, in

Structure, was applied to the data, resolution of all three populations became quite clear

(Figure 5 and Figure 6). This model indicates putative ancestry (historical gene flow)

between the populations. The triangle plot indicates that individuals from Alaska and

Florida Bay have previously contributed genes to the North Florida population and that

North Florida has contributed genes into the Florida Bay population.

DISCUSSION

The purpose of this study was to evaluate genetic differentiation between bald eagles from Florida Bay and those from Alaska and North Florida. Results from both mtDNA and microsatellite loci indicated that there is significant differentiation between populations.

Genetic diversity

Analysis of both mtDNA control region Domain I and Domain I + II revealed 22 and 24 haplotypes respectively. The majority, 75% (18/24), of haplotypes for both data sets were restricted to the Alaska population. The larger number of haplotypes within

Alaska may partly be due to the larger number of samples. Another factor possibly contributing to the number of unique Alaska haplotypes is that the majority, 58%, of samples were taken during the wintering season. In winter, bald eagles from interior

Alaska congregate at roosting sites along the coastline. This may increase the number of single haplotypes if individuals come from separate Alaska subpopulations.

The major haplotypes found in Alaska were H3, H4 and H10 making up 13%,

30% and 13% of the population, respectively. For the Florida Bay population, H3 makes up 50% percent of the population followed by H19 (25%), H20 (19%) and H21 (6%).

The majority of the North Florida samples were equally distributed for H4, H5, and H20 at 19%; and H19 at 13%. All populations share the major haplotype found in Florida

Bay, H3, however there is only one individual (6%) in North Florida and eight (13%) individuals in Alaska that represent this haplotype. The low number of individuals in

North Florida that represent this haplotype may be indicative of sampling bias; as data that FWC received with the eagle carcasses was limited and although we know the counties they were received from it is possible that these eagles could be from many different areas within Florida or even outside of Florida. The high percentage of H3 in

Florida Bay could indicate that individuals (8 samples) with this haplotype are breeding adult year-round residents or that female offspring with H3 are returning to their natal territory, Florida Bay, to reproduce. The high number of H3 may also be indicative of sampling bias; although every effort was made to eliminate possible errors, such as removing all known siblings and using only one individual from one year per key. It is difficult to determine whether offspring, from adult bald eagles that may have moved between keys within their territory in different breeding years, were re-sampled.

Mitochondrial DNA showed significant evidence of population differentiation between populations with highly significant FST values between Alaska and Florida Bay and Alaska and North Florida. There were no significant differences between the Florida

Bay and North Florida populations. The Florida Bay population is more depauperate for both haplotype and nucleotide diversity as compared to Alaska and North Florida. The differences between Alaska and Florida Bay populations indicate that there is little gene flow between the two populations and may be attributed to isolation by distance (Wright

1943). In the case of Florida Bay and North Florida, the populations are not significantly different from each other when comparing FST values; however, they do show significant differentiation when using the exact differentiation test. This suggests, that although each

population shares haplotypes, that there is limited gene flow between these two populations.

There are several possible explanations for lack of gene flow between populations. The first is isolation by distance, especially between Alaska and Florida

Bay. The second is founder effects, Florida Bay is at the edge of the species range and it is possible that only a few individuals founded this population. Third, sampling error may bias haplotype distribution, as discussed above.

In comparison, the results of this study show that overall haplotype (h = 0.896) and nucleotide diversity (π = 0.003) in Domain I+II of the control region is higher for bald eagles than for other raptor species. For example, the Spanish Imperial Eagle

(Aquila adalberti) reported haplotype diversity is h = 0.321 and nucleotide diversity is π

= 0.001 (Martinez-Cruz et al. 2004). Bollmer et al. (2006) reported haplotype diversity as 0.625 and nucleotide diversity as 0.0019 in the Galapagos hawk (Buteo galapagoensis), while Johnson et al. (2007) reported that gyrfalcons (Falco rusticolus) in

Alaska were found to have a haplotype diversity of h = 0.647 and π = 0.001. Several raptor species show lower haplotype diversity but higher nucleotide diversity than bald eagles. For example, Saker falcons (Falco cherrug) had h = 0.868 and π = 0.005 (Johnson et al. 2007) and Bollmer et al. (2006) reported h = 0.766 and π = 0.0059 for Swainson’s hawk (Buteo swainsoni). The White-tailed Sea Eagle (H. albicilla) overall populations studied by Hailer et al. (2007) also showed a lower haplotype diversity than bald eagles, h = 0.746; however, overall nucleotide diversity was higher, π = 0.0068. The Eastern

Imperial Eagle (Aquila heliaca) also follows this trend with lower haplotype diversity, h

= 0.779 and higher nucleotide diversity, π = 0.0055 (Martinez-Cruz et al. 2004).

On average, each of the studies above used 200 – 500 bp of Domain I of the control region. In comparison with the data for Domain I, from this study, overall population haplotype diversity (h) was 0.865±0.02 and nucleotide diversity (π) is 0.005.

Looking at the populations individually, Florida Bay diversity indices were lower, π =

0.0026 and h = 0.598, than other raptors; for Alaska (π = 0.0063, h = 0.880) and North

Florida (π = 0.0038, h = 0.838) both diversity indices remain higher than indices in other raptors.

Data analyses of both microsatellite loci indicated significant differentiation between bald eagle populations (FST = 0.133, P = 0.000), indicating the presence of genetic structure within the bald eagle range. Literák et al. (2007) used the same two microsatellite loci as this study and found significant differentiation between subpopulations in the west/north and east of the white-tailed sea eagles range (FST =

0.048, P = 0.008).

Comparison of data from both mitochondrial DNA and microsatellites indicated that there is population differentiation between Alaska, Florida Bay and North Florida bald eagles. Exact tests of differentiation tests reject the null hypothesis of panmixia for these populations. Although the proximity of Florida Bay and North Florida populations should not exclude dispersal and breeding of individuals between these populations, data indicates that there is limited gene flow among them. It is possible that the Florida Bay population is declining due to lack of significant gene flow into the population.

Implications for conservation of Florida populations

Additional conservation measures may be necessary in the future for this species, specifically for populations in Florida. Development of lands that are near conservation

areas will limit and fragment the habitat that is available for nesting and foraging. It is possible that Florida Bay and Everglades National Park could become a refuge for bald eagles from other regions of Florida, especially in light of the projected population growth that would expand into currently undeveloped and rural areas of Florida (Zwick and Carr 2006). This human population expansion is estimated to eliminate 1.9 million acres of land that is currently utilized by bald eagles in Florida; other species found in

Florida will also be affected by the loss of land. For example, both the burrowing owl and the wood stork are projected to lose 200,000 acres; the Florida panther is projected to lose 300,000 and the Florida black bear 2.3 million acres (Cerulean 2010). With this loss of habitat comes the loss of resources and territory for many more species in Florida and creates a situation where very limited landmass will be available for species currently endangered or of concern to wildlife managers. The loss of these species and the loss of the habitat in which they live may push species to utilize specific areas of land that have been conserved; however this is not necessarily a ―good‖ choice for these species as habitat and resources would become limited.

CONCLUSION

This study found significant genetic differentiation between three populations of bald eagles. Further analysis of these populations should include a more extensive survey of bald eagles in Florida Bay, and if possible, include sampling from areas that are not easily accessible. Investigation using a more extensive set of microsatellites may allow a definitive statement on the degree of differentiation within Florida; and inclusion of other nuclear or mitochondrial DNA loci may resolve the conflicting results between FST and the exact test of differentiation. Inclusion of other populations throughout the bald eagles range would also benefit future studies. By detailing differentiation in populations, and assessing amount of gene flow between populations, local, state or national entities in charge of conservation of the species can include genetic data to backup historical data, such as life histories or unique habitat niches.

It is also possible that the Florida Bay population, even though on the edge of the species range, will harbor beneficial alleles/genes that would benefit the overall population should another bottleneck occur. These alleles/genes may actually give the

Florida Bay bald eagles the ability to adapt to current environmental conditions and thus be more able to adapt to future environmental changes. Dissemination, via gene flow, of these beneficial alleles/genes, may help the overall population to adapt to future environmental changes as well.

In conclusion, Florida Bay may be on the verge of isolation from other mainland populations as indicated by lower genetic variation within this population as compared to

North Florida and Alaska populations. Although, mtDNA FST values did not indicate differentiation between North Florida and Florida Bay populations, the exact test of differentiation indicated that there is significant differentiation. Results from the exact test of differentiation are supported by the microsatellite results indicating significant differentiation between these two populations. As the Florida Bay population is on the edge of the species range, it is possible that the population will be of conservation concern in the future and should be monitored accordingly.

Table 1. Variable sites, numbers and frequency of 22 mtDNA haplotypes (H) based upon Domain I of mtDNA

H Nucleotide Position Samples (N; frequency) AK (67) FLB (27) NFL (17)

157 161 173 228 229 233 234 236 285 72 91 94 96

H1 T C A G G G T C A T T C A 3(0.0448) 0 0 H2 • • • • A A • • • C C • • 1(0.0149) 0 0 H3 • • • • A • • • • • C • • 8(0.119) 16(0.539) 1(0.0588) H4 • • • • A • • • • • • • • 19(0.284) 4(0.148) 6(0.353) H5 • • • • • • • • • • C • • 2(0.0299) 0 3(0.176) H6 C • • • A • C • • • • • • 1(0.0149) 0 0 H7 • • • • • • C • • • C • • 2(0.0299) 0 0 H8 • • • • A A • • • • • • • 4 (0.0597) 0 1(0.0588) H9 • • • • A • • • • C C • • 4(0.0597) 0 0 H10 C • • • • • C • • • C • • 9(0.134) 0 0 H11 C • • A • • C • G • C • • 3(0.0448) 0 0 H12 • A • • A • • • • • • • • 5(0.0746) 0 0 H13 • • • • • • • • G • • • • 1(0.0149) 0 0 H14 • • • • • • C • • • • • • 1(0.0149) 0 0 H15 • • • A A • • • • • • • • 1(0.0149) 0 0 H16 C • • • • • • • • • C • • 1(0.0149) 0 0 H17 C • • A • • C • G • C • G 1(0.0149) 0 0 H18 • • • • A A • • • • C • • 1(0.0149) 0 0 H19 • • • • A • • • • • • G • 0 6(0.222) 3(0.0176) H20 • • T • A • • T • • • • • 0 1(0.037) 0 H21 • • • • A • • T • • • • • 0 0 2(0.118) H22 • • • • A • • T • • C • • 0 0 1(0.0588)

Table 2. Overall population variation for 22 haplotypes from Domain I

Sum of Variance Percentage of Source of Variation d.f. Squares components Variation Among populations 2 8.641 0.109 Va 9.97

Within populations 108 106.098 0.982 Vb 90.03 Total 110 114.739 1.091

FST 0.099 P-value 0.000

Table 3. Matrix of within population FST values (below diagonal) and corresponding P-values (above diagonal) for Domain I of the mtDNA control region

Population Alaska Florida Bay North Florida

Alaska — 0.000 0.019 Florida Bay 0.118 — 0.059 North Florida 0.073 0.070 —

Table 4. Exact test of differentiation P-values for Domain I

Population Alaska Florida Bay

Florida Bay 0.000 — North Florida 0.011 0.000

Table 5. Molecular diversity estimates for Domain I haplotypes. Sample size (n), number of haplotypes (H), nucleotide diversity (π), haplotype diversity (h) and average number of nucleotide differences (k). Standard error is in parenthesis.

Population n H π h k Alaska 67 18 0.0063(±0.0005) 0.880(±0.026) 2.457 (±1.346) Florida Bay 27 4 0.0026(±0.0005) 0.598(±0.082) 1.008 (±0.699) North Florida 17 7 0.0038(±0.0005) 0.838 (±0.063) 1.485 (±0.943) Group Total 111 22 0.160 (±0.097) 0.865 (±0.019) 2.086 (±1.318)

Table 6. Variable sites, numbers and frequency of 24 mtDNA haplotypes (H) based upon Domains I and II of mtDNA control region

H Nucleotide Position Samples (N; frequency)

157 161 173 228 229 233 234 236 285 662 725 72 91 94 96 AK (63) FLB (16) NFL (12)

H1 T C A G G G T C A T T C A ― G 3(0.0476) 0 0 H2 • • • • A A • • • C C • • ― • 1(0.0159) 0 0 H3 • • • • A • • • • • C • • ― • 8(0.127) 8(0.5) 1(0.0667) H4 • • • • A • • • • • • • • ― • 19(0.302) 0 3(0.20) H5 • • • • • • • • • • C • • ― • 2(0.0317) 0 3(0.20) H6 C • • • A • C • • • • • • ― • 1(0.0159) 0 0 H7 • • • • • • C • • • C • • ― • 2(0.0317) 0 0 H8 • • • • A A • • • • • • • ― • 3(0.0476) 0 0 H9 • • • • A • • • • C C • • ― • 4(0.0635) 0 0 H10 C • • • • • C • • • C • • ― • 8(0.127) 0 0 H11 C • • A • • C • G • C • • ― • 3(0.0476) 0 0 H12 • • • • • • • • G • • • • ― • 1(0.0159) 0 0 H13 • • • • • • C • • • • • • ― • 1(0.0159) 0 0 H14 • • • A A • • • • • • • • ― • 1(0.0159) 0 0 H15 C • • • • • • • • • C • • ― • 1(0.0159) 0 0 H16 • A • • A • • • • • • • • ― • 3(0.0476) 0 0 H17 C • • A • • C • G • C • G ― • 1(0.0159) 0 0 H18 • • • • A A • • • • C • • ― • 1(0.0159) 0 0 H19 • • • • A • • • • • • G • T • 0 4(0.25) 2(0.133) H20 • • • • A • • • • • • • • T • 0 3(0.188) H21 • • T • A • • T • • • • • ― • 0 1(0.0625) 0 H22 • • • • A • • T • • • • • ― A 0 0 1(0.0667) H23 • • • • A • • T • • C • • ― • 0 0 1(0.0667) H24 • • • • A • • T • • • • • ― • 0 0 1(0.0667)

Table 7. Overall population variation for 24 haplotypes of Domains I and II

Source of Sum of Variance Percentage of Variation d.f. Squares components Variation Among populations 2 9.582 0.157 Va 12.29

Within populations 91 102.089 1.122 Vb 87.71 Total 93 111.670 1.279

FST 0.123 P-value 0.000

Table 8. Molecular diversity estimates for Domains I and II. Sample size (n), number of haplotypes (H), nucleotide diversity (π), haplotype diversity (h) and average number of nucleotide differences (k).

Population n H π h k Alaska 63 18 0.0033 0.873 2.424 Florida Bay 16 4 0.0016 0.692 1.183 North Florida 15 8 0.0021 0.905 1.543 Overall Total 94 24 0.003 0.896 2.402

Table 9. Matrix of FST values (below diagonal) and corresponding P-values (above diagonal) for Domains I and II of the mtDNA control region

Population Alaska Florida Bay North Florida

Alaska — 0.000±0.000 0.009±0.009 Florida Bay 0.160 — 0.387±0.041 North Florida 0.101 -0.0003 —

Table 10. Exact test of differentiation P-values for Domains I and II

Population Alaska Florida Bay

Florida Bay 0.000±0.000 — North Florida 0.0012±0.0005 0.0121±0.0013

Table 11. Overall population differentiation as weighted average over microsatellite loci

Sum of Variance Source of variation squares components Percentage variation

Among individuals 128.78 0.101 12.62

Within individuals 101 0.695 87.38

Total 229.78 0.796 Average FIS: 0.126 P-value 0.000

Table 12. Observed (HO) and expected (HE) heterozygosity and polymorphic information content (PIC) values for microsatellite loci

Locus HE HO PIC values Populations Populations Populations AK FLB NFL AK FLB NFL AK FLB NFL IEAAAG05 0.613 0.741 0.703 0.567 0.621 0.809 0.558 0.690 0.635 IEAAAG12 0.889 0.842 0.391 0.815 0.714 0.474 0.872 0.819 0.338

Table 13. Population specific FST values of pairs of populations for microsatellite loci; pairwise FST values (below diagonal) and P-values (above diagonal)

Population Alaska Florida Bay North Florida

Alaska — 0.000 0.000 Florida Bay 0.075 — 0.000 North Florida 0.221 0.177 —

Table 14. Averaged population statistics for microsatellite loci. Sample size (n), number loci typed (N), expected heterozygosity (HE, ±Standard error), observed (HO, ±Standard error), unbiased heterozygosity (unbiased HE) and mean number of alleles (A,±Standard error) averaged over both loci

Population n N HE HO Unbaised HE A Alaska 68 2 0.740±0.195 0.684±0.186 0.693±0.475 11.50±6.36 Florida Bay 69 2 0.759±0.059 0.698±0.022 0.654±0.563 8.00±4.24 North Florida 21 2 0.575±0.260 0.713±0.338 0.539±0.403 4.50±2.12

Table 15. Genetic differentiation using locus-by-locus AMOVA; results as weighted average over all microsatellite loci

Source of Variance Percentage variation Sum of squares components variation

Among populations 20.94 0.111 13.28

Among individuals within populations 107.85 0.032 3.84

Within individuals 101.00 0.695 82.88 Total 229.79 0.839

FIS: 0.044 (P = 0.061)

FST: 0.133 (P = 0.000)

FIT 0.171 (P = 0.000)

Table 16. Population specific FIS indices per polymorphic locus.

Locus Average FIS Alaska Florida Bay North Florida

IEAAAG05 0.014 0.083 0.049 -0.262 IEAAAG12 0.069 0.073 0.109 -0.218

Figure 1. Median-joining network of 24 mitochondrial DNA haplotypes.

Figure 2. Collapsed median-joining network utilizing 12 active mitochondrial DNA haplotypes. 47

Figure 3. Triangle plot from STRUCTURE, K=3. Each individual is represented by a colored dot and the color corresponds to the population as entered in the data file.

Figure 4. Bar plot from STRUCTURE, K=3. Each individual is represented by a colored bar and the color corresponds to the population as entered in the data file.

Figure 5. Triangle plot, showing number of putative ancestral migrants, from STRUCTURE, K=3. Each individual is represented by a colored dot and the color corresponds to the population as entered in the data file.

Figure 6. Bar plot, showing number of putative ancestral migrants, from STRUCTURE, K=3. Each individual is represented by a colored bar and the color corresponds to the population as entered in the data file. 49

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