GENETIC DIFFERENTIATION AMONG POPULATIONS OF
BALD EAGLES, HALIAEETUS LEUCOCEPHALUS
by
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
Copyright by Ericka Elizabeth Helmick 2011
ii
GENETIC DIFFERE TIAnON AMO G POPULATIO S OF
BALD EAGLES, HALIAEETUS LEUCOCEPHALUS
by
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 of Master 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.
iv
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.
v
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
vi
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
ix
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
x
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
1
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),
2
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
3
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
4
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).
5
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
6
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
7
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
8
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
9
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 sexbiased dispersal. Female biased dispersal tends to decrease variation in mtDNA between
populations and increase variability within populations (e.g., Huck et al. 2007). Sexbiased 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
10
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
11
(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;
12
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
13
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
14
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
15
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.
16
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 resampling 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.
17
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 25 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)
18
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
19
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).
20
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.
21
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 HardyWeinberg 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.
22
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).
23
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
24
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.
25
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
26
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
27
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
28
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)
29
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.
30
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
31
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
32
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).
33
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
34
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.
35
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.
36
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.
37
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)
285
236
234
233
229
228
173
161
157
96
94
91
72
H1
H2
H3
H4
H5
H6
H7
H8
H9
H10
H11
H12
H13
H14
H15
H16
H17
H18
H19
H20
H21
H22
T
•
•
•
•
C
•
•
•
C
C
•
•
•
•
C
C
•
•
•
•
•
Table 2.
C
•
•
•
•
•
•
•
•
•
•
A
•
•
•
•
•
•
•
•
•
•
A
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
T
•
•
G
•
•
•
•
•
•
•
•
•
A
•
•
•
A
•
A
•
•
•
•
•
G
A
A
A
•
A
•
A
A
•
•
A
•
•
A
•
•
A
A
A
A
A
G
A
•
•
•
•
•
A
•
•
•
•
•
•
•
•
•
A
•
•
•
•
T
•
•
•
•
C
C
•
•
C
C
•
•
C
•
•
C
•
•
•
•
•
C
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
T
T
T
A
•
•
•
•
•
•
•
•
•
G
•
G
•
•
•
G
•
•
•
•
•
T
C
•
•
•
•
•
•
C
•
•
•
•
•
•
•
•
•
•
•
•
•
T
C
C
•
C
•
C
•
C
C
C
•
•
•
•
C
C
C
•
•
•
C
C
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
G
•
•
•
A
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
G
•
•
•
•
•
3(0.0448)
1(0.0149)
8(0.119)
19(0.284)
2(0.0299)
1(0.0149)
2(0.0299)
4 (0.0597)
4(0.0597)
9(0.134)
3(0.0448)
5(0.0746)
1(0.0149)
1(0.0149)
1(0.0149)
1(0.0149)
1(0.0149)
1(0.0149)
0
0
0
0
0
0
16(0.539)
4(0.148)
0
0
0
0
0
0
0
0
0
0
0
0
0
0
6(0.222)
1(0.037)
0
0
0
0
1(0.0588)
6(0.353)
3(0.176)
0
0
1(0.0588)
0
0
0
0
0
0
0
0
0
0
3(0.0176)
0
2(0.118)
1(0.0588)
Overall population variation for 22 haplotypes from Domain I
Source of Variation
d.f.
Sum of
Squares
Among populations
2
8.641
0.109 Va
9.97
Within populations
108
106.098
0.982 Vb
90.03
110
114.739
1.091
Total
FST
0.099
P-value
0.000
38
Variance
components
Percentage of
Variation
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
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.
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)
40
Table 6.
Variable sites, numbers and frequency of 24 mtDNA haplotypes (H) based
upon Domains I and II of mtDNA control region
H
Samples (N; frequency)
662
285
236
234
233
229
228
173
161
157
96
94
91
72
725
H1
H2
H3
H4
H5
H6
H7
H8
H9
H10
H11
H12
H13
H14
H15
H16
H17
H18
H19
H20
H21
H22
H23
H24
Nucleotide Position
AK (63)
―
―
―
―
―
―
―
―
―
―
―
―
―
―
―
―
―
―
T
T
―
―
―
―
G
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
A
•
•
3(0.0476)
1(0.0159)
8(0.127)
19(0.302)
2(0.0317)
1(0.0159)
2(0.0317)
3(0.0476)
4(0.0635)
8(0.127)
3(0.0476)
1(0.0159)
1(0.0159)
1(0.0159)
1(0.0159)
3(0.0476)
1(0.0159)
1(0.0159)
0
0
0
0
0
0
T
•
•
•
•
C
•
•
•
•
A
•
•
•
•
G
•
•
•
•
G
A
A
A
•
G
A
•
•
•
T
•
•
•
•
C •
•
• A • C
•
•
•
C
C
•
•
•
C
•
C
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
T
•
•
•
•
•
•
•
A
•
•
A
•
•
A
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
A
•
•
•
•
•
•
•
•
•
A
A
•
•
•
•
A
•
A
•
A
A
A
A
A
A
A
•
A
•
•
•
•
•
•
•
•
•
A
•
•
•
•
•
•
C
•
•
C
C
•
C
•
•
•
C
•
•
•
•
•
•
•
C
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
T
T
T
T
A
•
•
•
•
•
•
•
•
•
G
G
•
•
•
•
G
•
•
•
•
•
•
•
T
C
•
•
•
•
•
•
C
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
T
C
C
•
C
•
C
•
C
C
C
•
•
•
C
•
C
C
•
•
•
•
C
•
C
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
G
•
•
•
•
•
41
A
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
G
•
•
•
•
•
•
•
FLB (16)
0
0
8(0.5)
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4(0.25)
3(0.188)
1(0.0625)
0
0
0
NFL (12)
0
0
1(0.0667)
3(0.20)
3(0.20)
0
0
0
0
0
0
0
0
0
0
0
0
0
2(0.133)
0
1(0.0667)
1(0.0667)
1(0.0667)
Table 7.
Overall population variation for 24 haplotypes of Domains I and II
Source of
Variation
d.f.
Sum of
Squares
Variance
components
Percentage of
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
Table 8.
FST
0.123
P-value
0.000
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
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
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
—
43
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
Source of variation
Sum of
squares
Variance
components
Percentage variation
Among individuals
128.78
0.101
12.62
Within individuals
101
0.695
87.38
229.78
0.126
0.000
0.796
Total
Average FIS:
P-value
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
44
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
Alaska
68
2
Florida Bay
69
North Florida
21
HE
HO
Unbaised HE
A
0.740±0.195 0.684±0.186
0.693±0.475
11.50±6.36
2
0.759±0.059 0.698±0.022
0.654±0.563
8.00±4.24
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
variation
Sum of squares
Variance
components
Percentage
variation
Among
populations
20.94
0.111
13.28
Among individuals
within populations
107.85
0.032
3.84
Within individuals
Total
101.00
229.79
0.695
0.839
82.88
FIS:
0.044 (P = 0.061)
FST:
0.133 (P = 0.000)
FIT
0.171 (P = 0.000)
45
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
46
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.
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.
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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