The Use of Genetics for the Management of a Recovering Population: Temporal Assessment of Migratory Peregrine Falcons in North America
et al. (2010) The Use of Genetics for the Management of a Recovering Population: Temporal
Assessment of Migratory Peregrine Falcons in North America. PLoS ONE 5(11): e14042. doi:10.1371/journal.pone.0014042
The Use of Genetics for the Management of a Recovering Population: Temporal Assessment of Migratory Peregrine Falcons in North America
Jeff A. Johnson 0
Sandra L. Talbot 0
George K. Sage 0
Kurt K. Burnham 0
Joseph W. Brown 0
Tom L. Maechtle 0
William S. Seegar 0
Michael A. Yates 0
Bud Anderson 0
David P. Mindell 0
Robert C. Fleischer, Smithsonian Institution National Zoological Park, United States of America
0 1 Department of Biological Sciences, Institute of Applied Sciences, University of North Texas, Denton, Texas, United States of America, 2 Alaska Science Center, U.S. Geological Survey, Anchorage, Alaska, United States of America, 3 High Arctic Institute, Orion, Illinois, United States of America, 4 Department of Biological Sciences, University of Idaho, Moscow, Idaho, United States of America , 5 Sheridan , Wyoming, United States of America, 6 Edgewood Research Development and Engineering Center, Department of Army, Aberdeen Proving Ground, Maryland, United States of America, 7 Raptor Research Center, Boise State University , Minden , Nevada, United States of America, 8 Falcon Research Group, Bow, Washington, United States of America, 9 California Academy of Sciences , San Francisco, California , United States of America
Background: Our ability to monitor populations or species that were once threatened or endangered and in the process of recovery is enhanced by using genetic methods to assess overall population stability and size over time. This can be accomplished most directly by obtaining genetic measures from temporally-spaced samples that reflect the overall stability of the population as given by changes in genetic diversity levels (allelic richness and heterozygosity), degree of population differentiation (FST and DEST), and effective population size (Ne). The primary goal of any recovery effort is to produce a longterm self-sustaining population, and these genetic measures provide a metric by which we can gauge our progress and help make important management decisions. Methodology/Principal Findings: The peregrine falcon in North America (Falco peregrinus tundrius and anatum) was delisted in 1994 and 1999, respectively, and its abundance will be monitored by the species Recovery Team every three years until 2015. Although the United States Fish and Wildlife Service makes a distinction between tundrius and anatum subspecies, our genetic results based on eleven microsatellite loci suggest limited differentiation that can be attributed to an isolation by distance relationship and warrant no delineation of these two subspecies in its northern latitudinal distribution from Alaska through Canada into Greenland. Using temporal samples collected at Padre Island, Texas during migration (seven temporal time periods between 1985-2007), no significant differences in genetic diversity or significant population differentiation in allele frequencies between time periods were observed and were indistinguishable from those obtained from tundrius/anatum breeding locations throughout their northern distribution. Estimates of harmonic mean Ne were variable and imprecise, but always greater than 500 when employing multiple temporal genetic methods. Conclusions/Significance: These results, including those from simulations to assess the power of each method to estimate Ne, suggest a stable or growing population, which is consistent with ongoing field-based monitoring surveys. Therefore, historic and continuing efforts to prevent the extinction of the peregrine falcon in North America appear successful with no indication of recent decline, at least from the northern latitude range-wide perspective. The results also further highlight the importance of archiving samples and their use for continual assessment of population recovery and long-term viability.
Funding: Funding for field work and sample collection was generously provided by the U.S. Armys Edgewood Research Development and Engineering Center,
The Wolf Creek Charitable Foundation, The Grassland Charitable Foundation, Ruth and Brian Mutch, the North American Falconers Association, The Offield Family
Foundation, and The Peregrine Fund. Funding for the genetic portion of this study was provided by The Peregrine Fund and the U. S. Geological Survey Alaska
Science Center. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
In cases where populations or species have a recent history of
decline followed by increase, the use of genetic data can be a
powerful tool for monitoring progress in conservation efforts .
For example, estimates of the genetic diversity (allelic and
heterozygosity), effective population size (Ne), gene flow or
dispersal, and population admixture can provide information
useful for making future management decisions to prevent further
population decline and extinction. For species previously
considered threatened or endangered, section 4(g)(1) of the Endangered
Species Act requires the U.S. Fish and Wildlife Service in
cooperation with the States to monitor a species for a minimum of
five years after being removed from the List of Endangered and
Threatened Wildlife and Plants to ensure they maintain
nonthreatened status. The incorporation of genetic monitoring into
such programs can provide information on the progress made in
creating and maintaining a self-sustaining population, regardless if
genetic measures were addressed in the original rulemaking or
listing process [e.g., 57]. This approach can be particularly
important with populations or species that have wide geographic
distributions over challenging terrain (e.g., mountainous) where
accurate demographic measures from the field are costly or
difficult to obtain.
The peregrine falcon (Falco peregrinus) provides an example
species recovery plan that could benefit from ongoing genetic
monitoring. Globally, the peregrine falcon consists of nineteen
subspecies and is found on every continent with the exception of
Antarctica [8,9]. In North America, three subspecies are currently
recognized . F. p. pealei is a year-round resident of the Pacific
Northwest from the coasts of northern Washington and British
Columbia extending to the Aleutian Islands in Alaska. F. p. tundrius
breeds throughout the Arctic tundra of Alaska, Canada and
western Greenland, and F. p. anatum breeds south of the tundra to
northern Mexico, except in coastal areas in the Pacific Northwest.
Both F. p. tundrius and anatum are migratory with tundrius wintering
as far south as central Argentina and Chile in South America
[11,12; see also 13].
Historic estimates of peregrine falcon abundance range from
400 to 500 pairs in Greenland , 1,000 to 3,500  to 7,548
pairs  in the Arctic, and 7,000 to 10,000 total pairs in North
America . Following the late 1940s, many peregrine falcon
populations suffered a steady decline due primarily to exposure
from organochlorines, including DDT
(1,1,1-trichloro-2,2-bis[pchlorophenyl]-ethane) and its principle metabolite DDE
(1,1dichloro-2, 2-bis[p-chlorophenyl]-ethylene), which caused direct
mortality or adversely affected their reproduction and egg
production . By 1964, peregrine falcons nesting east of
the Rocky Mountains south of the boreal forests in Canada (F. p.
anatum) were essentially extirpated [22,23]. To the west in the
Rocky Mountains, the number of peregrine falcons (F. p. anatum)
was also significantly reduced with only 15 (29%) of 51 known
historic nest sites occupied in 1964 , and by 1979 after a much
more extensive survey of historic nest site locations, only 12 (7.5%)
out of 160 were occupied covering an area of four million km2
. Declines in the Arctic (F. p. tundrius) were less severe;
however, it is estimated that their abundance was reduced
approximately 50 to 60% by 1975 [20,26]. For the Peales falcon
(F. p. pealei), abundance remained relatively stable during this time
period , presumably due to their specialized diet feeding
predominately on sea birds (e.g., alcids; [27,28] as opposed to
terrestrial avian prey that were more likely to be exposed to DDT.
In the past two decades, peregrine falcons in the U.S. and
Canada, including Europe [8,29], have made a remarkable
recovery due to the ban of DDT in 1969 and 1972 in Canada
and the U.S., respectively, the inclusion of F. p. anatum and F. p.
tundrius on the precursor of the federal Endangered Species List in
1970 (35 FR 16047), and extensive propagation and release efforts
made by many conservation groups . As a result, the species
was delisted in 1994 (F. p. tundrius; 59 FR 50796), and 1999 (F. p.
anatum; 64 FR 46541-46558) in the U. S., and reassigned Special
Concern status in April 2007 in Canada. Current estimate of total
breeding population size for both F. p. tundrius and F. p. anatum is
between 4,300 and 10,400 ; considering immature and floater
(non-breeders) individuals, the total population size could be
between 40,000 to 50,000 . These estimates are based on both
direct and indirect counts, including projections made from
available potential nesting sites. Because our current estimates of
population size are imprecise, significant uncertainty exists for
making decisions for management purposes.
The primary aim of this study was to determine the overall
stability and effective size of high latitude peregrine falcon
populations in North American and Greenland, F. p. anatum and
tundrius. This was accomplished using temporally segregated
samples from both migratory and breeding peregrine falcons
sampled during fall and spring migration through Padre Island,
Texas over a 21 year period (,7 generations) and from
southwestern Greenland over a 1214 year period (,4
generations), respectively. We assume that the sampled migratory
individuals from Padre Island used in this study possess high
latitude breeding distributions throughout Alaska, Canada and
Greenland as supported by band recovery , satellite telemetry
studies [12,32,33] and genetics (this study; see below), and therefore
represent the northern peregrine falcon breeding population in
North America. Our temporal sampling allowed the investigation
of overall population stability by assessing allele frequency change
over time and the estimation of Ne for both the migrant population
and a focal breeding population in Greenland. These results are
useful for conservation monitoring purposes and for making
decisions that may influence future peregrine falcon population
viability in North America.
Sampling and DNA extraction
All samples were obtained from birds caught and bled under
government permits, and all birds were released after processing.
A total of 292 peregrine falcons were sampled for genetic analyses
during migration through Padre Island, Texas. Blood samples
were taken during both autumn and spring migration for each of
the following temporal subsets: 1985/86, 1988/89, and 2006/07,
with additional samples from spring 2001 migration period (see
Table 1 for sample sizes). All samples were kept frozen, and DNA
extractions were performed using methods described elsewhere
. An additional 349 samples were obtained from the
contemporary northern breeding distribution of peregrine falcons
throughout Alaska, Canada and western Greenland, of which 168
samples were used in a previous study . The samples collected
from breeding territories were included in this study to verify the
degree of population subdivision throughout their northern
breeding distribution from which the Padre Island migrants likely
originated. For analysis purposes and adequate sample size
considerations, individuals were grouped into geographic sampling
regions for each of the subspecies (see Fig. 1; Table 1). Two
additional subspecies, F. p. cassini (n = 25) from South America and
F. p. macropus (n = 15) from Australia, were included in the analyses
for comparative purposes.
Eleven microsatellite loci originally developed for the peregrine
falcon (Fp5, Fp13, Fp31, Fp46-1, Fp54, Fp79-4, Fp82-2, Fp86-2,
Fp89, Fp92-1, Fp107; ) were used for the microsatellite
analyses. All microsatellite loci were dinucleotide repeats, and
protocols used for PCR amplification have been described
elsewhere [34,35,37]. Genotypic data generated in different
laboratories using different procedures were calibrated using a
subset of samples (n$4) for each of the eleven microsatellite loci.
No ambiguities were observed across all loci after calibration.
Microsatellite genotypes were tested for
and departure from Hardy-Weinberg
F. p. tundrius and anatum migrants (Padre Island, TX)
equilibrium within each population at each locus using the
computer program GDA . Sequential Bonferroni corrections
were applied to correct for multiple simultaneous comparisons
. Mean number of alleles per locus (A) and observed (Ho) and
expected (He) heterozygosity values were calculated using GDA.
Measures of allelic richness (AR) were calculated using the
program FSTAT version 188.8.131.52 . AR estimates control for
uneven sample sizes among populations . Differences in
microsatellite genetic diversity estimates between sample locations
and time periods were tested for significance using a Wilcoxon
signed-rank test. Measures of FIS and its significance for each
sampled population was calculated using Fishers exact test within
Genepop v. 4.0.10 ([42,43]; http://genepop.curtin.edu.au) after
adjusting the p-value to account for multiple simultaneous
Population subdivision. The degree of population
subdivision between sample locations and temporal sampling periods was
investigated using the Bayesian method of Pritchard et al. 
and Falush et al. , implemented in the program STRUCTURE
version 2.1. The number of genetically distinct clusters (K), or
populations, was identified based on allele frequencies across loci
while minimizing linkage and violations to Hardy-Weinberg
equilibrium. The most likely value of K is determined by
comparing the likelihood of the data for different values of K.
To determine the number of clusters, we also calculated the rate of
change in the log probability of the data between successive K
values (DK) plotted against K following Evanno et al. . Analyses
were performed with all samples and subspecies, including
additional analysis without F. p. pealei, F. p. cassini and F. p.
macropus samples to determine the influence the latter three
subspecies have on the overall approximation of K. Calculations
were conducted with a burn-in period of 105 iterations followed by
an additional 106 iterations. Each simulation from K = 1 to 8 was
performed four times using an ancestry model allowing admixture
where individual a was inferred from the data for each cluster
(alpha .1 means that most individuals are admixed; ), and a
model of correlated allele frequencies that did not include prior
information on sampling origin. Final results from STRUCTURE
were visualized using the program DISTRUCT . The degree of
population subdivision was also explored as implemented in the
software TESS . This latter approach determines the number
of groups similar to STRUCTURE, but differs by taking into account
the spatial organization of individuals and incorporates a
regularization procedure that helps facilitate the choice of K
. The method implemented in TESS is also less influenced
by Isolation by Distance compared to methods such as STRUCTURE
when identifying the number of distinct clusters when clinal
variation exists [50,51]. The MCMC algorithm was run under
Figure 1. Peregrine falcon population sampling locations in North America. Samples sizes for each area are given in Table 1.
admixture model with interaction parameter Y = 0.7, with 10,000
burn-in and 50,000 sweeps. Twenty independent iterations were
run for K = 27 and after identifying the value of K that produced
the highest likelihood, this was run 100 times and the 20 highest
likelihood runs for Kmax were averaged using CLUMPP version
1.1.2  applying the Full Search algorithm and the G pairwise
matrix similarity statistics.
Estimates of genetic differentiation based on pairwise FST and
Dest values were also obtained to further investigate the overall
stability between sampling locations and temporal sampling
periods. FST values were calculated following Weir & Cockerham
 as implemented in ARLEQUIN version 3.11 , and Dest
values  were calculated using SPADE  and bootstrap
proportions for estimates of 95% confidence intervals (CI) were
based on 1,000 permutations. The program Isolation By Distance
Web Service (IBDWS), version 3.16  was used to perform a
Mantel test with 10,000 randomizations to examine the
correlation between matrices of genetic distance (pairwise FST and Dest)
and geographic distance of breeding sample locations of anatum
and tundrius. A second set of analyses that included pealei samples
were also performed. Geographic distance between sample
locations was measured as the euclidean distance (km) using the
ruler implemented in Google Earth version 184.108.40.2069.
Effective population size. Genetic estimates of effective
population size (Ne) were calculated using different methodological
approaches to assess the robustness of our results. Two general
approaches to estimating Ne were explored: 1) analyzing single
time period population samples, and 2) analyzing multiple
temporal samples from the same population. Although the
temporal approach has been shown to outperform single-sample
methods when assuming a closed population [1,58,59], recent
analytical developments have improved both the precision and
accuracy of Ne estimates from individual population samples [e.g.,
60,61]. We used a method originally based on linkage
disequilibrium (LD; ) to estimate contemporary Ne from
individual sampling periods. The LD method included a bias
correction  as implemented in the program LDNE , which
has been shown to improve performance even with non-ideal
populations (e.g. skewed sex ratios or non-random variance in
reproductive success; ). Estimates of Ne were obtained for each
of the spring migratory sampling periods collected from Padre
Island, TX. We do not use this method to estimate Ne for any of
the breeding populations because we cannot assume a closed
population [64,65]. A jackknife method was used to obtain 95%
confidence intervals (CI) on loci, and estimates were calculated
assuming random mating and excluded all alleles #0.01 .
To estimate Ne based on multiple sampling periods for the
migrant population from Padre Island, we employed three
methods that are based on the premise that temporal variance
in neutral genetic allele frequencies is inversely proportional to Ne
due to the effects of genetic drift in the absence of migration and
mutation [66,67]. The first method is based on the standardized
variance of change in allele frequencies (Fk) between at least two
sampling periods (equation 11, ; see also ). Because bias
can exist with this method when estimates are based on small
sample sizes and skewed allele frequencies , we used the
weighing scheme of Jorde & Ryman  to provide an estimate of
Ne with our dataset. Using sampling plan I , estimates of Ne
and 95% confidence intervals were obtained using the program
TEMPOFS . Sampling plan I (i.e. nondestructive sampling)
requires an estimate of population census size (N) to calculate Ne.
Because we do not have a precise estimate of N for the peregrine
falcon population, we calculated Ne using a range of values from
1,000 to 100,000 individuals to determine if uncertainty in N
influences our estimate of Ne. Two additional estimates of Ne were
obtained for the Padre Island migrant population using a
coalescent-based method as employed in the program TM3 
and a pseudo-likelihood method implemented in the program
MLNE 2.3 [65,73]. Both methods were used to calculate Ne of the
migrant population while assuming an Ne-MAX of 10,000, no
immigration, and a generation time of three years.
To estimate Ne based on multiple temporal sampling periods for
the Greenland population, we used MLNE 2.3 while assuming 1) a
closed population (NeCLOSED), and 2) accounting for immigration
from a potential source population (NeOPEN). Estimates of Ne from
temporal data when mistakenly assuming a closed population are
likely to be incorrect because, in addition to the effects of genetic
drift, immigration will influence allele frequencies of the
population to an extent that is related to the amount of
differentiation between populations [59,64,65,74]. Therefore,
when immigration is present, methods that account for this effect
should be employed to generate accurate estimates of Ne. Using
MLNE, two temporally spaced datasets from Greenland (1990 and
2001-04; four generations) were used to estimate both NeCLOSED
and NeOPEN to assess the potential influence of immigration on our
estimate of Ne and for comparative purposes with our estimates
from the migrant Padre Island population Ne. Similar to the
migrant dataset, 10,000 was used as our NeMAX. We used the
pooled allele frequencies from contemporary tundrius and anatum
breeding locations as the potential source population for
immigrants into Greenland for estimating NeOPEN. Additional
estimates were also calculated using the spring migrants from
Padre Island, TX as the source population to evaluate the choice
of source population on NeOPEN.
Simulations. To assess the utility and precision of methods
used to estimate Ne, we used simulated data representing multiple
populations of specified size. This was done primarily to determine
our ability to estimate Ne in populations of large size where drift is
not likely to play a strong role influencing allele frequency change
over a short time periods, e.g., seven generations (as with this
study). Using the empirical data from spring 1986 as our initial
sampling period (T0), we used the program BottleSim  to
simulate seven generations (T7) at population sizes Ne = 50, 100,
200, 300, 500, 1000, 2000, and 5000. For each simulation based
on 1000 iterations, we used the settings for maximum generation
overlap (100%), random mating, three years for age at first
breeding, 12-year longevity, and equal sex ratios . Estimates of
Ne were calculated similar to the empirical data, and their
deviations from the specified Ne were then determined and directly
compared to results obtained using the migrant Padre, TX
temporal dataset. We also assessed levels of differentiation among
ten populations from each of the simulated datasets of known size
using similar samples sizes (n = 46). This was done to investigate
the development of genetic differentiation relative to Ne after seven
generations had passed similar to our empirical dataset.
Genetic diversity measures
Eight of the eleven microsatellite loci were polymorphic in all
peregrine falcon sampling locations. Locus Fp5 was monomorphic
in F. p. tundrius Northwest and Ungava Bay, F. p. anatum Alberta
and Northeast, F. p. cassini, and Padre Island spring 2001 migrant
sampling locations. Loci Fp54 and Fp92-1were both
monomorphic for F. p. macropus. After adjusting for multiple comparisons,
significant departures from Hardy-Weinberg equilibrium in the
form of heterozygote deficiencies were observed in one locus
(Fp92-1) among four sampling locations (Northwest and 1990
Greenland, F. p. tundrius; Northwest, F. p. anatum; Padre Island fall
migrants 1985). Similarly, significant FIS values were observed
with five sampled locations (Table 1; heterozygote deficit), and
three (Greenland 1990 & 2001-04, F. p. tundrius; Northwest, F. p.
anatum) of the five remained significant after excluding locus
Fp921 from the analysis. No pairwise comparisons testing for linkage
disequilibrium were significant after correcting for multiple
The majority of microsatellite genetic diversity estimates do not
differ significantly (Wilcoxon signed-rank test, p.0.05) between
geographic sampling locations in North America or between
temporal sampling periods from Padre Island, Texas or Greenland
(Table 1). Allelic richness (AR) varied from 3.760.5 (6 s.e.) alleles
per locus in F. p. pealei to 4.660.7 alleles per locus in F. p. tundrius
from western Greenland. The few cases for F. p. pealei possessed
significantly lower AR compared to the tundrius populations in
Nunavut (Z = 22.667; p = 0.008) and Greenland (Z = 22.667;
p = 0.008) and the anatum population in Ontario (Z = 22.134;
p = 0.033). Expected heterozygosity (He) ranged from 0.48560.092
in Ungava Bay F. p. tundrius from northeastern Canada to
0.55760.083 in F. p. anatum from eastern Canada. A significant
difference in He was observed between F. p. tundrius populations in
Ungava Bay and Nunavut (Z = 22.223; p = 0.026). Genetic
diversity estimates for F. p. cassini and macropus from Argentina
and Australia, respectively, were significantly lower in all
comparisons of AR when compared to all sampling locations for
the North American subspecies anatum, tundrius, and pealei (p,0.02);
Table 1). F. p. macropus He was significantly lower than He estimates
from all anatum, tundrius, and pealei populations (p,0.04), with the
exception of Ungava Bay F. p. tundrius (Z = 21.778; p = 0.075). F. p.
cassini He was not significantly different (p.0.06) from any of the
He estimates from anatum, tundrius, and pealei populations.
The posterior probability values for each value of K with
STRUCTURE, while using the complete dataset that included F. p.
cassini and macropus, plateau at K = 3 to K = 5 with Ln P(D) values
from multiple runs at K = 3 (SD = 2.2) and K = 4 (SD = 3.6) being
more consistent across runs compared to K = 5 (SD = 18.2; Fig. 2).
When we used DK to infer the number of clusters, K = 3 was
clearly inferred for the complete dataset using all sampled
subspecies. Results from STRUCTURE when using only data from
North American subspecies (F. p. pealei, tundrius, anatum) indicated
the highest posterior probability values for K = 2 (Ln
P(D) = 215843.5), while K = 1 and K = 3 had lower posterior
probability values (215886.7 and 215937.6, respectively). We are
unable to evaluate between K = 1 and K = 2 using the DK method
(see Evanno et al. 2005). For K = 2 in this second analysis, F. p.
tundrius and anatum sample locations possessed a relatively high
proportion of membership to the same inferred cluster (0.870 to
0.959 and 0.759 to 0.922, respectively) and the majority of the
pealei samples were assigned to the second cluster at a lower
proportion (proportion of membership = 0.661). Samples
collected from Padre Island across all years clustered with high support
(.0.924; data not shown) with samples collected from breeding
grounds identified as F. p. tundrius/anatum.
The results from TESS corroborated those found with
STRUCTURE. The minimum DIC value (30669) was achieved with
Figure 2. Results from STRUCTURE analysis for all sampled peregrine falcon populations. (A) Assignment of individuals to K = 3 to 5 inferred
clusters based on 11 microsatellite loci. Colors indicate different inferred clusters and their magnitude represents the posterior probability that the
individual belongs to a particular cluster. (B) Estimated log probability values [Ln (P(D)] for each run for K = 1 to 8. The box highlighting K = 3 to 5
indicates the lowest values of K with the highest likelihood values. These results remained similar after excluding F. p. cassini and macropus from the
analysis (data not shown).
K = 4 (K = 2, DIC value = 31736; K = 3, DIC value = 31163) and
plateaus with higher values of K. The average value for the 20%
best runs when K = 4 showed that the tundrius and anatum
individuals clustered in a single group, which also included all of
the migrant samples collected from Padre Island, whereas the
remaining three subspecies, pealei, macropus and cassini, were in
separate clusters each with high support (data not shown).
Estimates of FST and Dest largely agreed with the results from
STRUCTURE and TESS, indicating strong genetic differentiation
between subspecies with the exception of those comparisons
between F. p. tundrius and anatum which showed much lower levels
of differentiation (Table S1). Although 16 of the 20 pairwise FST
comparisons were significant (p,0.001) after correction for
multiple comparisons between F. p. tundrius and anatum sampled
breeding territories, the values were low (FST = 0.006 to 0.050) and
similar in magnitude to those obtained from within subspecies
comparisons (FST = 0.007 to 0.026 and 0.016 to 0.033 for tundrius
and anatum, respectively) of which some were also significant
(p,0.001; see Table S1). Estimates of Dest between F. p. tundrius
and anatum were also low (Dest = 0.000 to 0.032), with only one
comparison being significantly different from zero (Ontario &
Greenland_1990). Dest values between F. p. tundrius and anatum
were similar to those obtained from pairwise comparisons between
sample locations within F. p. anatum (Dest = 0.003 to 0.029), while
pairwise comparisons between tundrius subspecies locations were
consistently low (Dest = 0.000 to 0.012) and not significantly
different from zero (Table S1). After excluding samples from
Ungava Bay due to low population sample size (n = 15), significant
isolation by distance was observed among breeding sample
locations of F. p. tundrius and anatum using both pairwise FST
(r = 0.663; Mantel test P = 0.003) and Dest (r = 0.649; Mantel test
P = 0.001) measures. Similarly, significant isolation by distance was
observed among F. p. tundrius and anatum sample locations when
southwest Ontario samples were excluded from the analyses (FST,
r = 0.668, Mantel test P = 0.012; DEST, r = 0.579, Mantel test
P = 0.035). However, isolation by distance was not supported when
we included F. p. pealei genetic distance measures with tundrius and
anatum breeding locations (FST, r = 0.092, Mantel test P = 0.324;
Dest, r = 0.113, Mantel test P = 0.288).
When comparing migrant peregrines from Padre Island with
samples collected on breeding territories, far fewer pairwise FST and
Dest comparisons were significant with those made with F. p. tundrius
(23% and 0% out of 35 comparisons, respectively) than with anatum
samples (71% and 14% out of 28 comparisons, respectively; Table
S1). No pairwise FST or Dest comparisons between migrant Padre
Island temporal samples were significant across sampling periods,
indicating stable allele frequency distributions over a 22-year
period. The simulated datasets of known size at Ne of #300
following seven generations were all significantly different from each
other based on allele frequency distributions after sequential
Bonferroni correction; whereas at Ne of 500, eight of forty-five
comparisons were significant and at Ne of $1000, none of the
pairwise comparisons were significant (data not shown).
Effective population size
Point estimates of Ne for the migrant peregrine falcon
population varied depending on the choice of method, but in all
cases, the values were high and ranged from 509 to .10,000
breeding individuals (Table 2). Reported 95% confidence levels
around each point estimates were wide, with all of cases, regardless
of method, extending to infinity, or at least the maximum
allowable value (.10,000) used in each of the analyses (i.e.,
Ne-MAX). The choice of the census population size (N, 1,000 to
100,000; see Methods) used with the method implemented in the
program TempoFs did not substantially influence our estimate of
Ne. For example, Ne was 450 (117-infinity) and 516 (121-infinity)
using an N of 1,000 or 100,000, respectively. Estimates of Ne from
the method LDNe using spring migratory single time periods
ranged from 187.8 in the 2001 dataset and .10,000 in the
remaining three periods (1986, 1989, and 2007) with 95% CIs
ranging between 59.5 to infinity.
MLNE analyses on the Greenland population while allowing for
immigration (see Methods) produced an NeOPEN estimate of 122.7
(95% CI 55.3-590.7) with a joint migration estimate of 0.103 (95%
CI 0.024-0.234). The choice of source population did not
substantially affect our estimates of NeOPEN. When we defined
the source population for potential immigrants as Padre Spring
2007, our estimates of NeOPEN was 102.5 (95% CI 44.7-470.7) with
a joint migration estimate of 0.221 (95% CI 0.056-0.734). Estimate
of NeCLOSED for the Greenland peregrine falcon population was
158.9 (95% CI 75.7-804.5).
Results from our simulated datasets of known size ranging from
50 to 5,000 breeding individuals further supported our empirical
data suggesting that the migrant population is of large size. As
population size increased across simulations, the accuracy and
precision of each method for calculating Ne decreased (Fig. 3;
Table 3). Above Ne of 500, for example, point estimates from all
methods differed from the actual simulated size by more than 200
individuals with wide 95% confidence intervals (Fig. 3).
Interestingly, point count estimates of Ne at levels below 500 were more
often overestimated, while $500 tended to be underestimated.
The one obvious exception was with LDNe where estimates of
Ne$1,000 were overestimated (Fig. 3; Table 3). These results
suggest that our estimate of the migrant peregrine falcon
population Ne is at least 500 and possibly .1000 breeding
Method used to estimate Ne
95% confidence intervals are provided in parentheses below each point estimate. Values indicated as infinity represent .10,000 breeding individuals (Ne-MAX of
1Estimates of Ne from LDNe are based on a single time period (i.e., the 7th generation at the particular population size).
Figure 3. Estimates of Ne from simulated populations of known size. Three different temporal methods (TempoFs, TM3, MLNE) and a fourth
method (LDNe) based on a single time period were used to estimate Ne. Similar to our empirical data from Padre Island, TX, temporal estimates were
based on seven generations (T0 - T7), while the single time period estimate is from the simulated 7th generation (see methods). Bars represent
deviated estimates of Ne from simulated population size and the dotted vertical lines reflect 95% confidence intervals. = values beyond the range of
y-axis (see also Table 3 for point estimates used in this figure).
Table 3. Estimates of Ne from simulated populations of known size.
Method used to estimate Ne
95% confidence intervals are provided in parentheses below each point estimate. Values indicated as infinity represent .10,000 breeding individuals (Ne-MAX of
1Estimates of Ne from LDNe are based on a single time period (i.e., the 7th generation at the particular population size).
Population genetic data are a valuable tool for monitoring
populations and species [e.g., 24,7], particularly those in the
process of recovery. The peregrine falcon in North America is one
such species that has required extensive monitoring to assess its
progress toward achieving a sustainable population as defined by a
federally mandated monitoring plan  subsequent to the
species delisting from endangered status in 1999 (64 FR
4654146558). Here, we have utilized genetic data to assess the stability of
the migrant peregrine falcon population in North America. Our
results based on multiple methodological approaches indicate that
this species is stable from a population genetics perspective as
documented for breeding territories sampled throughout its entire
northern latitudinal distribution including its migratory population
at Padre Island, Texas.
Assessment of population structure F. p. tundrius and
To support our use of peregrine falcon samples collected during
migration at Padre Island to assess the species northern high
latitude population genetic stability, it was important to document
levels of genetic differentiation among all presumed northern
breeding areas that contribute to the migrant population. In a
previous study investigating the genetic structure of peregrine
falcon sampling locations across Canada, Brown et al. 
documented no differentiation based on FST and STRUCTURE
analyses between F. p. tundrius and anatum using samples collected
prior to their population decline in the 1950s. These results
suggested that a continuous phenotypic cline existed based on the
subtle morphological and behavioral characters used originally to
describe the separation of the two subspecies (e.g., ). In
contrast, however, Brown et al.  did document low but
significant differentiation between the two subspecies when using
contemporary samples. This was particularly the case with sample
locations from southeastern Ontario where stronger allele
frequency fluctuations and allelic introgression from captive
released birds were more likely to have occurred as compared to
their sampling locations further north and to the west (see also
[78,79]; Fig. 2; Table S1).
Nearly 7,000 peregrine falcons were released in the United
States and Canada between the years 1974 and 1999 . Many
of these birds originated from breeding stock that included
nonnative peregrine subspecies such as peregrinus and brookei from
Europe, cassini from South America, and macropus from Australia,
but to a lesser extent than individuals with either anatum, tundrius or
pealei pure or mixed-ancestry . Because peregrine falcons
were extirpated throughout southeastern Canada and the United
States east of the Rocky Mountains, the peregrine falcons that now
reside in these areas are largely the result of the release programs
[e.g., 82]. Therefore, it is not surprising that contemporary low
levels of genetic differentiation were observed between peregrine
falcon populations sampled in geographic areas that have been
reestablished with captive-bred individuals, while no genetic
differentiation was observed prior to the decline. Other release
and supplementation programs have also documented similar
effects [e.g., 8386]. For example, Jacobsen et al.  reported
significant microsatellite allele frequency changes before and after
the reintroduction project of peregrine falcons (F. p. peregrinus) in
southern Scandinavia .
Using F. p. tundrius and anatum samples from Brown et al. (;
n = 140) with additional sampling (n = 176) of both subspecies
from previously unsampled geographic areas in Alaska (tundrius
and anatum) and Greenland (tundrius), our results indicate little if no
genetic population differentiation (STRUCTURE, TESS, FST and
Dest) among sampling locations of these two subspecies. The
significant values that were observed were those comparisons that
included F. p. anatum samples from eastern Canada or
geographically distant locations, including three other subspecies (Fig. 2;
Table S1). A significant Mantel correlation between genetic (FST)
and geographic distance matrices suggests an isolation by distance
model of population differentiation (or regional equilibrium; )
throughout the high-latitude breeding distribution of tundrius and
anatum. These results were also supported by a new measure (i.e.,
Dest, ) that is useful for investigating population differentiation
between geographic locations when using highly polymorphic
markers such as microsatellite DNA [see also 8991]. In this case,
only a few pairwise tundrius/anatum or anatum/anatum comparisons
were significant, all of which were those comparisons made with
the Ontario sampled location (Table S1), and similar to FST, an
isolation by distance model of population structure could not
be rejected based on Dest. This is consistent with there being
considerable gene flow among the populations considered as
tundrius or anatum in Alaska, Canada and Greenland, and suggests
that enough time may have passed for localized gene flow and
genetic drift to stabilize and produce a pattern characteristic of
migration-drift equilibrium throughout their high-latitude
distribution (e.g., ). In contrast, all pairwise comparisons (FST and
Dest) that included other subspecies pealei, cassini and macropus were
Although the degree of differentiation between pealei and
tundrius/anatum was not as strong with STRUCTURE and TESS
compared to FST and Dest, the lower proportion of membership for
a few individuals from the two groups was likely due to pealei also
being used in the captive breeding program for which some post
release individuals subsequently possessed mixed ancestry [7981;
see Figure 2]. Similarly, a few of the individuals collected as pealei
may have been misidentified in the field where the subspecies
distribution overlaps with anatum. Although, F. p. pealei are
nonmigratory and they occupy coastal territories in the Pacific
Northwest, extensive plumage variation in immature individuals
exists, particularly in the southern portion of their range 
where our pealei samples originated. No significant isolation by
distance was observed after including the F. p. pealei sample
location with F. p. tundrius/anatum breeding territories further
suggesting no contemporary gene flow between pealei and tundrius/
anatum populations. Therefore, the STRUCTURE results were likely
influenced by introgression of alleles from individuals used in the
release program [e.g., 29] and/or the lack of power to detect
population structure in cases with relatively low levels of
differentiation (e.g., FST,0.03; [92,93]).
With the exception of those comparisons made between high
latitude populations of F. p. tundrius (see below), we remain cautious
and resist equating reported population differentiation estimates
with actual relative degree of differentiation between populations
[e.g., 55,9496]. Peregrine falcons in North America, particularly
those on the East Coast and Midwest in both Canada and the
U.S., have experienced dramatic changes in population
abundance over the past half-century [13,17,30]. These lower latitude
populations are unlikely to have reached the equilibrium
conditions necessary for a direct interpretation of measures of
differentiation. Additional work is necessary to explore how these
measures are influenced by data from populations with differing
demographic histories, particularly those that experienced recent
decline followed by a rapid recovery.
Our results from STRUCTURE, TESS, FST and Dest do indicate,
however, no genetic differentiation between F. p. tundrius and high
latitude anatum sample locations in Alaska and northwest Canada.
The high latitude sampling locations used in this study are all
geographic regions that have not experienced any direct
supplementation from translocated or captive bred individuals
. Similarly, the contemporary sampling locations from Alaska,
northern Canada and Greenland do not differ in allele frequencies
or levels of genetic diversity when compared to pre-decline historic
samples from Canada (; data not shown) suggesting genetic
stability over time at least in their northern distribution. This
conclusion is further supported by a significant isolation by
distance relationship among breeding sample locations of these
two subspecies, even after excluding samples from southern
Ontario where introgression from captive-released birds is likely
to have occurred (see ). A few studies have documented
reduced levels of genetic variation in declining populations of birds
of prey (e.g., ). However, populations that have
experienced recent declines yet have recovered in abundance
can retain levels of genetic diversity when certain demographic
conditions exist. For example, Hailer et al.  documented high
levels of genetic diversity in recovering white-tailed eagle (Haliaeetus
albicilla) populations suggesting that their long life span (,17 years)
has helped buffer against the effects of genetic drift and loss of
genetic variability [see also 102104]. In comparison, maximum
longevity of banded wild peregrine falcons ranges from 16 to 20
years , suggesting that this factor may have helped populations
maintain high levels of genetic variability in their northern
distribution in North America, despite significant declines in
After adjusting significance levels to account for multiple
simultaneous comparisons, five sample locations possessed
significant FIS values, or heterozygote deficiencies (Table 1). Possible
explanations for these results include null alleles, sampled multiple
populations (Wahlund effect), or nonrandom mating within
sampled locations. Two of the populations were no longer
significant after excluding locus Fp92-1 from the analysis,
suggesting possible null alleles with this locus. For the three
remaining sampled locations, despite observing little population
genetic differentiation (FST, DEST, STRUCTURE, TESS) between
breeding areas across their North American high latitude
distribution, the more plausible explanation for significant FIS
values is due to sampling multiple subpopulations within the three
areas (1990 & 2001-04 Greenland F. p. tundrius and Northwest F. p.
anatum). For example, the samples for Greenland 2001-04 were
obtained from both Thule (n = 15) and Kangerlussuaq (n = 27),
two geographic areas separated by .1,000 km, and the samples
from Northwest F. p. anatum were obtained from a large
geographic area throughout Alaska and northwest Canada [see
Figure 1]. Although fewer samples were obtained from Thule,
significant FIS values were observed, but not with Kangerlussuaq
after separating the two datasets (data not shown). More work is
required to investigate whether fine scale geographic structure
may exist in areas on the periphery of the species distribution and
whether recent expansion or growth may influence these results
(e.g., ) because peregrine falcons have recently expanded
northward into areas such as Thule .
We currently do not possess an adequate number of samples
collected in the United States to determine if peregrine falcons
(i.e., anatum) show a similar lack of population breeding structure
south of the Canadian and U.S. border (e.g., continental U.S.) and
whether contemporary gene flow exists throughout their
latitudinal distribution. We feel that this deficiency in sampling, however,
does not negate the utility of the current analysis because we were
primarily interested in determining overall genetic stability and
effective population size of migrant passage peregrine falcon
population. It has been shown that peregrine falcons that breed
south of the U.S./Canada border possess reduced migratory
behavior than those further north [11,13,32] and, therefore, less
likely sampled in Padre Island, TX. Although, anecdotal evidence
suggests that individuals with mixed tundrius ancestry that were
released in eastern U.S. may migrate further south than
predecline individuals (i.e., anatum) from the same area , other
work in the Midwestern U.S. has documented an increasing
number of urban-nesting peregrine falcons overwintering
consistently in or near their breeding territories . The most recent
USFWS Monitoring Results for F. p. anatum in 2003 
reported that while 92% of recorded nest sites throughout five of
the six defined regions for monitoring purposes in continental U.S.
were located on natural substrates (e.g., cliffs), 68% in the
Midwestern/Northeast region were on human-built structures in
urban settings such as tall buildings and bridges [see also
79,106,108,109]. Whether introgression of non-native genes into
the breeding population in the U.S. has had any negative effects
on fitness (e.g., outbreeding depression; ) or changes in
population dispersal patterns remains to be shown (; see also
). Additional work is required specifically to address the
genetic stability of populations in the contiguous U.S., whereas our
study is primarily focused on high latitude populations, which are
more likely the source of migrant birds passing through Padre
Migrant population genetic stability
Peregrine falcons sampled during migration at Padre Island,
Texas clustered with high support with individuals sampled
throughout their northern breeding distribution in Alaska, Canada
and Greenland (F. p. tundrius/anatum; Fig. 2). In contrast to
peregrine falcons sampled in southern Canada (anatum), the Padre
Island samples had consistently higher posterior probability
assignment values similar to peregrine falcons identified as
tundrius/anatum and possessed little if no signal indicating
admixture from the other sampled subspecies (Fig. 2). These
results, along with results from FST and DEST pairwise comparison
(Table S1), therefore suggest that peregrine falcons passing
through Padre Island were likely individuals with breeding
territories located further north than southeastern Canada.
Previous studies investigating migratory patterns of peregrine
falcons in North America using banding records or satellite
telemetry have also documented peregrines passing through Padre
Island that originate or finalize their migration in northern high
latitude areas rather than further south in southern Canada and
continental U.S. ([11,32,33]; see also Fig. 2 in ). Fuller et al.
, for example, identified a wide distribution of breeding
territories across northern latitudes for passage peregrines (n = 54),
including those surveyed migrating through Padre Island.
Across migratory seasons (fall and spring) and seven time
periods sampled over a twenty-two year period, no significant
changes in levels of microsatellite diversity were observed at Padre
Island, and diversity levels were similar to those obtained from
high latitude breeding peregrine falcon sample locations (Table 1).
These results suggest that the population is large enough in size to
offset the negative effects of drift, with adequate levels of gene flow
between areas (see also ). Further, no significant levels of
differentiation were observed between each of the sampled time
periods (Fig. 2; Table S1). The latter result is important because
monitoring changes in population differentiation (e.g., FST or
DEST) is often a more sensitive indicator of population decline than
is the loss of allelic diversity [116,117]. Similarly, no significant
change in diversity levels (Table 1) or population differentiation
(Table S1) was observed between the two temporal sample periods
(1990 and 2001-04) in western Greenland. Multiple studies have
documented significant allele frequency change associated with
increased population differentiation in small or declining
populations [74,118121]. With our simulated datasets of known size, for
example, the development of significant population differentiation
(FST) was observed in all pairwise comparisons at Ne of #300 in as
little as seven generations (the time period between our samples
collected from Padre). However, at Ne of 500 only eight of the
forty-five comparisons were significant (18%) and none of the
comparisons at Ne of $1000 were significant (0%).
These results, along with those from the USFWS nationwide
monitoring efforts , suggest that the higher latitude migratory
and breeding peregrine falcon population is stable with no
indication of decline. In fact, monitoring efforts in the field,
including multiple long-term migration watchsites (e.g., Cape May
Bird Observatory, Hawk Mountain Sanctuary, Hawk Ridge Bird
Observatory) suggest that this species continues to increase in
abundance [13,31,122124]. Similarly, levels of organochlorine
pesticides continue to decline in peregrine falcon migrants
returning from Central and South America. Henny et al. 
reported a 9697% decline in blood DDE concentrations in
female peregrine falcons sampled between 1994 (n = 45) and 2004
(n = 27) at Padre Island. Out of the 27 adult peregrine falcons
sampled in 2004, DDE concentrations were below detectible levels
(,0.02 mg/g) in 20 birds (77%), while in contrast only two of the
156 adult samples (1%) between 1978 and 1994 were below
the detectible limit . These are definitely reassuring signs that
the peregrine falcon population is moving toward full recovery.
Our inability to obtain a precise estimate of Ne for the migratory
population of peregrine falcons also suggests a large population
[see also 126,127]. Estimates of Ne ranged from 509 to infinity
(.10,000; Ne-MAX), with extremely wide 95% confidence intervals
(Table 2). The power to estimate Ne using genetic data is
dependent on multiple factors. When populations are of small size
(,500 breeding individuals), a variety of methods, some of which
were employed in this study, do exceptionally well in inferring how
strong genetic drift was or how large the Ne of the population must
be to cause the observed change in allele frequencies over time
when assuming no mutation, selection and migration during the
sampled time period [58,64,127]. When populations are of large
size, however, allele frequencies are less likely to change due to
drift and our ability to estimate Ne becomes much more difficult.
As was observed with our estimates of population differentiation
measures (FST and DEST; Table S1) between temporal Padre
Island sampling periods, no significant changes in allele
frequencies were identified over the 22-year period (,7 generations),
further supporting that the breeding population of high latitude
peregrine falcons is of large size. This conclusion is also supported
by our estimates of Ne from the local Greenland peregrine falcon
population (Ne = ,120) while accounting for immigration,
suggesting that a much larger migratory population must exist when
extrapolated to the remainder of its breeding distribution across
There are ways to improve the precision of our estimate of Ne.
Either we increase the sample size of each time period and/or
increase the number of loci characterizing allele frequency change
over time. In a recent study investigating Ne of an Australian tiger
prawn (Penaeus esculentus) population, Ovenden et al. 
determined using simulation that they would require 2,000
samples taken one generation apart to reliably estimate Ne of
about 8,000 breeding individuals with eight microsatellite loci
using similar temporal methods as this study. By increasing the
time between sampling periods to four generations, their
simulations suggested that the same sample size would produce
accurate estimates for Ne of 10,000; however, by decreasing the
sample size to 1,000 individuals, their ability to obtain finite
estimates dropped from 100% to 65% . In this study, our
samples sizes were 46 individuals for the majority of temporal
periods. We could possibly double the sample size to
approximately 100 individuals per time period, but sampling beyond that
number is unrealistic given the difficulty in trapping migratory
falcons. Palstra & Ruzzante  recommend that the S/Ne ratio
(where S is the sample size) be approximately 0.10 for adequate
sampling for the temporal approach for estimating Ne with genetic
data. With a sample size of 46 individuals, we should be able to
provide reliable estimates of Ne,460. Therefore, the wide 95%
confidence intervals, or low precision, we obtained from all of the
temporal methods for estimating Ne in this study suggests that the
actual Ne is of larger size.
Alternatively, increasing the number of loci would also increase
the power to estimate Ne from large populations using genetic
data; however, the extent of its improvement will depend on the
variability of the loci with increased polymorphism required .
In a recent review, Leberg  commented that increasing the
number of loci sampled will increase the precision of estimates to a
greater extent than increasing the number of individuals sampled.
(but see ). Therefore, because of difficulties obtaining large
sample sizes, it may prove worthwhile to explore additional
markers, such as SNPs [e.g., 129131] and additional
microsatellite loci [e.g., 132,133] to obtain a more precise estimate of Ne
that can be used to make future management decisions for this
The above results can inform future management decisions
impacting the full recovery of peregrine falcons breeding in North
America. Recently, the U.S. Fish and Wildlife Service allowed the
take in 2009 of up to 36 first-year (FY) autumn migrant passage
peregrine falcons east of 100uW longitude for use in falconry
[31,134]. The plan made a distinction between peregrines with
natal sites south of 54uN latitude (F. p. anatum) and those further
north, which includes both F. p. tundrius and northern F. p. anatum
subspecies. Peregrine falcons (F. p. anatum) south of 54uN latitude
possess reduced migratory behavior compared to those further
north, producing a leap-frog breeding/migratory peregrine
distribution [11,13,31,32]. This distinction between northern
migratory birds and those further south was important because
the two geographic groups differ in their current census estimates.
Approximately 2,700 to 8,000 pairs, which include both tundrius
and anatum subspecies, are estimated for the northern population
($54uN latitude) based on non-genetic methods. The southern
populations (,54 uN latitude) east of 100uW longitude (F. p.
anatum) is estimated at ,450 pairs, while west of 100uW longitude
(F. p. anatum and F. p. pealei) consist of ,1,400 to 1,800 pairs .
Therefore, the USFWS specifically targets the take of migratory
individuals that breed $54uN latitude.
Based on our genetic results, including census estimates from
the field, and because the proposal specified autumn first-year
individuals, the removal of 36 FY autumn migrants from the
population is unlikely to adversely affect their continued recovery
[see also 31]. First-year survivorship in the wild is estimated at 40
50%, while breeding adult survival for migrants is likely between
8085% . If the peregrine falcon population was of small size
(Ne,500), the methods that we employed should have had
sufficient power to provide precise estimates of Ne; yet, our
inability to obtain such an estimate suggests this population is of
larger size. The estimate of Ne as measured in this study (with the
exception of Ne from LDNe; see [1,73]) reflects the harmonic
mean effective size of the migratory high latitude peregrine falcon
population over the sampled seven-generation time period (e.g.,
19892007). It roughly approximates to the number of breeding
individuals that produce offspring that live to reproductive age
during the sampled time period, which is typically a value much
smaller than the actual census size (N) of the population
(Ne/N,0.11; see ). Genetic measures of Ne incorporate all
demographic effects in their estimate, such as fluctuating
population size, unequal sex ratio, and variance in reproductive
success [1,136138], which all decrease Ne relative to N.
Therefore, even if we assume that Ne of 500 is correct, the actual
census size is likely an order of magnitude larger; however, more
work is required to determine the actual ratio between the two
measures specific to peregrine falcon populations. The number of
non-breeders (floaters) can outnumber the actual breeders in some
areas by severalfold [13,139], and the numbers of migrants
recorded at specific monitoring sites in the U.S. are high. For
example, between 1999 and 2004, the mean annual fall migration
peregrine falcon count at Curry Hammock Florida State Park in
the middle Keys alone was 1,908 individuals (1,4322,858,
minmax; ). Taking into account the relatively high first year
mortality for migrant peregrine falcons ($5060%, ), the
Ne/N ratio is further reduced due to a large number of individuals
not producing offspring relative to fall census estimates.
Although we were unable to provide a precise point estimate of
Ne in this study, we can conclude that the Ne for the migrant
peregrine falcon population is unlikely to be smaller than 500. This
agrees with field data suggesting a much larger breeding
population size [13,31]. Reducing organochlorine pesticides and
other contaminants (e.g., mercury; [140,141]) in their
environment is of greater importance for securing the long-term viability
of peregrine falcon populations, and recent results from Henny et
al.  suggest that these conditions are improving.
Consequences of the illegal take of peregrine falcons in their wintering
distribution in South America  also deserves more attention,
and certainly, local monitoring of specific areas within the species
breeding distribution should continue. With the exception of our
results from Greenland, the analyses using migrant samples from
Padre Island provide a coarse description of population genetic
stability over time for the high-latitude breeding population of this
species, while more local demographic perturbations associated
with specific breeding locations (see ) would not necessarily
be reflected in our results using migrant samples alone.
Table S1 Pairwise estimates of FST (below diagonal) and Dest
(above diagonal) based on 11 microsatellite loci between regional
peregrine falcon sample locations.
Found at: doi:10.1371/journal.pone.0014042.s001 (0.09 MB
We thank the managers and staff of Laguna Atascosa National Wildlife
Refuge, managers and staff of USGS Bird Banding Laboratory, Dr.
William G. Mattox (Greenland Peregrine Falcon Survey), Dr. Mark R.
Fuller, and all the field researchers who captured peregrines and collected,
processed and archived samples. Marit Nesje and Jan Lifjeld graciously
provided a portion of the data and samples from Australia, and samples
from Alaska and Canada were collected by numerous field biologists and
provided by Jon Longmire. We thank The Danish Polar Center and
Greenland Home Rule Government for providing permits and allowing us
to work in Greenland. Mention of trade names or commercial products
does not constitute endorsement or recommendation for use.
Conceived and designed the experiments: JAJ. Performed the experiments:
JAJ ST GKS JWB. Analyzed the data: JAJ. Contributed reagents/
materials/analysis tools: JAJ ST GKS KKB JWB TLM WSS MAY BA
DPM. Wrote the paper: JAJ ST KKB JWB DPM.
1. Leberg P ( 2005 ) Genetic approaches for estimating the effective size of populations . Journal of Wildlife Management 69 : 1385 - 1399 .
2. Schwartz MK , Luikart G , Waples RS ( 2007 ) Genetic monitoring as a promising tool for conservation and management . Trends in Ecology and Evolution 22 : 25 - 33 .
3. Laikre L , Nilsson T , Primmer CR , Ryman N , Allendorf FW ( 2009 ) Importance of genetics in the interpretation of favourable conservation status . Conservation Biology 23 : 1378 - 1381 .
4. De Barba M , Waits LP , Garton EO , Genovesi P , Randi E , et al. ( 2010 ) The power of genetic monitoring for studying demography, ecology and genetics of a reintroduced brown bear population . Molecular Ecology 19 : 3938 - 3951 .
5. Hall D ( 2005 ) Memo: Policy on Genetics in Endangered Species Activity , January 27 , 2005 . US Fish & Wildlife Service. Available: www.peer.org/docs/ fws/05_24_5_hallmemo.pdf via the Internet. Accessed 30 Apr 2010 .
6. Morgenweck R ( 2005 ) Letter from Ralph Morgenweck to Dale Hall , March 11 , 2005 . Available: http://www.peer.org/docs/fws/05_24_5_morgenweck. pdf via the Internet. Accessed 30 Apr 2010 .
7. Laikre L , Allendorf FW , Aroner LC , Baker CS , Gregovich DP , et al. ( 2010 ) Neglect of genetic diversity in implemetation of the Convention on Biological Diversity . Conservation Biology 24 : 86 - 88 .
8. Ratcliffe D ( 1993 ) The Peregrine Falcon , 2nd edn. London: T & AD Poyser.
9. Ferguson-Lees J , Christie DA ( 2001 ) Raptors of the World . London: Christopher Helm.
10. White CM , Boyce DA , Jr. ( 1988 ) An overview of peregrine falcon subspecies . In: Cade TJ, Enderson JH , Thelander CG , White CM, eds. Peregrine Falcon Populations: Their Management and Recovery . Boise, Idaho: The Peregrine Fund . pp 789 - 810 .
11. Schmutz JK , Fyfe RW , Banasch U , Armbruster H ( 1991 ) Routes and timing of migration of falcons banded in Canada . Wilson Bulletin 103 : 44 - 58 .
12. Fuller MR , Seegar WS , Schueck LS ( 1998 ) Routes and travel rates of migrating Peregrine Falcons Falco peregrinus and Swainson's Hawks Buteo swainsoni in the Western Hemisphere . Journal of Avian Biology 29 : 433 - 440 .
13. White CM , Clum NJ , Cade TJ , Hunt WG ( 2002 ) Peregrine Falcon (Falco peregrinus) . In: Poole A, Gill F, eds. The Birds of North America, No. 660. Philadelphia: The Birds of North America, Inc.
14. Falk K , Mller S ( 1988 ) Status of the peregrine falcon in South Greenland: population density and reproduction . In: Cade TJ, Enderson JH , Thelander CG , White CM, eds. Peregrine Falcon Populations: Their Management and Recovery . Boise, Idaho: The Peregrine Fund . pp 37 - 43 .
15. Cade TJ ( 1982 ) The Falcons of the World . Ithaca, NY: Cornell University Press.
16. Fyfe RW ( 1969 ) The Peregrine Falcon in northern Canada . In: Hickey JJ, ed. Peregrine Falcon Populations: Their Biology and Decline . Madison, WI: University of Wisconsin Press. pp 101 - 114 .
17. Kiff LF ( 1988 ) Commentary - Changes in the status of the Peregrine in North America: an overview . In: Cade TJ, Enderson JH , Thelander CG , White CM, eds. Peregrine Falcon Populations: Their Management and Recovery . Boise, Idaho: The Peregrine Fund . pp 123 - 139 .
18. Jefferies DJ , Prestt I ( 1966 ) Post-mortems of peregrines and lanners with particular reference to organochlorine residues . British Birds 59 : 49 - 64 .
19. Ratcliffe D ( 1967 ) Decreases in eggshell weight in certain birds of prey . Nature 215 : 208 - 210 .
20. Cade TJ , Lincer JL , White CM , Roseneau DG , Swartz LG ( 1971 ) DDE residues and eggshell changes in Alaska falcons and hawkbills. Science 172 : 955 - 957 .
21. Peakall DB , Reynolds LM , French MC ( 1976 ) DDE in eggs of the peregrine falcon . Bird Study 23 : 183 - 186 .
22. Berger DD , Sidebar CR , Jr., Gamble KE ( 1969 ) The status of breeding Peregrines in the eastern United States . In: Hickey JJ, ed. Peregrine Falcon Populations: Their Biology and Decline . Madison, WI: University of Wisconsin Press. pp 165 - 173 .
23. Berry RB ( 2003 ) History and Extinction of the Appalachian Peregrine . In: Cade TJ, Burnham W, eds. Return of the Peregrine . Boise, ID: The Peregrine Fund . pp 35 - 55 .
24. Enderson JH ( 1969 ) Population trends among Peregrine Falcons in the Rocky Mountain region . In: Hickey JJ, ed. Peregrine Falcon Populations: Their Biology and Decline . Madison, WI: University of Wisconsin Press. pp 73 - 79 .
25. Burnham W ( 2003 ) Peregrine Falcon Restoration in the Rocky Mountains/ Northwest . In: Cade TJ, Burnham W, eds. Return of the Peregrine . Boise, ID: The Peregrine Fund . pp 127 - 153 .
26. Fyfe RW , Temple SA , Cade TJ ( 1976 ) The 1975 North American peregrine falcon survey . Canadian Field Naturalist 90 : 228 - 273 .
27. White CM , Emison WB , Williamson FL ( 1973 ) DDE in a resident Aleutian Island peregrine population . The Condor 75 : 306 - 311 .
28. Nelson RW ( 1990 ) Status of the Peregrine Falcon, Falco peregrinus pealei , on Langara Island, Queen Charlotte Islands, British Columbia , 1968 - 1989 . The Canadian Field-Naturalist 104 : 193 - 199 .
29. Jacobsen F , Nesje M , Bachmann L , Lifjeld JT ( 2008 ) Significant genetic admixture after reintroduction of peregrine falcon (Falco peregrinus) in southern Scandinavia . Conservation Genetics 9 : 581 - 591 .
30. Cade TJ , Burnham W ( 2003 ) Return of the Peregrine. A North American Saga of Tenacity and Teamwork . Boise, Idaho: The Peregrine Fund.
31. United States Fish and Wildlife Service ( 2008 ) Final environmental assessment and management plan: take of migrant peregrine falcons from the wild for use in falconry, and reallocation of nestling/fledgling take . Arlington, Virginia: U.S. Fish and Wildlife Service, Division of Migratory Bird Management.
32. McGrady MJ , Maechtle TL , Vargas JJ , Seegar WS , Porras Pen a MC ( 2002 ) Migration and ranging of peregrine falcons wintering on the Gulf of Mexico coast , Tamaulipas, Mexico. The Condor 104 : 39 - 48 .
33. Burnham KK , Burnham WA , Newton I , Johnson JA , Gosler AG ( 2010 ) Recent population expansion of Peregrine Falcons in northwest Greenland as a result of climate change . Meddelelser om Grnland, BioScience . In press.
34. Johnson JA , Burnham KK , Burnham WA , Mindell DP ( 2007 ) Genetic structure among continental and island populations of gyrfalcons . Molecular Ecology 16 : 3145 - 3160 .
35. Brown JW , Van Coeverden de Groot PJ , Birt TP , Seutin G , Boag PT , et al. ( 2007 ) Appraisal of the consequences of the DDT-induced bottleneck on the level and geographic distribution of neutral genetic variation in Canadian peregrine falcons, Falco peregrinus . Molecular Ecology 16 : 327 - 343 .
36. Nesje M , Red KH , Lifjeld JT , Lindberg P , Steen OF ( 2000 ) Genetic relationships in the peregrine falcon (Falco peregrinus) analysed by microsatellite DNA markers . Molecular Ecology 9 : 53 - 60 .
37. Talbot SL , Palmer AG , Sage GK , Sonthagen SA , Swem T , et al. ( 2010 ) Lack of genetic polymorphism among peregrine falcons of Fiji. Avian Biology . In review.
38. Lewis PO , Zaykin D ( 2001 ) Genetic Data Analysis: Computer Program for the Analysis of Allelic Data , Version 1.1. Storrs , Connecticut: University of Connecticut.
39. Rice WR ( 1989 ) Analyzing tables of statistical tests . Evolution 43 : 223 - 225 .
40. Goudet J ( 1995 ) FSTAT (version 1.2): a computer program to calculate Fstatistics . Journal of Heredity 86 : 485 - 486 .
41. Leberg P ( 2002 ) Estimating allelic richness: effects of sample size and bottlenecks . Molecular Ecology 11 : 2445 - 2449 .
42. Raymond M , Rousset F ( 1995 ) GENEPOP (version 1.2): population genetics software for exact tests and ecumenicism . Journal of Heredity 86 : 248 - 249 .
43. Rousset F ( 2008 ) Genepop'007: a complete reimplementation of the Genepop software for Windows and Linux . Molecular Ecology Resources 8 : 103 - 106 .
44. Pritchard J , Stephens M , Donnelly P ( 2000 ) Inference of population structure using multilocus genotype data . Genetics 155 : 945 - 959 .
45. Falush D , Stephens M , Pritchard J ( 2003 ) Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies . Genetics 164 : 1567 - 1587 .
46. Evanno G , Regnaut S , Goudet J ( 2005 ) Detecting the number of clusters of individuals using the software Structure: a simulation study . Molecular Ecology 14 : 2611 - 2620 .
47. Rosenberg N ( 2004 ) DISTRUCT: a program for the graphical display of population structure . Molecular Ecology Notes 4 : 137 - 138 .
48. Chen C , Durand E , Forbes F , Francois O ( 2007 ) Bayesian clustering algorithms ascertaining special population structure: a new computer program and a comparison study . Molecular Ecology Notes 7 : 747 - 756 .
49. Francois O , Ancelet S , Guillot G ( 2006 ) Bayesian Clustering Using Hidden Markov Random Fields in Spatial Population Genetics . Genetics 174 : 805 - 816 .
50. Durand E , Jay F , Gaggiotti OE , Francois O ( 2009 ) Spatial inference of admixture proportions and secondary contact zones . Molecular Biology and Evolution 26 : 1963 - 1973 .
51. Francois O , Blum MGB , Jakobsson M , Rosenberg NA ( 2008 ) Demographic history of European populations of Arabidopsis thaliana . PLoS Genetics 4 ( 5 ): e1000075 .
52. Jakobsson M , Rosenberg NA ( 2007 ) CLUMPP: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure . Bioinformatics 23 : 1801 - 1806 .
53. Weir BS , Cockerham CC ( 1984 ) Estimating F-statistics for the analysis of population structure . Evolution 38 : 1358 - 1370 .
54. Excoffier L , Laval G , Schneider S ( 2005 ) ARLEQUIN ver . 3 .0: An integrated software package for population genetics data analysis . Evolutionary Bioinformatics Online 1 : 47 - 50 .
55. Jost L ( 2008 ) GST and its relatives do not measure differentiation . Molecular Ecology 17 : 4015 - 4026 .
56. Chao A , Shen T-J SPADE . Available: http://chao.stat.nthu.edu.tw/ softwareCE.html via the Internet. Accessed 25 Dec 2009 .
57. Jensen JL , Bohonak AJ , Kelly ST Isolation by distance, web service . BMC Genetics 6 : 13 . [v.3.16 http://ibdws.sdsu.edu/].
58. Wang J ( 2005 ) Estimation of effective population sizes from data on genetic markers . Philosophical Transactions of the Royal Society Series B 360 : 1395 - 1409 .
59. Fraser DJ , Hansen MM , stergaard S , Tessier N , Legault M , et al. ( 2007 ) Comparative estimation of effective population size and temporal gene flow in two contrasting population systems . Molecular Ecology 16 : 3866 - 3889 .
60. Waples RS ( 2006 ) A bias correction for estimates of effective population size based on linkage disequilibrium at unlinked gene loci . Conservation Genetics 7 : 167 - 184 .
61. Tallmon DA , Koyuk A , Luikart G , Beaumont MA ( 2008 ) ONeSAMP: a program to estimate effective population size using approximate Bayesian computation . Molecular Ecology Resources 8 : 299 - 301 .
62. Hill WG ( 1981 ) Estimation of effective population size from data on linkage disequilibrium . Genetical Research 38 : 209 - 216 .
63. Waples RS , Do C ( 2008 ) LDNE: a program for estimating effective population size from data on linkage disequilibrium . Molecular Ecology Resources 8 : 753 - 756 .
64. Palstra FP , Ruzzante DE ( 2008 ) Genetic estimates of contemporary effective population size: what can they tell us about the importance of genetic stochasticity for wild population persistence? Molecular Ecology 17 : 3428 - 3447 .
65. Wang J , Whitlock MC ( 2003 ) Estimating effective population size and migration rates from genetic samples over space and time . Genetics 163 : 429 - 446 .
66. Krimbas CB , Tsakas S ( 1971 ) The genetics of Dacus oleae V. Changes of esterase polymorphism in a natural population following insecticide control: selection or drift? Evolution 25 : 454 - 460 .
67. Pollak E ( 1983 ) A new method for estimating the effective population size from allele frequency changes . Genetics 104 : 531 - 548 .
68. Waples RS ( 1989 ) A generalized approach for estimating effective population size from temporal changes in allele frequency . Genetics 121 : 379 - 391 .
69. Waples RS ( 1990 ) Conservation genetics of Pacific salmon: III, estimating effective population size . Journal of Heredity 81 : 277 - 289 .
70. Turner TF , Salter LA , Gold JR ( 2001 ) Temporal-method estimates of Ne from highly polymorphic loci . Conservation Genetics 2 : 297 - 308 .
71. Jorde PE , Ryman N ( 2007 ) Unbiased estimator for genetic drift and effective population size . Genetics 177 : 927 - 935 .
72. Berthier P , Beaumont MA , Cornuet J-M , Luikart G ( 2002 ) Likelihood estimation of the effective population size using temporal changes in allele frequencies: a genealogical approach . Genetics 160 : 741 - 751 .
73. Wang J ( 2001 ) A pseudo-likelihood method for estimating effective population size from temporally placed samples . Genetical Research 78 : 243 - 257 .
74. Johnson JA , Bellinger MA , Toepfer JE , Dunn PO ( 2004 ) Temporal changes in allele frequencies and low effective population size in greater prairie-chickens . Molecular Ecology 13 : 2617 - 2630 .
75. Kuo CH , Janzen FJ ( 2003 ) BottleSim: a bottleneck simulation program for long-lived species with overlapping generations . Molecular Ecology Notes 3 : 669 - 673 .
76. United States Fish and Wildlife Service ( 2003 ) Monitoring plan for the American Peregrine Falcon, a species recovered under the Endangered Species Act . PortlandOregon: U.S. Fish and Wildlife Service, Divisions of Endangered of Endangered Species and Migratory Birds and State Programs, Pacific Region.
77. White CM ( 1968 ) Diagnosis and relationships of the North American tundrainhabiting peregrine falcons . Auk 85 : 179 - 191 .
78. Nesje M , Red KH , Bell DA , Lindberg P , Lifjeld JT ( 2000 ) Microsatellite analysis of population structure and genetic variability in peregrine falcons (Falco peregrinus) . Animal Conservation 3 : 267 - 275 .
79. Tordoff HB , Redig PT ( 2001 ) Role of genetic background in success of reintroduced Peregrine Falcons . Conservation Biology 15 : 528 - 532 .
80. Temple SA ( 1988 ) Future goals and needs for the management and conservation of the Peregrine Falcon . In: Cade TJ, Enderson JH , Thelander CG , White CM, eds. Peregrine Falcon Populations: Their Management and Recovery . BoiseIdaho : The Peregrine Fund . pp 843 - 848 .
81. Cade TJ , Tordoff HB , Barclay JH ( 2000 ) Re-introduction of peregrines in the eastern United States: an evaluation . Re-Introduction News 19 : 19 - 21 .
82. Tordoff HB , Redig PT ( 1997 ) Midwest peregrine falcon demography , 1982 - 1995 . Journal of Raptor Research 31 : 339 - 346 .
83. DeYoung RW , Demarias S , Honeycutt RL , Rooney AP , Gonzales RA , et al. ( 2003 ) Genetic consequences of white-tailed deer (Odocoileus virginianus) restoration in Mississippi . Molecular Ecology 12 : 3237 - 3252 .
84. Vernesi C , Crestanello B , Pecchioli E , Tartari D , Caramelli D , et al. ( 2003 ) The genetic impact of demographic decline and reintroduction in the wild boar (Sus scrofa): a microsatellite analysis . Molecular Ecology 12 : 585 - 595 .
85. Eldridge WH , Naish KA ( 2007 ) Long term effects of translocation and release numbers on fine scale population structure among coho salmon (Onchorhynchus kisutch) . Molecular Ecology 16 : 2407 - 2421 .
86. Fitzpatrick BM , Johnson JR , Kump DK , Shaffer HB , Smith JJ , et al. ( 2009 ) Rapid fixation of non-native alleles revealed by genome-wide SNP analysis of hybrid tiger salamanders . BMC Evolutionary Biology 9 : 176 .
87. Lindberg P ( 1988 ) Reintroducing the peregrine falcon in Sweden . In: Cade TJ, Enderson JH , Thelander CG , White CM, eds. Peregrine Falcon Populations: Their Management and Recovery . Boise, Idaho: The Peregrine Fund . pp 619 - 628 .
88. Hutchison DW , Templeton AR ( 1999 ) Correlation of pairwise genetic and geographic distance measures: inferring the relative influences of gene flow and drift on the distribution of genetic variability . Evolution 53 : 1898 - 1914 .
89. Jost L ( 2009 ) D vs . GST: Response to Heller and Siegismund (2009) and Ryman and Leimar ( 2009 ). Molecular Ecology 18 : 2088 - 2091 .
90. Heller R , Siegismund H ( 2009 ) Relationship between three measures of genetic differentiation GST,DEST, and GST': how wrong have we been? Molecular Ecology 18 : 2080 - 2083 .
91. Gerlach G , Jueterbock A , Kraemer P , Deppermann J , Harmand P ( 2010 ) Calculations of population differentiation based on GST and D: forget GST but not all of statistics! Molecular Ecology 19 : 3845 - 3852 .
92. Latch EK , Dharmarajan G , Glaubitz JC , Rhodes OE , Jr. ( 2006 ) Rela- tive performance of Bayesian clustering software for inferring population substructure and individual assignment at low levels of population differentiation . Conservation Genetics 7 : 295 - 302 .
93. Waples RS , Gaggiotti O ( 2006 ) What is a population? An empirical evaluation of some genetic methods for identifying the number of gene pools and their degree of connectivity . Molecular Ecology 15 : 1419 - 1439 .
94. Balloux F , Bru nner H , Lugon-Moulin N , Hausser J , Goudet J ( 2000 ) Microsatellites can be misleading: an empirical and simulation study . Evolution 54 : 1414 - 1422 .
95. Balloux F , Lugon-Moulin N ( 2002 ) The estimation of population differentiation with microsatellite markers . Molecular Ecology 11 : 155 - 165 .
96. Ryman N , Leimar O ( 2009 ) GST is still a useful measure of genetic differentiation - a comment on Jost's D. Molecular Ecology 18 : 2084 - 2087 .
97. Ambrose RE , Ritchie RJ ( 2003 ) Peregrine falcons , naturalists, scientists, and biologists in Alaska, 1900 to 2000 . In: Cade TJ, Burnham W, eds. Return of the Peregrine . Boise, ID: The Peregrine Fund . pp 189 - 197 .
98. Groombridge JJ , Jones CG , Bruford MW , Nichols RA ( 2000 ) ' Ghost' alleles of the Mauritius kestrel . Nature 403 : 616 .
99. Ewing SR , Nager RG , Nicoll MA , Aumjaud A , Jones CG , et al. ( 2008 ) Inbreeding and loss of genetic variation in a reintroduced population of Mauritius kestrel . Conservation Biology 22 : 395 - 404 .
100. Gautschi B , Muller JP , Schmid B , Shykoff JA ( 2003 ) Effective number of breeders and maintenance of genetic diversity in the captive bearded vulture population . Heredity 91 : 9 - 16 .
101. Hailer F , Helander B , Folkestad AO , Ganusevich SA , Garstad S , et al. ( 2006 ) Bottlenecked but long-lived: high genetic diversity retained in white-tailed eagles upon recovery from population decline . Biology Letters 2 : 316 - 319 .
102. Halbert ND , Derr JN ( 2008 ) Patterns of genetic variation in US federal bison herds . Molecular Ecology 17 : 4963 - 4977 .
103. Johnson JA , Gilbert M , Virani MZ , Asim M , Mindell DP ( 2008 ) Temporal genetic analysis of the critically endangered oriental white-backed vulture in Pakistan . Biological Conservation 141 : 2403 - 2409 .
104. Lerner HRL , Johnson JA , Lindsay AR , Kiff LF , Mindell DP ( 2009 ) It's not too late for the harpy eagle (Harpia harpyja): high levels of genetic diversity and differentiation can fuel conservation programs . PLoS ONE 4 : e7336 . (doi:10.1371/journal.pone. 0007336 ).
105. Eckert CG , Samis KE , Lougheed SC ( 2008 ) Genetic variation across species' geographical ranges: the central-marginal hypothesis and beyond . Molecular Ecology 17 : 1170 - 1188 .
106. Septon G ( 2000 ) Overwintering by urban-nesting peregrine falcons Falco peregrinus in Midwestern North America . In: Chancellor RD, Meyburg B-U, eds. Raptors at Risk. Surrey, B.C.: Hancock House Publications , Ltd. pp 455 - 461 .
107. Green MG , Swem T , Morin M , Mesta R , Klee M , et al. ( 2006 ) Monitoring results for breeding American Peregrine Falcon (Falco peregrinus anatum) , 2003 . Washington DC: U.S. Department of Interior, Fish and Wildlife Service, Biological Technical Publication FWS/BTP-R1005-2006.
108. Cade TJ , Martell M , Redig P , Septon GA , Tordoff HB ( 1996 ) Peregrine falcons in urban North America . In: Bird DM, Varland DE , Negro JJ, eds. Raptors in Human Landscapes . London: Academic Press. pp 3 - 14 .
109. Wakamiya SM , Roy CL ( 2009 ) Use of monitoring data and population viability analysis to inform reintroduction decisions: peregrine falcons in the Midwestern United States . Biological Conservation 142 : 1767 - 1776 .
110. Edmands S ( 2007 ) Between a rock and a hard place: evaluating the relative risks of inbreeding and outbreeding for conservation and management . Molecular Ecology 16 : 463 - 475 .
111. Temple SA , Cade TJ ( 1988 ) Genetic issues associated with recovery efforts for three endangered raptors . In: Garcelon DK, Roemer GW, eds. Proceedings of the International Symposium on Raptor Reintroduction , 1985 . Arcata, CA: Institute of Wildlife Studies . pp 17 - 29 .
112. Hansson B , Bensch S , Hasselquist D , Lillandt BG , Wennerberg L , et al. ( 2000 ) Increase of genetic variation over time in a recently founded population of great reed warblers (Acrocephalus arundinaceus) revealed by microsatellites and DNA fingerprinting . Molecular Ecology 9 : 1529 - 1538 .
113. Keller LF , Jeffery KJ , Arcese P , Beaumont MA , Hochachka WM , et al. ( 2001 ) Immigration and the ephemerality of a natural population bottleneck: evidence from molecular markers . Proceedings of the Royal Society of London Series BBiological Sciences 268 : 1387 - 1394 .
114. Vila C , Sundqvist AK , Flagstad O , Seddon J , Bjornerfeldt S , et al. ( 2003 ) Rescue of a severely bottlenecked wolf (Canis lupus) population by a single immigrant . Proceedings of the Royal Society of London Series B-Biological Sciences 270 : 91 - 97 .
115. Ortego J , Aparicio JM , Calabuig G , Cordero PJ ( 2007 ) Increase of heterozygosity in a growing population of lesser kestrels . Biology Letters 3 : 585 - 588 .
116. Luikart G , Cornuet J-M , Allendorf FW ( 1999 ) Temporal changes in allele frequencies provide estimates of population bottleneck size . Conservation Biology 13 : 523 - 530 .
117. Spencer CC , Neigel JE , Leberg PL ( 2000 ) Experimental evaluation of the usefulness of microsatellite DNA for detecting demographic bottlenecks . Molecular Ecology 9 : 1517 - 1528 .
118. Pertoldi C , Hansen MM , Loeschcke V , Madsen AB , Jacobsen L , Baagoe H ( 2001 ) Genetic consequences of population decline in the European otter (Lutra lutra): an assessment of microsatellite DNA variation in Danish otters from 1883 to 1993 . Proceedings of the Royal Society Series B 268 : 1775 - 1781 .
119. Martnez-Cruz B , Godoy JA , Negro JJ ( 2007 ) Population fragmentation leads to spatial and temporal genetic structure in the endangered Spanish imperial eagle . Molecular Ecology 16 : 477 - 486 .
120. Segelbacher G , Manel S , Tomiuk J ( 2008 ) Temporal and spatial analyses disclose consequences of habitat fragmentation on the genetic diversity in capercaillie (Tetrao urogallus) . Molecular Ecology 17 : 2356 - 2367 .
121. Demandt MH ( 2010 ) Temporal changes in genetic diversity of isolated populations of perch and roach . Conservation Genetics 11 : 249 - 255 .
122. Lott CA ( 2006 ) A new raptor migration monitoring site in the Florida Keys: counts from 1999-2004 . Journal of Raptor Research 40 : 200 - 209 .
123. Farmer CJ , Goodrich LJ , Ruelas Inzunza E , Smith JP ( 2008 ) Conservation status of North America's birds of prey . In: Bildstein KL, Smith JP , Ruelas Inzunza E , Veit RR, eds. State of North America's Birds of Prey. Series in Ornithology 3. Cambridge, MA: Nuttall Ornithological Club: Washington DC: American Ornithologists Union . pp 303 - 415 .
124. Smith JP , Farmer CJ , Hoffman SW , Lott CA , Goodrich LJ , et al. ( 2008 ) Trends in autumn counts of migratory raptors around the Gulf of Mexico , 1995 - 2005 . In: Bildstein KL, Smith JP , Ruelas Inzunza E , Veit RR, eds. State of North America's Birds of Prey. Series in Ornithology 3. CambridgeMA: Nuttall Ornithological Club: Washington DC: American Ornithologists Union . pp 253 - 277 .
125. Henny CJ , Yates MA , Seegar WS ( 2009 ) Dramatic declines of DDE and other organochlorines in spring migrant peregrine falcons from Padre Island , Texas, 1978 - 2004 . Journal of Raptor Research 43 : 37 - 42 .
126. Poulsen NA , Nielsen EE , Schierup MH , Loeschcke V , Grnkjaer P ( 2006 ) Long-term stability and effective population size in North Sea and Baltic Sea cod (Gadus morhua) . Molecular Ecology 15 : 321 - 331 .
127. Antao T , Perez-Figueroa A , Luikart G ( 2010 ) Evolutionary Applications (in press) Early detection of population declines: high power of genetic monitoring using effective population size estimators . DOI: 10.1111/j .1752- 4571 . 2010 .00150.x.
128. Ovenden JR , Peel D , Street R , Courtney AJ , Hoyle SD , et al. ( 2007 ) The genetic effective and adult census size of an Australian population of tiger prawns (Penaeus esculentus) . Molecular Ecology 16 : 127 - 138 .
129. Morin PA , Luikart G , Wayne RK , SNP workshop group (2004) SNPs in ecology, evolution and conservation . Trends in Ecology and Evolution 19 : 208 - 216 .
130. Morin PA , Martien KK , Taylor BL ( 2009 ) Assessing statistical power of SNPs for population structure and conservation studies . Molecular Ecology Resources 9 : 66 - 73 .
131. Backstrom N , Fagerberg S , Ellegren H ( 2008 ) Genomics of natural bird populations: a gene-based set of reference markers evenly spread across the avian genome . Molecular Ecology 17 : 964 - 980 .
132. Caballero IC , Ashley MV , Bates JM ( 2006 ) Return of the peregrine: what microsatellites can tell us about population genetics dynamics in reintroduced peregrine falcons . Forth North American Ornithological Conference, Veracruz, Mexico.
133. Dawnay N , Ogden R , Wetton JH , Thorpe RS , McEwing R ( 2009 ) Genetic data from 28 STR loci for forensic individual identification and parentage analyses in 6 bird of prey species . Forensic Science International: Genetics 3 : e63 - e69 .
134. United States Fish and Wildlife Service ( 2008 ) Take of migrant peregrine falcons in the United States for use in falconry . Federal Register 73 ( 236 ): 74508 - 9 .
135. Frankham R ( 1995 ) Effective population-size adult-population size ratios in wildlife - a review . Genetics Research 66 : 95 - 107 .
136. Caballero A ( 1994 ) Developments in the prediction of effective population size . Heredity 73 : 657 - 679 .
137. Wang J , Caballero A ( 1999 ) Developments in predicting the effective size of subdivided populations . Heredity 82 : 212 - 226 .
138. Turner TF , Osborne MJ , Moyer GR , Benavides MA , Alo D ( 2006 ) Life history and environmental variation interact to determine effective population to census size ratio . Proceedings of the Royal Society Series B 273 : 3065 - 3073 .
139. Newton I ( 1988 ) Commentary - Population regulation in Peregrines: an overview . In: Cade TJ, Enderson JH , Thelander CG , White CM, eds. Peregrine Falcon Populations: Their Management and Recovery . BoiseIdaho : The Peregrine Fund . pp 761 - 770 .
140. Mora M , Skiles R , McKinney B , Paredes M , Buckler D , et al. ( 2002 ) Environmental contaminants in prey and tissues of the peregrine falcon in the Big Bend Region , Texas, USA. Environmental Pollution 116 : 169 - 176 .
141. Mora M , Skiles RS , Paredes M ( 2007 ) Further assessment of environmental contaminants in avian prey of the peregrine falcon in Big Bend National Park , Texas. The Southwestern Naturalist 52 : 54 - 59 .
142. Maseveu OB ( 2009 ) Is illegal take affecting the peregrine falcon population in central Peru: why and how ? In: Rich TD, Arizmendi C , Demarest DW , Thompson C, eds. Tundra to Tropics : Connecting Birds, Habitats and People. Proceedings of the Fourth International Partners in Flight Conference , 13 - 16 February 2008 . McAllen, TX: Partners in Flight . pp 171 - 178 .
143. Soule ME , Estes JA , Miller B , Honnold DL ( 2005 ) Strongly interacting species: conservation policy , management, and ethics. BioScience 55 : 168 - 176 .