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Submitted 23 December 2013
Accepted 25 February 2014
Published 13 March 2014
Corresponding author
Jason Munshi-South,
jason@NYCevolution.org
Academic editor
William Amos
Additional Information and
Declarations can be found on
page 17
DOI 10.7717/peerj.310
Copyright
2014 Munshi-South and Nagy
Distributed under
Creative Commons CC-BY 3.0
OPEN ACCESS
Urban park characteristics, genetic
variation, and historical demography of
white-footed mouse (Peromyscus
leucopus) populations in New York City
Jason Munshi-South1and Christopher Nagy2
1Department of Biological Sciences and the Louis Calder Center—Biological Field Station,
Fordham University, Armonk, NY, USA
2Mianus River Gorge Preserve, Bedford, NY, USA
ABSTRACT
Severe fragmentation is a typical fate of native remnant habitats in cities, and ur-
ban wildlife with limited dispersal ability are predicted to lose genetic variation in
isolated urban patches. However, little information exists on the characteristics of
urban green spaces required to conserve genetic variation. In this study, we examine
whether isolation in New York City (NYC) parks results in genetic bottlenecks in
white-footed mice (Peromyscus leucopus), and test the hypotheses that park size and
time since isolation are associated with genetic variability using nonlinear regres-
sion and information-theoretic model selection. White-footed mice have previously
been documented to exhibit male-biased dispersal, which may create disparities in
genetic variation between males and females in urban parks. We use genotypes of 18
neutral microsatellite data and four different statistical tests to assess this prediction.
Given that sex-biased dispersal may create disparities between population genetic
patterns inferred from bi- vs. uni-parentally inherited markers, we also sequenced
a 324 bp segment of the mitochondrial D-loop for independent inferences of his-
torical demography in urban P. leucopus. We report that isolation in urban parks
does not necessarily result in genetic bottlenecks; only three out of 14 populations
in NYC parks exhibited a signature of a recent bottleneck at 18 neutral microsatellite
loci. Mouse populations in larger urban parks, or parks that have been isolated for
shorter periods of time, also do not generally contain greater genetic variation than
populations in smaller parks. These results suggest that even small networks of green
spaces may be sufficient to maintain the evolutionary potential of native species
with certain characteristics. We also found that isolation in urban parks results in
weak to nonexistent sex-biased dispersal in a species known to exhibit male-biased
dispersal in less fragmented environments. In contrast to nuclear loci, mitochondrial
D-loop haplotypes exhibited a mutational pattern of demographic expansion after a
recent bottleneck or selective sweep. Estimates of the timing of this expansion suggest
that it occurred concurrent with urbanization of NYC over the last few dozens to
hundreds of years. Given the general non-neutrality of mtDNA in many systems and
evidence of selection on related coding sequences in urban P. leucopus, we argue that
the P. leucopus mitochondrial genome experienced recent negative selection against
haplotypes not favored in isolated urban parks. In general, rapid adaptive evolution
How to cite this article Munshi-South and Nagy (2014), Urban park characteristics, genetic variation, and historical demography of
white-footed mouse (Peromyscus leucopus) populations in New York City. PeerJ 2:e310;DOI 10.7717/peerj.310
driven by urbanization, global climate change, and other human-caused factors is
underappreciated by evolutionary biologists, but many more cases will likely be
documented in the near future.
Subjects Conservation Biology, Ecology, Evolutionary Studies, Genetics, Zoology
Keywords Population genetics, Genetic variation, Mitochondrial DNA, Urban ecology,
Sex-biased dispersal, Selective sweep, Genetic bottleneck, Historical demography,
Urban evolutionary biology, Peromyscus leucopus
INTRODUCTION
Populations in fragmented habitats are predicted to lose genetic variation due to drift and
local adaptation through natural selection (Varvio, Chakraborty & Nei, 1986), although
this decline may be opposed by gene flow and mutations that add new genetic variants
to individual populations (Slatkin, 1987). If sufficiently severe, fragmentation promotes
a cycle of reduced population size, inbreeding, and loss of genetic variation (Ellstrand &
Elam, 1993). The relative importance of genetic variation in this ‘extinction vortex’ has
been widely debated (Ashley et al., 2003), but the magnitude of inbreeding depression
(Soul´
e & Mills, 1998) and initial population sizes (Fagan & Holmes, 2006) both influence
the probability of population extinction. Hundreds of empirical studies indicate that
population genetic structure is magnified in fragmented habitats due to restricted gene
flow (Bohonak, 1999;Keyghobadi, 2007), but many of these studies do not test explicit
population genetic hypotheses (Emel & Storfer, 2012). Additionally, the interacting roles of
population density, fragment area, habitat quality, and spatial configuration in driving loss
of genetic variation vary widely across taxa or ecosystems (Gibbs, 2001;Fahrig, 2003).
Severe fragmentation is a typical fate of native remnant habitats in cities (Shochat
et al., 2006), and urban wildlife with limited dispersal ability are predicted to exhibit
genetic differentiation between urban habitat patches (often city parks or similar
semi-natural green infrastructure). A growing body of “urban conservation genetics”
(No¨
el & Lapointe, 2010) literature has documented genetic structure between populations
of multiple city-dwelling taxa, including mammals (Wandeler et al., 2003;Munshi-South
& Kharchenko, 2010;Chiappero et al., 2011), amphibians (Hitchings & Beebee, 1997;Noel
et al., 2007;Munshi-South, Zak & Pehek, 2013), reptiles (Delaney, Riley & Fisher, 2010),
birds (Bjorklund, Ruiz & Senar, 2010;Vangestel et al., 2011;Unfried, Hauser & Marzluff,
2013), and insects (Watts et al., 2004;Jha & Kremen, 2013). These studies reported either
stable or reduced genetic variability in urban vs. non-urban habitats, but few examined
associations between patch attributes and population genetic indicators. Larger urban
parks harbor increasingly greater numbers of species (Goddard, Dougill & Benton,
2010;Strohbach, Lerman & Warren, 2013), and may also protect individual populations
against genetic bottlenecks, inbreeding, and loss of genetic variation. Understanding the
relationship between park size and genetic variation will aid efforts to manage networks of
small urban patches (Millard, 2008;Vergnes, Viol & Clergeau, 2012).
Munshi-South and Nagy (2014), PeerJ, DOI 10.7717/peerj.310 2/23
In this study, we examine whether isolation in New York City (NYC) parks results in
genetic bottlenecks in white-footed mice (Peromyscus leucopus). We then use nonlinear
regression and information-theoretic model selection to test the hypotheses that park size
and time since isolation are associated with genetic variability. Our previous research
on this system found substantial genetic structure among urban white-footed mice,
and indicators of genetic variation at neutral microsatellites were moderately high but
not uniform across parks (Munshi-South & Kharchenko, 2010). In contrast, P. leucopus
in fragmented woodlots surrounded by agricultural matrix exhibit only weak genetic
structure and high genetic variability (Mossman & Waser, 2001). Emigration rates from
small patches may be higher than from large patches in these P. leucopus metapopula-
tions (Anderson & Meikle, 2010), presumably because the smallest patches contain the
highest population densities (Krohne & Hoch, 1999). Our previous estimates of both recent
and historical migration between parks were very low between most pairs of parks in
NYC (Munshi-South, 2012). Thus, the probability of bottlenecks and levels of genetic
variation in urban white-footed mice should be influenced more by park size and how long
the sites have been isolated than by migration rates. The short timeframe of urbanization
in NYC also indicates that mutations will be a weak contributor to contemporary genetic
diversity.
Natal dispersal in most mammals (Greenwood, 1980;Dobson, 1982), including P. leuco-
pus (Wolff, Lundy & Baccus, 1988), is male-biased. This pattern may result in lower average
relatedness and weaker genetic structure between members of the dispersing vs. philopatric
sex (Mossman & Waser, 1999;Munshi-South, 2008). In urban populations, we predict that
a male bias in dispersal will be weak to nonexistent due to an inability for either sex to
successfully disperse out of isolated urban patches. We use neutral microsatellite data and
four different statistical tests to assess this prediction. Given that sex-biased dispersal may
create disparities between population genetic patterns inferred from bi- vs. uni-parentally
inherited markers, we also sequenced a 324 bp segment of the mitochondrial D-loop. We
use these sequence data for independent inferences of population demography and genetic
variation of urban P. leucopus. Specifically, we used mismatch distribution analyses to
statistically assess the evidence for a population expansion after a bottleneck or selective
sweep in NYC parks, and estimate the number of generations since any such events.
Urban biodiversity is increasingly recognized as worthy of conservation atten-
tion (Elmqvist et al., 2013), but the population genetics of wildlife in cities has received
relatively little attention (Magle et al., 2012). This study is one of the first to examine
population bottlenecks, genetic variation, and sex-biased dispersal of wildlife in relation to
the characteristics of urban parks.
METHODS
Sampling and microsatellite data collection
To examine associations between urban park size and genetic variation, we trapped and
sampled genetic material from 294 white-footed mice from 14 urban parks in NYC from
2008 to 2009. These study sites encompass nearly all of the large forested areas known to
Munshi-South and Nagy (2014), PeerJ, DOI 10.7717/peerj.310 3/23
Table 1 Characteristics of study sites and results of bottleneck tests. Total area of site, area of potential white-footed mouse habitat, percent habitat,
and years since park founding (a proxy for isolation time) for 14 NYC parks analyzed in this study. Site abbreviations follow Fig. 1. Final column
represents the P-value calculated from 10,000 randomizations of the bottleneck test. Significant values appear in bold.
Site Borough Total area
(ha)
Habitat area
(ha)
Percent
habitat
Years since
founding
Bottleneck
P-value
Hunters Island (HI) Bronx 247.23 103.47 0.42 121 0.71
NY Botanical Garden (NYBG) Bronx 98.23 37.44 0.38 114 0.838
S. Pelham Bay (SPel) Bronx 126.24 64.06 0.51 121 0.567
Van Cortlandt Park (VC) Bronx 433.15 226.83 0.52 121 0.29
Central Park (CP) Manhattan 344.05 45.23 0.13 136 0.011
Inwood Hill Park (In) Manhattan 79.21 52.53 0.66 93 0.935
Alley Pond Park (AP) Queens 219.66 164.26 0.75 82 0.033
Cunningham Park (CH) Queens 188.31 123.50 0.66 71 0.517
Willow Lake (FM) Queens 42.09 25.84 0.61 75 0.009
Forest Park (FP) Queens 230.68 129.84 0.56 114 0.433
Fort Tilden (FT) Queens 248.96 66.71 0.27 92 0.416
Jamaica Bay (JB) Queens 263.38 263.38 1.00 71 0.071
Kissena Park (KP) Queens 61.44 17.68 0.29 103 0.959
Ridgewood Reservoir (RWR) Queens 50.58 28.40 0.56 103 0.695
harbor P. leucopus in the NYC boroughs of the Bronx, Manhattan, and Queens. Brooklyn
and Staten Island were excluded from the study a priori due to logistical constraints. The
trapping sites within each park were usually located in “Forever Wild” nature preserves
that are protected to maintain urban biodiversity and ecosystem services. These “Forever
Wild” sites are situated within a broader park matrix of mowed lawns, playgrounds,
athletic fields and other managed landscapes. Most trapping sites consisted of an invasive
understory and an Appalachian oak-hickory or successional northern hardwoods canopy
as defined by Edinger et al. (2002), with successional shrublands, oldfields, and salt marsh
edges at three Queens sites (Fort Tilden, Willow Lake, and Jamaica Bay, respectively;
Table 1).
Mice were trapped over 2–3 nights at each site using Sherman Live Traps (9′′ ×9′′ ×3′′ )
baited with birdseed. For genetic analysis, we snipped the terminal 1 cm or less of each
mouse’s tail before releasing them alive at the site of capture. Tail snips were stored
in 80–95% ethanol until DNA extraction. Next, we genotyped all mice at 18 unlinked
microsatellite loci, and calculated for each population across all loci the mean number of
alleles, effective number of alleles (i.e., the estimated number of equally frequent alleles in
an ideal population), number of private alleles, and observed heterozygosity in GenAlex
6.2 (Peakall & Smouse, 2006). All animal handling procedures were approved by the CUNY
Brooklyn College Institutional Animal Care and Use Committee (Protocol No. 229).
Permission to collect genetic samples from wild white-footed mice was granted by the
New York State Department of Environmental Conservation (License to Collect or Possess
Wildlife Nos. 1262 and 1603), Gateway National Recreation Area, the NYC Department
of Parks and Recreation, and the Central Park Conservancy. Full descriptions of study
Munshi-South and Nagy (2014), PeerJ, DOI 10.7717/peerj.310 4/23
sites, microsatellite loci, genotyping protocol, and calculation of basic population genetic
statistics are available in Munshi-South & Kharchenko (2010). The microsatellite genotypes
are also available on the Dryad Digital Repository (DOI 10.5061/dryad.1893).
Analysis of historical demography and sex-biased dispersal using
microsatellite loci
We tested for genetic bottlenecks in each park using the program BOTTLENECK 1.2
(Piry, Luikart & Cornuet, 1999) and the authors’ recommended settings for microsatellites
(two-phase mutations; 95% single-step and 5% multi-step mutations). We report results
of a one-tailed Wilcoxon’s signed rank test based on 10,000 randomizations to examine
the hypothesis of significant heterozygosity excess in bottlenecked populations (Cornuet &
Luikart, 1996).
To test for sex-biased dispersal between NYC parks in urban white-footed mice, we
used the “biased dispersal” module in FSTAT 2.9.3 (Goudet, Perrin & Waser, 2002) to
compare multiple indices between males and females: the mean and variance of a corrected
assignment index (AIc), average relatedness, and FST calculated separately for males and
females. The assignment index calculates the probability that an individual’s genotype
occurred by chance in a population, and thus individuals of the dispersing sex should
exhibit lower mean AIcvalues (Paetkau et al., 1995;Favre et al., 1997). We used one-sided
Pvalues calculated using 10,000 randomizations of the data to test the predictions of lower
mean AIc, greater variance in AIc, lower average relatedness, and lower FST for males than
for females. These indices were calculated for the entire dataset, as well as for two subsets of
populations: Bronx and Queens. Previous work indicated that migration rates are nonzero
between at least some populations in each subset (Munshi-South, 2012), but results were
not different from the total dataset.
Modeling of park size vs. genetic diversity
We modeled five basic measures of genetic variation against area and time since isolation
of each of the 14 NYC parks to examine the effect of park attributes on genetic diversity.
Genetic measures included the mean number of alleles (NA), effective number of alleles
(NE), number of private alleles (NP), and observed heterozygosity (HO) reported in
Munshi-South & Kharchenko (2010), as well as Θ(4NEµwhere µ=mutation rate)
estimated using MIGRATE-n(Beerli, 2006) and reported in Munshi-South (2012).
These genetic measures were modeled with three geographic covariates: total park area
(TA), natural habitat area defined as secondary or primary forest cover (HA), and the
proportion of habitat area out of the total area (PH). Geographic layers of park boundaries
were obtained from the New York City Department of Parks and Recreation. Habitat
delineations were digitized by hand in ArcGIS 10.1 using aerial photographs and our own
knowledge of each park’s layout.
We also modeled genetic variability with the number of years since each park became
isolated to examine the hypothesis that longer periods of isolation result in lower genetic
variability. We used two different proxies for time since isolation: (1) the years since each
park was officially founded, and (2) years since major infrastructure projects (primarily
Munshi-South and Nagy (2014), PeerJ, DOI 10.7717/peerj.310 5/23
multi-lane parkways and expressways) were erected around park perimeters. Our rationale
for the former date was that many parks in NYC were likely the last large green spaces
in the general area at the time of their establishment, and thus their founding dates
should reflect the order in which they became isolated. We used a time since isolation
based on infrastructure projects because many NYC parks are at least partially circled by
major roadways that likely present major obstacles to wildlife. Networks of parkways were
constructed during the Robert Moses era of park management in NYC (Caro, 1974), and
these parkways may have been the most important factor in loss of connectivity between
populations. Information that would facilitate more precise inference of the time since
isolation is generally not available. Aerial photos of NYC from 1924 and 1951 (Available at
http://maps.nyc.gov/doitt/nycitymap/) indicate that most parks were surrounded by urban
development by 1924, and thus isolated concurrent with or before their founding. Many
maps exist from throughout NYC’s history, but unfortunately these maps generally do
not contain information on the quality or extent of vegetation (Benson, 2013). Generating
habitat cover through more complex predictive approaches for even one snapshot of time
in NYC is a monumental effort outside the scope of this study (Sanderson, 2009).
Genetic (dependent) variables were examined with eighteen candidate models, each
of which consisted of various combinations of the three geographic covariates and time
since founding (F): an intercept-only model, the four univariate models (TA, HA, PH, and
F), the eight combinations of two covariates,the four combinations of three covariates,
and a global model with all four covariates (TA +HA +PH +F). Intercept-only
models were included in the candidate model sets of each genetic variable to serve as
a baseline for detecting a covariate effect: if a model performed substantially better
than the intercept-only model, we interpreted this result as evidence for an effect of
that model’s covariates upon the respective diversity index. We calculated maximum
likelihood estimates of model parameters for each model, and then models were ranked
using Akaike’s Information Criterion (AICc) corrected for finite sample sizes (Burnham
& Anderson, 2002). In brief, this approach compares a set of models, each representing
an a priori hypothesis, to determine which model is closer to a hypothetical model that
encompasses all of reality, i.e., one that perfectly models the dependent variable in all
instances. The advantages and general differences of an information-theoretic approach
versus traditional hypothesis testing were discussed by Anderson, Burnham & Thompson
(2000).
Each of the six genetic diversity indices was modeled with regression techniques
appropriate to the distribution of that index, based on the overall sample frequency
distribution, e.g., right-skewed variables used gamma regression. If we were unsure of
the proper regression method between a choice of two, the method that minimized
the deviance of the global model was used. Traditionally, a transformation (e.g., log,
square-root) is applied to non-normal data to facilitate the use of regression; however,
this approach may be inferior to using regression techniques that directly match the
distributions of the variables in question (Gea-Izquierdo & Ca˜
nellas, 2009). All modeling
was performed in R 2.15 (R Development Core Team, 2012). Gamma and GLM regression
Munshi-South and Nagy (2014), PeerJ, DOI 10.7717/peerj.310 6/23
were specifically performed using the MASS package (Venables & Ripley, 2002), and
analyses with a Tweedie distribution using the “tweedie” package (Dunn, 2013).
Mitochondrial DNA sequencing and analyses
We sequenced a 324 bp region of the mitochondrial D-loop for a subset of 110 individuals
from above to examine historical demography and genetic variation using a maternally
inherited marker. We designed D-loop PCR primers from a consensus sequence created
using all P. leucopus D-loop sequences available on GenBank in September 2009 (accession
numbers available from authors upon request). We created the consensus sequence from
a ClustalW alignment conducted in BioEdit 7 (Hall, 1999), and chose the primers using
the Primer3 web interface (Rozen & Skaletsky, 1999). We conducted PCR in 25 µl volumes
using Illustra PuReTaq Ready-to-Go PCR beads (G.E. Life Sciences, Piscataway, NJ) with
one µl forward primer (Pleucopus DloopFor 5′-ACCATCCTCCGTGAAATCAG-3′),
one µl reverse primer (Pleucopus DloopRev 5′-AAAAAGCATATGAGGGGAGTG-3′),
and one µl of template DNA with concentrations of 25–50 ng/µl. We performed PCR on a
thermocycler for 30 cycles of 95 ◦C for 30 s, 55 ◦C for 30 s, and 72 ◦C for 1 min, and then
cleaned PCR products using Qiaquick PCR purification kits (Qiagen, Valencia, CA). We
then sequenced both forward and reverse strands using the standard GenomeLab DTCS
quick start protocol on a Beckman Coulter CEQ 8000 sequencer (Beckman Coulter, Brea,
CA). Finally, we edited and aligned the sequences using Sequencer 4.8 (Gene Codes, Ann
Arbor, MI) and BioEdit 7. All unique, unaligned D-loop haplotypes have been deposited
on GenBank (Accession: KF986735–KF986771), and a Nexus haplotype file used for the
analyses below is available on the Figshare digital repository (DOI 10.6084/m9.figshare.
881830).
We calculated summary statistics for all D-loop sequences and subsets from Bronx,
Manhattan, and Queens using DnaSP 5.1 (Rozas et al., 2003). Statistics we used to describe
D-loop variation included the number of polymorphic sites, nucleotide diversity, number
of haplotypes, haplotype diversity, and the average number of nucleotide differences.
To examine deviations from neutrality and population size changes, we also calculated
Tajima’s Dand Fu’s Fs, and assessed their significance using 10,000 coalescent simulations.
We also calculated mismatch distributions (i.e., the observed pairwise nucleotide site
differences) under a model of population expansion to examine demographic changes, and
assessed significance of the observed distributions using 10,000 coalescent simulations of
the raggedness statistic, r(Rogers & Harpending, 1992), and the R2 statistic (Ramos-Onsins
& Rozas, 2002).
RESULTS & DISCUSSION
Analysis of historical demography and sex-biased dispersal using
nuclear loci
Tests for genetic bottlenecks did not detect significant heterozygosity excess in most
NYC parks (Table 1), indicating that bottlenecks have not been a general phenomenon
in these populations. Three populations tested positive for recent bottlenecks, but
there was no general trend towards bottlenecks in large or small parks (Table 1). The
Munshi-South and Nagy (2014), PeerJ, DOI 10.7717/peerj.310 7/23
estimated habitat area (HA) was among the lowest of the 14 parks for two of the
populations exhibiting bottlenecks (Central Park and Willow Lake), but other small
habitat patches were not positive for bottlenecks. These results suggest that very small
urban forest fragments (e.g., <50 ha) may support sufficiently large populations of
native small mammals to prevent severe genetic drift from population crashes. The
relatively high population densities of P. leucopus that have been recorded in small patches
may explain this resiliency (Krohne & Hoch, 1999). However, the lack of evidence for
bottlenecks is still surprising given that substantial genetic drift has occurred in these
populations over the past century (Munshi-South & Kharchenko, 2010), and drift efficiently
reduces allelic diversity in isolated populations (Allendorf, 1986). This type of analysis
should be interpreted cautiously because the results may be influenced by deviations
from the underlying assumptions about microsatellite mutation rates. Many of our
microsatellite markers were first identified in the closely-related P. maniculatus, and thus
in P. leucopus may not strictly adhere to a stepwise mutation model with a low frequency
of multi-step mutations. These microsatellites generally adhered to other expectations
(e.g., Hardy–Weinberg equilibrium) and performed well in a number of other analyses,
and thus we feel it is unlikely that we are failing to detect true bottlenecks in these
populations. Changes to the assumed frequencies of single- vs. multi-step mutations in
our Bottleneck runs did not significantly alter the results.
We also found little evidence of sex-biased dispersal in urban white-footed mice,
either across all NYC populations or clustered sites in Bronx or Queens (Table 2). Only
one of four statistics varied in the predicted direction for male-biased dispersal (mean
AIc), but this sex difference was not robustly supported. Males in our sample had less
likely genotypes than females given the overall genetic characteristics of our sample, as
has been argued previously to support male-biased dispersal in this species (Mossman
& Waser, 1999). Our previous findings of very low migration rates between urban
populations (Munshi-South, 2012) coupled with these dispersal results suggest that
neither males nor females migrate between urban patches at anywhere near the high
rates reported for less severe fragmentation scenarios (Anderson & Meikle, 2010). However,
males may still disperse more often or farther away from their natal sites within patches
than females disperse. Our study design and sample sizes for each site did not allow us to
test within-patch dispersal patterns.
Few studies have examined bottlenecks or sex-biased dispersal in species isolated
in urban forest fragments. Our results for white-footed mice suggest that other small
vertebrates with limited dispersal ability (especially non-volant species) can avoid
genetic bottlenecks if they maintain high population densities in small urban parks.
Common forest dwellers in eastern North America that are known to thrive in urban
parks include red-backed salamanders, Plethodon cinereus (No¨
el & Lapointe, 2010), and
northern short-tailed shrews, Blarina brevicauda (Brack Jr, 2006). The former species
responds similarly to urban forest fragmentation as P. leucopus in terms of rapid genetic
differentiation between fragments but little apparent loss of genetic diversity (Gibbs, 1998;
Noel et al., 2007). We predict that B. brevicauda will exhibit similar patterns. In contrast,
Munshi-South and Nagy (2014), PeerJ, DOI 10.7717/peerj.310 8/23
Table 2 Tests for male-biased dispersal in urban white-footed mice. Results of sex-biased dispersal
analysis for white-footed mice across all 14 NYC parks, a subset of six parks in Bronx, and a subset of
parks in Queens.
NMean AIcVariance AIcRelatedness FST
All NYC Parks 301 – – 0.14 0.08
Females 165 0.43 31.8 0.15 0.09
Males 136 −0.52 32.2 0.14 0.08
Bronx 104 – – 0.16 0.09
Females 52 0.47 35.7 0.15 0.09
Males 52 −0.47 36.8 0.15 0.08
Queens 157 – – 0.14 0.08
Females 90 0.40 31.8 0.15 0.09
Males 67 −0.53 26.3 0.13 0.08
the northern dusky salamander, Desmognathus fuscus, loses genetic variation in isolated
urban stream/seepage habitats (Munshi-South, Zak & Pehek, 2013), but this pattern
may not hold for species such as the northern two-lined salamander, Eurycea bislineata,
that maintain higher population densities and occupy a greater diversity of streams in
urbanized watersheds (Pehek, 2007).
Modeling of park characteristics vs. genetic diversity
The values for NA,NE, and NPwere right skewed continuous and thus were modeled
using gamma regression (Gea-Izquierdo & Ca˜
nellas, 2009), while HOwas best modeled
with a standard generalized linear model (GLM). Θwas highly right-skewed and thus was
best modeled using a Tweedie distribution with an inverse Gaussian dispersion parameter
(i.e., p=3). The inverse Gaussian parameterization of the generalized Tweedie distribution
is useful in modeling variables that are right-skewed and continuous (Jorgensen, 1987;
Dunn & Smyth, 2001). In all model sets the global model deviance/degrees of freedom
were less than or equal to 1.0, indicating adequate model fit (i.e., no overdispersion). For
all diversity measures except Θthe intercept-only models had the most parsimonious fit
(Table 3), indicating no discernible pattern between the geographic covariates/time since
park founding and the genetic diversity measures (Fig. 1). Θincreased as the percent of
habitat area in each park increased (Table 3;Fig. 1S). The model analyzing Θwith percent
habitat and time since founding was also highly ranked, but due to the effect of percent
habitat rather than time since founding. Two geographic covariate pairs—total area (TA)
and habitat area (HA), and HA and percent habitat (PH)—were correlated (r=0.68 and
0.63, respectively); however, models with these combinations were universally poor.
While there was considerable variation in measures of allelic diversity among NYC
parks, there was no clear relationship between park size/time since founding and genetic
variation. Neither estimate of the time since founding was successful at explaining genetic
variation, and we only report the results from the time since founding (Table 3,Fig. 1).
Observed heterozygosity did not differ very much between most populations. This
latter result suggests that even the smallest and most isolated habitat patches maintain
Munshi-South and Nagy (2014), PeerJ, DOI 10.7717/peerj.310 9/23
Figure 1 Scatterplots of genetic variation vs. characteristics of NYC parks. Scatterplots of observed
heterozygosity (A–D), number of alleles (E–H), number of effective alleles (I–L), number of private alleles
(M–P), and Θ(4NEµ; Q–T) on the y-axis vs. (from left to right) total park area (ha), habitat area (ha),
percent habitat, and years since founding on the x-axis. Each of 14 NYC parks is labeled within each
scatterplot with an abbreviation following Table 1.
Munshi-South and Nagy (2014), PeerJ, DOI 10.7717/peerj.310 10/23
Table 3 Model selection for park characteristics vs. genetic diversity. Results of model selection for
park characteristics vs. genetic diversity indices.
Model LogLike kΔAICcModel LogLike kΔAICc
Number of alleles (NA) Effective number of alleles (NE)
Intercept −25.91 2 0.00 Intercept −15.20 2 0.00
TA −25.54 3 2.58 F −15.13 3 3.17
HA −25.61 3 2.71 HA −15.15 3 3.21
PH −25.85 3 3.19 HA ∗F−10.60 5 3.21
F−25.87 3 3.22 TA −15.15 3 3.22
HA ∗F−22.49 5 5.56 PH −15.19 3 3.29
TA +F−25.35 4 6.23 PH +F−14.98 4 6.91
TA +PH −25.45 4 6.43 HA +F−15.04 4 7.03
TA +HA −25.51 4 6.55 TA +F−15.11 4 7.18
HA +PH −25.60 4 6.73 TA +HA −15.14 4 7.24
HA +F−25.60 4 6.74 HA +PH −15.14 4 7.24
PH +F−25.85 4 7.23 TA +PH −15.14 4 7.25
TA ∗F−24.43 5 9.45 TA ∗F−13.60 5 9.22
TA +HA +F−25.33 5 11.25 HA +PH +F−14.97 5 11.96
TA +PH +F−25.35 5 11.28 TA +PH +F−14.97 5 11.96
TA +HA +PH −25.36 5 11.31 TA +HA +F−14.99 5 11.99
HA +PH +F−25.53 5 11.65 TA +HA +PH −15.14 5 12.30
Global −25.26 6 17.60 Global −14.97 6 18.46
Number of private alleles (NP) Observed heterozygosity (HO)
Intercept −33.40 2 0.00 Intercept 25.75 2 0.00
HA −33.29 3 3.07 PH 26.13 3 2.56
PH −33.31 3 3.12 F 26.11 3 2.60
TA −33.40 3 3.31 HA 26.03 3 2.75
F−33.40 3 3.31 TA 25.75 3 3.31
TA +HA −33.21 4 6.98 TA +HA 26.49 4 5.87
PH +F−33.22 4 6.99 HA +F 26.38 4 6.10
HA +PH −33.27 4 7.09 HA +PH 26.20 4 6.45
HA +F−33.28 4 7.11 PH +F 26.20 4 6.46
TA +PH −33.30 4 7.16 TA +F 26.17 4 6.52
TA +F−33.40 4 7.35 TA +PH 26.13 4 6.59
HA ∗F−32.59 5 10.78 HA ∗F 27.74 5 8.44
TA ∗F−32.88 5 11.35 TA ∗F 26.70 5 10.51
TA +HA +F−33.07 5 11.74 TA +HA +PH 26.65 5 10.61
TA +HA +PH −33.11 5 11.83 TA +HA +F 26.53 5 10.85
HA +PH +F−33.21 5 12.03 HA +PH +F 26.38 5 11.16
TA +PH +F−33.22 5 12.04 TA +PH +F 26.23 5 11.44
Global −33.03 6 18.15 Global 26.80 6 16.81
(continued on next page)
Munshi-South and Nagy (2014), PeerJ, DOI 10.7717/peerj.310 11/23
Table 3 (continued)
Model LogLike kΔAICc
Θ
HA ∗F –*– –
Global –*– –
PH −39.69 3 0.00
PH +F−38.99 4 2.63
Intercept −43.10 2 3.51
HA +PH −39.44 4 3.54
TA +PH −39.50 4 3.66
HA −42.27 3 5.15
TA +HA −40.32 4 5.30
F−42.57 3 5.76
HA +F−40.68 4 6.01
TA +F−40.82 4 6.30
TA −43.09 3 6.79
TA +PH +F−38.98 5 7.67
HA +PH +F−38.98 5 7.68
TA +HA +PH −39.44 5 8.59
TA +HA +F−39.64 5 8.99
Notes.
TA, total area of park; HA, undeveloped habitat area of park; PH, proportion of park habitat area to total park area; F,
years since founding of park.
*Denotes model that did not converge.
population densities that are adequate to preserve heterozygosity. Additionally, the parks
we sampled in NYC may all fall below a size threshold beyond which white-footed
mice maintain high population densities and genetic variation, sometimes referred
to as a “synurbic” threshold (Francis & Chadwick, 2012). Larger urban parks support
higher population densities of gray squirrels (Sciurus carolinensis), although the relative
proportions of tree and building cover also influence these densities (Parker & Nilon,
2012). Most of the parks analyzed in this study contained similar types of mouse habitat:
an invasive vegetative understory with an oak-hickory or successional northern hardwoods
canopy (Munshi-South & Kharchenko, 2010). However, it is possible that unmeasured
ecological variability between NYC parks, such as differences in habitat quality, food
availability, or predator abundance (Levi et al., 2012), would better explain genetic
variation than park size or the time since founding. Genetic variation in P. leucopus may
alternatively respond to ecological variables in non-generalizable ways. For example,
competition between squirrels, chipmunks (Tamias striatus), and white-footed mice
in northeastern forest fragments is weak to nonexistent, except in certain sites with
idiosyncratic characteristics (Brunner et al., 2013).
NYC parks with the largest habitat areas may be instructive about the influence of highly
site-specific characteristics on genetic diversity. Jamaica Bay (JB) had one of the largest
habitat areas but the lowest measures of genetic diversity (Fig. 1). This site differs from the
Munshi-South and Nagy (2014), PeerJ, DOI 10.7717/peerj.310 12/23
others in that the habitat is composed of salt marsh and sandy scrub in addition to forests,
and thus may be lower-quality habitat than the typical urban forest. We ran the modeling
procedure without Jamaica Bay, but the overall results did not change. In contrast to
Jamaica Bay, Van Cortlandt (VC) is one of the largest parks in NYC and also exhibited the
highest allelic diversity. Besides large size, Van Cortland contains a diversity of forest and
meadow habitats, and roads may promote weak genetic differentiation between mice in the
park (Munshi-South & Kharchenko, 2010). The remaining parks may have not sufficiently
differed in size and genetic diversity to identify a general trend, although the Van Cortland
results suggest that very large urban parks may harbor the greatest genetic variation if they
are diverse in vegetation and structure.
Park isolation time was generally not successful at explaining genetic variation. Similar
to park size, there may not be enough variation in the time since isolation of NYC parks
to observe a general trend in genetic variation. Alternatively, our use of the years since
a park was founded or the years since construction of major infrastructure as proxies
for how long parks have been ecologically isolated may not have been accurate. Given
that much of NYC was heavily urbanized concurrent with the founding of these parks
(particularly outside Manhattan), we feel that our choices were justified. However, future
historical reconstructions of the NYC landscape (Sanderson, 2009) may fruitfully revisit
this question. For the specific question of ecological and genetic isolation of wildlife, some
parks may also have a complicated history of human transformation that will be difficult
to account for in studies such as ours. For example, both Central Park in Manhattan and
Flushing Meadows-Willow Lake in Queens are largely human-made habitats constructed
after periods of heavy human disturbance (farms and villages in Central Park, and a
massive ash dump in Flushing Meadows). Thus, it is not clear whether white-footed
mouse populations have always been present at these sites, or recolonized after some
period of absence. Relatively high levels of genetic variation suggest the former scenario,
but definitive historical trapping records are not available.
Mitochondrial DNA, demographic changes, and selection
We identified 37 haplotypes among 324 bp mitochondrial D-loop sequences obtained for
110 individuals (Table 4). Haplotype diversity was very high, but the average number of
nucleotide differences between haplotypes was low to moderate. Analysis of haplotypes
by landmass (Bronx, Manhattan & Queens) revealed similar patterns, although Queens
exhibited lower diversity and average number of differences than the other two landmasses
or the total sample, despite a larger number of haplotypes and polymorphic sites (Table 4).
Significant Tajima’s Dand Fu’s Fs values for the total NYC sample and Queens indicate
that urban P. leucopus underwent a recent population expansion after a bottleneck or
selective sweep. Alternatively, the D-loop may have experienced genetic hitchhiking
(i.e., genetic draft) due to negative selection on linked mitochondrial genes. The observed
mismatch distributions for NYC and Queens closely fit the expected unimodal distribution
for a recent population expansion (Fig. 2). This fit was statistically supported by the
raggedness and R2 statistics (Table 4). Statistics indicating a demographic expansion were
Munshi-South and Nagy (2014), PeerJ, DOI 10.7717/peerj.310 13/23
Table 4 Statistical analysis of 324 bp of the mtDNA D-loop from 110 white-footed mouse individuals.
Population NaPbπcHdHdeKfDgFshriR2jτk
Bronx 26 13 0.0089 12 0.91 2.70 −0.71 −4.38* 0.04 0.099 2.70
Manhattan 22 10 0.0113 7 0.82 3.61 1.08 0.81 0.13 0.177 2.93
Queens 56 22 0.0051 17 0.74 1.62 −2.1** −11.5** 0.029*0.041** 0.88
All 110 30 0.0084 37 0.91 2.51 −1.73*−34.29** 0.058 0.04* 2.09
Notes.
Significant values are presented in bold text at *P<0.05 or **P<0.01 based on 10,000 coalescent simulations in
DNASP.
aNumber of individuals haplotyped.
bNumber of polymorphic sites in D-loop sequence.
cNucleotide diversity.
dNumber of D-loop haplotypes.
eHaplotype diversity.
fAverage number of pairwise nucleotide differences.
gTajima’s D.
hFu’s Fs.
iRaggedness statistic for mismatch distribution.
jRamos-Onsins & Rozas R2 statistic for mismatch distribution.
kτ(2µt) calculated for mismatch distribution.
generally not significant for the Bronx and Manhattan subsamples, but this discrepancy
may be due to a much smaller sample size for these areas of the city. Environmental or
demographic stochasticity can exert considerable influence on mismatch distributions,
and such an effect would be enhanced for small sample sizes.
We estimated the time (in generations) since the bottleneck or selective sweep reflected
in the mismatch distributions (Fig. 2) using the τparameter (2 µt; Table 4) and four
pedigree-derived estimates of the mitochondrial D-loop mutation rate (3.52 ×10−5,
1.92 ×10−5, 1.28 ×10−5, and 4.19 ×10−6/site/generation) in humans (Santos et al.,
2005). To our knowledge, no similar D-loop mutation rate estimates have been published
for rodents. The four human mutation rates were multiplied by 324 bp, and then used
to calculate t=τ/2µfor Queens (38.5, 70.6, 105.9, and 323.9 generations, respectively)
and the entire NYC sample (91.5, 167.8, 251.7, and 769.8 generations). These results
suggest that demographic expansion after a bottleneck or selective sweep was concurrent
with urbanization of NYC (i.e., in the last few hundred years), assuming a conservative
generation time in urban P. leucopus between 0.5 and 1.0 years. The expansion also
occurred more recently in Queens than NYC overall, potentially because Queens was
not heavily urbanized until after the construction of bridges, tunnels, and commuter
rail connecting Queens to Manhattan in the early twentieth century. However, these
estimated times should be interpreted cautiously given that we used D-loop mutation rates
estimated from human pedigrees. These human estimates were calculated over sequences
that were a few hundred bp longer than those analyzed in this study. If mutation rates are
heterogeneous along the D-loop, then these human estimates may over- or under-estimate
the mutation rate for P. leucopus. Additionally, mitochondrial mutation rates vary widely
Munshi-South and Nagy (2014), PeerJ, DOI 10.7717/peerj.310 14/23
Figure 2 Mitochondrial mismatch distribution analyses for white-footed mice in NYC that show the
influence of a recent population expansion after a bottleneck of selective sweep. Mismatch distributions
for 324 bp segment of the mtDNA D-loop for Queens (N=56; top graph) and all NYC samples (N=110;
bottom graph). The solid line indicates the observed distribution, and the dotted line indicates the
expected distribution for a demographic expansion.
across mammals with variation in body size, temperature, metabolic rates (Gillooly et al.,
2005), age at female sexual maturity, and lifespan (Nabholz, Gl´
emin & Galtier, 2008). All of
these factors predict that P. leucopus will have a higher mitochondrial mutation rate than
humans, and thus our reported times since a bottleneck or selective sweep are likely all
underestimated. If mutation rates are substantially higher in P. leucopus, then the timing
estimates could indicate that the demographic event or sweep occurred considerably more
recently than urbanization/isolation of NYC Parks.
In the absence of independent evidence, it is difficult to distinguish between bottlenecks
and selection as explanations for the mismatch distributions observed in this study. Only
a few NYC populations exhibited evidence of recent bottlenecks at nuclear microsatellite
loci, thus undermining the bottleneck argument for the mtDNA data. Alternatively, the
mtDNA results may reflect a bottleneck that occurred further back in time than could
be detected by the microsatellite data, or the mtDNA reflects a bottleneck specific to
Munshi-South and Nagy (2014), PeerJ, DOI 10.7717/peerj.310 15/23
this matrilineal marker. These alternatives are unlikely given that the D-loop is also
a hypervariable marker appropriate for detecting recent demographic events, and the
events creating the mismatch distribution were estimated to occur in the last few dozens
to hundreds of generations. A matrilineal bottleneck signature is also unlikely given that
males and females did not differ at multiple population genetic parameters estimated using
nuclear data (Table 2).
The mismatch distributions may be better explained by negative selection against
unfavorable mtDNA haplotypes once these populations became isolated in urban habitat
patches with novel selection pressures. In this scenario, the D-loop would have hitchhiked
along with the surviving haplotypes containing favored alleles in mtDNA protein-coding
regions. The mitochondrial genome is now widely acknowledged to have experienced
selective sweeps in many if not most taxa, and thus some authors have called into
question its utility in demographic estimation (Bazin, Gl´
emin & Galtier, 2006;Nabholz
et al., 2008;Balloux, 2010). Theoretical arguments and some empirical results show that
factors such as changes in population density (Lankau & Strauss, 2011) and increased
temperature (Franks, Weber & Aitken, 2014;Schilthuizen & Kellermann, 2014) are likely to
produce evolutionary responses in human-altered environments (Sih, Ferrari & Harris,
2011;Mueller et al., 2013). These factors or others related to metabolism (such as a
dietary shift in urban habitats) may have driven mitochondrial selective sweeps in NYC’s
white-footed mice.
Mitochondrial DNA sequencing of contemporary and museum specimens of P. leucopus
from the Chicago area indicated that mtDNA haplotypes changed rapidly over a timeframe
corresponding to human development of natural areas (Pergams, Barnes & Nyberg, 2003).
Mismatch distributions of D-loop haplotypes from these populations (Pergams & Lacy,
2008) closely resemble those presented here for NYC mice, indicating that mitochondrial
selection during urbanization may have been a general phenomenon throughout the range
of P. leucopus.Pergams & Lacy (2008) argued that these patterns in the Chicago area were
due to replacement of the original residents by migrants with a selective advantage; this
scenario seems less likely than selection on standing mtDNA variation in NYC because
of the high isolation of NYC populations (Munshi-South, 2012). The case for selection
on mtDNA is further bolstered by our recent finding of several genes exhibiting elevated
signatures of selection in urban vs. rural P. leucopus populations in the NYC area (Harris
et al., 2013). Two of these candidate nuclear genes encode mitochondrial proteins (39S
ribosomal protein L51 and Camello-like protein 1), suggesting that mitonuclear pathways
may be active targets of selection in urban populations (Dowling, Friberg & Lindell, 2008).
Full mitochondrial genome sequences and large-scale mRNA-Seq datasets for urban and
rural populations of white-footed mice can be used in the future to examine potential
mitonuclear associations.
CONCLUSIONS
We report here that isolation in urban parks does not necessarily result in genetic
bottlenecks or substantial loss of genetic variation in urban wildlife populations.
Munshi-South and Nagy (2014), PeerJ, DOI 10.7717/peerj.310 16/23
White-footed mouse populations in larger urban parks, or parks that have been isolated
for fewer years, do not generally contain greater genetic variation than smaller or older
parks, although we could not address site-specific variability between parks that may
exert a greater influence on genetic variation than size alone. These results should be
encouraging to conservation biologists working in human-dominated landscapes, as even
small networks of green spaces may be sufficient to maintain self-sustaining populations
and evolutionary potential of some native species (Goddard, Dougill & Benton, 2010). We
also found that isolation in urban parks results in weak to nonexistent sex-biased dispersal
in a species known to exhibit male-biased dispersal in less fragmented environments. The
breakdown of this dispersal mechanism likely explains the pervasive genetic differentiation
among P. leucopus populations in different NYC parks. In contrast to nuclear loci,
mitochondrial D-loop haplotypes exhibited a mutational pattern of demographic
expansion after a recent bottleneck or selective sweep. Estimates of the timing of this
expansion indicate that it occurred concurrent with urbanization of NYC over the last few
dozens to hundreds of years. Given the general non-neutrality of mtDNA in many systems
and evidence of selection on coding sequences in urban P. leucopus, we argue that the
P. leucopus mitochondrial genome experienced recent negative selection against haplo-
types not favored in isolated urban parks. In general, rapid adaptive evolution driven by
urbanization, global climate change, and other human-caused factors is underappreciated
by evolutionary biologists, but many more cases will likely be documented in the near
future.
ACKNOWLEDGEMENTS
We thank Anna Bernstein and Stephen E. Harris for assistance with the mitochondrial
sequencing. William Amos, Seth Magle, Charles Nilon, and two anonymous reviewers
provided many constructive comments that greatly improved our manuscript. The New
York State Department of Environmental Conservation, the NYC Department of Parks
& Recreation, Central Park Conservancy, and New York Botanical Garden graciously
provided permission to sample white-footed mice in urban parks.
ADDITIONAL INFORMATION AND DECLARATIONS
Funding
This work was funded by grants from the National Science Foundation (DEB 0817259)
and National Institute of General Medical Sciences/National Institutes of Health
(1R15GM099055-01A1) to Jason Munshi-South. The funders had no role in study design,
data collection and analysis, decision to publish, or preparation of the manuscript.
Grant Disclosures
The following grant information was disclosed by the authors:
National Science Foundation: DEB 0817259.
National Institute of General Medical Sciences/National Institutes of Health:
1R15GM099055-01A1.
Munshi-South and Nagy (2014), PeerJ, DOI 10.7717/peerj.310 17/23
Competing Interests
Christopher Nagy is an employee of the Mianus River Gorge Preserve.
Author Contributions
•Jason Munshi-South conceived and designed the experiments, performed the experi-
ments, analyzed the data, contributed reagents/materials/analysis tools, wrote the paper,
prepared figures and/or tables, reviewed drafts of the paper.
•Christopher Nagy analyzed the data, contributed reagents/materials/analysis tools,
wrote the paper, prepared figures and/or tables, reviewed drafts of the paper.
Animal Ethics
The following information was supplied relating to ethical approvals (i.e., approving body
and any reference numbers):
All animal handling procedures were approved by the CUNY Brooklyn College
Institutional Animal Care and Use Committee (Protocol Number 229). The lead author
is currently employed by Fordham University, but completed the field work for this study
while employed by CUNY.
Field Study Permissions
The following information was supplied relating to ethical approvals (i.e., approving body
and any reference numbers):
Permission to collect genetic samples from wild white-footed mice was granted by the
New York State Department of Environmental Conservation (License to Collect or Possess
Wildlife Nos. 1262 and 1603), Gateway National Recreation Area, the New York City
Department of Parks and Recreation, and the Central Park Conservancy.
DNA Deposition
The following information was supplied regarding the deposition of DNA sequences:
GenBank KF986735–KF986771.
Data Deposition
The following information was supplied regarding the deposition of related data:
NEW: Figshare DOI 10.6084/m9.figshare.881830.
PREVIOUSLY DEPOSITED, REANALYZED DATA: Dryad DOI 10.5061/dryad.1893.
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