Molecular Ecology (2009) 18, 1848–1862
doi: 10.1111/j.1365-294X.2009.04112.x
Landscape genetics of California mule deer (Odocoileus
hemionus): the roles of ecological and historical factors in
generating differentiation
Blackwell Publishing Ltd
K A T H E R I N E M . P E A S E ,* A D A M H . F RE E D M A N ,* J O H N P. P O L L I N G E R ,* J O H N E . M C C O R M A C K ,†
WO L F G A N G B U E R M A N N ,† J E F F R O D Z E N ,‡ J I M B A N K S ,‡ E R I N M E RE D I T H ,‡ VE R N O N C . B L E I C H ,§
R O B E R T J . S C H A E F E R ,¶ K E N J O N E S ** and R O B E R T K . WA Y N E *†
*Department of Ecology and Evolutionary Biology, †Center for Tropical Research, Institute of the Environment, University of
California, Los Angeles, CA 90095, USA, ‡Wildlife Forensics Laboratory, Law Enforcement Division, California Department of Fish
and Game, 1701 Nimbus Road, Rancho Cordova, CA 95670, USA, §Sierra Nevada Bighorn Sheep Recovery Program, California
Department of Fish and Game, 407 W. Line St., Bishop, CA 93514, USA, ¶California Department of Fish and Game, 9701 Scott River
Road, Fort Jones, CA 96032, USA, **Genetic Identification Services Inc., 9552 Topanga Canyon Blvd, Chatsworth, CA 91311, USA
Abstract
Landscape genetics is an emerging discipline that utilizes environmental and historical
data to understand geographic patterns of genetic diversity. Niche modelling has added a
new dimension to such efforts by allowing species–environmental associations to be
projected into the past so that hypotheses about historical vicariance can be generated and
tested independently with genetic data. However, previous approaches have primarily
utilized DNA sequence data to test inferences about historical isolation and may have
missed very recent episodes of environmentally mediated divergence. We type 15 microsatellite loci in California mule deer and identify five genetic groupings through a Structure
analysis that are also well predicted by environmental data. We project the niches of these
five deer ecotypes to the last glacial maximum (LGM) and show they overlap to a much
greater extent than today, suggesting that vicariance associated with the LGM cannot
explain the present-day genetic patterns. Further, we analyse mitochondrial DNA (mtDNA)
sequence trees to search for evidence of historical vicariance and find only two wellsupported clades. A coalescence-based analysis of mtDNA data shows that the genetic
divergence of the mule deer genetic clusters in California is recent and appears to be mediated
by ecological factors. The importance of environmental factors in explaining the genetic
diversity of California mule deer is unexpected given that they are highly mobile species
and have a broad habitat distribution. Geographic differences in the timing of reproduction
and peak vegetation as well as habitat choice reflecting natal origin may explain the
persistence of genetic subdivision.
Keywords: landscape genetics, niche modelling, mule deer, mtDNA, microsatellites
Received 27 August 2008; revision received 20 December 2008; accepted 7 January 2009
Introduction
Spatio-temporal variation in the environment, as well as
historical isolation, can cause genetic differentiation.
However, understanding the role of specific environmental
factors in causing genetic differentiation as well as assessing
their importance relative to historical isolation has been
challenging. Recently, conceptual and analytical tools have
Correspondence: Robert K. Wayne, E-mail: [email protected]
been developed that allow the environmental influences
on genetic variation to be more precisely quantified
(Manel et al. 2003; Storfer et al. 2007). A potential approach
to disentangle the direct effects of environment from
historical barriers to gene flow involves the projection
of the environmental niche of genetically defined extant
populations back in time to determine the historical overlap
predicted by environmental changes alone. This approach
has been used to project contemporary species–environment
relationships to the last glacial maximum (LGM) and
© 2009 The Authors
Journal compilation © 2009 Blackwell Publishing Ltd
M U L E D E E R L A N D S C A P E G E N E T I C S 1849
predict patterns of genetic isolation that, through the use
of genealogical models, can be tested with genetic data
(Waltari et al. 2007). However, the use of DNA sequence
data alone has made it difficult to detect more recent,
environmentally mediated divergence. Understanding how
environmental preferences (Geffen et al. 2004; Wiens &
Graham 2005; Kozak & Wiens 2006; Brown et al. 2007)
and historical isolation influence the process of divergence
and ultimately, speciation, is a fundamental problem in
evolutionary biology.
The flora and fauna of California are ideal for testing
approaches to understanding the importance of environmental and topographic factors in causing differentiation
among populations. Previous studies have found significant
genetic divisions in many taxa across geographic units
in California including the Transverse Ranges, the Sierra
Nevada Mountains, the Coast Range Mountains, the Los
Angeles basin, Monterey Bay, the Channel Islands, the
Central Valley, San Francisco Bay/Sacramento River Delta,
and the Tehachapi Mountains (Calsbeek et al. 2003; Feldman
& Spicer 2006). California also exhibits considerable habitat
heterogeneity and can be divided into 10 bioregions based
on topography, climate, and plant community (Hickman
1993). This pronounced heterogeneity has greatly influenced the pattern of genetic differentiation within many
species. For example, the population genetic structure of
California coyotes is correlated with habitat bioregions
(Sacks et al. 2004), reflecting natal-habitat-biased dispersal.
Consequently, environment as well as recent and past
topographic barriers to dispersal has clearly influenced
current genetic patterns in numerous California species.
We explore the influence of ecological and historical
factors on genetic subdivision in the mule deer, Odocoileus
hemionus, in California. Mule deer are large ungulates
found in a wide variety of habitats from coastal sage to
alpine woodlands and are commonly found in disturbed
areas associated with human development. O. hemionus is
divided into 7 to 11 subspecies, with 5 to 6 occurring in
California (Cowan 1956; Dasmann 1975; Mackie et al. 2003).
Subspecies classifications rely on body size, tail colour, tail
pattern, and metatarsal gland length. Mule deer occur
throughout all defined California bioregions, with limited
distribution in the Great Central Valley, and the Mojave
and Sonoran deserts. Further, mule deer are highly mobile
herbivores; average dispersal distances range from
15.2–25.7 km for males and 12.2–36.9 km for females
(Bunnell & Harestad 1983; Hamlin & Mackie 1989). Deer
can be migratory or resident, and migration frequently
occurs altitudinally in mountain-foothill habitats and over
distances of a few kilometres to more than 160 km (Mackie
et al. 2003). Consequently, we predict limited fidelity to
specific environments and high rates of gene flow in the
absence of distinct ecological preferences or pronounced
topographic barriers to dispersal.
© 2009 The Authors
Journal compilation © 2009 Blackwell Publishing Ltd
We use microsatellite loci to identify distinct genetic
clusters within California mule deer. To test the association
of these clusters with the environment and historical isolation, we determine the influence of a wide range of ecological
factors on genetic differentiation through multivariate
ordination. Further, we test whether the geographic distribution of these genetic clusters can be well estimated with
ecological niche models. We then project the present-day
environmental envelope of each of the genetic clusters to
the LGM to determine if they have a long history of being
discrete. In order to determine whether distribution changes
since the LGM have contributed to genetic divergence
among clusters, we use a coalescence-based analysis of
mitochondrial DNA data to time their divergence. Such
coupling of present and past ecological niche models with
genetic data is a novel approach to understanding divergence and speciation and has only recently been applied
to test specific historical hypotheses (see Hugall et al. 2002;
Knowles et al. 2007; Richards et al. 2007).
Materials and methods
Specimens examined
The California Department of Fish and Game (CDFG)
Wildlife Forensic Laboratory (WFL) provided the samples
from their tissue archive for the laboratory studies at the
University of California, Los Angeles. The samples (blood
and tissue) were obtained from road kills, hunter check
stations, and deer telemetry and health evaluation studies.
The samples were collected from 1994 to 2004 and the
majority of samples (75%) were collected between the
months of September and March. This time period generally
corresponds to the mule deer winter range. However,
given that migration is generally less than a few kilometres
and no more than 160 km (Mackie et al. 2003), we feel that
the restriction to this time period is unlikely to substantially
affect our results.
We analysed 587 mule deer samples (Fig. 1) from 49 of 58
counties in California. The samples were male-biased:
N males = 399; N females = 121; N unknown = 67. The
latitude and longitude for each sample is known and
multiple deer samples came from the same geographic
location.
Laboratory methods
Total genomic DNA from blood and tissue was extracted
with the QIAamp DNA Mini Kit (QIAGEN) according to
the manufacturer’s protocol. We genotyped the deer for 18
tetranucleotide microsatellite loci: B, C, D, F, G, H, I, J, K, L,
M, N, O, P, Q, R, S, V (GenBank Accession nos AF102240–
AF102260) (Jones et al. 2000). Eight loci were originally
genotyped by Genetic Identification Services, Inc. (Jones
1850 K . M . P E A S E E T A L .
Fig. 1 Sampling locations of 587 deer in
California (some locations include multiple
deer samples). The deer locations are colourcoded by the genetic cluster to which deer
were assigned by Structure. The five genetic
clusters are: green, northwestern cluster;
blue, eastern cluster; grey, central cluster;
red, southern cluster; orange, San Diego
cluster. The average posterior probability of
assignment to a cluster is 94.5%; four deer
were assigned at a probability of less than
50% and are shown on the map with a red
circle. The 10 Jepson bioregions (Hickman
1993) are indicated. The GIS data layer of the
bioregions was obtained from the University
of California, Santa Barbara California Gap
Analysis Project (Davis et al. 1998).
et al. 2000). Ten loci were genotyped at the University
of California, Los Angeles (UCLA) and all data analyses
were performed at UCLA. For the 10 loci typed at UCLA,
an M-13 hybrid primer process was used to dye-label the
primer (Boutin-Ganache et al. 2001). The polymerase chain
reactions (PCR) were carried out with QIAGEN Multiplex
PCR kits using the manufacturer’s protocol with 10 μL
reactions and two-stage PCR cycle annealing temperatures
of 59 °C and 53 °C. PCR products were analysed on an ABI
3700 capillary sequencer and allele sizes were determined
using GeneMapper software (Applied Biosystems).
From the 587 genotyped deer, we chose 65 deer for
mtDNA sequencing to evenly span California and represent
approximately equal numbers of deer from the five genetic
clusters identified by Structure (see Results). We used PCR
to amplify a 624-bp segment of the control region of the
mtDNA genome with primers L15926 and H16498 (Kocher
et al. 1989; Shields & Kocher 1991). PCR cycles were the
following: 3 min denaturation at 94 °C followed by 35
cycles of 94 °C for 30 s, 50 °C for 30 s, and 72 °C for 45 s,
with a final extension of 10 min at 72 °C. Sequencing
reactions were performed with the L15926 primer and ABI
BigDye 3.1 and products were sequenced on an ABI 3700
capillary sequencer. Sequences were edited with the program Sequencher 4.1 (GeneCodes) and visually checked
for accuracy.
Data analysis
Microsatellite loci. We used Structure 2.1 (Pritchard et al.
2000) to identify genetic clusters within the set of 587
genotyped mule deer. Structure is a program that uses a
Bayesian statistical design to cluster individuals into
population groupings that are in Hardy–Weinberg (H–W)
equilibrium. The program computes the likelihood L(K)
for division of the total sample into an assumed number of
population units (K). All individuals were combined into
one data set for analysis, without any a priori population
© 2009 The Authors
Journal compilation © 2009 Blackwell Publishing Ltd
M U L E D E E R L A N D S C A P E G E N E T I C S 1851
assignments. We utilized a burn-in of 50 000 iterations,
followed by 500 000 iterations of the Gibbs sampler;
admixture was allowed. We first evaluated cluster assignments and L(K) estimates for K-values from 1 to 20. We
then performed five run iterations each for K-values from
1 to 10 to evaluate stability and calculated the ΔK parameter
(Evanno et al. 2005). Cluster assignment results for each
individual were evaluated with respect to its capture
location, each individual was assigned to a population, and
the posterior probability of correct population assignment
was calculated in Structure using the ancestry model with
admixture and migration parameters set to v = 0.1.
For the entire sample and the five clusters (based on
the results from Structure; see Results), we used GenePop
(Raymond & Rousset 1995) to determine allele and genotype
frequencies, linkage disequilibrium (LD), and conformance
to H–W expectations. Bonferroni corrections were applied
to linkage disequilibrium and H–W equilibrium calculations (Rice 1989). Allelic richness, which takes into account
sample size, and F-statistics for the five clusters were
calculated in fstat 2.9.3 (Goudet 1995). Genetic distances
among the 5 clusters were calculated as Nei’s standard
genetic distances (DS) (Nei 1972) with Populations 1.2.28
(www.cnrs-gif.fr/pge). BayesAss + 1.3 (Wilson & Rannala
2003) was used to determine migration rates among the
five clusters. We calculated molecular analysis of variance
(amova) for microsatellite data at the population (northwestern, central, eastern, southern, San Diego) and group
(northwestern/central vs. eastern/southern/San Diego)
levels (999 permutations) using the program GenAlEx
(Peakall & Smouse 2006).
Dependence of genetic structure on ecological factors: present.
To determine if ecological factors are primarily responsible
for maintaining divergence among the five clusters
inferred from the Structure analysis, we conducted an
integrative analysis employing multivariate ordination
and niche modelling techniques. First, we obtained data on
environmental variables from weather stations and remote
sensing that captured the major variation in temperature,
precipitation, and vegetation. These included 11 bioclimatic
variables from WorldClim (version 1.4; Hijmans et al. 2005)
interpolated to 1-km spatial resolution: annual mean
temperature, temperature mean diurnal range, temperature
seasonality, maximum temperature of the warmest month,
minimum temperature of the coldest month, annual
precipitation, precipitation seasonality, precipitation of
the wettest quarter, precipitation of the driest quarter,
precipitation of the warmest quarter, and precipitation of
the coldest quarter. To quantify spatial and temporal
vegetation patterns, we used satellite-based monthly 1-km
normalized difference vegetation index (NDVI) data from
the Moderate Resolution Imaging Spectroradiometer
(MODIS) of year 2001 (Justice et al. 1998). The NDVI, a widely
© 2009 The Authors
Journal compilation © 2009 Blackwell Publishing Ltd
used vegetation index, is indicative of photosynthetic
activity. From the monthly data, we computed five
vegetation metrics: annual maximum NDVI (max), annual
mean NDVI (mean), mean dry season NDVI (May–October;
dry), mean wet season NDVI (November–April; wet) and
vegetation seasonality (difference between mean wet and
dry seasons; wet–dry). For an additional fine-scale metric
of land cover, we also used the MODIS-derived vegetation
continuous field (VCF) product as a measure of the
percentage of tree canopy cover at 1-km resolution (Hansen
et al. 2002). Finally, we included elevation data at 1-km
resolution from the Shuttle Radar Topography Mission
(SRTM).
Second, we selected a subset of these variables that
characterized the environmental differences among genetic
clusters identified in Structure. To do this, we designed a
variable reduction procedure that maintained sufficient
information to make robust predictions, highlighted variables
responsible for differences in distribution among genetic
clusters, and improved interpretability of model results
(by eliminating extraneous and correlated variables).
Specifically, variables were selected using canonical correspondence analysis (CCA) implemented in the program
Canoco (ter Braak & Smilauer 2002). CCA is an ordination
technique that describes variation in species abundance
information at sites, constrained by multivariate axes that
capture environmental variation at those sites. All deer
locations were entered as samples, and binary-coded
according to their genetic cluster determined by Structure.
Multiple samples at a locality assigned to the same genetic
cluster were eliminated to reduce potential biases arising
from uneven sampling. We performed a stepwise forward
CCA and evaluated significance of selected variables with
permutation tests. When the next variable to be selected
had a correlation coefficient (r) > 0.75 with any of the previously entered variables, we excluded that variable, and
re-ran the CCA. This process was iterated until additional
variables did not provide a significant improvement to
the model. Bivariate correlations used to exclude variables
were computed at 1000 random points throughout the
study area.
Using the subset of selected environmental variables
from the CCA analysis, we then modelled the spatial distributions of individual clusters with Maxent (version 3.0.4),
a recently developed general-purpose algorithm for
modelling the distribution of species with presence-only
data (Phillips et al. 2006). Maxent allows for the fitting of
complex response curves (Phillips et al. 2006) and it is insensitive to correlations among predictor variables used to
build niche models (S.J. Phillips, personal communication).
Finally, we compared the Maxent models based on the
selected variables to those based on all 18 environmental
variables that were used in this study. If the geographic
separation of genetic clusters is driven by ecologically
1852 K . M . P E A S E E T A L .
based niche divergence, we would expect that the spatial
distributions predicted using only the variables that distinguish clusters (i.e. the subset selected with CCA) should
be similar to those predicted with all variables. In other
words, the inclusion of variables deemed unimportant
in distinguishing among genetic clusters should not
substantially alter the Maxent predictions.
The importance of environmental variables in defining
distributions was determined by the percent contribution
of each variable to the model, and the loss of predictive
power when each variable was left out. For range overlap
analyses, we adopted more conservative thresholds of
species occurrence in order to avoid overprediction of
co-occurrence among clusters in areas where, because
habitat suitability is low for those clusters, their joint
probability of occurrence is extremely small (see Supporting
Information). We evaluated Maxent predictions on the
basis of spatial accuracy using threshold-dependent
(omission and predicted area) and threshold-independent
[area under the receiver operator curve (AUC)] measures
following Phillips et al. (2006) (see Supporting Information).
To determine the extent to which ecological factors
explain genetic differentiation above and beyond differentiation due simply to isolation by distance (IBD), we
performed partial Mantel tests. We compared matrices of
genetic distance (Nei’s DS) vs. geographic distance and
genetic distance vs. ecological distance, controlling for
ecological distance and geographic distance, respectively.
We calculated ecological distance by performing a principal
components analysis (PCA) on climate, vegetation and
elevation variables for all individual deer, and then calculating the Euclidean distance between ‘population’ pairs in
principal components space defined by the first two PC
axes. Because deer are distributed continuously in California,
we performed these tests at two different spatial scales.
First, we examined differentiation among 36.9 × 36.9 km
grid cells corresponding to the average dispersal distance
of mule deer (Hamlin & Mackie 1989), within which we
pooled individuals irrespective of genetic cluster. If genetic
clusters are defined by discrete niches, we would expect
genetic differentiation to be unrelated to geographic distance
or environmental distance within clusters. Thus, for the
second analysis, we performed partial Mantel tests on
distances between individuals within each genetic cluster.
Mantel tests were performed with the program zt (Bonnet
& Van de Peer 2002) and 10 000 permutations were used in
significance testing.
Dependence of genetic structure on ecological factors: past.
To explore whether mule deer genetic clusters may be the
result of historical adaptation to ecologically differentiated
and geographically isolated glacial refugia, we projected
the present-day climate–genetic-cluster relationships onto
the LGM [21 000 years before present (bp)] climate.
Geographic isolation among genetic groups at the LGM
would support a role for past allopatry, whereas greater
overlap among clusters at the LGM would support more
recent post-glacial expansion from core habitat areas and
subsequent ecologically mediated divergence. LGM climate
was simulated with the Paleoclimate Modelling Intercomparison Project (PMIP; http://www.pmip2.cnrs-gif.fr)
Community Climate System Model (CCSM, http://www.
ccsm.ucar.edu/, [Kiehl & Gent 2004]) downscaled to the
resolution of contemporary environmental variables (Waltari
et al. 2007). Projections of distributions into the past based
on present-day relationships included only climate variables
since information on vegetation distribution at the LGM is
typically not available at the required spatial resolution.
However, we note that exclusion of vegetation variables
did not influence interpretations because no vegetation
variables were selected by the CCA (see Results). If the
relationship among variables changes, holding them
constant may distort results. Consequently, we also excluded
elevation variables in the past climate projections. Prior
to inclusion in niche models, we verified the output of
palaeoclimate models by comparing them to known patterns
of climate in California at the LGM (see Supporting
Information).
mtDNA sequencing analysis. Sequences were aligned and
haplotypes were identified with Collapse 1.1 (Posada
1999). The program DnaSP 4.10.9 (Rozas et al. 2003) was
used to determine percent divergence between cluster
pairs. ModelTest 3.06 (Posada & Crandall 1998) was used
to find the best-fitting model of evolution. We used paup*
(Swofford 2002) to reconstruct phylogenetic trees with
distance (neighbour-joining) and maximum parsimony
(MP) algorithms. The neighbour-joining tree was constructed using HKY85 distances and node support was
evaluated with a neighbor-joining bootstrap analysis using
1000 replicates. We performed a heuristic search with 100
replicates to find the most parsimonious trees. We also
constructed a maximum-likelihood (ML) tree using
TreeFinder (Jobb et al. 2004) with the best-fitting model of
evolution. Bootstrapping with 100 replicates was performed
to determine support for MP and ML trees. Finally, we
used MrBayes version 3.1.2 (Huelsenbeck & Ronquist
2001) to create a Bayesian tree. Markov chain Monte Carlo
(MCMC) simulation used four chains that ran for 2 million
generations, from which trees were sampled every 100
generations for a total of 20 001 trees. Burn-in was determined to have occurred when likelihood scores reached
stationarity and the first 2000 trees were discarded. The
consensus tree was obtained from 36 002 trees from the two
runs. All trees were rooted with Alces alces (moose) GenBank
Accession no. AF016951, and we used GenBank Accession
no. AF016952 as a subspecies reference for the Columbian
black-tailed deer (O. hemionus columbianus).
© 2009 The Authors
Journal compilation © 2009 Blackwell Publishing Ltd
M U L E D E E R L A N D S C A P E G E N E T I C S 1853
Understanding the timing of divergence among clusters
is important for determining the extent to which changes
in geographic isolation among them since the LGM may
have influenced patterns of genetic divergence. Thus, we
estimated divergence times between adjacent population
pairs using mtDNA data and the program ima, which
implements the isolation-with-migration coalescence
model (Hey & Nielsen 2004). ima uses an MCMC method to
jointly estimate several demographic parameters of two
populations that have recently diverged. For this study, we
use ima to estimate t, time since divergence. An important
assumption of ima is that the populations in question have
most recently split from one another. In the absence of a
well-resolved phylogeny, we limited analyses to plausible
adjacent population pairs that could be sister taxa, resulting
in four pairwise analyses: central vs. southern, eastern vs.
southern, southern vs. San Diego, and eastern vs. central.
Northwestern populations were excluded from this analysis
because their haplotypes are highly divergent, with the few
shared haplotypes clearly resulting from recent migration,
not incomplete lineage sorting (see Results). While some of
those comparisons probably violate this ima assumption,
all analyses gave similar results, and thus the conclusions
drawn are robust. A similar approach has been taken in
other recent applications of ima (Niemiller et al. 2008).
We ran several initial analyses to determine limits for the
different parameters that encompassed their full distribution.
Conditions of the runs varied slightly, but generally we ran
each pairwise analysis for at least 20 million steps after a
burn-in of 1 million steps using 40 to 50 chains. We used a
geometric heating scheme, with heating parameters for g1
ranging from 0.6 to 0.9 and g2 = 0.8. Mixing of the chains
was monitored by observing effective sample size (ESS)
values and inspecting parameter plots for trends, per ima
instructions. Results were verified with one other long run
with a different random starting seed.
ima allows a range of mutation rates to be inputted prior to
the analysis for scaling parameter estimates in demographic
units. We used a per-locus mutation rate of 6.24 × 10–5
for our 624-bp segment, corresponding to a mutation rate
of 10% per million years, which is within the range found
for control region in an intraspecific study of artiodactyls
(Birungi & Arctander 2000).
Results
Characteristics of microsatellite loci and population
structure
Three microsatellite loci (I, O, Q) showed consistent
heterozygote deficiency and were dropped from further
analysis. The number of alleles ranged from 2 to 18 for the
15 microsatellite loci, with an average of 7.47 alleles per
locus (Table S1, Supporting Information). For all 587 deer,
© 2009 The Authors
Journal compilation © 2009 Blackwell Publishing Ltd
Table 1 FST values and Nei's genetic distance between pairs of the
5 genetic clusters identified by Structure. Nei’s standard genetic
distances (DS) (Nei 1972) are shown above the diagonal and FST
values are shown below the diagonal. Significant values (P < 0.05)
are indicated by an asterisk
San
Central Southern Eastern Northwestern Diego
Central
Southern
Eastern
Northwestern
San Diego
—
0.038*
0.042*
0.023*
0.072*
0.081
—
0.032*
0.087*
0.041*
0.086
0.065
—
0.092*
0.054*
0.046
0.169
0.181
—
0.134*
0.169
0.097
0.118
0.295
—
the average observed heterozygosity (HO) was 58%, with
an expected value (HE) of 66%, and 13 of the 15 loci showed
significant heterozygote deficiency (Table S1). These results
are consistent with the existence of population structure,
which is to be expected at this broad geographic level.
Linkage disequilibrium was not apparent for any pair of
loci after performing Bonferroni corrections.
Five distinct genetic clusters were ultimately resolved
with Structure analysis (northwestern, central, eastern,
southern, and San Diego clusters). L(K) values asymptote
at K = 8 and above, and ΔK was a maximum at K = 4
(Figs S5 and S6, Supporting Information). However, visual
analysis of the genetic cluster assignments revealed distinct
geographic groupings at K = 5 (Fig. 1; Fig. S7; Supporting
Information). Consequently, we follow Falush et al. (2003)
and Evanno et al. (2005) by using L(K) and ΔK values as
well as biological knowledge of the study system to choose
the best clustering assignment fit as K = 5. The clusters
range in sample size from 27 to 177, with an average size of
117 ± 57.7. The average Structure posterior probability of
assignment to a cluster was 0.95, suggesting that deer can
be confidently assigned to one of these five clusters. Ninety
per cent of the deer (528/587) were assigned at a probability
of > 0.90 and 99.3% of deer (583/587) were assigned at
a probability of > 0.50. Three of the four individuals
assigned at a probability of < 0.50 were all located in
‘hybrid’ zones, where clusters overlap geographically
(Fig. 1; Supporting Information), indicating possible hybridization or migration.
Genetic differentiation among clusters is also suggested
by FST values, Nei’s genetic distance values, and migration
rates (Tables 1 and 2). The FST values (Table 1) ranged from
0.023 to 0.134 and all values were statistically significant.
The San Diego and the northwestern clusters are the two
most geographically distant groups and had the highest
pairwise FST value. The next largest FST values included
the northwestern cluster in comparison to eastern deer, and
southern deer. However, the amova showed that 94% of
the variance was within populations (Table S5, Supporting
1854 K . M . P E A S E E T A L .
Table 2 BayesAss+ analysis of migration rates among and within the five genetic clusters as identified by Structure. Means of the posterior
distributions of migration rate are shown. The rows list the populations from which each individual was sampled (population into which
individuals migrated). The columns list the populations from which the individuals migrated. Standard deviation for all distributions
were < 0.05
Migration from
Migration to
Central
Southern
Eastern
Northwestern
San Diego
Central
Southern
Eastern
Northwestern
San Diego
0.917
0.012
0.002
0.017
0.006
0.032
0.904
0.001
0.002
0.006
0.011
0.034
0.995
0.003
0.006
0.036
0.003
0.001
0.975
0.005
0.004
0.047
0.001
0.002
0.977
Dependence of genetic differentiation on environmental
factors: present
Fig. 2 Canonical correspondence analysis biplot of genetic cluster
centroids (hollow triangles) and environmental variables: BIO2
(mean temperature diurnal range), BIO4 (temperature seasonality),
BIO5 (maximum temperature of the warmest month), BIO15
(precipitation seasonality), BIO16 (precipitation of the wettest
quarter), BIO18 (precipitation of the warmest quarter), and SRTM
(elevation). The length of an arrow indicates the strength of the
variable in explaining inter-cluster environmental divergence,
while the direction of the arrow indicates the direction in which
that variable shows increasing values and its degree of correlation
with an ordination axis. Axes 1 and 2 explain 18.1% and 17.6% of
variation among clusters, respectively.
Information), which is consistent with the low FST values
observed between them. The values for Nei’s genetic
distance followed the same pattern as the FST values
(Table 1). There was little migration among clusters with
most showing less than 2% of the population migrating per
generation (Table 2).
CCA confirmed niche differentiation among clusters by
mapping them into distinctive regions of multivariate
niche space (Fig. 2). The first two axes explained 35.7% of
the variation among clusters, while the first four axes
explained 48.7%. Tests of the first axis, and of all axes were
highly significant (for both tests, P = 0.0002). Seven
variables explained niche differences: mean temperature
diurnal range, temperature seasonality, maximum temperature of the warmest month, precipitation seasonality,
precipitation of the wettest quarter, precipitation of the
warmest quarter, and elevation. Correlations of environmental variables with species axes indicate that differentiation of niche space occurs primarily along gradients
of precipitation of the wettest quarter (first axis) and
precipitation seasonality (second axis), with additional
separation along the second axis due to differences in
the seasonality and mean diurnal range of temperature
(Table S3, Supporting Information). Collectively, these
results suggest that, despite overlap at the margins of their
respective distributions, the genetic clusters occupy different
areas of niche space (Figs 1 and 2; Supporting Information).
For all five genetic clusters, the Maxent niche modelling
shows a clear correspondence between the predicted
and observed distributions, there is very little overlap
of the predicted distributions (Fig. 3; Fig. S4, Supporting
Information), and models performed well (Table S4, Supporting Information).
Variables important in differentiating niche space in
CCA typically were also important for defining predicted
distributions in the Maxent analysis (Table 3; Supporting
Information). In addition, predicted distributions based
upon model scenarios that included only variables that
describe divergence among clusters in multivariate niche
space and those that included all variables were similar
(Fig. 3; Fig. S2, Supporting Information). This provides
further evidence for ecologically mediated geographic isolation contributing to genetic divergence.
© 2009 The Authors
Journal compilation © 2009 Blackwell Publishing Ltd
M U L E D E E R L A N D S C A P E G E N E T I C S 1855
Fig. 3 Maxent predictions of modern deer
distributions for each of the five genetic
clusters as identified by Structure. Distributions are defined by probabilities of
occurrence above the minimum predicted
probability at a deer location (lower limit of
the area shaded in blue). The niches were
modelled using the seven environmental
variables shown in Fig. 2. The last panel
shows the number of range overlaps among
the five distributions, using the more conservative threshold of minimum predicted
probability of occurrence (MPPO) + 10%
(see Supporting Information).
Partial Mantel tests across groups of individuals within
36.9 × 36.9 km grid cells detected a weak but significant
relationship between genetic distance and geographic
distance (controlling for ecological distance), and between
genetic distance and ecological distance (controlling for
geographic distance) (Table 4). Among individuals within
genetic clusters, these associations were weaker than that
found at the grid cell level, and in nearly all instances were
not significant (Table 4).
Dependence of genetic differentiation on environmental
factors: past
Reconstructions of palaeoclimate by the CCSM model for
California showed a pattern broadly consistent with those
© 2009 The Authors
Journal compilation © 2009 Blackwell Publishing Ltd
inferred from palaeopollen records, validating our use of
the CCSM output for reconstructing the distribution of
deer at the LGM. In particular, for all plant species detected
in the palaeopollen records and for all climate variables
used in the Maxent models, CCSM predictions of LGM
climate fell within the range of values those species
currently occupy. When we project the present-day climate
relationship of genetic clusters onto reconstructed climate
at the LGM, distributions of clusters overlap more than for
the present, suggesting decreased isolation among clusters
during glacial periods (Fig. 4; Fig. S4). A large portion
of California is encompassed by the overlap of at least two
ranges in the LGM (Fig. 4) whereas in modern times, the
range overlaps are more restricted (Fig. 3). From the LGM
to the present day, the net area of range overlap decreased
1856 K . M . P E A S E E T A L .
Table 3 Measures of importance of environmental variables in
Maxent niche models. Values indicate variance explained (%) in
model runs with all variables included*
Environmental variables
Genetic cluster BIO2 BIO4 BIO5 BIO15 BIO16 BIO18 SRTM
Northwestern 1.9 10.4
0.7
Central
3.2 30.0† 5.0
Eastern
11.1† 13.2
1.8
Southern
4.9
3.4 15.5
San Diego
2.6 19.6 10.4
2.1
3.2
11.0
33.8
16.8
79.0†
53.0
7.9
20.8
32.2†
1.2
2.3
2.1
4.8
17.5
4.8
3.3
52.8
16.8†
0.9
*Boldface indicates the variable contributing most to the model
and †indicates variable with largest unique contribution to the
model (i.e. greatest loss of gain when excluded from the model).
Variables are BIO2 (mean temperature diurnal range), BIO4
(temperature seasonality), BIO5 (maximum temperature of the
warmest month), BIO15 (precipitation seasonality), BIO16
(precipitation of the wettest quarter), BIO18 (precipitation
of the warmest quarter), and SRTM (elevation).
Table 4 Correlations of genetic distance (Nei’s DS) with
geographic and ecological distance as measured with partial
Mantel tests
Geographic
distance*
Ecological
distance†
Spatial scale
r
P
r
Grid cell§
Within cluster¶
Eastern
Southern
Northwestern
Central
San Diego
0.173
0.0001
0.064
0.069
0.078
0.151
0.044
0.076
0.060
0.021
0.0001
0.303
P
n‡
0.117
0.003
176
0.096
–0.040
–0.066
0.000
0.035
0.015
0.151
0.070
0.489
0.282
94
108
91
159
26
*controlling for ecological distance.
†controlling for geographic distance.
‡analyses exclude duplicate individuals that are from the same
geographic location and genetic cluster.
§distances computed among 36.9 × 36.9 km grid cells, with deer
sampling locations pooled within grid cells.
¶distances computed among individuals within genetic clusters.
from between 49 to 97%, depending upon the threshold
used (Figs 3 and 4; Fig. S4), indicating more range overlap
and greater potential for genetic exchange during the LGM.
mtDNA sequences
We found 53 haplotypes based on 624-bp of the control
region sequenced from 65 deer throughout California. The
Bayesian tree of these haplotypes does not support long-
term isolation of the genetic units defined by Structure
analysis of microsatellite loci. Rather, only two distinct,
well-supported clades are defined (Fig. 5). The trees
obtained from MP and ML analyses are not shown but
the topologies are identical to Fig. 5 and the two major
clades were well supported in all analyses. The clades are
geographically distinct, with one clade consisting of deer
from northwestern California and the other clade encompassing eastern, central, and southern California deer. The
northwestern clade is very distinct from the other deer as
the net nucleotide substitutions per site between the clades
is 6.5% and the FST value is 0.77. The reference sample of the
black-tailed deer (O. hemionus columbianus) is contained in
the northwestern clade and all the individuals from the
northwestern clade fall within the range of that subspecies
(Fig. 5; Fig. S1, Supporting Information). With the
exception of four deer, the northwestern clade contains
individuals assigned to the northwestern cluster by
Structure. The four deer (of the 17 in the northwestern
clade) that are not assigned to the northwestern cluster are
from bordering regions suggesting hybridization (Fig. 5).
Consequently, mtDNA sequence data suggest that the
genetic differentiation of the California deer into five
ecotypes is a relatively recent phenomenon, as implied by
niche projections.
Estimates of time since divergence from ima indicate
population subdivision near the LGM (eastern vs. central,
27 000 ± 11 000 bp; central vs. southern, 22 000 bp) or more
recently (southern vs. San Diego, 13 000 bp; eastern vs.
southern, 13 000 bp). Results were highly consistent among
runs for the same data set. Parameter plots were free from
trends and ESS values were very high (usually >> 100 000),
indicating convergence on the true posterior density of
the parameters. We present results from the longest run for
each population pair. It should be noted that although
estimates of t had high peaks in posterior probability for all
analyses, the right tail of the distribution often approached
zero slowly, resulting in an inability to arrive at a 90% HPD
for three of the four analyses.
Discussion
Ecological factors are clearly important in maintaining
divergence among the genetic clusters of deer. This
conclusion is based on the strong correspondence between
the five Structure clusters and California bioregions
(Hickman 1993) (Fig. 1; Table S2, Supporting Information),
which are defined by topography, climate, and plant
community, and by the CCA analyses, which revealed that
the five clusters occupy different climatic regimes.
Further, ecological niche models showed that, in modern
times, the five genetic clusters have distinct geographic
distributions with little overlap (Fig. 3; Fig. S4). Similarity
between distributions predicted using variables that
© 2009 The Authors
Journal compilation © 2009 Blackwell Publishing Ltd
M U L E D E E R L A N D S C A P E G E N E T I C S 1857
Fig. 4 Maxent predictions of projected past
deer distributions for each of the five genetic
clusters and their overlap at the Last Glacial
Maximum (LGM ~ 21 000 bp). Variables
included, thresholds used to define distributions and range overlaps are the same as
in Fig. 3.
underlie niche differences, and those including all
environmental variables suggest that deer respond to a
specific subset of environmental variables that limit gene
flow. The weak correlations detected among grid cells with
partial Mantel tests (Table 4) are in stark contrast to the
pronounced spatial genetic structuring of genetic clusters,
indicating that neither geographic distance nor strictly
clinal environmental variation alone can explain the
patterns of genetic differentiation. This conclusion is even
more evident for the Mantel tests within genetic clusters at
the individual level where little if any association between
genetic differentiation and ecological or geographic
distances is found. These findings suggest each genetic
© 2009 The Authors
Journal compilation © 2009 Blackwell Publishing Ltd
cluster can be defined by a specific ecological niche, and we
propose that they represent specific ecotypes that roughly
correspond to subspecies (see Supporting Information).
The correspondence between the genetic clusters and the
phenotypically defined subspecies (Fig. S1) justifies their
classification as distinct genetic and ecological units (e.g.
Crandall et al. 2000).
Further supporting the role of ecology in generating and
maintaining genetic differentiation in California mule deer
are previous studies showing the influence of habitat and
environmental differences on genetic differentiation in
highly mobile carnivores (Geffen et al. 2004; Sacks et al.
2004; Stenseth et al. 2004; Pilot et al. 2006) and ungulates
1858 K . M . P E A S E E T A L .
Fig. 5 Bayesian analysis of 625 bp of
mtDNA control region sequence. Consensus
topology of 36 002 trees obtained after 2
million generations based on the HKY + I + G
model. Numbers along branches represent
node support from Bayesian analysis
(above) and neighbor joining (below);
numbers are shown for values 95% or
greater in both analyses (one exception is
the lower clade supported at 94% and
100%). One asterisk corresponds to nodes
that obtained a support value between 50%
and 94% in both analyses and two asterisks
correspond to nodes that obtained a support
value between 50% and 94% in one analysis
and between 95% and 100% in the other
analysis. Haplotype designations are
shown at branch tips and correspond to
the Structure cluster to which the deer
was assigned (NW, northwestern cluster;
C, central cluster; SC, southern cluster;
E, eastern cluster; SD, San Diego cluster).
The maps show the locations of all the
deer (N = 65) from which sequences were
obtained and are separated by the two
major clades. The top map shows the deer
that fall into the northwestern clade; all
deer in this clade were assigned to the
northwestern genetic cluster by Structure
except for the four individuals that are
highlighted with circles on the map and
with boxes on the tree.
(Courtois et al. 2003; Brown et al. 2007). Specifically in
California, a comparative phylogeographic analysis of nine
California species spanning different ecologies (Lapointe &
Rissler 2005) revealed a supertree (composed of trees from
the nine species) with five genetic groups that correspond
to distinct geographic regions in California. The genetic
groupings found by Lapointe & Rissler (2005) are roughly
similar in geographic extent to the genetic clusters found in
California mule deer. Moreover, Lapointe & Rissler (2005)
found that these groups were characterized by significantly different climatic regimes, analogous to our finding
that temperature, precipitation, and elevation are all influential variables in distinguishing the five genetic clusters of
mule deer.
We propose two possible mechanisms for the dependence
of genetic differentiation on ecological factors. The first
is temporal mismatch due to seasonality. Elevation and
climatic factors (precipitation, temperature, and their
seasonality) have a central role in determining when food
is available for deer and in turn, food availability constrains
where deer will be found. The timing of reproduction for
each ecotype may reflect differing patterns of seasonality in
climate, rainfall, and plant availability specific to each
ecological region. Mule deer subspecies are known to breed
at slightly different times of the year from September to
January with mule deer subspecies in northern California
breeding earlier than those in the south (Wallmo 1978;
Mackie et al. 2003). Mule deer subspecies can interbreed as
suggested by mixed Structure assignments but habitat and
ecological differences among clusters may be enough to
reduce gene flow. Further exploration into the seasonality
of the five ecoregions and remote sensing data on ‘green
up’ are needed to further test these hypotheses.
Second, the dependence of genetic differentiation on ecological factors may be due to natal-habitat-biased dispersal
[proposed for wolves (Geffen et al. 2004) and coyotes
(Sacks et al. 2004)]. Deer may be preferentially dispersing
to areas that are environmentally similar to their area of
birth by gauging temperature, precipitation, elevation, or
seasonality directly. Alternatively, they may associate
© 2009 The Authors
Journal compilation © 2009 Blackwell Publishing Ltd
M U L E D E E R L A N D S C A P E G E N E T I C S 1859
indirectly with those factors through vegetation and habitat.
Deer are opportunistic browsers and will likely eat vegetation from any ecoregion; however, it is quite possible that
they make dispersal decisions based on food availability
(seasonality), climatic factors, and elevation, all of which
define the genetic clusters. To test this hypothesis, deer
could be followed by radio telemetry along the contact
zones of the genetic clusters to determine fidelity to specific
habitats and food preferences could by determined through
faecal DNA studies (Hoss et al. 1992; Kohn & Wayne 1997).
Historical mechanisms of genetic differentiation
Although current ecological factors are clearly important
in defining mule deer genetic structure, historical factors
such as topographic or habitat barriers may also have
influenced contemporary genetic structure. We assessed
historical factors by modelling the distributions of the
five genetic clusters of deer during the LGM (Fig. 4). We
hypothesized that if isolation in LGM refugia was a strong
force in determining modern genetic structure, then
marginal or no range overlap of the five genetic clusters
should be evident during the LGM. We found instead that
during the LGM, there was more range overlap among
the five genetic clusters than today, suggesting that the
divergence among the five mule deer clusters is primarily
ecologically mediated and likely of recent origin. This
conclusion is supported by the mitochondrial DNA
sequence data because the five Structure clusters were not
observed as distinct clades, indicating insufficient time for
reciprocal monophyly. However, the two clades found in
the mtDNA phylogeny suggest at least one historical barrier
may have influenced the genetic divergence of mule deer
(see below).
Coalescent-based analyses of mtDNA data are in accord
with a relatively recent timing of divergence, with peak
posterior probability of divergence among four population
pairs ranging from 13 000 to 27 000 bp. Two of the pairs
produced peaks in t corresponding to postglacial dates
(< 18 000 bp; southern vs. San Diego and eastern vs. southern). Although tight confidence intervals could not be
obtained because the right tail of the distribution gradually
approached zero, peaks for t were generally high and
much older dates, such as those expected if divergence was
initiated before the last glaciation, had much lower probability. Although we are unable to precisely estimate the
timing of niche divergence relative to genetic divergence,
our results suggest that ongoing niche divergence was
facilitated by climatic changes following the LGM, which
led to reduced genetic exchange among clusters adapting
to local environmental conditions.
Predictions of suitable habitat from Maxent niche modelling (Fig. 3) also give additional insight in the detection
of modern and historical barriers. For example, if there is
© 2009 The Authors
Journal compilation © 2009 Blackwell Publishing Ltd
suitable habitat predicted but no deer from that cluster
occur there, it is likely that historical factors or physical barriers are responsible for the discrepancy. This pattern is
seen in only a few instances, such as in the northwestern
cluster around the San Francisco Bay/Sacramento Delta
(SFB/SD). Genetic partitions were also found across the
SFB/SD in other California species (Feldman & Spicer
2006), supporting its role as a historical barrier, forming in
the mid-Pleistocene (Feldman & Spicer 2006). The Monterey
Bay also appears to act as a barrier for the southern genetic
cluster. A large historical marine embayment occurred
there 5–2.5 million years ago (Jacobs et al. 2004), and the
Monterey Bay was found to a major barrier for two mammals
(the wood rat and the ornate shrew) (Maldonado et al. 2001;
Calsbeek et al. 2003).
The recent integration of niche modelling into phylogeographic studies has provided an unprecedented opportunity to test hypotheses concerning genetic differentiation
in a spatial context, both within and among species (Kozak
& Wiens 2006; Richards et al. 2007). Niche differences have
been detected among more ancient lineages representing
cryptic species (Rissler & Apodaca 2007), tests of sister
species pairs have found compelling support for niche
conservatism (Kozak & Wiens 2006), and glacial refugia
postulated from phylogeographic data have been confirmed
with ecological niche models (Waltari et al. 2007). However,
applications of this new approach have thus far defined
lineages and based predictions on analyses of DNA
sequence data. Thus, these studies necessarily sample
older lineages and are biased against the detection of rapid
niche evolution. In contrast, we defined genetic clusters
within species using rapidly evolving microsatellites, and
contrasted these clusters with the near lack of spatial structure
in mitochondrial DNA. By quantifying niche differences
with multivariate ordination, we identified the factors that
explain differences among genetic clusters. We then used
ecological niche model projections of modern, LGM,
and future (Fig. S3, Supporting Information) distributions
to visualize the spatial outcomes of niche differences, and
to test predictions of pre- and post-glacial niche diversification. Our results support post-glacial differentiation
induced by environmental differences as the cause of
differentiation and identified possible areas of geographic
isolation as those with suitable habitat but missing deer
of the appropriate ecotype. Our approach employing
rapidly evolving genetic markers is complementary to
the recent applications of niche modelling noted above,
but allows investigation into the more recent past.
Acknowledgements
We are indebted to the many biologists and game wardens of the
California Department of Fish and Game (CDFG) for assisting in
the collection of the samples used in this project. Without their help,
1860 K . M . P E A S E E T A L .
this project would not have been possible. We specifically want to
thank Craig Stowers and Mary Sommer, both from the CDFG
Deer Management Program, for their support and assistance
during this project. Funding for this project was also provided by
the CDFG Law Enforcement Division under interagency contract.
We also thank the Sacramento Safari Club and the California Deer
Association for their additional financial support and dedication.
We are grateful to the Paleoclimate Modelling Intercomparison
Project for access to palaeoclimate data. We appreciate laboratory
assistance from Bridgett vonHoldt, Hanna Shohfi, Megan Motta,
and Daniel Greenfield. David Jacobs provided useful comments
and suggestions. P. Klimov kindly loaned processor time for
coalescence analysis.
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Katherine Pease is a PhD student at UCLA interested in landscape
genetics and the genetic impacts of urbanization and fragmentation.
Adam Freedman studies how demography, history, and environment influence patterns of diversification. John Pollinger develops
molecular genetic techniques for conservation applications
and serves as Director of UCLA’s Conservation Genetics Resource
Center. John McCormack and Wolfgang Buermann are members
of the Center for Tropical Research at UCLA. Jeff Rodzen, Jim
Banks, Erin Meredith, Vernon Bleich, and Robert Schaefer work
with the California Department of Fish and Game as field biologists
and wildlife forensic specialists. Ken Jones develops custom
genetic markers for a wide variety of applications. Robert Wayne
applies molecular genetic techniques to study questions in ecology
and evolutionary biology.
Supporting Information
Additional Supporting Information may be found in the online
version of this article:
Fig. S1 Mule deer subspecies ranges and sampling locations of
587 deer in California.
Fig. S2 Maxent predictions of modern deer distributions for each
of the five genetic clusters as identified by Structure using all
environmental variables.
Fig. S3 Maxent predictions of projected future deer distributions
for each of the five genetic clusters and their overlap under climate
change.
Fig. S4 Spatial extent of overlapping Maxent predictions between
mule deer genetic clusters for the Last Glacial Maximum, the
present, and the future under a CCM3 scenario of doubled CO 2
atmospheric concentrations.
Fig. S5 Plot of L(K) vs. K (one iteration for each K value) for the
initial Structure analysis of 587 genotyped deer samples for K = 1
to 10, showing a maximum in L(K) at K = 8.
Fig. S6 Plot of ΔK (Evanno et al. 2005) vs. K (five iterations per
K value) for K values up to K = 10, showing a maximum in ΔK at
K = 4.
1862 K . M . P E A S E E T A L .
Fig. S7 Plots of genetic cluster assignment for 587 California deer
grouped by county and based on Structure analysis for K values of
2, 3, 4 and 5.
Table S4 Niche model summary statistics and results from threshold-dependent and threshold-independent significance tests
based upon 10-fold validation
Table S1 Observed (HO) and expected (HE) heterozygosity, allelic
number (A), and allelic richness (AR) for all deer and for the five
clusters identified by Structure.
Table S5 Molecular analysis of variance for microsatellite data (15
microsatellites and 587 samples) at the population (northwest,
central, east, southern, San Diego) and group (northwest/central
vs. east/southern/San Diego) levels
Table S2 Proportion of deer from each Structure group that fall
within each Jepson ecoregion
Table S3 Correlations between CCA species axes and the selected
environmental variables
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© 2009 The Authors
Journal compilation © 2009 Blackwell Publishing Ltd