the walk is never random: subtle landscape effects shape ...€¦ · the population. we found...

Molecular Ecology (2012) doi: 10.1111/j.1365-294X.2012.05681.x

The walk is never random: subtle landscape effectsshape gene flow in a continuous white-tailed deerpopulation in the Midwestern United States

STACIE J . ROBINSON,* MICHAEL D. SAMUEL,† DAVIN L. LOPEZ‡ and PAUL SHELTON§

*Department of Forest & Wildlife Ecology, University of Wisconsin-Madison, Rm 208 Russell Labs, 1630 Linden Dr., Madison,

WI 53706, USA, †U.S. Geological Survey, Wisconsin Cooperative Research Unit, University of Wisconsin-Madison, Rm 204

Russell Labs, 1630 Linden Dr., Madison, WI 53706, USA, ‡Wisconsin Department of Natural Resources, PO Box 7921,

Madison, WI 53707, USA, §Illinois Department of Natural Resources, 1 Natural Res. Way, Springfield, IL 62702, USA

Corresponde

E-mail: stacie

Published 20

Abstract

One of the pervasive challenges in landscape genetics is detecting gene flow patterns

within continuous populations of highly mobile wildlife. Understanding population

genetic structure within a continuous population can give insights into social structure,

movement across the landscape and contact between populations, which influence

ecological interactions, reproductive dynamics or pathogen transmission. We investi-

gated the genetic structure of a large population of deer spanning the area of Wisconsin

and Illinois, USA, affected by chronic wasting disease. We combined multiscale

investigation, landscape genetic techniques and spatial statistical modelling to address

the complex questions of landscape factors influencing population structure. We

sampled over 2000 deer and used spatial autocorrelation and a spatial principal

components analysis to describe the population genetic structure. We evaluated

landscape effects on this pattern using a spatial autoregressive model within a model

selection framework to test alternative hypotheses about gene flow. We found high

levels of genetic connectivity, with gradients of variation across the large continuous

population of white-tailed deer. At the fine scale, spatial clustering of related animals

was correlated with the amount and arrangement of forested habitat. At the broader

scale, impediments to dispersal were important to shaping genetic connectivity within

the population. We found significant barrier effects of individual state and interstate

highways and rivers. Our results offer an important understanding of deer biology and

movement that will help inform the management of this species in an area where

overabundance and disease spread are primary concerns.

Keywords: generalized linear model, landscape genetics, model selection, spatial ordination,

white-tailed deer

Received 27 January 2012; revision received 13 April 2012; accepted 27 April 2012

Introduction

Examining gene flow patterns for large continuous

wildlife populations presents a substantial challenge

within the landscape genetics field. Yet there is increas-

ing need to understand genetic processes within these

nce: Stacie J. Robinson, Fax: 608 262 9922;

[email protected]

12. This article is a US Government work and is in the p

populations, particularly in cases with overabundant

wildlife (Garroway et al. 2008), disease risks (Cross

et al. 2008), human–wildlife conflicts (Henderson et al.

2000) and negative ecological impacts of overpopulation

(Cote et al. 2004). Understanding population genetic

structure within a continuous population can provide

insights into social structure, movement, reproductive

dynamics, ecological interactions or pathogen transmis-

sion. While many landscape genetics studies focus on

ublic domain in the USA.

2 S . J . R O B I N SO N E T A L.

identifying clusters of individuals and measuring

genetic differentiation among distinct subpopulations

(Storfer et al. 2010), there is a growing interest in large-

scale continuous animal populations (Balkenhol et al.

2009; Schwartz & McKelvey 2009; Segelbacher et al.

2010; Storfer et al. 2010). Several novel methods have

recently been developed or applied in landscape genet-

ics to address these subtle questions about population

structure (Borcard et al. 2004; Dray et al. 2006; Jombart

et al. 2008; Epperson et al. 2010).

The white-tailed deer (Odocoileus virginianus) popula-

tion in the Midwestern United States provides an excel-

lent case study for application of these emerging

landscape genetic methods. The population is burgeon-

ing, widespread and continuously distributed over the

landscape. Deer are highly mobile, habitat generalists

and adapted to human populations throughout the

region (Hirth 1977; Lesage et al. 2000; Kilpatrick et al.

2001). Deer densities frequently exceed 40 per km2,

impact natural ecosystems (Bartelt et al. 2003) and are

important to local economies (over 600 000 hunting

licences annually in Wisconsin alone; Wisconsin Depart-

ment of Natural Resources, 2003). But overpopulation

also has negative impacts including forest disturbance

(Rooney 2001; Cote et al. 2004), agricultural damage

(Fagerstone & Clay 1997; Retamosa et al. 2008), elevated

risk of deer–vehicle collisions (�1.5 million annually in

the US; Mastro et al. 2008), and transmission and

spread of bovine tuberculosis and chronic wasting dis-

ease (O’Brien et al. 2002; Williams et al. 2002).

Given the high mobility and complex social structure

of white-tailed deer, different processes act to shape

population structure at different scales. At the fine

scale, deer populations are organized by social groups

of related female deer, with successive generations of

females establishing home ranges overlapping their

mother’s (as described by the ‘rose petal hypothesis’

refering to the expanding pattern of home ranges of

related females; Porter et al. 1991). This social organiza-

tion can produce localized clusters of genetically related

animals within populations (Mathews & Porter 1993;

Purdue et al. 2000; Wang & Schreiber 2001; Grear et al.

2006). Local habitat quality and arrangement can influ-

ence the home range requirements of individuals and

the degree of overlap between social groups (Kohn &

Mooty 1971; Larson et al. 1978; Schauber et al. 2007).

At a broader scale, different biological processes and

landscape features affect genetic connectivity and

admixture beyond the localized groups. In white-tailed

deer, males disperse at higher rates than females and

may move considerable distances, homogenizing

genetic composition (Hawkins & Klimstra 1970; Nixon

et al. 1991; Nelson 1993; Rosenberry et al. 1999).

Although average male dispersal distances in the Mid-

Published 2012. This article is a

west range from 5 to 10 km, and commonly up to

20 km (Larson et al. 1978; Wozencraft 1978; Ishmael

1984; Skuldt et al. 2008), both direction and distance of

dispersal may depend on habitat quality and arrange-

ment (Marchinton & Hirth 1984; Wiegand et al. 1999;

Wiens 2001; Long et al. 2005). Landscape features such

as roads, fences and rivers might additionally restrict

deer movement (Coulon et al. 2006; Perez-Espona et al.

2008).

We employed a novel combination of multiscale

investigation, multivariate techniques and spatial mod-

elling to address the complex questions of how land-

scape factors affect the degree of genetic clustering

among social groups at the local scale and shape

genetic connectivity at the landscape scale. We

addressed three research questions: (i) whether the pop-

ulation structure of white-tailed deer in the Wisconsin–

Illinois area is best described by distinct population

subdivisions or a continuous gradient of genetic varia-

tion; (ii) what habitat characteristics affect local genetic

structure at the scale of social groups; and (iii) what

habitat factors or potential dispersal barriers shape gene

flow within the population at a landscape scale. We

used a combination of spatial statistics and ordination

to describe landscape genetic patterns, but these analy-

ses fall short of explaining the mechanisms behind the

observed genetic structure. Therefore, we constructed

spatial autoregressive models within a model selection

framework to test alternative hypotheses about land-

scape influences on gene flow.

Materials and methods

Study area and landscape variables

Our research took place in chronic wasting disease

management zones of southern Wisconsin (approxi-

mately 23 300 km2 in 15 counties) and northern Illinois

(approximately 8800 km2 in six counties) (Fig. 1) where

deer tissues were collected for disease surveillance

(Shelton & McDonald 2010; Wisconsin Dept. Natural

Resources, 2010). Our study area encompassed a hetero-

geneous landscape covering several ecoregions with

varying levels of urbanization and potential natural and

anthropogenic barriers to animal movement. The region

was comprised of the heavily forested Western Coulee

and Ridge (WCR) and grassland Southwest Savanna

ecoregions (SWS) in the west, and the agriculture-domi-

nated Southeast Glacial Plains (EGP) in the east (Omer-

nik 1987). The EGP was the most urbanized landscape

in the region, containing Madison, WI, and Rockford,

IL, as well as other metropolitan areas along interstate

corridors. A continental climate dominates the study

area with warm summers and cold, snowy winters (WI

US Government work and is in the public domain in the USA.

Fig. 1 Study area for landscape genet-

ics analysis of white-tailed deer in the

Midwestern (Wisconsin and Illinois),

USA. Sample townships (black) are

shown relative to the systematic grid

used to ensure even sample coverage

over the broad study area. Data from a

pilot area (dashed outline) were used to

determine the sampling design, which

included approximately 30 individuals

from each of 64 townships.

LANDSCAPE G ENETICS IN A C ONTINUOUS DEER POPULATION 3

State Climatology Office, 2008). But winters were mild

enough that deer were not seasonally migratory, as seen

in more severe climates (Nelson 1995).

We investigated habitat characteristics and dispersal

barriers previously shown to impact deer movement

(Long 2006; Blanchong et al. 2008). Landscape variables

were summarized at the scale of Public Land Survey

System townships (9.6 km · 9.6 km) (classifications for

each township shown in Fig. 2). We used three vari-

ables to describe the broad-scale ecological characteris-

tics and site-specific habitat in each township.

Ecoregion classification (ECO) described the ecological

community in and surrounding the township (based on

the ecoregions defined above). Per cent forest canopy

cover (CAN) provided a measure of potential deer habi-

tat in a township (Rolley 2007; Storm 2011) and can

affect both population density and dispersal patterns

(Long et al. 2005; Joly et al. 2006). CAN was calculated

from the national landcover database as the per cent of

each township covered by deciduous forest canopy (Fry

et al. 2009). Because habitat fragmentation has been

related to deer space use and dispersal (Nixon et al.

1991, 2008; Etter et al. 2002; Long et al. 2005), we used

the landscape metric per cent like adjacency (ADJ) to

indicate the level of continuity or fragmentation of for-

est patches within each township (calculated as the pro-

portion of deciduous forest patches adjacent to another

forest patch vs. nonforest, using FRAGSTATS; McGarigal &

Marks 1995). We also evaluated three variables fre-

quently suggested as impediments to deer gene flow:

interstates, state-level highways and major rivers (Long

et al. 2005; Coulon et al. 2006; Joly et al. 2006; Blan-

chong et al. 2008). Interstates comprised large divided

Published 2012. This article is a US Government work and is in the p

(federally managed) highways with 2–3 traffic lanes in

each direction, whereas state-managed highways were

typically not divided and consisted of 1 (sometimes 2)

traffic lane in each direction. Major rivers were desig-

nated by the Wisconsin Department of Natural

Resources as those that maintained permanent high-vol-

ume water flow. We classified townships based on their

location on opposite sides of interstate highways I39 ⁄ 90,

I43 and I94 (INT); state highways 12, 14, 18, 20, 51, 151

and (HWY); and the Wisconsin, Rock and Yahara Riv-

ers (RIV), thus creating categorical variables represent-

ing geographic zones as shown in Fig. 2.

Sampling

We conducted a pilot study using microsatellite data

from over 1300 samples collected from 2001 to 2006

(pilot area shown in Fig. 1, data from microsatellites

obtained as described for the full data set below; Grear

et al. 2010) to determine sampling needed to represent

the genetic variation in the local population and detect

subtle genetic patterns in a large, dense, continuous

deer population with relatively high gene flow (Grear

et al. 2006; Blanchong et al. 2008). We found no tempo-

ral trend in fine-scale genetic patterns (no significant

change in allele frequencies from 2001 to 2006, calcu-

lated in GENEPOP; Raymond & Rousset 1995) and there-

fore pooled deer sampled across several years. We also

found no subdivision of populations within the four

township area considered (performed in STRUCTURE, Prit-

chard et al. 2000, K = 1 within the 310-km2 pilot area),

and thus we concluded that a single township could

adequately represent a neighbourhood of at least four


(A)

(B)

(C)

(D)

(E)

(F)

Fig. 2 Study area maps show variation

in the predictor variables used in spatial

genetic models for white-tailed deer in

the Midwestern USA. Variables mea-

sured at each sample township described

potential dispersal barriers [INT

(A) = zones defined by intervening inter-

state highways, 4–6 lanes divided roads

with high traffic, HWY (B) = zones

defined by intervening state highways,

typically 2 lanes with moderate traffic,

RIV (C) = zones defined by intervening

rivers of class four or five water volume]

and habitat features [ECO (D) = ecore-

gion classification, CAN (E) = % forest

canopy, ADJ (F) = % like adjacency mea-

suring continuity of forest habitat).


townships. Our pilot analysis (rarefaction curve of allele

numbers for samples of 5–500 per township) further

indicated that the allelic composition of a township was

adequately captured by 30 deer.

We employed a hierarchical design to achieve a sam-

ple intensity representing fine-scale genetic patterns

and also sufficiently widespread to capture landscape-

scale patterns in gene flow. We divided the study

region into a grid with cells of 19.3 km · 19.3 km

(encompassing four townships; Fig. 1). We then ran-

domly sampled deer from one township from most grid

cell (in the few cases, samples were inadequate from

the randomly selected township, the township contain-

ing the most deer samples was selected, and grid cells

in which no township contained adequate samples were

skipped). This created a systematic coverage of the area

with 64 sampled townships separated by a variety of

distances. Within each township, we attempted to sam-

ple 40 adult female deer to characterize the local gene


pool. We used only females to maximize the detection

of population structure and minimize noise introduced

by male migrants (Rassmann et al. 1997).

Deer were sampled between 2001 and 2010 as part of

disease monitoring programmes conducted by the WI

and IL Departments of Natural Resources (DNRs).

Lymph nodes were collected by DNR staff when animals

were harvested or registered. Samples were georefer-

enced to the Public Land Survey System section

(2.56 km2) where the animal was harvested. All WI sam-

ples were collected from hunter-harvested deer. Harvest

intensity was lower in IL, so samples were collected from

hunter-harvest and sharpshooting by IL DNR (part of

CWD monitoring and control efforts). Because sharp-

shooting was highly localized, with many deer collected

from a single shooting station, we sampled individuals

from as many shooting stations as possible and only used

animals culled on different dates to avoid sampling-

related groups that might arrive at sharpshooting



stations together and to distribute samples over the land-

scape similar to hunter-harvested samples.

Genetic data

Whole-genomic DNA was extracted from approxi-

mately 10 mg of lymph node using the Qiagen DNeasy

tissue extraction kit (Qiagen Ltd., Crawley, West Sussex,

UK). Extracted DNA was quantified using a Sapphire II

UV spectrometer, then diluted to 25 ng DNA ⁄ lL H2O.

Individuals were genotyped using a suite of 15 highly

variable microsatellite markers (Table S1, Supporting

information). All PCR were performed using a Qiagen

multiplex kit containing a pre-optimized mix of Taq

enzyme, dNTPs, MgCl2 and buffering chemicals. Reac-

tions were run using the thermoprofile prescribed by

the Qiagen kit, with marker-specific annealing tempera-

tures and primer concentrations detailed in Table S1

(Supporting information). The labelled fragments were

visualized using an ABI 3730 capillary sequencer

(Applied Biosystems, Foster City, CA, USA) and analy-

sed using GENEMARKER software from Softgenetics (Soft-

genetics, State College, PA, USA). We replicated PCR

and genotype analysis for approximately 10% of all

samples and determined error rates between replicates.

As preliminary analysis, we tested for deviations from

Hardy–Weinberg equilibrium (HWE) and linkage equi-

librium (LE) using GENEPOP (Raymond & Rousset 1995).

Detection of population subdivision

We used a spatially explicit Bayesian population assign-

ment algorithm to determine whether the study area con-

stituted a single population or should be subdivided into

genetically distinct groups. The test was performed in

BAPS v5.1 (Corander et al. 2008), which finds the most

likely grouping of individuals to maximize HWE and LE

through stochastic optimization. Unlike nonspatial popu-

lation assignments, which use uninformative priors, the

spatial test assumes that populations should be spatially

cohesive with higher probability of nearby samples being

assigned to the same population (Corander et al. 2008).

Data for the assignment test consisted of each individ-

ual’s multilocus genotype and spatial coordinates of the

harvest location (based on the centroid of the PLSS sec-

tion (2.6 km · 2.6 km) identified by the hunter). We con-

ducted five iterations of tests for each potential number

of subpopulations (K) varying from 1 to 10 to extend far

beyond numbers of populations that might be expected.

Fine-scale social structure

At the fine scale, we used correlograms to explore the

patterns of spatial clustering among genetically related


animals. Correlograms have been used to explore con-

tinuous spatial genetic patterns across the full range of

a species (Quemere et al. 2010) or spanning heteroge-

neous habitats within a species range (Gauffre et al.

2008) (see overviews in Wagner et al. 2005; Guillot et al.

2009). We constructed correlograms showing the degen-

eration of spatial association with increasing distance

classes (using the ‘r’ statistic in the ‘Multiple D Class’

routine in GENALEX 6; Peakall & Smouse 2006). We used

distance classes of 2 km because this approximates the

PLSS section (1 mi2 or 2.6 km2) at which samples were

collected, and ensured an adequate number of pairs

within each distance class. We plotted separate correlo-

grams for each ecoregion to assess the influence of

broad-scale landscapes on local patterns of relatedness

and to identify genetic neighbourhoods.

We hypothesized that local genetic structure would

be correlated with habitat characteristics within the

township that affect social group organization and

genetic neighbourhood size (Hirth 1977; Wang & Schre-

iber 2001). We constructed generalized linear models

(glm) in R (R Development Core Team, 2008) to relate

habitat variables to the average level of genetic related-

ness (rxy) between all deer pairs within each township

(calculated in SpagEdi; Hardy & Vekemans 2007; Quel-

ler & Goodnight 1989). These models used ECO, CAN

and ADJ as the habitat predictor variables. The best

model was chosen based on AIC score, and for closely

competing models (DAIC £ 2), we averaged model coef-

ficients (Burnham & Anderson 2002). Fit of the final

model was assessed using residual deviance to measure

improvement in likelihood between the selected and

the null models.

Broad-scale population connectivity

To identify broad-scale spatial genetic patterns, we

employed spatial principal component analysis (sPCA)

(see overviews in Jombart et al. 2008, 2009), which com-

bines multivariate allele frequencies and spatial auto-

correlation, to summarize genetic variation and its

spatial pattern. This multivariate ordination technique

can summarize a large multivariate data set using a few

highly informative and uncorrelated factors, the ‘princi-

pal components’ (McGarigal & Cushman 2000). Our

data matrix consisted of allele frequencies in each sam-

ple township, and the resulting sPCA component axes

were scaled to preserve this genetic relationship among

townships, as well as spatial dependence between them.

This is accomplished by optimizing the maximum

eigenvector scores based on the product of genetic vari-

ance and Moran’s I values. We plotted the principal

component eigenvalues to evaluate their relative infor-

mation and determine the number of informative sPCA



axes (Legendre & Legendre 1998; Jombart et al. 2008,

2009). Additionally, we used factor loading plots to

judge the contribution of each allele and locus to the

sPCA and to assess the degree of clinal or clustered

genetic structure in the population. We performed the

sPCA using the adegenet package in R; we chose the

inverse distance weighting option to include all possible

township pairs in the spatial neighbourhood matrix

(Jombart et al. 2008).

Each sampled township was assigned a score on each

sPCA axis representing its position in the multidimen-

sional space of genetic variability relative to all other

townships. These sPCA scores provided site-specific

measures of genetic structure that simultaneously

accounted for genetic variability and broad-scale spatial

structure. We constructed regression models to test the

association of landscape variables with this genetic

structure using sPCA scores on each axis as response

variables for each township. These site-based measures,

as opposed to pairwise genetic distances, allowed us to

determine which landscape variables predicted genetic

structure (Jombart et al. 2009), were essential to meeting

the independence assumptions of regression analysis

and facilitated the use of model selection methods

(Burnham & Anderson 2002).

We hypothesized that landscape-scale genetic struc-

ture might be influenced by habitat characteristics at

each township, barriers to movement between town-

ships or a combination of these factors. As an explor-

atory step, we computed partial Mantel tests to assess

whether each variable (ECO, CAN, ADJ, INT, HWY,

RIV) explained significant genetic variation after

accounting for isolation by distance. We considered

genetic distance measured as the Euclidean distance

between sampled township coordinates on the two

sPCA axes. We measured landscape distance as the dif-

ference in numerical measures (CAN, ADJ) or as cate-

gorical zones (Fig. 2B) for barrier variables (i.e.

distance = 0 between townships within the same barrier

zone, distance = 1 between townships separated by a

putative barrier, tests calculated using the ZT software;

Bonnet & Van de Peer 2007).

While the sPCA scores represent the broad-scale spa-

tial patterns of genetic variation, they do not specifically

account for the isolation-by-distance process (Jombart

et al. 2008). Therefore, we constructed spatial autore-

gressive models to simultaneously account for isolation

by distance and evaluate the effect of landscape features

on the observed genetic structure. We used a spatially

lagged regression to assess the effect of multiple param-

eters while accounting for spatial autocorrelation in the

response variable (Legendre & Legendre 1998). Using

the lagsarlm routine in the spdep package (Bivand et al.

2011) within R, we fit the model:


Gi¼q�Wij � Gj þ b � X þ e ðeqn 1Þ

where Gi represents the genetic measure (sPCA score)

at township i and Gj represents scores measured at

other townships. Wij is the weight matrix that deter-

mines the relative influence of township j on township

i. The weight matrix was constructed for all possible

pairs of townships based on the inverse of the distance

between them. We chose the inverse distance weighting

because this best approximates the expectation of spa-

tial autocorrelation from isolation by distance. In the

spatially lagged model, q accounts for the lack of inde-

pendence among townships. The contribution of predic-

tor variables is determined by fitting the linear function

b *X and estimating the effects (b) of the included envi-

ronmental variables (X). Finally, e is the residual error.

We used a hierarchical model selection procedure

based on an information theoretic approach. We sepa-

rately evaluated the contribution of either habitat vari-

ables (ECO, CAN and ADJ) or barrier variables (INT,

HWY and RIV) in explaining the sPCA genetic pattern.

Alternative models from either set were selected based

on the lowest AIC score (or within DAIC < 2 from the

lowest). We then considered the best models from each

set and combinations thereof. The final set of candidate

models containing habitat and barrier variables were

also evaluated based on AIC score (and ⁄ or averaged

based on AIC weights in the event of closely competing

models with DAIC < 2) (Burnham & Anderson 2002).

We evaluated model goodness of fit by residual devi-

ance (as above) and R2. While inclusion in the model

indicated that a predictor variable impacted genetic

structure, we also tested whether specific landscape fea-

tures (individual interstates, highways, rivers) consti-

tuted significant barriers to gene flow. To accomplish

this, we used z tests to compare model regression coeffi-

cients between zones separated by a single potential bar-

rier (i.e. INT zones A vs. B separated by interstate

I90 ⁄ I94). If regression coefficients differed significantly

between zones on either side of a putative barrier, we

concluded that sample townships were more similar to

one another within zones and differed significantly

between zones, thus indicating a barrier effect differenti-

ating townships on either side of that landscape feature.

Results

Genetic data

In total, we genotyped 2069 deer from 64 townships,

averaging 32 deer per sampled township (range 10–72).

We sampled adult female deer with an average age of

2.76 years. The locus OBCAM performed poorly with



15% missing data and >5% error, so it was dropped from

the data set, leaving 14 loci for analysis. Error rates for all

other loci were low (1–3%) and missing data were mini-

mal (Table 1). Five locus pairs (RT23-RT7, BM1225–

BM4208, RT27-BM4208, RT27-OarFCB193, and RT9-Oar-

FCB193) showed linkage disequilibrium after Bonferroni

correction for multiple tests (0.05 ⁄ 91 pairwise tests signif-

icance = a < 0.0006). However, Wilson et al. (1997) dem-

onstrated no linkage among these loci; thus, our result

likely relates to structure within the population rather

than physical linkage between loci. Six loci were out of

HWE after correcting for multiple tests (0.05 ⁄ 14 locus-

based tests significance = a < 0.0036). Deviations from

HWE were also likely attributable to population sub-

structure as genotyping error rates were not elevated for

these loci and they showed strong spatial structuring

based on loading scores for the sPCA axes (below).

Detection of population subdivision

The Bayesian assignment test indicated that deer in our

study area were not divided into genetically distinct

subpopulations (posterior probability of K = 1 was 1),

indicating a single genetic population, continuous over

a broad geographic scale. Thus, our remaining land-

scape genetic analyses examine subtle genetic patterns

within a single genetic population rather than between

separate populations.

Fine-scale social structure

Correlograms showed that clustering of genetic similar-

ity was greatest at short distances (2–6 km), although

Table 1 Microsatellite characteristics and data quality for genotypes

of data quality, we present the number of alleles for each polymorph

cients (Fis) and observed vs. expected ratios of homozygotes (HO), a

exact test of Hardy–Weinberg equilibrium (Pval HWE)

Locus No. of alleles % Missing data Fis

BM 6506 14 4.592 0.099

BM 1225 12 3.142 0.040

N 24 5.607 0.038

RT 23 20 3.287 0.020

BM 4107 17 1.982 0.017

Oar 193 18 6.235 0.015

RT 27 18 4.592 0.006

BM 6438 13 7.347 0.010

BM 4208 19 6.815 0.085

Cervid 1 19 2.127 0.011

Cervid 2 15 4.447 0.166

IGF 1 15 2.320 0.003

RT 7 17 3.335 0.029

RT 9 11 2.465 0.121

*Corrected a = 0.0036.


differences in fine-scale population structure were

apparent among ecoregions (Fig. 3). The degree of

genetic autocorrelation declined with increasing dis-

tance beyond which individuals became mixed more

randomly. The EGP and SWS had similar correlograms,

while genetic autocorrelation was substantially higher

in the WCR region. While the correlograms indicated

differences across ecoregions, linear models identified

the amount and arrangement of forest habitat as the

best predictors of relatedness within townships

(CAN + ADJ was the model with lowest AIC, DAIC = 3

for the second best model including ECO). Genetic

relatedness was higher in heavily forested townships

(CAN coefficient = 0.0007, P = 0.001) and also increased

with higher forest fragmentation (ADJ coeffi-

cient = )0.001, P = 0.0002).

Broad-scale population connectivity

The sPCA revealed clinal patterns in allele frequencies

across our study landscape. The first two sPCA axes

accounted for most of the spatial genetic pattern in the

data. The first two eigenvalues were 0.020 and 0.011,

while the remaining eigenvalues were <0.004, indicating

that the majority of the data structure was accounted

for on the first two axes. Factor scores for the first two

sPCA axes displayed a different, orthogonal gradient in

the genetic structure (Fig. 4B,D) and were retained for

further modelling. sPCA1 showed a genetic cline from

the northwest to southeast, and alleles from Cervid1, N

and RT 7 were the most informative (Fig. 4A). sPCA2

showed northeast to southwest cline and was related to

variation in BM4107, BM6438 and RT7 allele frequencies

generated for Midwestern white-tailed deer. As basic measures

ic locus, the amount of data missing for each, inbreeding coeffi-

nd heterozygotes (HE), and finally, P values associated with an

HOexp : HOobs HEexp : HEobs Pval HWE

0.518 0.878 <0.000*

0.890 1.041 <0.000*

0.716 1.061 <0.000*

0.822 1.021 0.014

0.912 1.022 0.040

0.986 1.002 0.013

0.901 1.019 0.135

0.873 1.027 0.035

0.484 1.122 <0.000*

0.889 1.029 <0.000*

0.522 1.212 <0.000*

0.998 1.000 0.902

0.839 1.025 0.069

0.936 1.017 0.140


Fig. 3 Variation among ecoregions in

fine-scale population genetic structure

of white-tailed deer in the Midwestern

USA. Correlograms for each ecoregion

show the distance-based decline in spa-

tial clustering of related genotypes.


(Fig. 4C). sPCA1 had a more consistent and gradual

gradient, while sPCA2 showed abrupt spatial changes

in factor scores.

As expected for a spatial ordination, the genetic pat-

tern depicted by each sPCA axis was strongly related to

distance, so accounting for local spatial autocorrelation

in our model of sPCA scores was crucial in distinguish-

ing the role of landscape features from spatial distance.

Inclusion of the spatial autocorrelation parameter (q)

substantially improved model performance (sPCA 1

q = 0.827, SE = 0.115, DAIC = 8.350 from nonspatial lin-

ear model: sPCA 2 q = 0.796, SE = 0.137, DAIC = 4.560

from nonspatial linear model). Noting the strong spatial

cline in sPCA scores, we can expect that the q parame-

ter alone should have substantial power to explain the

data; we judged this by looking at the R2 for the q-only

model (sPCA1, R2 = 0.794; sPCA2, R2 = 0.787). To deter-

mine the contribution of landscape factors, we then

evaluated the improvement of model fit achieved with

the addition of explanatory landscape variables. Land-

scape variables substantially improved model fit (see

AIC scores in Table S2, Supporting information) and

predictive performance (increases to R2 for sPCA1 =

0.112 and for sPCA2 = 0.103).

Our spatial autoregressive models indicated that,

beyond isolation by distance, potential impediments to

dispersal played the biggest role in shaping broad-scale

genetic connectivity among Midwestern deer. For both

sPCA1 and sPCA2, all three barrier variables were

included in all of the final models (for sPCA1:

q + INT + HWY + RIV, q + INT + HWY + RIV + ECO,

q + INT + HWY + RIV + ECO + CAN all with DAIC < 2


and for sPCA2: q + INT + HWY + RIV, q + INT +

HWY + RIV + ECO + ADJ, q + INT + HWY + RIV + E-

CO + ADJ + CAN all with DAIC < 2). Parameter esti-

mates were averaged among the top models (Table S2,

Supporting information). After accounting for model

uncertainty through model averaging, some of the

included parameters were no longer significant. The

regression effect estimates were highest and were signifi-

cant for the barrier variables, whereas ECO effects were

not significant in either the sPCA1 or sPCA2 models, and

CAN and ADJ had very minor effects (near 0 after model

averaging). This indicated that, while habitat features may

have a minor impact on genetic structure, the barriers we

measured made the most important contribution to pre-

dicting genetic structure across the study landscape. Par-

tial Mantel tests confirmed that habitat and barrier

variables were significantly correlated with genetic differ-

entiation after accounting for isolation by distance (ADJ:

r = )0.070, P = 0.130; ECO: r = )0.228, P = 0.0001; CAN:

r = )0.154, P = 0.004; INT: r = 0.252, P = 0.0001; HWY:

r = 0.147, P = 0.0001; RIV: r = 0.011, P = 0.006).

Subsequent analysis of regression coefficients showed

that there were specific gene flow impediments of

importance within the general INT, HWY and RIV cate-

gories (Table 2, Fig. 5). A primary north–south barrier

was formed by the coincidence of the I39 ⁄ 90 corridor,

the Rock River and WI highway 51S. While the INT was

only marginally significant in the sPCA1 model, it was

highly significant in the sPCA2 model. Conversely, the

Rock River was highly significant on sPCA1. In an east–

west direction, WI highway 18E-W formed a significant

barrier to gene flow in both sPCA models. The coinci-


Fig. 4 sPCA of genetic structure in a white-tailed deer population in the Midwestern USA. Bar graphs show the allelic contributions

(weights) to loading on spatial principal component axes sPCA1 (A) and sPCA2 (C). Height of bars indicates the weight of each

allele (based on 14 microsatellite loci) on each sPCA axis. The map plots show factor loading scores for each sample unit on sPCA1

(B) and sPCA2 (D). Each square represents a sample township. Dark squares indicate positive values, light squares indicate negative

values, and size of the square indicates magnitude of the score on each axis, representing each township’s position relative to the

overall genetic structure of the population. Contour lines illustrate the orthogonal genetic gradients based on interpolation of the

sPCA scores across the study area.


dence of HWY 18E with interstate I94 may have com-

pounded the effects of these two barriers. The concentra-

tion of major roadways in the eastern and southeastern

portion of the study area led to several significant road

barrier effects (interstates I39 ⁄ 90, I43 and I94 and state

highways 12E, 18E, 20E and 151N, Fig. 5) and coincided

with the steepest portions of the genetic gradient dis-

played in the sPCA axes (Fig. 4B,D).

Discussion

Landscape genetics of white-tailed deer

Our analysis revealed that landscape processes shape

white-tailed deer population structure at varying

scales. At the ecoregion scale, we found that genetic

relatedness was most clustered in the highly forested

WCR and least in the more agricultural ⁄ grassland EGP


and SWS regions (Fig. 3). We further found that

genetic relatedness within sample townships increased

with both the amount and fragmentation of forested

habitat. We expect this reflects higher density and

philopatry of related social groups in areas with dense

forest habitat. Previous research has shown that home

range requirements are smaller in contiguous high-

quality habitat (Lesage et al. 2000), leading to higher

population density and greater spatial clustering of

genetically related individuals (Wang & Schreiber

2001). Female deer are also more likely to disperse in

open landscapes where habitat resources are sparse

(Nixon et al. 1991; Nelson & Mech 1992). Dispersal

distances also tend to be greater in areas with less for-

est cover or more fragmented forest habitat (Long

et al. 2005; Nixon et al. 2008). Animals might also shift

between summer- and winter-use areas in agricultural

habitat as in the EGP, where fields become unsuitable


Tab

le2

Eff

ects

esti

mat

esre

late

dto

gen

efl

ow

bar

rier

sd

etec

ted

thro

ug

hsp

atia

lau

tore

gre

ssiv

em

od

els

of

gen

etic

pat

tern

sin

wh

ite-

tail

edd

eer

inth

eM

idw

este

rnU

SA

Est

imat

eS

E

(a)

sPC

A1

q0.

697

0.25

8

Inte

rcep

t)

0.07

90.

088

CA

N0.

000

0.00

0

AD

JN

ot

inm

od

el

Wo

fI3

9⁄9

0E

of

I39

⁄90

NE

of

I39

⁄90

C

Eo

fI3

9⁄9

0N

)0.

065

0.04

70.

163

Eo

fI3

9⁄9

0C

0.02

60.

046

0.57

20.

048

Eo

fI3

9⁄9

0S

)0.

052

0.04

70.

268

0.09

6

20W

—18

W18

W—

14W

14W

—12

W12

W—

151E

151E

—18

E18

E—

12E

12E

—14

E14

E—

20E

18W

—14

W0.

067

0.03

90.

085

14W

—12

W0.

057

0.05

00.

827

12W

—15

1E0.

027

0.04

50.

509

151E

—18

E)

0.05

90.

060

0.15

0

18E

—12

E0.

122

0.08

40.

031

12E

—14

E0.

127

0.07

80.

947

14E

—20

E0.

141

0.08

10.

861

So

f20

)0.

062

0.05

60.

267

0.00

0

Eo

fR

ock

No

fW

isc

Yah

-Ro

ck

No

fW

isc

0.10

50.

094

Yah

-Ro

ck0.

120

0.10

00.

016

Wis

c-Y

ah0.

123

0.09

10.

007

0.84

00.

975

EG

PS

WS

SW

S0.

021

0.01

40.

134

WC

R)

0.00

30.

023

0.88

90.

294

(b)

sPC

A2

q0.

752

0.31

2

Inte

rcep

t0.

056

0.16

1

CA

N0.

000

0.00

0

AD

J)

0.00

30.

001

Wo

fI3

9⁄9

0E

of

I39

⁄90

NE

of

I39

⁄90

C

Eo

fI3

9⁄9

0N

0.20

20.

086

0.01

9

Eo

fI3

9⁄9

0C

0.17

80.

083

0.03

10.

776

Eo

fI3

9⁄9

0S

0.05

20.

053

0.32

70.

017

20W

—18

W18

W—

14W

14W

—12

W12

W—

151E

151E

—18

E18

E—

12E

12E

—14

E14

E—

20E

10 S . J . ROB I N SO N ET AL.

Published 2012. This article is a US Government work and is in the public domain in the USA.

Tab

le2

Con

tin

ued

Est

imat

eS

E

18W

—14

W0.

028

0.04

30.

514

14W

—12

W0.

069

0.06

30.

521

12W

—15

1E0.

101

0.06

90.

645

151E

—18

E)

0.03

30.

067

0.04

5

18E

—12

E0.

288

0.13

10.

014

12E

—14

E0.

106

0.08

50.

031

14E

—20

E0.

062

0.07

80.

574

So

f20

)0.

076

0.06

70.

257

0.04

0

Eo

fR

ock

No

fW

isc

Yah

-Ro

ck

No

fW

isc

0.16

80.

115

Yah

-Ro

ck0.

161

0.12

10.

153

Wis

c-Y

ah0.

068

0.09

40.

397

0.28

90.

325

EG

PS

WS

SW

S)

0.01

80.

021

0.38

7

WC

R0.

027

0.03

60.

448

0.20

5

WC

R

AD

J,ad

jace

ncy

;W

CR

,W

este

rnC

ou

lee

and

Rid

ge.

Reg

ress

ion

mo

del

par

amet

eres

tim

ates

and

stan

dar

der

ror

ind

icat

eth

eef

fect

of

each

bar

rier

zon

eo

rh

abit

atv

aria

ble

on

gen

etic

dif

fere

nti

atio

n.

Th

em

atri

xp

ort

ion

sho

ws

Pv

alu

es

ind

icat

ing

the

stat

isti

cal

sig

nifi

can

ceo

fea

chp

ote

nti

alg

ene

flo

wb

arri

ers

bas

edo

nst

atis

tica

lco

mp

aris

on

of

par

amet

eres

tim

ates

bet

wee

nre

gio

ns

on

eith

ersi

de

of

the

bar

rier

.

Bo

ldan

dit

alic

ized

tex

tin

dic

ates

sig

nifi

can

tef

fect

s(a

<0.

05);

ital

ics

on

lyin

dic

ates

mar

gin

alsi

gn

ifica

nce

(a<

0.1)

.

LANDSCAPE GENETICS IN A CO NTI NUOUS DEER POPULATION 11

Published 2012. This article is a US Government work and is in the public domain in the USA.

Fig. 5 Landscape patterns illustrating

population genetic structure of white-

tailed deer in the Midwestern USA. Bar-

riers to genetic connectivity were

detected through multiple regression in

a spatial autoregressive format. This

map combines factors that contributed

significantly to explaining genetic gradi-

ents portrayed by both sPCA1 and

sPCA2. Sample townships are shown in

colour gradients representing relative

positions on the sPCA axes; gradation

of red indicates sPCA1; gradation of

green indicates sPCA2.


once crops are cut. Higher dispersal or seasonal move-

ment could lead to looser organization of kin groups,

which might produce lower autocorrelation in the less

forested ecosystems and the positive association of for-

est cover with fine-scale genetic relatedness.

At the broader scale, landscape connectivity among

deer was determined primarily by natural and anthro-

pogenic impediments to dispersal (Fig. 5). These land-

scape features may exert different influences on deer

movement (whether altering behaviour, habitat choice

or survival as in road mortality; Etter et al. 2002) over

different temporal scales (rivers pose a historic barrier

to gene flow compared with anthropogenic features).

We note that because our data were female biased, the

genetic structure we detected may emphasize the

impact of these dispersal barriers. Although barrier

effects were statistically significant, the assignment test

indicated that sufficient gene flow occurred across these

barriers to maintain connectivity within the population.

We found particularly strong effects where several

impediments occurred in near proximity. For example,

the potential combined effects of the interstate I39 ⁄ 90

corridor along with WI highway 51S and the Rock

River may present substantial limits to gene flow. Such

confounding of features may be common on the land-

scape, for example river valleys frequently facilitate

highway construction, and interstates may often aug-

ment existing highway routes. The coincidence of land-

scape features makes it difficult to quantify the separate

contribution of each feature.

The combination of landscape genetic techniques and

spatial modelling provided a powerful tool for under-

standing connectivity and movement within a dense and

continuous population of white-tailed deer. The sPCA


method provided genetic metrics that could be assigned

to each sample township and used in a general linear

model framework allowing us to simultaneously evalu-

ate several landscape variables without subjective, a

priori assignment of weights to any particular feature

(e.g. cost path analysis). Multivariate and regression-

based methods have been recommended as powerful

alternatives to linear distance-based relationships (as in

the Mantel test framework) (Legendre & Fortin 2010).

Such tests are widely used in ecology (Borcard & Legen-

dre 2002; Borcard et al. 2004; Dray et al. 2006) and are

gaining popularity in landscape genetics (Geffen et al.

2004; Guinand 2008; Jombart et al. 2009). The model

selection framework using AIC helped us identify impor-

tant landscape features affecting gene flow and the

spatial association parameter (q) allowed us to account

for isolation by distance while assessing the additional

impact of landscape variables and specific features

(highways, rivers or interstates) that were influential in

differentiating adjacent groups of deer.

Our results complement previous white-tailed deer

research in the Midwest and other areas in North Amer-

ica and offer a valuable expansion or our knowledge of

genetic structure at both fine and broad scale. We found

fine-scale spatial genetic clustering similar to Grear et al.

(2010), who found that first- or second-order relatives

were more likely to be in close proximity than unrelated

deer. Blanchong et al. (2008), using a subset of our study

area, found that highways WI 18W and WI 14W and the

Wisconsin River were barriers to local gene flow. Our

analysis of the wider area confirmed that highways and

rivers act as gene flow barriers. We also found that large

INT in the eastern and western portions of our study

region acted as a major impediment to gene flow between



deer. Rogers et al. (2011) found that certain mitochon-

drial DNA sequences from a portion of our study area

(363 deer from 12 of the WI sample pops) varied across

the landscape similarly to either the sPCA1 or sPCA2

gradients in our findings; though, mtDNA haplotypes

were more clustered on the landscape than our microsat-

ellite data. Our results in the Midwest were similar to

those for white-tailed deer spanning the Canadian prov-

inces of Alberta and Saskatchewan, where Cullingham

et al. (2011) found little evidence for population subdivi-

sion, but detected significant isolation by distance. How-

ever, we found that roads or rivers are impediments to

dispersal and gene flow, as previously suggested for sev-

eral ungulate species (Nussey et al. 2005; Coulon et al.

2006; Perez-Espona et al. 2008; Long et al. 2010), and

these landscape features have become an important focus

of animal movement studies (Forman & Alexander 1998;

Jackson 2000), as well as in landscape genetics (Balkenhol

& Waits 2009).

Our results, however, contrast with those of Kelly

et al. (2010), who found significant population substruc-

ture for a study area of similar size, including a portion

overlapping our study area in north-central Illinois.

Kelly et al. (2010) identified four genetic populations,

including two admixed populations in the central part

of our study area and differentiated populations in spa-

tially disjoint areas to the west and south. Difference in

sample designs appears to be the primary cause of the

different conclusions about population subdivision.

Whereas we sampled deer in random locations within a

systematic grid, Kelly et al. (2010) sampled areas sepa-

rated by 100s of km. Because isolation by distance is a

prevailing genetic pattern for white-tailed deer popula-

tions, sampling clusters of individuals at far-spaced

intervals along the genetic cline may over-represent the

degree of population subdivision (Frantz et al. 2009;

Schwartz & McKelvey 2009). Additionally, we used

more spatially random samples from hunter-harvest,

while Kelly et al. (2010) relied on groups of deer har-

vested at DNR sharpshooting stations. This method is

likely to collect related animals that arrive at baited sta-

tions together and fawns constituted over 38% of their

data set, indicating a significant portion of the data may

have represented undispersed relatives. Studies of alter-

native sampling design have shown that if local areas

of relatedness are confounded with sampling patterns,

assignment programmes may find an artificial associa-

tion between sampling blocks and population clusters

(Schwartz & McKelvey 2009; Storfer et al. 2010).

Management implications

Understanding deer population structure on the Mid-

west landscape has important implications for the


future management of this abundant species. In general,

the large spatial scale of gene flow and high connectiv-

ity within the deer population suggests that large man-

agement units should be used to determine harvest and

population goals. It is also important to note that

although we found impediments to gene flow, there

were no absolute barriers subdividing the population.

Thus, the study area contains many potential deer man-

agement units that are clearly interconnected through

high levels of gene flow. Thus, emigration and immi-

gration between management units could impact demo-

graphics used by wildlife managers to establish

population goals. It will be important to treat manage-

ment units as open populations to accurately design

population control measures or harvest regulations.

Infectious disease is increasingly a priority in cervid

management because chronic wasting disease, brucello-

sis, bovine tuberculosis and bovine viral diarrhoea

affect wild cervid populations and raise concern for

livestock health. The processes investigated here rela-

tive to population connectivity and dispersal relate

directly to epidemiological processes of disease trans-

mission between individuals at the fine scale (Altizer

et al. 2003) and spread of disease among populations

with broad-scale connectivity (Ostfeld et al. 2005; Joly

et al. 2006; Real & Biek 2007). Our study area in WI and

IL contained two distinct CWD outbreak areas in the

northwest (WCR between highways 14W and 18W) and

southeast (EGP east of interstate I39 ⁄ 90 and the Rock

River). Our results indicate that fine-scale population

structure differs among these regions with more cluster-

ing in the WCR around the western CWD outbreak.

This level of overlap among social groups indicated

higher potential contact rates among individuals that

could increase local transmission of disease around the

western outbreak area. Conversely, disease may spread

faster across the EGP landscape where more frag-

mented forest habitat lead to longer dispersals. At the

broader scale, dispersing animals are likely to spread

disease beyond social groups to other areas across the

landscape. Knowing that highways, interstates and riv-

ers provide some level of impediment to deer dispersal

might give management agencies the opportunity to

maximize such barriers in efforts to contain or other-

wise manage disease spread.

Acknowledgements

We thank the Wisconsin Department of Natural Resources,

particularly those at the Black Earth sample archive facility,

and the Illinois Department of Natural Resources, particularly

those working at deer check stations. We appreciate pilot data

and information from Dr. Julie Blanchong and Dr. Dan Grear.

Thanks also to all laboratory technicians and staff at the UW

Biotechnology Core Facility. Our manuscript benefited from



comments on previous drafts from Dr. Lisette Waits and Dr.

Kim Scribner. Funding was provided by the U.S. Geological

Survey, a U.S. Department of Agriculture Hatch grant and the

Wisconsin Department of Natural Resources. Thanks to the

University of Wisconsin Department of Forest & Wildlife Ecol-

ogy for assistance with publication costs. Note that any use of

trade, product or firm names is for descriptive purposes and

does not imply endorsement by the US Government.

References

Altizer S, Nunn CL, Thrall PH et al. (2003) Social organization

and parasite risk in mammals: integrating theory and

empirical studies. Annual Review of Ecology Evolution and

Systematics, 34, 517–547.

Balkenhol N, Waits LP (2009) Molecular road ecology:

exploring the potential of genetics for investigating

transportation impacts on wildlife. Molecular Ecology, 18,

4151–4164.

Balkenhol N, Gugerli F, Cushman SA et al. (2009) Identifying

future research needs in landscape genetics: where to from

here? Landscape Ecology, 24, 455–463.

Bartelt G, Pardee J, Thiede J (2003) Environmental Impact

Statement on Rules to Eradicate Chronic Wasting Disease in

Wisconsin’s Free-Ranging White-tailed Deer Herd. Wisconsin

Department of Natural Resources, Madison, Wisconsin.

Bivand R, Altman M, Anselin L et al. (2011) R Package

‘spdep’: spatial dependence: weighting schemes, statistics

and models. Available from http://cran.r-project.org/web/

packages/spdep/index.html.

Blanchong JA, Samuel MD, Scribner KT et al. (2008) Landscape

genetics and the spatial distribution of chronic wasting

disease. Biology Letters, 4, 130–133.

Bonnet E, Van de Peer Y (2007) zt: a software tool for simple

and partial Mantel tests.

Borcard D, Legendre P (2002) All-scale spatial analysis of

ecological data by means of principal coordinates of

neighbour matrices. Ecological Modelling, 153, 51–68.

Borcard D, Legendre P, Avois-Jacquet C, Tuomisto H (2004)

Dissecting the spatial structure of ecological data at multiple

scales. Ecology, 85, 1826–1832.

Burnham KP, Anderson DR (2002) Model Selection and

Multimodel Inference: A Practical Information-Theoretic

Approach. Springer Verlag, New York, NY, USA.

Corander J, Marttinen P, Siren J, Tang J (2008) Enhanced

Bayesian modelling in BAPS software for learning genetic

structures of populations. BMC Bioinformatics, 9, 539.

Cote SD, Rooney TP, Tremblay JP, Dussault C, Waller DM

(2004) Ecological impacts of deer overabundance. Annual

Review of Ecology, Evolution, and Systematics, 35, 113–147.

Coulon A, Guillot G, Cosson J-F et al. (2006) Genetic structure

is influenced by landscape features: empirical evidence from

a roe deer population. Molecular Ecology, 15, 1669–1679.

Cross P, Drewe J, Patrek V et al. (2008) Wildlife population

structure and parasite transmission: implications for disease

management. In: Management of Disease in Wild Mammals

(eds Delahay R, Smith G, Hutchings M), pp. 9–29. Springer,

New York City, New York.

Cullingham C, Merrill E, Pybus M et al. (2011) Broad and fine

scale genetic analysis of white-tailed deer populations:


estimating the relative risk of chronic wasting disease

spread. Evolutionary Applications, 4, 116–131.

Dray S, Legendre P, Peres-Neto PR (2006) Spatial modelling: a

comprehensive framework for principal coordinate analysis

of neighbour matrices (PCNM). Ecological Modelling, 196,

483–493.

Epperson BK, McRae BH, Scribner K et al. (2010) Utility of

computer simulations in landscape genetics. Molecular

Ecology, 19, 3549–3564.

Etter D, Hollis K, Van Deelen T et al. (2002) Survival and

movements of white-tailed deer in suburban Chicago,

Illinois. Journal of Wildlife Management, 66, 500–510.

Fagerstone KA, Clay WH (1997) Overview of USDA animal

damage control efforts to manage overabundant deer.

Wildlife Society Bulletin, 25, 413–417.

Forman RTT, Alexander LE (1998) Roads and their major

ecological effects. Annual Review of Ecology and Systematics,

29, 207–231.

Frantz AC, Cellina S, Krier A, Schley L, Burke T (2009) Using

spatial Bayesian methods to determine the genetic structure

of a continuously distributed population: clusters or isolation

by distance? Journal of Applied Ecology, 46, 493–505.

Fry JA, Coan MJ, Homer CG, Meyer DK, Wickham JD (2009)

Completion of the National Land Cover Database (NLCD)

1992–2001 Land Cover Change Retrofit product. U.S.

Geological Survey Open-File Report 2008–1379, 18 p.

Garroway CJ, Bowman J, Carr D, Wilson PJ (2008)

Applications of graph theory to landscape genetics.

Evolutionary Applications, 1, 620–630.

Gauffre B, Estoup A, Bretagnolle V, Cosson J (2008) Spatial

genetic structure of a small rodent in a heterogeneous

landscape. Molecular Ecology, 17, 4619–4629.

Geffen E, Anderson MJ, Wayne RK (2004) Climate and habitat

barriers to dispersal in the highly mobile grey wolf.

Molecular Ecology, 13, 2481–2490.

Grear DA, Samuel MD, Langenberg JA, Keane D (2006)

Demographic patterns and harvest vulnerability of chronic

wasting disease infected white-tailed deer in Wisconsin.

Journal of Wildlife Management, 70, 546–553.

Grear DA, Samuel MD, Scribner KT, Weckworth BV, Langenberg

JA (2010) Influence of genetic relatedness and spatial proximity

on chronic wasting disease infection among female white-

tailed deer. Journal of Applied Ecology, 47, 532–540.

Guillot G, Leblois R, Coulon A, Frantz AC (2009) Statistical

methods in spatial genetics. Molecular Ecology, 18, 4734–4756.

Guinand B (2008) Use of a multivariate model using allele

frequency distributions to analyse patterns of genetic

differentiation among populations. Biological Journal of the

Linnean Society, 58, 173–195.

Hardy O, Vekemans X (2007) SPAGeDi 1.2: a program for Spatial

Pattern Analysis of Genetic Diversity, Available from http://

www.ulb.ac.be/sciences/ecoevol/docs/manual_SPAGeDi_1-

2.pdf.

Hawkins RE, Klimstra WD (1970) A preliminary study of the

social organization of white-tailed deer. Journal of Wildlife

Management, 34, 407–419.

Henderson DW, Warren RJ, Cromwell JA, Hamilton RJ (2000)

Response of urban deer to a 50% reduction in local herd

density. Wildlife Society Bulletin, 28, 902–910.

Hirth DH (1977) Social behavior of white-tailed deer in relation

to habitat. Wildlife Monographs, 53, 3–55.



Ishmael WE (1984) White-Tailed Deer Ecology and Management in

Southern Wisconsin. University of Wisconsin, Madison, WI,

USA.

Jackson SD (2000) Overview of transportation impacts on

wildlife movement and populations. In: Wildlife and

Highways: Seeking Solutions to an Ecological and Socio-Economic

Dilemma. The Wildlife Society, Attachment to EPIC Comment

Letter on 2006 RMP dated 10-23-06., pp. 7–20.

Joly DO, Samuel MD, Langenberg JA et al. (2006) Spatial

epidemiology of chronic wasting disease in Wisconsin white-

tailed deer. Journal of Wildlife Diseases, 43, 578–588.

Jombart T, Devillard S, Dufour A-B, Pontier D (2008) Revealing

cryptic spatial patterns in genetic variability by a new

multivariate method. Heredity, 101, 92–103.

Jombart T, Pontier D, Dufour A (2009) Genetic markers in the

playground of multivariate analysis. Heredity, 102, 330–341.

Kelly AC, Mateus-Pinilla NE, Douglas M et al. (2010) Utilizing

disease surveillance to examine gene flow and dispersal in

white-tailed deer. Journal of Applied Ecology, 47, 1189–1198.

Kilpatrick HJ, Spohr SM, Lima KK (2001) Effects of population

reduction on home ranges of female white-tailed deer at

high densities. Canadian Journal of Zoology, 79, 949–954.

Kohn BE, Mooty JJ (1971) Summer habitat of white-tailed deer

in north-central Minnesota. Journal of Wildlife Management,

35, 476–487.

Larson TJ, Rongstad OJ, Terbilcox FW (1978) Movement and

habitat use of white-tailed deer in southcentral Wisconsin.

Journal of Wildlife Management, 42, 113–117.

Legendre P, Fortin M (2010) Methodological advances –

inference of population structure: comparison of the Mantel

test and alternative approaches for detecting complex

multivariate relationships in the spatial analysis of genetic

data. Molecular Ecology Resources, 10, 831–844.

Legendre P, Legendre L (1998) Numerical Ecology. Elsevier

Science, Amsterdam, Netherlands.

Lesage L, Crete M, Huot J, Dumont A, Ouellet J-P (2000) Seasonal

home range size and philopatry in two northern white-tailed

deer populations. Canadian Journal of Zoology, 78, 1930–1940.

Long ES (2006) Landscape and Demographic Influences on

Dispersal of White-Tailed Deer. The Pennsylvania State

University, State College, PA, USA.

Long ES, Diefenbach DR, Rosenberry CS, Wallingford BD,

Grund MD (2005) Forest cover influences dispersal distance

of white-tailed deer. Journal of Mammalogy, 86, 623–629.

Long ES, Diefenbach DR, Wallingford BD, Rosenberry CS

(2010) Influence of roads, rivers, and mountains on natal

dispersal of white-tailed deer. The Journal of Wildlife

Management, 74, 1242–1249.

Marchinton RL, Hirth DH (1984) Behavior. In: White-Tailed

Deer Ecology and Management (ed. Halls LK), pp. 129–168.

Stackpole Books, Harrisburg, Pennsylvania.

Mastro LL, Conover MR, Frey SN (2008) Deer–vehicle collision

prevention techniques. Human-Wildlife Conflicts, 2, 80–92.

Mathews NE, Porter WF (1993) Effect of social structure on

genetic structure of free-ranging white-tailed deer in the

Adirondack Mountains. Journal of Mammalogy, 74, 33–43.

McGarigal K, Cushman S (2000) Multivariate Statistics for

Wildlife and Ecology Research. Springer-Verlag, New York

City, New York.

McGarigal K, Marks BJ (1995) FRAGSTATS: Spatial Pattern

Analysis Program for Quantifying Landscape Structure. Gen.


Tech. Report PNW-GTR-351. USDA Forest Service, Pacific

Northwest Research Station, Portland, Oregon.

Nelson ME (1993) Natal dispersal and gene flow in white-

tailed deer in northeastern Minnesota. Journal of Mammalogy,

74, 316–322.

Nelson ME (1995) Winter range arrival and departure of

white-tailed deer in northeastern Minnesota. Canadian Journal

of Zoology, 73, 1069–1076.

Nelson ME, Mech LD (1992) Dispersal in female white-tailed

deer. Journal of Mammalogy, 73, 891–894.

Nixon CM, Hansen LP, Brewer PA, Chelsvig JE (1991) Ecology

of white-tailed deer in an intensively farmed region of

Illinois. Wildlife Monographs, 118, 3–77.

Nixon CM, Mankin PC, Etter DR et al. (2008) Migration behavior

among female white-tailed deer in central and northern

Illinois. The American Midland Naturalist, 160, 178–190.

Nussey DH, Coltman DW, Coulson T et al. (2005) Rapidly

declining fine-scale spatial genetic structure in female red

deer. Molecular Ecology, 14, 3395–3405.

O’Brien DJ, Schmitt SM, Fierke JS et al. (2002) Epidemiology of

Mycobacterium bovis in free-ranging white-tailed deer,

Michigan, USA, 1995–2000. Preventive Veterinary Medicine, 54,

47–63.

Omernik JM (1987) Ecoregions of the conterminous United

States. Annals of the Association of American Geographers, 77,

118–125.

Ostfeld RS, Glass GE, Keesing F (2005) Spatial epidemiology:

an emerging (or re-emerging) discipline. Trends in Ecology &

Evolution, 20, 328–336.

Peakall R, Smouse PE (2006) GenAlEx 6: genetic analysis in

Excel. Population genetic software for teaching and research.

Molecular Ecology Notes, 6, 288–295.

Perez-Espona S, Perez-Barberia FJ, McLeod JE et al. (2008)

Landscape features affect gene flow of Scottish Highland red

deer (Cervus elaphus). Molecular Ecology, 17, 981–996.

Porter WF, Mathews NE, Underwood HB, Sage RWJ, Donald AB

(1991) Social organization in deer: implications for localized

management. Environmental Management, 15, 809–814.

Pritchard JK, Stephens M, Peter D (2000) Inference of

population structure using multilocus genotype data.

Genetics, 155, 945–959.

Purdue JR, Smith MH, Patton JC (2000) Female philopatry and

extreme spatial genetic heterogeneity in white-tailed deer.

Journal of Mammalogy, 81, 179–185.

Queller DC, Goodnight KF (1989) Estimating relatedness using

genetics markers. Evolution, 43, 258–275.

Quemere E, Crouau-Roy B, Rabarivola C, Louis E, Chikhi L

(2010) Landscape genetics of an endangered lemur

(Propithecus tattersalli) within its entire fragmented range.

Molecular Ecology, 19, 1606–1621.

R Development Core Team (2008) R: A Language and

Environment for Statistical Computing. R Foundation for

Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0,

URL http://www.R-project.org.

Rassmann K, Tautz D, Trillmich F, Gliddon C (1997) The

microevolution of the Galapagos marine iguana

Amblyrhynchos cristatus assessed by nuclear and

mitochondrial genetic analyses. Molecular Ecology, 6, 437–452.

Raymond M, Rousset F (1995) GENEPOP (Version 1.2):

population genetics software for exact tests and

ecumenicism. Journal of Heredity, 86, 248–249.



Real LA, Biek R (2007) Spatial dynamics and genetics of

infectious diseases on heterogeneous landscapes. Journal of

the Royal Society Interface, 4, 935–948.

Retamosa MI, Humberg LA, Beasley JC, Rhodes OE Jr (2008)

Modeling wildlife damage to crops in northern Indiana.

Human-Wildlife Conflicts, 2, 225–239.

Rogers KG, Robinson SJ, Samuel MD, Grear DA (2011)

Diversity and distribution of white-tailed deer mtDNA

lineages in CWD outbreak areas in southern Wisconsin,

USA. Journal of Toxicology and Environmental Health, 74, 1521–

1535.

Rolley RE (2007) White-tailed Deer Population Status.

Department of Natural Resources, Wisconsin, Madison, WI,

USA.

Rooney TP (2001) Deer impacts on forest ecosystems: a North

American perspective. Forestry, 74, 201.

Rosenberry CS, Lancia RA, Conner MC (1999) Population

effects of white-tailed deer dispersal. Wildlife Society Bulletin,

27, 858–864.

Schauber EM, Storm DJ, Nielsen CK (2007) Effects of joint

space use and group membership on contact rates among

white-tailed deer. Journal of Wildlife Management, 71, 155–163.

Schwartz MK, McKelvey KS (2009) Why sampling scheme

matters: the effect of sampling scheme on landscape genetic

results. Conservation Genetics, 10, 441–452.

Segelbacher G, Cushman SA, Epperson BK et al. (2010)

Applications of landscape genetics in conservation biology:

concepts and challenges. Conservation Genetics, 11, 375–385.

Shelton P, McDonald P (2010) Illinois Chronic Wasting Disease:

2009–2010 Surveillance ⁄ Management Summary Forest Wildlife

Program. Illinois Department of Natural Resources, Rockford,

Illinois.

Skuldt LH, Mathews NE, Oyer AM (2008) White-tailed deer

movements in a chronic wasting disease area in south-

central Wisconsin. Journal of Wildlife Management, 72, 1156–

1160.

Storfer A, Murphy MA, Spear SF, Holderegger R, Waits LP

(2010) Landscape genetics: where are we now? Molecular

Ecology, 19, 3496–3514.

Storm D (2011) Chronic Wasting Disease in White-Tailed Deer:

Evaluation of Aerial Surveys; Age-Estimation; and the Role of

Deer Density and Landscape in Disease Transmission. University

of Wisconsin, Madison, Wisconsin.

Wagner HH, Holderegger R, Werth S et al. (2005) Variogram

analysis of the spatial genetic structure of continuous

populations using multilocus microsatellite data. Genetics,

169, 1739–1752.

Wang M, Schreiber A (2001) The impact of habitat

fragmentation and social structure on the population

genetics of roe deer (Capreolus capreolus L.) in Central

Europe. Heredity, 86, 703–715.

WI State Climatology Office (2008) Available from http://

www.aos.wisc.edu/~sco/clim-history/state/index.html.

Wiegand T, Moloney KA, Naves J, Knauer F (1999) Finding the

missing link between landscape structure and population

dynamics: a spatially explicit perspective. American

Naturalist, 154, 605–627.


Wiens JA (2001) The landscape context of dispersal. In:

Dispersal (eds Clobert J, Danchin E, Dhondt AA, Nichols JD),

pp. 96–109. Oxford University Press, Oxford.

Williams ES, Miller MW, Kreeger TJ, Kahn RH, Thorne ET

(2002) Chronic wasting disease of deer and elk: a review

with recommendations for management. The Journal of

Wildlife Management, 66, 551–563.

Wilson GA, Strobeck C, Wu L, Coffin JW (1997)

Characterization of microsatellite loci in caribou, Rangifer

tarandus, and their use in other artiodactyls. Molecular

Ecology, 6, 697–699.

Wisconsin Department of Natural Resources (2003) Wisconsin

deer harvest and license sales, 1966–2002. Retrieved March

19, 2004, from State of Wisconsin, Department of Natural

Resources Available from: http://dnr.wi.gov/org/land/

wildlife/hunt/deer/harvest02.htm.

Wisconsin Dept. Natural Resources (2010) Wisconsin’s Chronic

Wasting Disease Response Plan: 2010–2025. Wisconsin

Department of Natural Resources, Madison, Wisconsin.

Wozencraft WC (1978) Investigations Concerning a High-Density

White-Tailed Deer Population in South Central Wisconsin.

University of Wisconsin, Madison, WI, USA.

S.J.R. is interested in the intersection of landscape genetics and

landscape epidemiology; she is currently a postdoctoral fellow

in the Department of Forest & Wildlife Ecology at the Univer-

sity of Wisconsin-Madison. M.D.S. is an Associate Professor of

Wildlife Ecology and assistant unit leader of the Wisconsin

Cooperative Wildlife Research Unit; his research focuses on

wildlife epidemiology and modeling. D.L. and P.S. work for

the Wisconsin and Illinois Departments of Natural Resources

respectively.

Data accessibility

R code and microsatellite data: DRYAD entry doi:10.5061/

dryad.p7639.

Supporting information

Additional supporting information may be found in the online

version of this article.

Table S1 Microsatellite primers and PCR conditions for geno-

types generated for Midwestern USA white-tailed deer.

Table S2 Model selection determining landscape variables that

best explain genetic patterns in white-tailed deer in the Mid-

western USA.

Please note: Wiley-Blackwell is not responsible for the content

or functionality of any supporting information supplied by the

authors. Any queries (other than missing material) should be

directed to the corresponding author for the article.


the walk is never random: subtle landscape effects shape ...€¦ · the population. we found...

Documents