ORIGINAL ARTICLE

Genetic assessment of environmental features that influence deerdispersal: implications for prion-infected populations

Amy C. Kelly • Nohra E. Mateus-Pinilla • William Brown •

Marilyn O. Ruiz • Marlis R. Douglas • Michael E. Douglas •

Paul Shelton • Tom Beissel • Jan Novakofski

Received: 10 April 2013 / Accepted: 8 December 2013 / Published online: 8 January 2014

� The Society of Population Ecology and Springer Japan 2014

Abstract The landscape can influence host dispersal and

density, which in turn, affect infectious disease transmis-

sion, spread, and persistence. Understanding how the

landscape influences wildlife dispersal and pathogen epi-

demiology can enhance the efficacy of disease manage-

ment in natural populations. We applied landscape genetics

to examine relationships among landscape variables, dis-

persal of white-tailed deer hosts and transmission/spread of

chronic wasting disease (CWD), a fatal prion encepha-

lopathy. Our focus was on quantifying movements and

population structure of host deer in infected areas as a

means of predicting the spread of this pathology and pro-

moting its adaptive management. We analyzed microsat-

ellite genotypes of CWD-infected and uninfected deer from

two disease foci (Southern Wisconsin, Northern Illinois).

We quantified gene flow and population structure using

FST, assignment tests, and spatial autocorrelation analyses.

Gene flow estimates were then contrasted against a suite of

landscape variables that potentially mediate deer dispersal.

Forest fragmentation and grassland connectivity promoted

deer movements while rivers, agricultural fields and large

urbanized areas impeded movement. Landscape variables,

deer dispersal, and disease transmission covaried signifi-

cantly and positively in our analyses. Habitats with ele-

vated host gene flow supported the concept of dispersal-

mediated CWD transmission by reflecting a concomitant,

rapid CWD expansion. Large, interrelated social groups

isolated by movement barriers overlapped disease foci,

suggesting that philopatry exacerbated CWD transmission.

Our results promote adaptive management of CWD by

predicting patterns of its spread and identifying habitats at

risk for invasion. Further, our landscape genetics approach

underscores the significance of topography and host

behavior in wildlife disease transmission.

Keywords Chronic wasting disease � Gene flow �Illinois � Isolation-by-distance � White-tailed deer �Wisconsin

Introduction

Human activities can influence abundance, movement and

survival of wildlife (Lubchenco et al. 1991). Therefore,

habitats subjected to human alteration are of interest for

monitoring ecological responses of species to land use

Electronic supplementary material The online version of thisarticle (doi:10.1007/s10144-013-0427-9) contains supplementarymaterial, which is available to authorized users.

A. C. Kelly � N. E. Mateus-Pinilla � J. Novakofski

Department of Animal Sciences, University of Illinois,

1503 S. Maryland Dr., Urbana, IL 61801, USA

A. C. Kelly (&) � N. E. Mateus-Pinilla �M. R. Douglas � M. E. Douglas

Illinois Natural History Survey, Prairie Research Institute,

University of Illinois, 1816 S. Oak St., Champaign,

IL 61820, USA

e-mail: kellyac@mail.nih.gov

W. Brown � M. O. Ruiz

Department of Pathobiology, College of Veterinary Medicine,

University of Illinois, 2001 S Lincoln, Urbana, IL 61801, USA

Present Address:

M. R. Douglas � M. E. Douglas

Department of Biological Sciences, University of Arkansas,

Fayetteville, AR 72701, USA

P. Shelton � T. Beissel

Division of Wildlife Resources, Illinois Department of Natural

Resources, One Natural Resources Way, Springfield,

IL 62702, USA

123

Popul Ecol (2014) 56:327–340

DOI 10.1007/s10144-013-0427-9

changes (Fischer and Lindenmayer 2007). For example, the

Midwestern United States has been iteratively fragmented

for more than 100 years by human agriculture and urban-

ization (Samson and Knopf 1994). In this region, extensive

overlap of human and wildlife habitats has contributed to

the expansion of zoonotic pathogens (Wilcox and Gubler

2005) with repercussions to wildlife, livestock and people

(Daszak et al. 2000). Environmental heterogeneity can

influence disease transmission, spread, and persistence

(Real and Biek 2007) by altering wildlife host dispersal

(Taylor et al. 1993). This in turn can affect the spatial

extent of infection (Cullingham et al. 2009; Paull et al.

2012), contact rates among infected and susceptible hosts

(Hess 1996; Farnsworth et al. 2005), and pathogen spread

to new areas (Real and Biek 2007). Clarifying relationships

between landscape, wildlife dispersal and pathogen epi-

demiology is therefore vital for understanding infectious

diseases and effectively managing them in complex

habitats.

Intimate spatial proximity of hosts and pathogens

facilitates direct transmission of disease (McCallum et al.

2001). As a consequence of this pathogen/host co-ecology

movement barriers in either species will be reflected in

both (Smith et al. 2002; Biek et al. 2007) which allows

deducing patterns of disease spread by examining host

behaviors. Recent epidemiological studies have evaluated

pathogen spread and transmission indirectly (Ernest et al.

2010) by tracking gene flow of the host into uninfected

terrain (Archie et al. 2009). This approach has identified

possible disease barriers based on host gene flow (Blan-

chong et al. 2008), characterized outbreak progression by

quantifying pathogen genetic structure (Walsh et al. 2005),

and predicted nascent disease ‘fronts’ by modeling host

movements (Rees et al. 2008). Use of gene flow informa-

tion as a surrogate indicator for potential disease spread can

be useful in management strategies for disease control

(Cullingham et al. 2009).

The pathogen of chronic wasting disease (CWD) is a

prion (Miller and Williams 2003) while its host in the

Midwestern United States, the white-tailed deer, (Odocoi-

leus virginianus) is a large vertebrate with a well under-

stood ecology (Nixon et al. 1991) and a long history of

hunter-harvest at regional levels (Roseberry and Woolf

1991). Transmission of CWD occurs through direct contact

with kin or social groups (Miller and Williams 2003), or

indirectly through exposure to infectious prions in the

environment (Williams et al. 2002). Deer behavior can

facilitate disease spread, particularly given that male dis-

persal can exceed 100 km (Kelly et al. 2010), thereby

creating opportunities for long-distance prion dispersion.

Likewise, philopatry in females can exacerbate transmis-

sion within matriarchal groups (Grear et al. 2010), either

through heightened contact with infected kin or by habitual

use of infected habitats. Certain prion gene alleles are

associated with reduced risk of CWD, though none of the

prion genotypes characterized in deer are completely pro-

tective against infection (Kelly et al. 2008). Understanding

CWD dynamics is critical, given the risks associated with

transmission of CWD to livestock (Hamir et al. 2005) and

potentially humans (Barria et al. 2011).

Chronic wasting disease in Midwestern North America

Natural dispersal and human transport of deer (Joly et al.

2003) within the last decade have spread CWD across

North America (Sigurdson and Aguzzi 2007). In 2002, the

disease was detected in South-Central Wisconsin and

subsequently along the Wisconsin-Illinois border *90 km

South (Illinois Department of Natural Resources (IDNR)

2012; Wisconsin Department of Natural Resources

(WDNR) 2012). The landscapes involved in these two

outbreaks are markedly different (Osnas et al. 2009; IDNR

2012). In the South-Central Wisconsin outbreak area,

habitats on average are [70 % forested (Joly et al. 2006),

and CWD is largely found within a *300 km2 core area.

This stands in contrast with Northern Illinois/South-Eastern

Wisconsin, where the landscape is, on average, \15 %

forested, and forest patches are imbedded within agricul-

tural fields, suburbs, and metropolitan centers all dissected

by interstate highways and narrow rivers/streams. Here,

CWD has two foci immediately adjacent to a metropolitan

area (Rockford, IL; population = *150,000), with preva-

lence dwindling outward 30–50 km (IDNR 2012).

In a previous report, we (Kelly et al. 2010) applied

genetic methods to quantify gene flow and dispersal in

white-tailed deer from Northern Illinois. While this study

characterized the genetic structure of deer, the effects of

environmental variables on deer movement were not

assessed. In this paper, we expand our examination of

white-tailed deer population dynamics by conducting a

multi-state landscape genetic analysis to determine how the

environment influences movement in CWD-infected habi-

tats. Dispersal mediates long-distance ([100 km) genetic

exchanges for deer in Illinois (Kelly et al. 2010), thus a

broad-scale analysis of movement patterns is warranted to

interpret CWD transmission, and offer predictions about its

expanding disease front in Midwestern North America.

With samples collected from diverse habitats spanning

[100,000 km2, we characterized regional population

structure and quantified genetic admixture using Bayesian

assignment tests and global spatial autocorrelation analy-

ses. We then used local spatial autocorrelation analyses to

quantify fine-scale population structure and identify

philopatric groups of deer that are at risk for accelerated

transmission of CWD (Grear et al. 2010; Kelly et al. 2010).

These data allow us to predict admixture between infected

328 Popul Ecol (2014) 56:327–340

123

and susceptible deer, infer patterns of spreading infection

by pathogenic prions, and delineate potential management

actions.

To date, ours is the largest landscape genetic study to

quantify both male and female movement patterns of deer

residing in CWD-infected habitats in the Midwestern US.

With data collected over a broad geographic scale, we are

able to map with considerable detail the landscape genetics

of deer. Movements of deer tend to be dictated by prop-

erties of the landscape, with less occurring within dense

forest as compared to more heterogeneous agro-urban

regions (Nixon et al. 2007). Therefore, we hypothesized

that patterns of CWD spread and transmission would differ

in our study region according to landscape variables, with

increased gene flow and inferred disease spread occurring

in fragmented habitats compared to larger, more continu-

ous forest patches. Regression analysis was applied in

tandem with our estimates of gene flow and fine-scale

population structure to explore which landscape variables

contributed to deer movement patterns and CWD preva-

lence or spread.

Materials and methods

Deer sampling

We utilized 1,988 tissue samples collected between

2003–2009 through CWD surveillance and population

control programs in Illinois and Southern Wisconsin. Free-

ranging deer were culled in areas at increased risk for

disease based on prior CWD proximity and disease

occurrence, and from uninfected populations geographi-

cally distant from the outbreak region. Deer population or

disease control programs in the following five sampling

areas provided tissues:

1. North-Central Illinois (NIL) which included Winne-

bago, Boone, Ogle, DeKalb and McHenry counties;

2. North-Western Illinois (GTA) which included Galena

Territory Association and JoDaviess county;

3. North-Eastern Illinois (DuP), which included DuPage

County Forest Preserve and parts of DuPage county;

4. East-Central Illinois (RAP) which included University

of Illinois Robert Allerton Park;

5. Southern Wisconsin (WI) which included Grant,

Layfayette, Green, Rock, Walworth, Kenosha and

Racine counties.

Sampling areas examined in the study spanned

[100,000 km2. We documented gender, age and harvest

locations of samples to the nearest township/range/section

(TRS; 2.6 km2). For all analyses, geographic coordinates

of sample harvest locations corresponded to the TRS

centroid. Deer were collected at the broadest spatial scale

possible, from 307 different TRS. To investigate fine-scale

genetic structure and explore a range of geographic dis-

tances and landscape characteristic between potential

genetic populations, we divided the five sampling areas

into 31 separate study sites (Table 1; Fig. 1a). Study site

delineations were based on subjective grouping of samples

from surveillance and population control programs which

generally reflected locations of forest patches with high

deer density and/or disease prevalence that were targeted

for population reduction. The 31 study sites included

multiple TRS harvest locations, which were also examined

individually in fine-scale genetic structure analysis. We

utilized all CWD-positive deer that were available at the

time of the study in the five sampling areas. The number of

male, female and CWD-positive deer sampled in the 31

study sites within the five sample areas are reported in

Table 1.

We created a map of CWD prevalence across the study

area by combining the total number of tested samples from

each 2.6 km2 section and all adjoining sections. For each of

these areas, prevalence was calculated (as the number of

CWD cases/the total number of deer tested for each area)

and assigned to the center section. We used kriging (ESRI

2011) to interpolate a 300 m 9 300 m grid from the

prevalence estimates at these points.

Laboratory procedures and descriptive measures

Ten microsatellite markers validated for use in our white-

tailed deer population were employed for genotyping [full

description in Electronic Supplementary Material (ESM)].

These markers, in addition to all laboratory procedures and

marker verification techniques are described in Kelly et al.

(2011). Arlequin (ver 3.0; Excoffier et al. 2005) was used

to calculate pairwise FST values (population differentia-

tion) between the 31 study sites and assess their signifi-

cance using 1,000 random permutations. To visualize

patterns of genetic differentiation across our study area, we

used the Genetic Landscapes GIS Toolbox in ArcGIS

(Perry et al. 2010) to interpolate a surface representing

genetic structure based on FST values of nearest neighbor

study sites. Technical details of the FST mapping procedure

are described in ESM. We also used Arlequin to generate

observed (HO) and expected (HE) heterozygosities for each

locus, and average expected heterozygosity for each sam-

pling area. We used GenePop (ver. 4.0; Rousset 2008) to

examine deviations from Hardy–Weinberg Equilibrium

(HWE: FIS, the inbreeding coefficient was calculated by

locus, by sampling area and overall with significance

determined using 10,000 dememorization steps, and 5,000

iterations in each of 100 batches). The rarefaction approach

implemented in ADZE (Szpiech et al. 2008) was used to

Popul Ecol (2014) 56:327–340 329

123

calculate estimates of the mean number of distinct and

private alleles per locus. Estimates were standardized to the

number of samples in DuP, (n = 49), the sampling area

with the smallest sample size.

Bayesian assignment of individuals

We inferred population structure using Bayesian clustering

methods to assign individuals to genetic clusters. Bayesian

clustering was implemented in STRUCTURE (v. 2.3.1;

Pritchard et al. 2000) which estimates the natural logarithm

of the probability [LnP(D)] that individual genotypes

belong to a given cluster (K = number of clusters to which

individuals are assigned). Several pilot simulations for

K 1–20 were conducted with and without location infor-

mation to determine the appropriate burn in and Markov

chain Monte Carlo (MCMC) replicates needed to generate

stable parameter estimates. In the final model, we ran

twenty replicates for K values 1–7 since averages and

standard deviations of LnP(D) for K [ 7 worsened sub-

stantially in pilot simulations. We then used the methods of

Evanno et al. (2005) to determine the number of clusters

based on the relative rate of change (DK) in mean

LnP(D) for all values of K. The simulation yielding the

largest DK was selected as the optimal model.

In STRUCTURE, we incorporated as background

information the TRS for each deer, specified study site as

population identifiers and utilized correlated allele fre-

quencies to account for shared ancestry (initial a = 1.0,

max a = 10.0, SD of a = 0.05) (Pritchard et al. 2000;

Falush et al. 2003). Burn-in periods of 100,000 and

100,000 MCMC replicates were used to model admixture

Table 1 Deer density and

sample size (n) of male, female,

and CWD? deer in 31 study

sites sampled from five

sampling areas in Illinois and

Wisconsin

DuP DuPage County Forest

Preserve, NIL North-Central

Illinois, GTA Galena Territory

Association, RAP Robert

Allerton Park, WI Southern

Wisconsin, NC not calculateda Densities for Illinois are based

on deer counted during aerial

censuses conducted during

winter 2010–2011 (IDNR

2012). Densities for Wisconsin

are estimated primarily from the

total number of deer harvested

in the fall of 2010 (WDNR

2012)

Study site Sampling area n females n males n CWD? (number females,

number males)

Density

(number deer/mi2)a

1 WI 12 15 0 14

2 WI 9 21 0 19

3 WI 20 16 5 (3F, 2M) 19

4 WI 15 18 0 12

5 WI 22 16 1F 11

6 WI 26 43 21 (4F, 17M) 11

7 WI 26 20 7 (3F, 4M) 11

8 WI 22 28 9 (2F, 7M) 14

9 WI 20 20 1M 14

10 WI 22 16 1M 12

11 WI 26 17 0 12

12 WI 25 14 0 12

13 GTA 154 66 0 NC

14 NIL 45 28 0 NC

15 NIL 17 10 0 105

16 NIL 7 7 1M 109

17 NIL 17 8 0 43

18 NIL 19 18 1F 49

19 NIL 101 71 26 (18F, 8M) 43

20 NIL 11 8 1F 43

21 NIL 45 28 8 (3F, 5M) 49

22 NIL 24 22 11 (9F, 2M) 49

23 NIL 98 48 27 (16F, 11M) 43

24 NIL 26 24 6 (3F, 3M) 43

25 NIL 14 12 3 (2F, 1M) 27

26 NIL 41 35 4 (2F, 2M) 34

27 NIL 16 8 1F 27

28 NIL 27 9 5 (3F, 2M) 82

29 NIL 39 27 2 (1F, 1M) 65

30 DuP 30 19 0 NC

31 RAP 234 86 0 NC

Total 1,210 778 141 (76F, 65M)

330 Popul Ecol (2014) 56:327–340

123

because these conditions yielded consistent parameter

estimates across replicate runs during pilot simulations.

Individuals were assigned to inferred clusters by highest

percentage of membership (q), with q [ 0.6 indicating that

an individual’s genotype was representative of allele fre-

quencies for their assigned cluster. Values of q \ 0.6 were

interpreted as a weak assignment, indicating that an indi-

vidual’s cluster membership could be allocated into mul-

tiple clusters or that the genotype was uninformative. After

assigning individuals, we used GenePop (Rousset 2008) to

examine deviations from Hardy–Weinberg equilibrium

within clusters.

Spatial autocorrelation

We used global spatial autocorrelation to explore spatial

structuring across the entire sampled region by examining

interrelations between geographic and genetic distance

matrices at varying spatial scales (Sokal and Oden 1978).

To complement results from Bayesian assignments, tests

for global spatial autocorrelation were performed using the

single population procedure by pooling deer from NIL,

DuP, and WI (GenAlEx ver. 6.2; Peakall and Smouse

2006). Deer from GTA and RAP were omitted because of

limited sampling between these study sites and other

sampled locations. Individuals were analyzed separately by

gender and age class (fawn, yearling or adult). For all

individuals, pairwise geographic distances were calculated

from the x/y-coordinates of the TRS centroid. Pairwise

geographic and squared genetic distance matrices were

used to calculate r, the autocorrelation coefficient for all

individuals in gender-age classes at eight increasing,

overlapping distance classes. Distances were divided into

the following classes: 0–0.5 km (deer sampled in the same

TRS), 0–2 km (home range size for females; Nixon et al.

1991), 0–3 km (deer in adjacent TRS), 0–6, 0–12, 0–24 km

(typical of 1 dispersal event; Nixon et al. 2007), 0–48,

0–100 km (long-distance dispersal; Oyer et al. 2007). We

a

b

Fig. 1 Genetic clusters of deer in Illinois as revealed by Bayesian

assignment of individuals. Clusters were simulated using STRUC-

TURE and study sites (numbered 1–31; adjacent to site centroids)

were assigned to the inferred cluster with the highest percentage of

membership. Yellow membership assigned to WI-NIL, green mem-

bership assigned to NILSW, red membership assigned to GTA, and

blue membership assigned to RAP (a). Membership proportions (q) in

the four inferred clusters are shown for all 31 study sites (bars

adjacent to study sites). Individual q values (b) were sorted from high

to low for each inferred cluster to demonstrate genetic admixture

among adjacent clusters

Popul Ecol (2014) 56:327–340 331

123

used the distance classes to determine the detection

threshold for genetic structure as indicated by the distance

class at which r becomes non-significant (Double et al.

2005). A one-tailed distribution was derived from 999

random permutations and bootstrapped to determine sig-

nificance and 95 % confidence limits.

To examine the location and size of genetic social

groups, we conducted two-dimensional local spatial auto-

correlation analyses in GenAlEx. Unlike global tests, local

autocorrelation tests were used to detect patterns at specific

locations, because autocorrelation coefficients were calcu-

lated for each individual and its nearest neighbors, rather

than for all deer within a given distance class (Sokal et al.

1998). We conducted tests of local autocorrelation on all of

the gender-age classes examined in the global tests.

Additionally, to examine differences in local autocorrela-

tion between deer that are generally philopatric and deer

with the highest propensity for dispersal (according to

global autocorrelation analyses), we grouped adult females

and fawns of both sexes (philopatic deer), and adult males

and female yearlings (dispersers) for local analyses. Five,

15, and 25 nearest neighbors were examined for all groups

to examine the consistency of autocorrelation patterns in

groups of variable sizes.

We examined individual local autocorrelation coeffi-

cients (r values) to identify family members and examine

the size and location of philopatric groups. Individual

r values were considered significant when P \ 0.05, based

on comparison with a distribution of r values generated

from 999 conditional permutations of the data (Peakall and

Smouse 2006). We mapped the location of the ten females

having the highest, most significant r values to indicate

areas where a single deer showed relatively high genetic

autocorrelation with its nearest neighbors, i.e., a philopatric

family.

Evaluation of landscape variables

To explore how landscape variables influenced gene flow

as a surrogate for CWD spread in Southern Wisconsin and

Northern Illinois, we performed linear regression using

landscape characteristics as predictors of FST values

between study sites. We explored how landscape variables

were effective predictors of gene flow at several spatial

scales by using 3, 5 and 10 km-wide buffers. During an

initial assessment, we found that landscape variables

measured in 10 km-wide buffers were more predictive of

FST than were variables measured in 3 and 5 km-wide

buffers and we subsequently focused only on the model

based on 10 km buffers. We measured dominant landscape

characteristics [Table S1 in Electronic Supplementary

Material (ESM)], along a buffered straight-line transect

between each site and the other 30 study sites. We included

landscape variables in the region that have been reported to

influence deer movement and CWD dynamics (Finder et al.

1999; Farnsworth et al. 2005; Long et al. 2005; Blanchong

et al. 2008; Witt et al. 2012).

To characterize land cover between study sites, a GIS

database was created to map variables for the region using

ArcGIS (ESRI, Redlands, CA, USA). The landscape vari-

ables represented in our regression model were: (1) forest;

(2) agriculture; (3) water and rivers/streams (4) developed

areas; (5) grasslands; and (6) slope. These variables were

derived from the 2001 National Land Cover Dataset

(NLCD) (http://www.mrlc.gov/nlcd.php), the National

Hydrography Dataset (NHD: http://nhd.usgs.gov/) or Uni-

ted States Geological Survey National Elevation Dataset

(NED: http://ned.usgs.gov/) (Table S1 in ESM). We reco-

ded the original NLCD land cover classes into binary maps

where ‘‘1’’ was the target variable, and ‘‘0’’ was all other

variables (Table S1 in ESM). For example, the ‘‘agricul-

ture’’ category contained NLCD 81 pasture/hay plus NLCD

82 cultivated crops, since crop rotation practices might

result in variation of planted species over years but in both

cases the fields would be relatively open, unprotected space

roughly 9–10 months of the year. Other classes were

combined for the variables representing forest, grassland

and developed areas (See Table S1 in ESM for details). In

addition to measuring water variables and wetlands from

the NLCD, we used the NHD, flow lines converted in the

GIS to a 30 m grid to map rivers and streams, again as a

binary GIS map. All of these binary maps were used to

calculate the percentage of the area covered by a variable

within the 10 km wide buffered transect (calculated as

[(area classified as each variable/total area in tran-

sect) 9 100]), We also used the NED to determine average

slope of the terrain and the NLCD Percent Developed

Imperviousness layer as an additional factor related to

developed area, measured as the percentage of impervi-

ousness. In addition to the landscape variables, Euclidean

distance was calculated between study site centroids.

In addition to quantifying the amount of landscape

variables in buffered transects, we also characterized their

spatial distribution using four metrics calculated in

FRAGSTATS (McGarigal et al. 2002). Metrics that

quantified fragmentation and connectivity (Kupfer 2012)

were chosen because these were expected to be biologi-

cally significant for white-tailed deer (Long et al. 2005;

O’Brien et al. 2006; Witt et al. 2012). Two of these metrics

measured connectivity among patches, where patches are

groups of adjacent grid cells representing the same land-

scape variables in maps: the Connectance Index (CON-

NECT), and the Patch Cohesion Index (COHESION)

(Schumaker 1996). CONNECT measures functional con-

nectivity by numerating patches that are connected to other

patches within buffered transects, while COHESION

332 Popul Ecol (2014) 56:327–340

123

measures physical connectivity considering the size and

shape of patches (detailed in ESM). The other two

FRAGSTATS metrics measured the scattering and shape of

patches. The Clumpiness Index (CLUMPY), measured the

clustering of patches relative to a spatially random distri-

bution, and the Perimeter-Area Fractal Dimension (PA-

FRAC), measured patch shape complexity over a range of

spatial scales. The four FRAGSTATS indices were calcu-

lated for forest, grassland, agriculture, developed and water

variables represented by the single class described above.

A total of 28 variables measuring Euclidean distance,

slope, the amount (% in buffered transects) and spatial

structure (FRAGSTATS indices) (Table S1 in ESM) of

landscape variables were compiled into 31 9 31 matrices.

These matrices were then used as predictors of a matrix of

FST values, such that for each observation, the dependent

variable and each independent variable corresponded to a

single value representing a pairwise comparison between

two study sites. Note that we analyzed landscape variables

between pairs of study sites rather than inferred genetic

clusters because analyzing at a smaller spatial scale

allowed us to explore ecological processes at resolutions

more similar to deer dispersal. Furthermore, quantifying

meaningful landscape variables between the inferred

genetic clusters from STRUCTURE was problematic given

the broad spatial scale and pronounced landscape hetero-

geneity within a single inferred genetic cluster.

Prior to modeling gene flow, we tested for correlation

among explanatory variables and removed seven variables

that were highly correlated (described in Table S2 in

ESM). We also tested for heteroscedasticity of residuals

using Chi-square, tested for outliers using influence sta-

tistics (detailed in the ESM), and examined normality of

errors with a normal probability plot of residuals. We

accounted for non-independence between pairwise FST

values by using a repeated measures linear mixed model

(Yang 2004; Pavlacky et al. 2009; Meeuwig et al. 2010;

PROC MIXED, SAS 2010) to model different covariance

structures for the full model. Descriptions of the four

modeled covariance structures can be found in Wolfinger

(1993) and ESM. Likelihood ratios and Akaike’s infor-

mation criterion (AIC) were used to identify the covariance

structure that best fit the data.

Given the large number of variables assessed (21 after

removing correlated predictors), we used model averaging

to identify a set of important landscape predictors, rather

than limit selection to a single, best model (Burnham and

Anderson 2002). We used model averaging as imple-

mented in PROC GLMSELECT (SAS 2010) to perform

100 rounds of stepwise model selection on resampled

datasets. For each round of selection, we used Schwarz

Bayesian information criterion to assess whether a variable

should enter/exit the model (Schwarz 1978). After 100

rounds of sampling with replacement from all possible

models, we used the selection percentage for each variable

as an indicator of predictor importance. We chose variables

with [50 % selection frequency for inclusion in the final

model, as we considered this conservative compared to the

recommended 20 % (SAS 2010). Parameter estimates,

standard errors and P values for variables in the final model

were obtained using PROC MIXED (SAS 2010) specifying

a first-order autoregressive covariance structure.

Results

Descriptive measures

Multi-locus genotypes were obtained for 1,988 deer in

Illinois and Southern Wisconsin, a region with CWD

prevalence between 0–12.5 % (Fig. 2b). Observed hetero-

zygosities ranged from 0.71 to 0.72 for the five sampling

areas. None of the 10 microsatellites violated HWE

assumptions, suggesting that null alleles and large-allele

drop-out did not affect genotype scoring. One marker in

NIL (RT23) showed weak heterozygote deficiency (ESM),

but FIS was low (FIS = 0.018), and there was no apparent

deficiency of a particular genotype, suggesting subpopula-

tion structure rather than inbreeding or null alleles. Global

tests showed no evidence of heterozygote excess in any of

the sampling areas. Pairwise FST values between the 31

study sites were weak (FST = 0.0003–0.05) though most

were significant (303/465 significant at Bonferroni adjusted

P \ 0.0001), and the distribution of FST values was sig-

nificantly higher for females than males (mean female

FST = 0.013, mean male FST = 0.006, P \ 0.01). The

spatial pattern of genetic differentiation based on interpo-

lation of FST values is shown in Fig. 2. Areas of JoDavies

(study site 13), Winnebago (study sites 15 and 17) and

South-Central Ogle (study site 14) counties showed pro-

nounced genetic differentiation as compared to the rest of

the study area. The standardized mean number of distinct

alleles per locus was highest for RAP at 8.2 and lowest for

GTA at 7.6. The mean number of private alleles per locus

was highest for RAP at 0.45 and lowest for DuP at 0.14.

Bayesian assignment of individuals

Bayesian clustering showed weak evidence for four

clusters (Fig. 1a), as K = 4 produced the highest average

likelihood and relative rate of change (DK) across 20 runs

(Table 2). All inferred clusters were in HWE (Bonferroni

corrected P \ 0.001), although some study sites had

membership apportioned across multiple clusters. The

four-cluster model recognized GTA and RAP as separate

but divided NIL and WI into two clusters (Fig. 1a). The

Popul Ecol (2014) 56:327–340 333

123

a

b

Fig. 2 Genetic divergence, CWD prevalence and philopatry across

Northern Illinois and Southern Wisconsin. a The Genetic Landscapes

GIS Toolbox in ArcGIS was employed to interpolate a surface

representing genetic structure based on FST values of nearest neighbor

study sites. b CWD prevalence was calculated and kriging was used

to interpolate a 300 m grid from each data point. The shaded grey

areas in the central portion of the map are calculated from cases in the

Northern Illinois/South-Eastern Wisconsin CWD outbreak, whereas

the shaded portion in the upper left part of the map is calculated from

cases in the South-Central Wisconsin CWD outbreak. Local spatial

autocorrelation coefficients (r values) for each deer and its 25 nearest

neighbors were calculated for females/fawns. Asterisks show loca-

tions of philopatric families with the strongest genetic autocorrelation

334 Popul Ecol (2014) 56:327–340

123

inferred cluster RAP was the most genetically distinct,

having an average q value of 0.9, whereas WI-NIL,

NILSW and GTA were much less so, having an average

q value of 0.6 (Table 3). Sampling intensity accounted

for less than 11 % of the variation in average TRS

q values, indicating that our sampling scheme has not

produced substantial bias in our genetic data (Kelly et al.

2010).

Admixture was high along contact zones for WI-NIL

and NILSW, and membership for a few study sites was

more or less evenly divided among multiple clusters (e.g.,

17, 30), indicating extensive gene flow along peripheries of

inferred populations. Figure 1b shows a gradual decrease

in individual q values (proportion of membership into

clusters) following largest-to-smallest sorting, suggesting

that while some deer in WI-NIL and NILSW had high

cluster membership, most were of migrant (=mixed)

ancestry or had uninformative genotypes. For these clus-

ters, boundaries represent contact zones between different

gene pools rather than population delineations. The

majority of deer in RAP and GTA had high fidelity to their

clusters, with a small proportion demonstrating mixed

ancestry.

Spatial autocorrelation

Adult males were not spatially autocorrelated in global

analyses at distances up to 100 km whereas male yearlings

were correlated at 0–2, 0–6, and 0–12 km (Fig. 3). At

B0.5 km, male fawns demonstrated the strongest positive

spatial autocorrelation of any group, averaging r = 0.035.

This significant autocorrelation was maintained at dis-

tances B3 km, then declined sharply. Adult females were

also positively autocorrelated at distances B48 km, with a

gradual decrease in r as distances increased between 0 and

48 km (Fig. 3). Female yearlings were not spatially

autocorrelated at any distance, with average r values sim-

ilar to those of adult males. Like male fawns, female fawns

were also positively autocorrelated, although with signifi-

cant structure at larger distances (B6 km) than males.

Local spatial autocorrelation analyses revealed that a

large proportion of adult females remained spatially asso-

ciated in large philopatric groups. When 25 nearest

neighbors were analyzed, 20 % of adult females were

significantly associated with relatives. This number

increased to nearly 25 % when fawns were included in the

analysis (Table S3 in ESM). Nearly ten percent of male

fawns and [17 % of female fawns were genetically asso-

ciated with their five nearest neighbors, suggesting that

they remain close to a few cohort members during their

first year. In contrast, adult males and female yearlings had

much lower maximum r values and fewer significant

genetic associations compared to the other groups within

their gender, which implies that very few associate spa-

tially with relatives.

When local spatial autocorrelation was assessed for

adult females and fawns with 25 nearest neighbors, several

locations showed trends for philopatry, as evidenced by the

presence of multiple autocorrelated families sampled

within a single study site. Locations of the ten female deer

that had the strongest genetic autocorrelation with their

nearest neighbors are shown in Fig. 2b. These ten deer

belonged to four different family groups, and interestingly,

two were located in the IL-CWD disease focus along the

Winnebago-Boone County border. Adult males/female

yearlings had very few significant r values with 25 nearest

neighbors, and correlations were substantially weaker than

those for adult females/fawns.

Evaluation of landscape variables

For the landscape analysis, covariance modeling indicated

that first-order autoregressive with a moving average best

fit the error term of pairwise FST values and was specified

in the final model. After removing seven correlated pre-

dictors (Table S2 in ESM), 21 landscape variables were

subjected to 100 rounds of stepwise model selection on

resampled datasets. The frequency (%) of selection for

each variable in the 100 sampled models is shown in

supplementary Fig. S1. Eight landscape variables proved

stably influential as demonstrated by their inclusion in

more than 50 % of resampled models: percent rivers/

streams, forest COHESION, agriculture PAFRAC, devel-

oped CLUMPY, developed COHESION, grassland

CLUMPY, grassland COHESION and grassland CON-

NECT (Table 4). In the final model including these eight

variables, errors were homoscedastic and normally dis-

tributed, and the adjusted R2 value indicated a 39 %

reduction in FST error. For comparison, we have shown

Table 2 Average likelihood, standard deviation and DK for

STRUCTURE Bayesian clustering of white-tailed deer in Northern

Illinois and Southern Wisconsin averaged across 20 replicates

K Average LnP(D) SD LnP(D) DKa

1 -65828.0 0.1 0.0

2 -65534.5 24.7 2.2

3 -65295.7 37.1 1.6

4 265115.8 49.5 5.3

5 -65197.8 132.0 1.8

6 -65520.6 307.1 0.9

7 -65566.6 300.3 0.2

Bold indicates the selected model having the highest LnP(D) and

DK values across 20 replicatesa DK was calculated per Evanno et al. 2005

Popul Ecol (2014) 56:327–340 335

123

alternate models that were frequently selected during

model averaging in Table S4 in ESM.

Parameter estimates showed that grassland COHESION,

grassland CONNECT and developed COHESION facili-

tated gene flow (i.e., were negatively related to FST;

Table 4). In contrast, grassland CLUMPY, agriculture

PAFRAC, forest COHESION, developed CLUMPY and

more area as rivers/streams restricted deer movement (i.e.,

positively related to FST). The most important predictors of

deer movement were grassland CLUMPY, agriculture

PAFRAC and developed CLUMPY (Table 4).

Discussion

Population genetic structure of white-tailed deer

in Midwestern North America

Our estimates of genetic admixture and gene flow indi-

cated deer in Southern Wisconsin and Northern Illinois

move extensively. Assignment tests and FST values

revealed a generally weak population structure across the

study area (Fig. 1), especially in Southern Wisconsin and

near Rockford. Most deer were admixed, with moder-

ately-low assignment to a particular genetic cluster

(Table 3). Clusters of deer spanned large geographic

areas and experienced extensive gene flow with sur-

rounding habitats, particularly along the Illinois-Wis-

consin border and to the North-East of Rockford IL

(Fig. 1). Although deer in Kentucky (Doerner et al.

2005), Michigan (Blanchong 2003), South Carolina and

Georgia (Purdue et al. 2000) exhibited higher genetic

(FST) differentiation at similar spatial scales, similarly

low levels of genetic structure have been reported for

deer in Canada (Cullingham et al. 2011a, b). Low levels

of genetic structuring observed in this study could also

be partially attributable to mutation-drift disequilibrium,

as the Illinois deer population experienced a bottleneck

and subsequent radiation during the early 20th century.

Such contemporary, drastic events might have prevented

detection of genetic definition at the time of sampling

(Cullingham et al. 2011a). Additionally, high effective

population size coupled with extensive gene flow at the

Table 3 Number of deer (n) per cluster, average q value and n with

q C 0.6 for each of the K = 4 clusters

GTA RAP NILSW WI-NIL

n per cluster 204 318 957 468

Cluster average qa 0.6 0.9 0.6 0.6

n with q C 0.6 (%) 78.4 94.0 59.2 48.1

a Membership proportions (q) for individuals in the four inferred

clusters: WI-NIL, NILSW, GTA, and RAP

Fig. 3 Global spatial autocorrelation for male and female deer grouped as fawn, yearling, and adult. Error bars surrounding the average

autocorrelation coefficient (r) represent 95 % confidence limits calculated with 999 bootstraps

Table 4 Estimates, standard errors and P values for landscape pre-

dictors of FST selected in [50 % of models in 100 rounds of model

averaging

Effect Parameter

estimate

Standard

error

P value

Grassland CLUMPY 0.1900 0.0189 \0.0001

Agriculture PAFRAC 0.0606 0.0194 0.0019

Developed CLUMPY 0.0539 0.0084 \0.0001

% Rivers/streams 0.0036 0.0004 \0.0001

Forest COHESION 0.0013 0.0003 \0.0001

Developed COHESION -0.0012 0.0003 \0.0001

Grassland COHESION -0.0026 0.0003 \0.0001

Grassland CONNECT -0.0276 0.0031 \0.0001

336 Popul Ecol (2014) 56:327–340

123

regional level would result in weak genetic structure,

similar to the patterns observed herein.

Populations exhibit positive spatial autocorrelation that

declines with increasing distance when relatives are spa-

tially adjacent (Smouse and Peakall 1999). Our global

analyses did not detect genetic structure among adult

males, but did so among fawns at smaller spatial scales,

and adult females at B48 km. This pattern is not unusual

since males are the dispersing sex and tend to occupy

larger home ranges (typically 3–5 km2) than females in

our study area (Nixon et al. 1991). Female deer, on the

other hand, usually remain with fawns and female off-

spring (Hawkins and Klimstra 1970) on home ranges that

vary seasonally, but tend to span 0.5–1.7 km2 in our study

area (Nixon et al. 1991). Philopatry on home ranges was

reflected in the strong spatial autocorrelation observed for

fawns and adult females at smaller distances (\3 km).

Surprisingly, we detected positive autocorrelation in male

(but not female) yearlings. Most studies report male

yearling dispersal (Nelson and Mech 1984; Nixon et al.

1991) and ecological studies have shown that females

tend to share home ranges with their mothers (Hawkins

and Klimstra 1970) for up to 2 years before they establish

home ranges nearby (Ozoga et al. 1982; Ozoga and Verme

1984; Mathews and Porter 1993). Our findings suggested

dispersal prior to sampling (January–March in Illinois,

November–December in Wisconsin) for female yearlings,

implying that they did not share home ranges with their

relatives. Though, long distance movements (*100 km)

have been documented for yearling females in our study

area (Oyer et al. 2007), dispersal of young females is

generally rare, and it is possible that herd reductions have

instigated this behavior.

Although global trends indicated autocorrelation for

yearling males, fawns, and adult females, local autocor-

relation analyses and assignment tests identified only a

few sites with strong philopatry and these influenced

observed patterns. Even when adult females and fawns

were analyzed, more than 75 % of the individuals were

not spatially autocorrelated with their nearest neighbors.

Overall, deer in most sampled TRS were not locally

autocorrelated and showed migrant ancestry. However,

sites in JoDaviess County (GTA: site 13), Southern Ogle

County (site 14), North-Western DeKalb County (site

24), and along the Winnebago-Boone county border

(study sites 19, 23) showed relatively high levels of

cluster membership (q C 0.6) with assignment tests and

strong female/fawn philopatry (Figs. 1, 2b). These areas

included forest patches in a large nature preserve and a

golf course, which could be acting as refuges for large

philopatric groups because they provide cover in an

otherwise developed/open landscape, or are located near

private properties where hunting/culling is not allowed.

Given their protection from harvest, these areas could

elicit increased deer densities (Hansen et al. 1997; Brown

et al. 2000) thereby contributing to our observed patterns

of spatial autocorrelation.

Landscape variables and their effects on population

genetic structure

Our multivariate regression analysis characterized gene

flow patterns in fragmented, agricultural habitats. Overall,

parameter estimates for landscape variables were very low

(range 0.19–0.0012), indicating that even significant pre-

dictor variables only weakly influence deer movement.

Nonetheless, large forested patches promoted genetic dif-

ferentiation among sites, though this is most likely because

they provide open niches for deer to settle in, not because

they create physical barriers to deer movement. Hence,

forests intensify genetic differentiation by contributing to

local genetic mixing (Scribner et al. 2005), while deterring

dispersal to other locales (Long et al. 2005). In support of

our findings, a meta-analysis of deer populations through-

out the US showed that increased forest cover was asso-

ciated with decreased dispersal distances (Long et al.

2005). Similarly, telemetry studies in Illinois report

reduced female dispersal in heavily forested versus frag-

mented habitats (Nixon et al. 1991, 2007). In contrast to

large forests where food and cover are readily available,

extensive movements may be needed in fragmented habi-

tats to utilize isolated resource patches (Long et al. 2005),

especially for females seeking protected, secluded fawning

sites (Nixon et al. 1991).

In addition to fragmented forests, physical connectivity

of grasslands promoted genetic mixing, suggesting that

these habitats act as corridors for deer movement. In our

study area, grasslands are often positioned between forests

and agricultural lands, thus allowing deer to utilize an

optimal foraging strategy among these edge habitats.

Edges, like grasslands, are a preferred ecotone (Whitehead

1972) because deer can forage in agricultural fields while

remaining near the protection of forest (Williamson and

Hirth 1985). Alternatively, deer move through grassland

habitats because they provide browse when resources are

scarce, particularly following harvest (Beier and McCul-

lough 1990). Interestingly, larger, more continuous grass-

lands impeded deer movement. Though, the reasons for

this finding cannot be directly ascertained from our results,

ecological studies suggest that deer avoid large open

grasslands because they provide less protection from

hunters or inclement weather (Nixon et al. 1991).

Our regression model indicated that urban development,

agricultural fields and rivers/streams deterred deer move-

ment. These landscape effects are apparent in the popula-

tion structure of deer in our study area, particularly along

Popul Ecol (2014) 56:327–340 337

123

the border of the NIL-SW and WI-NIL clusters, which are

separated by a large river, metropolitan Rockford, and a

major interstate highway (Fig. 1a). Nonetheless, while

parameter estimates for these variables were significant,

they were also low, which demonstrates elevated mobility

for male and female deer across the fragmented, heterog-

enous landscape.

Landscape, population structure and implications

for CWD

While the effects of environmental variables on deer

behavior and movement are subtle, landscape attributes,

nonetheless, are expected to influence CWD dynamics to an

extent. In our study area, the patchy distribution of CWD is

oftentimes coincident with habitat fragments that support

large deer densities. Sites 19 and 23, for example, are iso-

lated by urban development to the West and surrounded by a

large, forested conservation area to the East. Interestingly,

these two sites not only have the highest CWD prevalence in

Illinois (IDNR 2012), but deer sampled at these sites showed

increased philopatry, evidenced by relatively high levels of

cluster membership (q C 0.6) with assignment tests (Fig. 1)

and strong female/fawn local autocorrelation (Fig. 2b). In

contrast to the highly urbanized habitat of sites 19 and 23, an

undeveloped habitat in Ogle County (site 14) consists of a

state park and nature preserve with larger, forested patches

along a river. Though, deer density in Ogle County is rela-

tively high compared to other sampling sites in Northern

Illinois (Table 1), only one case of CWD has been detected

here (IDNR 2012), possibly because hunting pressure and

proactive disease surveillance prevented its establishment

by removing individuals prior to the shed of infectious pri-

ons (Williams et al. 2002). Large forest patches, particularly

those embedded in urbanized areas, could contribute to

CWD epidemics because they provide sustainable resources

for large, philopatric groups (Kelly et al. 2010). Farnsworth

et al. (2005) found higher CWD prevalence in developed

versus undeveloped habitats, presumably because local

factors such as replenishable food resources and smaller

habitat fragments act to concentrate deer (Wolfe et al. 2002).

Our landscape genetic data support the hypothesis that

the landscape influences deer movements, which in turn,

facilitates CWD spread. The distribution of CWD in our

study region, much like deer dispersal, appears limited to

the West of metropolitan Rockford (Fig. 2), where forests

and grassland habitats are relatively continuous. Further,

CWD is spreading South and East of Rockford where

habitats are fragmented and genetic admixture is high

(Kelly et al. 2010). If deer disperse post-infection, they can

directly transmit CWD when in contact with other deer.

Dispersing males are often accepted into bachelor groups

and, once formed, members participate in social grooming

behaviors (Marchinton and Hirth 1984) that would transmit

infected prions through saliva (Mathiason et al. 2006).

Regions experiencing genetic admixture from dispersal,

such as those observed in Southern Wisconsin and South

and East of Rockford, are at heightened risk for disease

spread. Deer from study sites 1–4 were sampled along the

fringes (\40 km from the core) of the large CWD outbreak

in South-Central Wisconsin, yet they genetically cluster

with deer sampled along the Wisconsin-Illinois border and

within infected habitats East of Rockford, Illinois (Fig. 1:

WI-NIL cluster). Given the extent of genetic admixture

among these fragmented, heterogeneous habitats, we

expect continued disease spread between the two CWD

outbreaks in Illinois and Wisconsin, despite the fact that

they currently appear spatially separated (Fig. 2b).

Overall, genetic structure of deer in our study area was

low, and while certain landscape variables can deter

movement, there are no absolute barriers to deer dispersal.

We should therefore expect that CWD will continue to

spread in accordance with observed dispersal patterns. In

fragmented habitats, larger forest patches should be targeted

for disease surveillance and culling, as these locales can

exacerbate CWD transmission by providing sustained hab-

itats for large, social groups and philopatric families of deer.

Furthermore, uninfected habitats that are physically con-

nected to infected populations through grassland corridors

could be at risk for pathogen influx via dispersal, and should

be closely monitored to promote early disease detection.

Widespread disease surveillance, particularly in con-

junction with landscape genetic analyses, is an effective

adaptive management mechanism for identifying related

populations within fragmented, heterogeneous environ-

ments. Our broad-scale landscape genetics study identified

environmental variables that influence population structure

of deer, and we hope our data can be used to further refine

landscape models and guide future studies of CWD.

Although it is out of the scope of the current analysis,

understanding the impacts of intense culling on deer popu-

lation structure is of great interest to guide further man-

agement of CWD.

Acknowledgments The Illinois Department of Natural Resources,

Wisconsin Department of Natural Resources, United States Depart-

ment of Agriculture Wildlife Services collected all samples and

numerous Wildlife Biologists generously donated both time and effort

to this study. The Illinois Department of Agriculture Animal Disease

Laboratory (Galesburg) provided invaluable assistance, while funding

was provided by IDNR, United States Geological Survey, United

States Department of Agriculture, University of Illinois Department

of Animal Sciences, Illinois Natural History Survey and the U.S. Fish

and Wildlife Service Federal Aid in Wildlife Restoration Project

W-146-R.

338 Popul Ecol (2014) 56:327–340

123

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