RESEARCH ARTICLE

Integrating multiple analytical approaches to spatially delineateand characterize genetic population structure: an applicationto boreal caribou (Rangifer tarandus caribou) in central Canada

Mark C. Ball • Laura Finnegan • Micheline Manseau •

Paul Wilson

Received: 18 September 2009 / Accepted: 8 June 2010 / Published online: 3 July 2010

� Springer Science+Business Media B.V. 2010

Abstract Individual-based clustering (IBC) methods

have become increasingly popular for the characterization

and delineation of genetic population units for numerous

species. These methods delineate populations based on the

genetic assumptions of a breeding unit which may provide

a better representation of the behaviour of the species. The

increasing use of IBC has resulted in the development of

several analytical models all of which vary in their theo-

retical assumptions to infer genetic population structure. In

this paper, we report a comparative strategy utilizing three

IBC methods to characterize the spatial genetic structure of

the boreal population of woodland caribou (Rangifer tar-

andus caribou) in central Canada. In addition, we imple-

ment both tests for isolation-by-distance (IBD) and

frequency-based assignment tests to validate the consensus

genetic clusters as defined by IBC. We also compare

indirect metrics of genetic diversity and gene flow using

both a priori defined herds and the IBC defined popula-

tions. Although our results show some concordance

between both pre-defined herds and IBC derived genetic

clusters, the IBC analyses identified a cluster that was

cryptic to observation-based caribou herds and found no

difference between several adjacent herds. By comparing

multiple IBC methods and integrating both IBD and indi-

rect genetic diversity metrics a posteriori, our strategy

provides an effective means to delineate wildlife popula-

tion structure and accurately assess genetic diversity and

connectivity.

Keywords Assignment test � Population structure �Bayesian � Rangifer tarandus � Faeces � Range delineation

Introduction

For most wildlife species, populations are defined based on

distribution of radio-collared animals or general range

characteristics (e.g., Rettie and Messier 2001; Schaefer

et al. 2001; Mosnier et al. 2003). In most cases, these

populations are considered closed and regulated primarily

by births and deaths rather than immigration and emigra-

tion. This is no different for the delineation of boreal

woodland caribou (Rangifer tarandus caribou), herein

referred to as boreal caribou, herd boundaries (herd syn-

onymous with subpopulation as in Cronin 2006). As a

result, subsequent analyses such as enumeration, cohort

descriptions and recruitment data are collected assuming

the discrete nature of these herds even though the extent of

dispersal events among herds is unconfirmed. Acknowl-

edging this, classical genetic approaches used to indirectly

infer genetic structure and associations through gene flow

M. C. Ball � L. Finnegan � P. Wilson

Natural Resources DNA Profiling and Forensic Centre,

Trent University, 1600 East Bank Drive,

Peterborough, ON K9J 7B, Canada

M. Manseau

Western Canada Service Centre, Parks Canada, 145 McDermot

Avenue, Winnipeg, MB R3B 0R9, Canada

M. Manseau

Natural Resources Institute, University of Manitoba,

70 Dysart Road, Winnipeg, MB R3T 2N2, Canada

M. C. Ball (&)

Fisheries and Wildlife Management Division,

Government of Alberta, 7th Floor, O.S. Longman Building,

6909-116 Street, Edmonton, AB T6H 4P2, Canada

e-mail: Mark.Ball@gov.ab.ca

123

Conserv Genet (2010) 11:2131–2143

DOI 10.1007/s10592-010-0099-3

typically ignore the systemic issues of these geographic

boundaries. Using a priori defined population units may

lead to the misinterpretation of genetic associations as a

response to the sampling scheme (i.e. overlapping seasonal

ranges) and may bias the results in favour of gene flow, or

the inability to accurately detect boundaries of genetic

structure (Pritchard et al. 2000; Mank and Avise 2004;

Latch and Rhodes 2006). Such inaccuracies in the delin-

eation of herds can have a significant impact on the pop-

ulation demographic parameters that are critical to the

management and conservation of this species.

Recent advances in molecular genetics have enabled

researchers to help clarify complexities in the relationships

between populations at varying scales (Falush et al. 2003;

Corander et al. 2004; Guillot et al. 2005a). A recent con-

tribution has been the development of individual-based

clustering analyses (IBC) where the operational units are

the individual genotypes rather than arbitrarily defined

population units, providing both the number of populations

and their spatial limits based on the genetic characteristics

of each sample (Mank and Avise 2004). This is an

advantage over other analyses that produce indirect esti-

mations of population structure and gene flow (F-statistics)

based on allele frequencies and under a priori designation

of population units. With these traditional approaches, the

use of pre-defined population units may hinder accurate

characterization of population structure (Rueness et al.

2003), the identification of populations with overlapping

seasonal ranges (Latch and Rhodes 2006) and the identi-

fication of cross-assigned and admixed individuals within

and among sub-populations (Mank and Avise 2004; Latch

and Rhodes 2006). This is particularly relevant in the

increasing number of studies examining fine-scale genetic

population structure (Rowe and Beebee 2007; Zamudio

and Wieczorek 2007; McDevitt et al. 2009).

Individual-based clustering approaches depict popula-

tion structure based on the genetic ancestry of individuals

which may or may not correspond to a priori defined

population units based on geographic sample sites, move-

ment data and taxonomy (Falush et al. 2003; Guillot et al.

2005b; Corander and Marttinen 2006). The estimation of

the number of genetic clusters (K) is based on probabilities

of individual genotypes to conform to prior assumptions of

theoretical population inheritance (e.g. Hardy–Weinberg

equilibrium (HWE), allele frequencies, inbreeding coeffi-

cients, admixture), using Bayesian statistics. The individual

proportional membership is then utilized to derive genetic

clusters and provides a direct assignment to a breeding unit

and admixture among populations. There are currently two

groups of IBC approaches that are widely used to delineate

population structure. These include spatially non-explicit

models such as that employed in the widely used clustering

program STRUCTURE (Pritchard et al. 2000; Falush et al.

2003) and spatially explicit models where explicit spatial

priors are directly added to the Bayesian model as in the

algorithm BAPS and GENELAND (Guillot et al. 2005a) or the

algorithm incorporates models for geographical continuity

of allele frequencies or cluster membership using hidden

Markov random fields (HMRF) as prior distributions as

used in TESS (Francois et al. 2006; Chen et al. 2007).

Although the development of IBC methods to assist with

population genetic analyses has proved valuable, these

methods do have limitations that can hinder interpretation

and may result in conflicting results among IBC algo-

rithms. For instance, Latch et al. (2006) described the

difference between two IBC algorithms, STRUCTURE 2.1 and

BAPS 3.1, to determine genetic structure using simulated

data sets with FST estimates as low as 0.02. Given the

ability of these analyses to differentiate genetic clusters

with low levels of genetic exchange, it is important to

discriminate such situations from instances of genetic iso-

lation. This is achievable using F-statistics (i.e. pairwise

FST) after the IBC characterization of spatial genetic

clusters. Furthermore, IBC methods do not provide

descriptive statistics such as inbreeding coefficients (e.g.

FIS, FST) or genetic diversity that are useful to the under-

standing of local genetic structure for recovery and man-

agement actions.

Additionally, there has been recent discussion on the

inability of various IBC algorithm to accurately charac-

terize genetic structure where patterns of genetic clines or

IBD occur (Rowe and Beebee 2007; Guillot et al. 2008;

Frantz et al. 2009). The response in such cases was the

overestimation of genetic structure by artefacts created by

the underlying IBD patterns (Frantz et al. 2009). Also, each

IBC algorithm varied in its ability to discern genetic

clusters under patterns of IBD and clines resulting in

conflicting results between applications (Rowe and Beebee

2007; Frantz et al. 2009). Guillot et al. (2008) strongly

suggested scrutinizing the outputs from several IBC algo-

rithms used on a single data set, in addition to testing for

patterns of IBD and cluster compliance to HWE to deci-

pher a genetic structure that can be explained empirically.

We used a broad analytical approach involving multiple

IBC analyses to first assess the genetic structure of the

boreal caribou in central Canada. Next, we verified the

strength of genetic structuring among the genetic clusters

using analyses of IBD, graph theory [isolation by graph

distance (IBGD)] and F-statistics, assessing both the levels

of gene flow and genetic diversity within and among each

of the genetic clusters. This comparative approach allows

us to critically refine the spatial partitioning of genetic

populations to reduce and eliminate many of the biases

inherent to ecologically pre-defined population units and

various deficiencies within and among IBC clustering

models.

2132 Conserv Genet (2010) 11:2131–2143

123

Methods and materials

Study area

For the purpose of this study, five caribou ranges were

studied. In Manitoba, the study area included ranges at the

southern boundaries of the species distribution, the North

Interlake herd, and from there extended to the west (The

Bog herd), to the northwest (the Kississing–Naaosap herd)

and to the north (Wabowden herd) (Fig. 1a). The range of

the Bog herd extended into Saskatchewan and included the

Pasquia Hill. The fifth herd, the Smoothstone–Wapeweka,

was in central Saskatchewan. Limited data are available on

these herds; a conservation strategy produced by Manitoba

Conservation suggest a population estimate of 50–75 ani-

mals for the North Interlake and The Bog respectively, the

Kississing–Naosap and Wabowden being larger at 150–275

and 200–225 animals, respectively and the Smoothstone–

Wapewaka being the largest at 700 animals (Environment

Canada 2008). The range of the Smoothsone-Wapewaka

herd has however experienced significant amount of

anthropogenic activities in the last 40 years and the popu-

lation is declining (Arsenault 2003; Arsenault and Manseau

2010; Arlt and Manseau 2010). Recent non-invasive fecal

DNA surveys have estimated caribou numbers in the North

Interlake at 100 animals and also declining (Hettinga 2010).

The other herds are stable (Manitoba Conservation 2005).

Sample collection

Collections of faecal samples took place from December

12, 2003 to January 25, 2006. Systematic aerial surveys

were conducted using fixed-wing aircraft following tran-

sects spaced 3 km apart. Flights occurred within 2–3 days

of a snowfall to allow for identification of recent caribou

cratering activity. Cratering sites were accessed the same

or following day by helicopter and the location recorded by

GPS. Only faecal pellets consolidated in a single mass and

of C20 pellets were collected as they are more likely

(compared to single pellets) to preserve embedded intesti-

nal epithelial cells for DNA extraction. Faeces were

scooped into separate, labelled freezer bags with the aid of

clean plastic spoons to avoid contamination.

DNA extraction and quantification

Extraction of all boreal caribou faecal samples was used to

qualify and quantify target DNA using the procedure out-

lined in Ball et al. (2007). As faecal material may contain

other sources of DNA (i.e. bacterial, plant), it is essential to

assess the quantity of target DNA from the total amount of

DNA extracted from the faecal samples. The method pro-

posed by Ball et al. (2007) provides a simple comparative

amplification assay, using species specific nuclear marker

amplification, to determine the target to total DNA ratio,

Fig. 1 Maps of study region

showing spatial delineation of:

a Sampled herds, b Genetic

clusters as determined by BAPS

5.1 using spatial data (K = 3),

c clusters defined by GENELAND

3.0. (K = 4) and d Genetic

clusters as determined by both

STRUCTURE 2.1 and TESS 2.1.

(Boundaries defined by areas

with 80% individuals

with [ 0.80 cluster probability)

Conserv Genet (2010) 11:2131–2143 2133

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allowing researchers to enter appropriate amounts of target

DNA into PCR reactions reducing the problems of allelic

dropout and PCR inhibition. In summary, sex identification

using caribou-specific Zfx/Zfy amplification characterized

only the caribou nuclear template DNA and identified those

samples with quantities greater than 500 pg; the lower limit

to which there is limited risk for allele drop-out (Ball et al.

2007).

Microsatellite amplification

Following quantification, 5 ng of target caribou DNA from

each sample was amplified using 11 polymorphic, fluo-

rescent-labeled microsatellite markers (Multiplex 1: Rt6,

Rt9, Rt24 (Wilson et al. 1997); Multiplex 2: Map2C, Bl42

and BM848 (Bishop et al. 1994); Multiplex 3: Bms1788

and Rt7 (Cronin et al. 2005) and Multiplex 4: Rt5, BM888

and Rt30 (Wilson et al. 1997)). All reactions were con-

ducted in a 10 ll volume containing: 19 PCR buffer;

2.0 mM MgCl; 0.2 lg/ml of BSA; 0.4 lM of each of both

the forward and reverse primers (more than one set in

multiplex reactions); 0.2 lM of each dinucleotide tri-

phosphate; 0.5 unit of Taq polymerase (Invitrogen Life

Technologies) and 5 ng of DNA template. The thermocy-

cling protocol for individual and multiplexed loci consisted

of 94�C for 5 min, then 29 cycles of 94�C for 30 s, 56�C

for multiplex 1 and 3 (60�C for multiplex 2 and 4) for 30 s

and 72�C for 30 s, then a final extension time of 60�C for

45 min. Post-amplification, each sample was desalted prior

to genotyping using a MegaBace workstation 1000 (GE

Healthcare Life Sciences, Quebec). Alleles were defined

based upon size (base pairs) for each of the 11 amplified

loci. All samples were amplified and genotyped in tripli-

cate to verify profile data consistency and genotyping error.

Individual genotypes, Hardy–Weinberg equilibrium

and linkage disequilibrium

The complete genotypes of all samples were compared

using the Microsoft Excel Macro, GENECAP (Wilberg and

Dreher 2004) to determine the number of identical geno-

types contained in the sample set. Genotypes which dif-

fered by 1–2 alleles were re-genotyped to confirm the

profile. All duplicate genotypes were discarded from fur-

ther analysis. After comparing the triplicate profiles, in any

case where allele dropout was discovered in a single locus,

that locus was amplified separately (93) to confirm its

profile. FSTAT 2.1 (Goudet 1995) was utilized to evaluate

HWE and linkage disequilibrium (LD) for each of the 11

loci. LD was tested using a Markov Chain method utilizing

10,000 de-memorization, 5,000 batches and 10,000 itera-

tions. Sequential Bonferroni correction was used to adjust

a-values (0.05) for multiple comparisons (Rice 1989).

F-statistics (FST and FIS) were performed using the soft-

ware FSTAT 2.1 (Goudet 1995) with significance levels set

with a nominal level of 0.001 and 100,000 randomizations.

The presence of null alleles and loci deviating from LD

were further identified using the software package CERVUS

3.0 (Kalinowski et al. 2007) with an estimated null-allele

frequency greater than 5%.

Population structure [individual-based clustering (IBC)]

We utilized three different individual Bayesian software

packages to examine genetic population structure. These

included: STRUCTURE 2.1 (Pritchard et al. 2000; Falush et al.

2003); BAPS 5.1 (Corander and Marttinen 2006); and TESS

1.2 (Francois et al. 2006; Chen et al. 2007). Each of these

IBC methods estimate genetic structure based on different

assumptions of theoretical inheritance and from multi-

locus genotypes without assuming pre-defined populations.

STRUCTURE 2.1 (Pritchard et al. 2000; Falush et al. 2003)

is an IBC analysis commonly used in contemporary pop-

ulation genetics. This algorithm estimates Pr (X|K) prob-

ability of the data to conform to (K) assumed clusters. It

also estimates the probability of membership of each

individual q to each cluster through the use of a Monte

Carlo Chain maximizing HWE and minimizing LD of the

clustered group. To determine the optimal K-clusters for

the data set, we utilized a method developed by Evanno

et al. (2005) based on a rate of change in the log probability

of the data (DK) among 10 runs of each assumed K. This

method performs better at deciphering the appropriate K

in situations where gene flow patterns among populations

are not homogenous, which could lead to over or under

estimation of K when assuming the highest estimated Ln

probability of the data or Ln Pr(X|K) as proposed by

Pritchard et al. (2000). Clustering was performed under the

F-model as proposed by Falush et al. (2003) (assuming

admixture and correlated allele frequencies). Each run was

set to a burn-in of 500,000 and a MCMC data collection

chain of 500,000. These values were determined after

several trial runs to identify the size required for conver-

gence of the posterior distribution (Pritchard et al. 2000;

Falush et al. 2003). We tested K = 1–10 replicated 10

times as required by Evanno et al. (2005) to quantify

the standard deviation among each run for a particular

assumed K.

To define the geographic boundaries of each genetic

cluster using the STRUCTURE 2.1 results, we plotted the

location of all high ancestry individuals, characterized as

those having q C 0.80 using ArcGIS 9.2 (ESRI, Redlands,

California) and applied minimum convex polygons to

determine the extent of the cluster area. Next, all individual

samples collected within each of these newly defined areas

were used for the frequency-based individual assignment

2134 Conserv Genet (2010) 11:2131–2143

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regardless of their genetic affiliation to confirm cluster

affiliation. The two frequency-based assignment tests we

applied consisted of the allele frequency assignment

approach (Paetkau et al. 1995, 1999, 2004) and the

Bayesian assignment method (Rannala and Mountain

1997) as implemented in Geneclass v2 (Piry et al. 2004).

Both assignment tests are commonly used in genetic

analyses (Kraaijeveld-Smit et al. 2005; Kim et al. 2006;

MacAvoy et al. 2007; Zamudio and Wieczorek 2007). For

the Bayesian assignment tests an assignment threshold of

P\0.01 was applied using the leave one out approach and

for the Paetkau et al. (1995) procedure a default frequency

for missing alleles was set at 0.01.

Secondly, the clustering program TESS 1.2 (Francois

et al. 2006; Chen et al. 2007) was used. This IBC utilizes a

MCMC approach to define genetic clusters under the

assumptions of HWE to reduce inbreeding coefficients

(Francois et al. 2006). This algorithm also utilizes a Hidden

Markov Random Field (HMRF) model on tessellations.

The developers of the algorithm claim it characterizes

spatial relationships to nearest neighbours, under the

assumption that individuals who are spatially, closely

located should be genetically similar (Francois et al. 2006).

An incremental increase of the HMRF parameter from 0

(full cline) to 1 (hard clusters) permits the user to visualize

genetic clustering that may be overestimated or diminished

using the previously described non-spatial IBC analyses,

which have difficulties differentiating genetic clusters

under various sampling distributions along a continuous

cline in genetic variation (Francois et al. 2006). Our anal-

yses using TESS involved the Markov chain Monte Carlo

algorithm under the assumption of admixture. The spatial

locations of all individuals were also incorporated. Ini-

tially, four starting values of the HMRF parameter at 0.2,

0.6, 0.8 and 0.9 were used to select the optimal HMRF,

determined to be 0.6. Next, we performed 50 independent

simulations for each assumed K over a range of values (1–

10) using a 30,000 cycles with a burn-in of 20,000 to

identify which assumed K produced the highest likelihoods

(Kmax). Kmax was then run 100 times and the estimated

cluster membership probabilities were determined from the

20 highest likelihood simulations (Chen et al. 2007; Fedy

et al. 2008; McDevitt et al. 2009).

BAPS 5.1 (Bayesian Analysis of Population Structure)

(Corander and Marttinen 2006; Corander et al. 2006), akin

to STRUCTURE and TESS, utilizes a Bayesian clustering

algorithm to determine hidden genetic structure of multi-

locus genotypic data. However, unlike the former pro-

grams, BAPS 5.1 infers clusters based upon similarities in

variance of baseline allele frequency data from assumed

source populations (i.e. a priori defined herds) (Corander

et al. 2006) and as such inference of K-clusters in indi-

vidual clustering approach is set not to exceed the number

of sampling areas (putative populations). This algorithm

infers the number of ancestral populations (K) utilizing a

stochastic optimization algorithm which significantly

reduces computational effort, a significant issue for users of

STRUCTURE 2.1 utilizing large data sets. Additionally, BAPS

5.1 permits users to define genetic structuring using the

spatial distribution of each individual in the data set. The

spatial prior distributions are then specified under the

constraint that Kmax is determined by the application, and

the corresponding posterior optimal number of clusters is

estimated using the stochastic learning algorithm (Coran-

der et al. 2006; Guillot et al. 2009). The spatial priors

provide more weight for clustering solutions that are

expected to be sensible from a biological perspective

a priori, which strengthens the inferences for sparse

molecular data. Using spatial clustering of individuals, we

inferred the maximum K to be between 1 and 10 with 20

replications of each inferred Kmax.

Finally, the IBC clustering algorithm GENELAND 3.0

(Guillot et al. 2005a, b, 2008; Guillot 2008) was used in the

comparison. This algorithm is implemented through an

extension of program R 2.4.1 (Ihaka and Gentleman 1996).

GENELAND uses individual multi-locus genotype data to

detect genetic population structure based on compliance to

best fit HWE and LE. It also incorporates spatial data

directly under the assumption that populations are spatially

organized. This model however does not assume admixture

and any genetic boundaries found are assumed to separate

K random mating populations (Manel et al. 2007; Guillot

et al. 2005b). Improvements to the model suggest the

ability to increase the detection of clusters having low

genetic differentiation (Guillot 2008). Our data set was

analysed over 20 independent runs assuming a Kmin = 1

and a Kmax = 20 using the dirichlet model as recom-

mended in (Guillot et al. 2005a) and filtering for null

alleles. Each run included 100,000 iterations, collecting or

thinning data at every 100th iteration. The maximum

number of nuclei was set at 400 and the maximum rate of

the Poisson process was fixed at 100. Once this initial run

converged on a K solution, this K was fixed for 20 inde-

pendent runs using the same parameters as mentioned

above.

Isolation-by-distance and graph theory

We tested for patterns of IBD and gene flow between the

inferred genetic clusters using a graph theoretic approach.

Graph theory allows the assessment of genetic connectivity

beyond the pairwise comparisons of FST. Nodes on the

graph represent sampling areas which are connected by

edges which represent gene flow (Dyer and Nason 2004;

Dyer 2007; Garroway et al. 2008). We calculated three

metrics to describe connectivity between nodes within our

Conserv Genet (2010) 11:2131–2143 2135

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graph including: degree (a measure of node connectivity)

and eigenvalue centrality (how connected a node’s imme-

diate connections are) in either GENETIC STUDIO (Dyer 2009)

or IGRAPH (Csardi and Nepusz 2006) for R 2.7.1. (R

Development Core Team 2008). In our case, we were

particularly interested in IBD among our genetic clusters

and whether this was affecting patterns of genetic structure.

We compared the matrix of shortest path length between

nodes to FST and also to pairwise measures of geographic

distance using a Mantel test with 9,999 permutations

implemented in ADE4 (Chessel et al. 2004) for the R

package. Further, we assessed patterns of IBD between

individual node pairs, scaled against the overall pattern

within the sampling range, using the export to Google-

EarthTM function in GENETIC STUDIO. This function uses a

v2 test to test for edges between nodes which are outside

the 95% CI of the overall IBGD regression line. Those

nodes which are spatially more distant than expected from

graph distance are called extended, while those which are

closer are compressed.

Results

Tests of genetic diversity and disequilibrium

Genetic profiles at 11 microsatellite loci were obtained for

a total of 876 caribou samples. All profiles were genotyped

in triplicate (876 93), heterozygote allele dropout occur-

ring in eight profiles at various loci and genotyping error

occurring in nine profiles for an error rate of 1.0% each (or

an overall error rate of 1.9%). We attribute this success to

quantifying and qualifying the caribou DNA used for each

reaction (Ball et al. 2007). All samples consistently yielded

caribou DNA template in excess of amounts associated

with allelic dropout (range from 25 to 250 ng per indi-

vidual DNA extraction elute) (Ball et al. 2007). The

comparison of profiles identified 443 unique genotypes due

to multiple sampling of the same individuals. The number

of alleles ranged among the 11 loci from 9 (BM848) to 13

(BMS1788). Mean number of alleles per locus (Na) ranged

from 7.6 to 9.1 (Table 1). Bonferroni correction for mul-

tiple testing suggested the absence of non-random associ-

ation (i.e. LD) among all loci. Two of our loci (Rt30 and

Bm888) were found to be deviating from HWE in several

of our pre-determined local populations each having fewer

heterozygotes than expected following a Bonferroni cor-

rection on the P-value for each locus from each herd.

Locus Rt30 deviated from HWE in all local populations

and was removed from further analyses. Population struc-

ture analyses were performed both with and without

BM888 with no change in the clustering output. With the

exception of Wabowden, all the pre-defined populations

had positive FIS values; the highest being in North Interlake

(Table 2). These values suggest population deviation from

HWE as a response to several potential factors including a

Wahlund effect or non-random mating. We did not detect

the presence of null alleles for any of the amplified loci

used in downstream analyses.

Population structure (individual clustering)

Resolving DK through our STRUCTURE 2.1 data analyses

applying the method proposed by Evanno et al. (2005)

identified the presence of two modes (Fig. 2a). The heights

of each DK mode describe the strength of genetic struc-

turing at each assumed K (Evanno et al. 2005) with a first

mode at K = 2 (DK = 479.0) and the second mode at

K = 5 (DK = 67.5). As described by Evanno et al. (2005),

this bimodal phenomenon describes the presence of a

‘‘contact’’ zone, whereby the first mode at K = 2 is

describing the uppermost level of structuring occurring in

our dataset and where gene flow is restricted (FST between

regions = 0.047). The second mode at K = 5 defines sub-

structuring within each of these areas which was confirmed

by the spatial representation of those individuals having

Table 1 Descriptive statistics (mean ± SD) of each ‘‘a priori’’ defined boreal caribou herd

Herd n Hexp Hobs A FIS Pair wise FST \ Nei’s genetic distance

North

Interlake

The

Bog

Kississing–

Naaosap

Wabowden Smoothstone–

Wapeweka

North Interlake 101 0.68 ± 0.04 0.61 ± 0.02 7.6 ± 2.2 0.10 0 0.08 0.14 0.17 0.16

The Bog 117 0.72 ± 0.03 0.67 ± 0.01 7.5 ± 1.6 0.04 * 0.04 0 0.13 0.15 0.14

Kississing–Naaosap 109 0.76 ± 0.02 0.70 ± 0.01 9.1 ± 1.7 0.08 0.05 0.04 0 0.10 0.07

Wabowden 38 0.70 ± 0.03 0.73 ± 0.02 7.8 ± 2.2 -0.05 * 0.07 0.05 0.02 * 0 0.11

Smoothstone–Wapeweka 78 0.75 ± 0.02 0.71 ± 0.02 8.7 ± 1.4 0.06 0.06 0.04 0.02 0.04 0

All FST and FIS values significantly different from 0 (P \ 0.001) except those marked with *

2136 Conserv Genet (2010) 11:2131–2143

123

q C 0.80 assignment. In the southern area, three clusters

were defined, one in the Bog collection area and two

clusters in the North Interlake area. In the north, two

clusters were identified, one in the greater Smoothstone–

Wapeweka area and one containing both Kississing–Na-

aosap and Wabowden (Figs. 1d, 2d).

BAPS 5.1 detected a significant spatial partition at K = 3

(P = 1.0; Ln Pr (X|K) = -16,206) (Fig. 2b), remaining

consistent at greater inferred Kmax (K = 4–10). Geo-

graphically, these clusters defined as: Cluster 1: including

the Kississing–Naaosap, Wabowden and the Smoothstone–

Wapeweka sample areas; Cluster 2: The Bog and the

northern area of the North Interlake (UNI) sampling areas

and Cluster 3: exclusively the lower area of the North In-

terlake (LNI) sampling area (Figs. 1b, 2b).

Our results from TESS, using individual spatial infor-

mation showed that under HMRF = 0.6, and compared

over assumed various assumed K (1–10), Kmax was esti-

mated to be five (average log likelihood -12,334)

(Figs. 1d, 2c). This yielded a genetic structure comparable

to those results characterized by STRUCTURE.

GENELAND 3.0 characterized four genetic partitions

across our study area with a mean Ln Pr (X|K) = -13,562.

These clusters were defined as Cluster 1: Smoothstone–

Table 2 Number of alleles,

size range and Hardy–Weinberg

equilibrium deviation for each

of 11 loci used to generate

individual caribou profiles

Locus Alleles Size range (bp) Herds deviating

from HWE (of 5)

Reference

BMS1788 13 103–131 0 (Cronin et al. 2005)

BM888 12 170–192 0 (Bishop et al. 1994)

Rt5 12 90–124 0 (Wilson et al. 1997)

Rt7 12 212–238 0 (Wilson et al. 1997)

Bm848 9 360–380 2 (Bishop et al. 1994)

Rt9 12 109–131 0 (Wilson et al. 1997)

Rt6 10 95–117 0 (Wilson et al. 1997)

Map2C 10 93–115 0 (Bishop et al. 1994)

Bl42 10 235–253 0 (Bishop et al. 1994)

Rt30 10 185–203 5 (Wilson et al. 1997)

Rt24 12 204–236 5 (Wilson et al. 1997)

Fig. 2 Individual based clustering results: a Bimodal distribution of

STRUCTURE 2.1 generated DK at K = 2 and K = 5 based on Evanno

et al. (2005) (solid line) with LnP(d) values at each tested K as proposed

by Pritchard et al. (2000) (dashed line), b clusters as determined by BAPS

5.1, K = 3, c clusters as determined by TESS 2.1 K = 5, d box plot of

STRUCTURE 2.1 generated individual proportional membership at K = 2

and K = 5 and e probability maps of spatially derived genetic clusters

by GENELAND 3.0 (K = 4)

Conserv Genet (2010) 11:2131–2143 2137

123

Wapeweka; Cluster 2: Kississing–Naaosap, Wabowden;

Cluster 3: Bog and UNI and Cluster 4: LNI (Figs. 1c, 2e).

Additional analyses utilized the greater genetic sub-struc-

turing of K = 5 from STRUCTURE/TESS in an attempt to

describe a consensus genetic clustering pattern among all

IBC results.

Using both the proportional admixture and geographic

data for each individual from our consensus spatial

structure (K = 5) from STRUCTURE/TESS, it was possible to

ascertain information regarding migrants from the iden-

tification of cross-assigned individuals for each area.

Furthermore, comparison of these results with assignment

tests requiring a priori population information was used to

validate our geographic/genetic cluster association. From

our STRUCTURE/TESS results, we identified a total of 21

cross-assigned individuals relative to the 5 genetic clus-

ters (Table 3). The range of q value assignment to a

particular genetic cluster ranged from 0.60–0.99.

Although it is common when using STRUCTURE/TESS to

assess genetic cluster assignment based upon a stringent

criteria of proportionate membership of above 0.80

(Rueness et al. 2003), the range given here was evaluated

and compared against the assignment scores across other

commonly used assignment tests. Our comparisons

showed that among the cross-assigned individuals, we

achieved concordance of the STRUCTURE/TESS assignments

for 60% of the individuals when using the Rannala and

Mountain (1997) method and for 66% of the individuals

when using the Paetkau et al. (1995, 2004) method. By

incrementally increasing the lower limit of the q value by

0.1 from 0.60 to 1.0 when assigning individuals using the

STRUCTURE/TESS output, we observed an increase in con-

cordance with the frequency-based assignment tests.

Comparison of Rannala and Mountain (1997) assignments

to the STRUCTURE/TESS with a lower assignment threshold

of 0.75 and 0.80 showed 78 and 87% identical cross-

assignments respectively, while under the Paetkau et al.

(1995, 2004) method cross-assignments at the same

threshold increased to 84 and 92%. In all cases, where

both the Rannala and Mountain (1997) and Paetkau et al.

(1995, 2004) assignments differed from STRUCTURE/TESS,

the individual was assigned as a resident of the a priori

defined population and were not cross-assigned to another

population (Table 4). In addition, for each of these cases,

the second most likely population assignment, based on

the -log(L) of assignment, was the locality primarily

chosen by STRUCTURE/TESS. As a result, an ancestry or

membership (q) lower threshold of 0.80 was implemented

for this study.

There were few cross-assigned individuals among

localities within our sampling area. The largest numbers of

cross-assigned individuals were found in both the Kissis-

sing–Naaosap–Wabowden and Bog localities (total of 10

and 7 respectively). All individuals in the Lower North

Interlake were highly assigned to that area (Table 3).

Isolation-by-distance and graph theory

The graph that best fit our data had 10 edges connecting the

five nodes (i.e. was completely saturated), therefore con-

nectivity did not vary among nodes. The average path

length was 4.43. The mantel tests found no evidence for a

correlation between graph distance and FST (r = 0.151,

P = 0.378), nor evidence of overall IBD (r = -0.24,

P = 0.594) or IBGD (r = 0.473, P = 0.069). However

more subtle patterns were revealed after exporting the

graph to GoogleEarthTM (Fig. 3). Some patterns of sig-

nificant IBGD were now apparent as well as deviations

from IBGD. There was a greater geographic distance than

expected from graph distance between node 1 and 3, 4 and

5 (extended edges indicating high gene flow). Conversely

there was less gene flow than expected between the four

Manitoba nodes (2–5).

Descriptive statistics of genetic clusters

F-statistics derived for each of the described genetic

cluster areas showed moderate differentiation based upon

FST results (Table 3). There was a strong differentiation

between the northern and southern clusters alike our

previous descriptive analyses. One interesting finding was

the strong differentiation of the Lower North Interlake

(LNI) area with all other clusters (0.06 B FST C 0.08),

and in particular, its differentiation with the Upper North

Interlake (UNI) area; these two corresponding areas are

only 40 km apart and within a contiguous forest cover.

FIS values for each area were positive, ranging from 0.05

in the LNI to a high of 0.07 in the UNI. However, as

previously shown, some of these areas do contain a

number of migrants identified through our assignment

tests, as well as a number of admixed individuals which

are likely influencing a continued Wahlund effect in

these areas. The high FIS calculated for the LNI is likely

Table 3 Number of individuals from each herd to genetic clusters

derived from individual-based clustering with individual proportional

memberships (q) above 0.80

Genetic cluster n Cluster membership

1 2 3 4 5

Smoothstone–Wapeweka (1) 78 44 0 2 0 0

Kississing–Naaosap–Wabowden (2) 147 6 42 0 4 0

The Bog (3) 117 0 0 43 7 0

Upper North Interlake (4) 53 0 1 0 27 1

Lower North Interlake (5) 48 0 0 0 0 48

2138 Conserv Genet (2010) 11:2131–2143

123

to be accurately reflecting the effects of non-random

mating where the immigration and admixture is likely

limited.

Fig. 3 A network showing the spatial genetic relationship among

caribou clusters (K = 5; Fig. 1d). Significant relationships between

geographic and graph distance (isolation-by-graph distance) are

presented in grey. Edges which are longer than expected geneti-

cally than predicted by geographic distance, indicating high gene

flow, are ‘extended’ (solid lines) while those that have less gene

flow than expected from geographic distance are ‘compressed’

(dashed lines)

Table 4 Comparison of cross-assigned individuals from IBC determined genetic clusters with two frequency-based assignment tests utilizing

IBC spatial genetic clusters as ‘‘a priori’’ defined populations

Individual ID Cluster IBC (S) Ranalla and Mountain (RM)a Paetkau (P)b Assignment

agreementAssign q Assign UNI LNI Bog Kiss S–W Assign UNI LNI Bog Kiss S–W

421 Bog UNI 0.936 UNI 7.9c 12 10.2 12.8 15 UNI 7.9d 12 10.3 12.8 14 All

2357 Bog UNI 0.929 UNI 11 17 10.7 14 18 UNI 11 17 10.7 14 18 All

99 Bog UNI 0.908 Bog 11 17 10.7 14 18 Bog 11 16 10.7 14 16 RM and P

2331 Bog UNI 0.886 UNI 7.8 10 9.71 11 13 UNI 7.8 10 9.7 11 13 All

91 Bog UNI 0.882 UNI 9.9 15 10.2 15.8 18 UNI 9.9 15 10.2 16 16 All

137 Bog UNI 0.861 UNI 8.1 13 8.4 10.2 12 UNI 8.1 12 8.5 10.2 11 All

425 Bog UNI 0.818 UNI 11 16 12 13.2 19 UNI 11 15 12 13.3 16 All

2549 Kiss S–W 0.934 S–W 15 19 16.6 11 9.1 S–W 14 14 13.9 11.1 9.1 All

636 Kiss UNI 0.929 UNI 11 17 11.2 13.2 19 UNI 11 16 11.2 13.2 16 All

1966 Kiss S–W 0.873 S–W 16 17 17.7 11.4 11 S–W 15 14 13.5 11.4 11 All

2072 Kiss S–W 0.861 S–W 16 16 14.9 10.8 10 S–W 15 13 12.1 10.8 10 All

681 Kiss UNI 0.843 UNI 9.4 13 12.8 12 12 UNI 9.4 14 12.9 12.1 11 All

2535 Kiss UNI 0.843 UNI 9 12 10.6 10.3 14 UNI 9 13 10.6 10.3 12 All

2482 Kiss S–W 0.829 S–W 15 19 17.3 9.2 9.2 S–W 14 15 12.8 9.9 9.2 All

1974 Kiss S–W 0.821 Kiss 22 26 23.5 14.8 16 Kiss 19 21 17.8 14.8 15 RM and P

185 Kiss S–W 0.812 S–W 17 22 19.5 11.8 11 S–W 16 16 15.4 11.8 11 All

675 Kiss UNI 0.800 Kiss 12 15 13.3 11.4 16 UNI 11 15 11.8 11.4 13 P and S

2366 UNI LNI 0.886 LNI 10 8.8 10.7 10.7 13 LNI 10 8.7 10.7 10.7 13 All

361 UNI Bog 0.824 Bog 13 18 13 17.5 17 Bog 14 16 13 17.7 16 All

1962 S–W Kiss 0.883 Kiss 20 22 18.7 11.9 14 Kiss 19 20 17.5 11.9 14 All

2000 S–W Kiss 0.818 Kiss 15 18 18.3 11.9 13 Kiss 15 17 17.1 11.9 13 All

UNI Upper North Interlake, LNI Lower North Interlake, Bog The Bog, Kiss Kississing–Naaosap–Wabowden and S–W Smoothstone–Wapewekaa Rannala and Mountain 1997, bPaetkau et al. 2004c,d -Log (likelihood)

Fig. 4 Consensus genetic population structure showing: course scale

genetic structure (dashed lines; FST = 0.047) and fine-scale structur-

ing (solid lines; see Table 5 for FST values)

Conserv Genet (2010) 11:2131–2143 2139

123

Discussion

In order to accurately define and characterize the genetic

attributes of a particular caribou herd, it is critical to first

delineate its spatial boundaries. While IBC analyses are

useful to identify genetic structure at varying spatial scales

and characterize genetic boundaries, these methods can

also be used to identify dispersing individuals (Cegelski

et al. 2003; Rueness et al. 2003; Berry et al. 2004) and

resolve incidents of genetic mixing among different clus-

ters/groups (Pritchard et al. 2000; Schaefer and Wilson

2002; Seldin et al. 2007). Analyses such as these afford

researchers the opportunity to more accurately define the

genetic identity of populations for genetic quantification

purposes Table 4.

Discerning the genetic structure of boreal caribou within

our sampling area was challenging. With the exception of

the STRUCTURE and TESS concordance in results, different

clustering solutions were attained by both BAPS and

GENELAND. However, examining these clustering solutions

with the aid of FST and IBD/IBGD metrics, we were able to

find agreement. All clustering solutions did acknowledge a

significant separation regionally between the northern areas

and the southern areas of our sampling area (FST = 0.047)

(Fig. 4). In the northern areas, both GENELAND and STRUC-

TURE/TESS characterized additional sub-structuring between

the Smoothstone–Wapeweka cluster and the Kississing–

Naaosap–Wabowden cluster which had an underlying

FST = 0.03. The inclusion of the Wabowden sampling area

with that of the Kississing–Naaosap sampling area as a

single cluster was not unexpected given the low FST (0.015)

between them (Table 1).There was no indication that this

structuring was a result of spurious clustering resulting

from patterns of IBD/IBDG.

In the southern areas, all IBC algorithms characterized a

single cluster in the LNI area, separating it from the UNI

area (FST = 0.07) (Table 3). Interestingly, UNI and LNI

animals were not previously considered as discrete herds

and are currently managed as one local population (Man-

itoba Conservation 2005) which may have introduced

inaccuracies to critical population demographic data

interpretation (i.e. sex-ratios, parturition rates). Further-

more, the separation of the NI into two discrete clusters

would have remained unidentified using a priori defined

populations in the analyses. As characterized through both

FST and BAPS and GENELAND methods, UNI caribou are

more genetically similar to the BOG (FST = 0.02) than to

its nearest neighbour the LNI. The most obvious reason for

this disconnect is the limited effective dispersal between

the LNI and UNI. The evidence provided by assignment

tests suggest that movement into the UNI from the LNI is

limited and asymmetrical northward as the only migrants

(one) found was located in the UNI. Coupled with this, is

the presence of a high percentage of admixed individuals in

addition to high residency UNI individuals, and migrants.

This is likely implying that the UNI is receiving genetic

contributions from several proximate herds, and is sus-

taining its own genetic identity through breeding in this

area. Considering this, the data are suggestive of a ‘‘sink

habitat’’ in the UNI (Watkinson and Sutherland 1995). The

marginal habitat of the UNI is supported by observational

data that suggest that there is high occurrence of highway

mortality and local harvest in this area, in addition to other

selective factors such as predation. Under these limitations,

the genetic cluster observed here is likely to be supple-

mented by emigration (e.g. density-dependant, rescue

effect) from the BOG and LNI. However, due to the sto-

chastic nature of demography in this area, allele

Table 5 Descriptive statistics (mean ± SD) of each geographic genetic cluster boundary and pairwise FST (below diagonal) and Nei’s genetic

distance (above diagonal)

Genetic cluster n Hexp Hobs A FIS Pair wise FST \ Nei’s genetic distance

Upper North

Interlake

Lower North

Interlake

The

Bog

Kississing–

Naaosap–

Wabowden

Smoothstone–

Wapeweka

Upper North

Interlake

48 0.69 ± 0.04 0.65 ± 0.02 6.9 ± 2.0 0.07 0 0.15 0.10 0.18 0.20

Lower North

Interlake

53 0.63 ± 0.04 0.58 ± 0.02 5.9 ± 1.7 0.05 0.07 0 0.14 0.19 0.22

The Bog 117 0.72 ± 0.03 0.67 ± 0.01 7.5 ± 1.6 0.04 * 0.02 0.06 0 0.13 0.14

Kississing–

Naaosap–

Wabowden

147 0.75 ± 0.02 0.71 ± 0.02 9.3 ± 1.5 0.06 0.05 0.07 0.04 0 0.07

Smoothstone–

Wapeweka

78 0.75 ± 0.02 0.71 ± 0.02 8.7 ± 1.4 0.06 0.07 0.08 0.04 0.03 0

All FST and FIS values significantly different from 0 (P \ 0.001) except those marked with *

2140 Conserv Genet (2010) 11:2131–2143

123

frequencies are unable to homogenize; limiting the occur-

rence of genetic panmixia with its most likely source the

BOG (Table 5). Recent data from DNA Capture-Mark-

Recapture work supports this theory as the North Interlake

herd is declining in numbers, and the results point to a

higher decline in females of the UNI when compared to the

LNI (Hettinga 2010).

Our results show that there is no significant overall

pattern of IBD or IBGD that would influence the discor-

dance of the clustering solutions determined by each IBC

used here. With this said and given our low sample size

(n = 5), it must be acknowledged that the power to detect a

significant pattern of IBD was low. Nevertheless, the

expansion of this analysis, using the graph theoretic

approach, supported the lack of significant IBD, with the

majority of edge lengths among nodes falling outside the

95% CI for IBGD (Fig. 3). This IBGD method, which

applies a graph theoretic approach, reveals patterns of

association between individual pairs of nodes, rather than

across the matrix as a whole. Therefore, it can reveal subtle

patterns of the effect of geographic distance on the genetic

differentiation among nodes, and can more easily be

compared with genetic clustering analyses. The increased

structuring found in the STRUCTURE/TESS results suggests, at

least for our data set, the inability of both BAPS and

GENELAND to differentiate genetic clusters at low FST val-

ues. BAPS did not characterize structure at FST B 0.03 and

GENELAND did not characterize structure at FST = 0.02.

We were able to delineate geographic boundaries

between each of the genetic clusters by examining the

spatial representation of each individual with proportional

assignment q C 0.80. In doing so, we were able to identify

genetic localities pertaining to a particular cluster as areas

with a high concentration of individuals with assignment to

a single cluster. Once these genetic localities were identi-

fied, all individuals within the area, regardless of assign-

ment, were assigned to this locality and subsequently used

for descriptive genetic and frequency-based assignment

tests. Comparison of the proportional assignment results

obtained from IBC analyses and the methods described by

Rannala and Mountain (1997) and Paetkau et al. (1995,

2004), showed high concordance of individual cluster

assignment when using q values above 0.80. Arbitrary

lower limit thresholds of q values in the range of 70–90%

ancestry are often used (Manel et al. 2002; Cegelski et al.

2003; Rueness et al. 2003) to identify individuals belong-

ing to genetic clusters; however the observed concordance

between Bayesian and frequency-based analyses validates

the 80% lower threshold used for this study. This validation

step can also be applied to the establishment of specific

thresholds of ‘‘core’’ individuals for other studies.

As allele frequency-based assignment tests would likely

produce individual assignment discordance to the IBC

assignment if our genetic clusters were erroneously delin-

eated, the fact that both of these methods were relatively

concordant provides a validation of each genetic cluster as

a discrete genetic unit. It should be noted here that the clear

identification of genetic clusters in this study is likely due

to the landscape characteristics of our study area. The

results may not be as clear in other parts of the overall

range (Thomas and Gray 2002) where animals present a

more contiguous distribution or demonstrate seasonal shifts

in distribution (Latch and Rhodes 2006).

Our comparison of descriptive statistics derived from

the pre-defined populations and IBC-based genetic clus-

ters, did not differ in the Bog and Smoothstone–

Wapeweka sampling areas as a result of genetic and

geographic concordance. There was no concordance

between surveyed area and genetic cluster identification

in the North Interlake area and subsequent the high FIS

value determined using all samples within the region

indicated a Wahlund effect which was later verified by

IBC analyses. This finding is significant to management

and recovery planning of boreal caribou in this area, as

characterization of both migrants and admixture events

using results from both IBC and assignment tests showed

that the LNI is likely isolated to immigration from all

proximate herds. We did not observe any cross-assigned

individuals in this genetic locality and all individuals here

were assigned to this cluster (q C 0.80) with no admixed

individuals observed. We did observe emigration events

away from this area into the UNI area, supporting

asymmetrical emigration as another likely contributor to

the significant genetic differentiation of this genetic

locality. As a result, the significant FIS value of 0.05 is

likely due to the effects of genetic drift or non-random

mating in this area.

It is clear from our analyses that the use of several IBC

techniques, IBD/IBGD analyses and comparative assign-

ment testing to delineate genetic areas can identify struc-

turing events that would not be characterized using pre-

defined units. Without IBC analyses, it would not have

been possible to determine spatial boundaries of the two

discrete genetic clusters of the North Interlake and the

relatively low structuring of caribou occurring over long

distances between Saskatchewan and the northern sampling

area of Manitoba. Although the limitations of specifically

using pre-defined populations has been recognized in the

literature (Mank and Avise 2004; Latch and Rhodes 2006),

these methods are still being used and their results continue

to be used to support research planning (Boulet et al. 2007).

We are not criticizing the importance of indirect estimates

of population structure, but rather we recommend that they

be used post hoc with estimates obtained from individually

based genetic clustering algorithms. Furthermore, it is

important to note that a complete depiction of population

Conserv Genet (2010) 11:2131–2143 2141

123

structure is only possible when sampling is done over the

entire range.

The results of this paper outline an analytical framework

for the integration of several IBC analyses, in association

with IBD/IBGD analyses, assignment testing and indirect

methods to provide an effective means to delineate and

characterize genetic populations for a proper assessment of

genetic diversity and connectivity. It is hoped that our results

will be useful for future management of boreal caribou in

this area and that our strategy to define genetic structure

amid the analytical discordance of several IBC algorithms

will aid other researchers examining other species.

Acknowledgments Financial support for this project was provided

in part by NSERC to PJW and an NSERC Graduate Scholarship to

MCB, Species at Risk Recovery Fund, Parks Canada, Manitoba

Department of Conservation, Manitoba Hydro, Saskatchewan Envi-

ronment and the Prince Albert Model Forest. We would also like to

express our appreciation to all those involved with winter field col-

lections, volunteering and technical support from the University of

Manitoba and Trent University.

References

Arlt M, Manseau M (2010) Changes in caribou distribution and landcover

in and around Prince Albert National Park: different management

strategies and different landscapes. Rangifer (in press)

Arsenault A (2003) Status and conservation management framework

for woodland caribou in Saskatchewan. Saskatchewan Environ-

ment, Fish and Wildlife Technical Report 2003-03

Arsenault A, Manseau M (2010) Land management strategies for the

recovery of boreal woodland caribou in central Saskatchewan.

Rangifer (in press)

Ball MC, Pither R, Manseau M et al (2007) Characterization of target

nuclear DNA from faeces reduces technical issues associated

with the assumptions of low-quality and quantity template.

Conserv Genet 8:577–586

Berry O, Tocher MD, Sarre SD (2004) Can assignment tests measure

dispersal? Mol Ecol 13:551–561

Bishop MD, Kappes SM, Keele JW et al (1994) A genetic-linkage

map for cattle. Genetics 136:619–639

Boulet M, Couturier S, Cote SD, Otto RD, Bernatchez L (2007)

Integrative use of spatial, genetic and demographic analyses for

investigating genetic connectivity between migratory, montane,

and resident caribou herds. Mol Ecol 16:4223–4240

Cegelski CC, Waits LP, Anderson NJ (2003) Assessing population

structure and gene flow in Montana wolverines (Gulo gulo) using

assignment-based approaches. Mol Ecol 12:2907–2918

Chen C, Durand E, Forbes F, Francois O (2007) Bayesian clustering

algorithms ascertaining spatial population structure: a new

computer program and a comparison study. Mol Ecol Notes

7:747–756

Chessel D, Dufour A-B, Thioulouse J (2004) The ade4 package-I: one

table methods. R News 4:5–10

Corander J, Marttinen P (2006) Bayesian identification of admixture

events using multilocus molecular markers. Mol Ecol 15:2833–

2843

Corander J, Waldmann P, Marttinen P, Sillanpaa MJ (2004) BAPS 2:

enhanced possibilities for the analysis of genetic population

structure. Bioinformatics 20:2363–2369

Corander J, Marttinen P, Mantyniemi S (2006) A Bayesian method

for identification of stock mixtures from molecular marker data.

Fish Bull 104:550–558

Cronin MA (2006) A proposal to eliminate redundant terminology for

intra-species groups. Wildl Soc Bull 34:237–241

Cronin MA, MacNeil MD, Patton JC (2005) Variation in mitochon-

drial DNA and microsatellite DNA in caribou (Rangifertarandus) in North America. J Mammal 86:495–505

Csardi G, Nepusz T (2006) The igraph software package for complex

network research. InterJournal, Complex Systems 1695. http://

igraph.sf.net

Dyer RJ (2007) The evolution of genetic topologies. Theor Popul Biol

71:71–79

Dyer RJ (2009) Genetic studio: a suite of programs from spatial

analysis of genetic-marker data. Mol Ecol Resour 9:110–113

Dyer RJ, Nason JD (2004) Population graphs: the graph theoretic

shape of genetic structure. Mol Ecol 13:1713–1727

Environment Canada (2008) Scientific Review for the Identification

of critical habitat for woodland caribou (Rangifer taranduscaribou), Boreal Population, in Canada. August 2008. Environ-

ment Canada, Ottawa

Evanno G, Regnaut S, Goudet J (2005) Detecting the number of

clusters of individuals using the software STRUCTURE: a simula-

tion study. Mol Ecol 14:2611–2620

Falush D, Stephens M, Pritchard JK (2003) Inference of population

structure using multilocus genotype data: linked loci and

correlated allele frequencies. Genetics 164:1567–1587

Fedy BC, Martin K, Ritland C, Young J (2008) Genetic and

ecological data provided incongruent interpretations of popula-

tion structure and dispersal in naturally subdivided populations

of white-tailed ptarmigan (Lagopus leucura). Mol Ecol 17:1905–

1917

Francois O, Ancelet S, Guillot G (2006) Bayesian clustering using

hidden Markov random fields in spatial population genetics.

Genetics 174:805–816

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? J Appl Ecol 46:493–505

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

graph theory to landscape genetics. Evol Appl 1:620–630

Goudet J (1995) FSTAT (Version 1.2): a computer program to

calculate F-statistics. J Hered 86:485–486

Guillot G (2008) Inference of structure in subdivided populations at

low levels of genetic differentiation. The correlated allele

frequencies model revisited. Bioinformatics 24:2222–2228

Guillot G, Estoup A, Mortier F, Cosso JF (2005a) A spatial statistical

model for landscape genetics. Genetics 170:1261–1280

Guillot G, Mortier F, Estoup A (2005b) Geneland: a computer

package for landscape genetics. Mol Ecol Notes 5:712–715

Guillot G, Kan-King-Yu D, Michelin J, Huet P (2006) Inference of a

hidden spatial tessellation from multivariate data: application to

the delineation of homogeneous regions in an agricultural field. J

R Stat Soc Ser C Appl Stat 55:407–430

Guillot G, Santos F, Estoup A (2008) Analysing georeferenced

population genetics data with Geneland: a new algorithm to deal

with null alleles and a friendly graphical user interface.

Bioinformatics 24:1406–1407

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

in spatial genetics. Mol Ecol 18:4734–4756

Hettinga P (2010) Use of sampled genetic information from woodland

caribou fecal pellets to estimate population demographics.

Dissertation, University of Manitoba

Ihaka R, Gentleman R (1996) R: a language for data analysis and

graphics. J Comput Graph Stat 5:299–314

2142 Conserv Genet (2010) 11:2131–2143

123

Kalinowski ST, Taper ML, Marshall TC (2007) Revising how the

computer program CERVUS accommodates genotyping error

increases success in paternity assignment. Mol Ecol 16:1006–

1099

Kim KS, Cano-Rios P, Sappington TW (2006) Using genetic markers

and population assignment techniques to infer origin of boll

weevils (Coleoptera : Curculionidae) unexpectedly captured near

an eradication zone in Mexico. Environ Entomol 35:813–826

Kraaijeveld-Smit FJL, Beebee TJC, Griffiths RA, Moore RD, Schley

L (2005) Low gene flow but high genetic diversity in the

threatened Mallorcan midwife toad Alytes muletensis. Mol Ecol

14:3307–3315

Latch EK, Rhodes E (2006) Evidence for bias in estimates of local

genetic structure due to sampling scheme. Anim Conserv 9:308–

315

Latch EK, Dharmarajan G, Glaubitz JC, Rhodes OE (2006) Relative

performance of Bayesian clustering software for inferring

population substructure and individual assignment at low levels

of population differentiation. Conserv Genet 7:295–302

MacAvoy ES, McGibbon LM, Sainsbury JP et al (2007) Genetic

variation in island populations of tuatara (Sphenodon spp)

inferred from microsatellite markers. Conserv Genet 8:3053

Manel S, Berthier P, Luikart G (2002) Detecting wildlife poaching:

identifying the origin of individuals with Bayesian assignment

tests and multilocus genotypes. Conserv Biol 16:650–659

Manel S, Berthoud F, Bellemain E, Gaudeul M, Luikart G, Swenson

JE, Waits LP, Taberlet P (2007) A new individual-based spatial

approach for identifying genetic discontinuities in natural

populations. Mol Ecol 16:2031–2043

Manitoba Conservation (2005) Manitoba’s conservation and recovery

strategy for woodland caribou (Rangifer tarandus caribou).

Manitoba Conservation—Wildlife Branch, Winnipeg

Mank JE, Avise JC (2004) Individual organisms as units of analysis:

Bayesian-clustering alternatives in population genetics. Genet

Res 84:135–143

McDevitt AD, Mariani S, Hebblewhite M, Decesare NJ, Morgantini

L, Seip D, Weckworth BV, Musiani M (2009) Survival in the

Rockies of an endangered hybrid swarm from diverged caribou

(Rangifer tarandus) lineages. Mol Ecol 18(4):665–679

Mosnier A, Ouellet JP, Sirois L, Fournier N (2003) Habitat selection

and home-range dynamics of the Gaspe caribou: a hierarchical

analysis. Can J Zool 81:1174–1184

Paetkau D, Calvert W, Stirling I, Strobeck C (1995) Microsatellite

analysis of population-structure in Canadian polar bears. Mol

Ecol 4:347–354

Paetkau D, Amstrup SC, Born EW, Calvert W, Derocher A, Garner G,

Messier F, Stirling I, Taylor M, Wigg O, Strobeck C (1999)

Genetic structure of the world’s polar bear populations. Mol Ecol

8:1571–1584

Paetkau D, Slade R, Burden M, Estoup A (2004) Genetic assignment

methods for the direct, real-time estimation of migration rate: a

simulation-based exploration of accuracy and power. Mol Ecol

13:55–65

Piry S, Alapetite A, Cornuet JM et al (2004) GENECLASS2: a

software for genetic assignment and first-generation migrant

detection. J Hered 95:536–539

Pritchard JK, Stephens M, Donnelly P (2000) Inference of population

structure using multilocus genotype data. Genetics 155:945–959

Rannala B, Mountain JL (1997) Detecting immigration by using

multilocus genotypes. Proc Natl Acad Sci USA 94:9197–9201

Rettie WJ, Messier F (2001) Range use and movement rates of

woodland caribou in Saskatchewan. Can J Zool 79:1933–1940

Rice WR (1989) Analyzing tables of statistical tests. Evolution

43:223–225

Rowe G, Beebee TJC (2007) Defining population boundaries: use of

three Bayesian approaches with microsatellite data from British

natterjack toads (Bufo calamita). Mol Ecol 16:785–796

Rueness EK, Jorde PE, Hellborg L et al (2003) Cryptic population

structure in a large, mobile mammalian predator: the Scandina-

vian lynx. Mol Ecol 12:2623–2633

Schaefer JA, Wilson CC (2002) The fuzzy structure of populations.

Can J Zool 80:2235–2241

Schaefer JA, Veitch AM, Harrington FH et al (2001) Fuzzy structure

and spatial dynamics of a declining woodland caribou popula-

tion. Oecologia 126:507–514

Seldin MF, Tian C, Shigeta R et al (2007) Argentine population

genetic structure: large variance in Amerindian contribution. Am

J Phys Anthropol 132:455–462

Thomas DC, Gray DR (2002) Update COSEWIC status report on the

woodland caribou Rangifer tarandus caribou in Canada, in

COSEWIC assessment and update status report on the Woodland

Caribou Rangifer tarandus caribou in Canada. In: Committee on

the Status of Endangered Wildlife in Canada, pp 1–98

Watkinson AR, Sutherland WJ (1995) Sources, sinks and pseudo-

sinks. J Anim Ecol 64:126–130

Wilberg MJ, Dreher BP (2004) GENECAP: a program for analysis of

multilocus genotype data for non-invasive sampling and capture-

recapture population estimation. Mol Ecol Notes 4:783–785

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

microsatellite loci in caribou Rangifer tarandus, and their use in

other artiodactyls. Mol Ecol 6:697–699

Zamudio KR, Wieczorek AM (2007) Fine-scale spatial genetic

structure and dispersal among spotted salamander (Ambystomamaculatum) breeding populations. Mol Ecol 16:257–274

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