Figures
Abstract
Evaluating the permeability of potential barriers to movement, dispersal and gene exchanges can help describe spreading patterns of wildlife diseases. Here, we used landscape genetics methods to assess the genetic structure of the striped skunk (Mephitis mephitis), which is a frequent vector of rabies, a lethal zoonosis of great concern for public health. Our main objective was to identify landscape elements shaping the genetic structure of this species in Southern Québec, Canada, in an area where the raccoon rabies variant has been detected. We hypothesised that geographic distance and landscape barriers, such as highways and major rivers, would modulate genetic structure. We genotyped a total of 289 individuals sampled across a large area (22,000 km2) at nice microsatellite loci. Genetic structure analyses identified a single genetic cluster in the study area. Major rivers and highways, however, influenced the genetic relatedness among sampled individuals. Sex-specific analyses revealed that rivers significantly limited dispersal only for females while highways only had marginal effects. Rivers and highways did not significantly affect male dispersal. These results support the contention that female skunks are more philopatric than males. Overall, our results suggest that the effects of major rivers and highways on dispersal are sex-specific and rather weak and are thus unlikely to prevent the spread of rabies within and among striped skunk populations.
Citation: Talbot B, Garant D, Rioux Paquette S, Mainguy J, Pelletier F (2012) Lack of Genetic Structure and Female-Specific Effect of Dispersal Barriers in a Rabies Vector, the Striped Skunk (Mephitis mephitis). PLoS ONE 7(11): e49736. https://doi.org/10.1371/journal.pone.0049736
Editor: Paul Sunnucks, Monash University, Australia
Received: June 15, 2012; Accepted: October 11, 2012; Published: November 14, 2012
Copyright: © 2012 Talbot et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by a team research grant from the Fonds de recherche du Québec - Nature et technologies (grant number: 138658) to Daniel Fortin, DG, JM and FP and by the Canada Research Chair in Evolutionary Demography and Conservation of Prof. FP from Canada Foundation for Innovation (grant number: 211722). BT was funded by scholarships from Natural Sciences and Engineering Research Council of Canada (scholarship number: 125498295) and FRQNT (scholarship number: 138987). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Studies investigating patterns of gene flow and dispersal among and within populations can help understand evolution on ecological time scales (reviewed in [1], [2]). Gene flow and the resulting genetic structure of populations are typically shaped by landscape heterogeneity (reviewed in [3]–[5]). For example, in addition to geographical distance, landscape barriers to animal movement, whether of natural or anthropogenic origin, have been shown to often cause a reduction in gene flow among populations [6]–[9]. Landscape genetics approaches have been developed to detect reductions in gene flow caused by these elements at different spatial scales [10]–[12]. They offer a robust framework to improve our understanding of the evolutionary processes affecting a species in a given area, as well as insights into habitat preferences, sex-biased dispersal and community association [13]–[16].
Recently, landscape genetic approaches have been increasingly used for applied purposes [17]–[20], especially in the study and management of wildlife diseases [21]–[23]. As several diseases are propagated by wild animals, identifying landscape elements that can limit or prevent gene flow can be used to better target intervention actions [13], [24], [25] or to make predictions about the most probable paths for disease spread [22], [26]. For instance, in order to prevent further raccoon rabies in Ontario, the Ontario Ministry of Natural Resources and its partners have designated oral rabies vaccination (ORV) zones on the New York/Ontario international border along the St. Lawrence River [27] and along the Niagara River [28], where raccoons tested positive for rabies were euthanized. The efficiency of these ORV zones can be improved if integrated along landscape features that naturally limit disease vector dispersal. Assessing the permeability of landscape elements is therefore important for such applied purposes.
Landscape features and geographic distance may influence gene flow differently for each vector species depending on their ecology, their life-history traits, and the scale at which the system is studied. Generally, geographic distance has a stronger effect on gene flow in species with a limited dispersal potential compared to those able to disperse over great distances (e.g., [29]). For example, pairwise geographic distance explained more of the observed genetic variation between sampling sites for the American mink (8%, Neovison vison; [30]), than for American martens (0.3%, Martes americana; [31]), the latter exhibiting generally greater average dispersal distances than the former [32]. Barriers to gene flow will also have differential permeability depending on the capacity of a species to cross or bypass them and it is thus important to characterize broad and fine scale genetic structure of each specific disease vector independently.
A fine-scale genetic structure can be detected using patterns of genetic relatedness among individuals (e.g., [33]–[35]). Such fine-scale structure analyses are helpful to assess population kin structure [36] and have been shown to be linked with the rate of inbreeding (e.g. [37]). In addition, it allows detecting landscape features that may have sex-specific effects. Male-biased dispersal and female philopatry have been suggested as possible mechanisms for inbreeding avoidance in mammals because the direction of dispersal bias is tightly linked to the mating system (reviewed in [38]) and most mammals are polygynous with mate defence (reviewed in [39]). In carnivores for instance, evidence shows that most species exhibit male-biased dispersal, as previously documented in American black bears (Ursus americanus, [40]), wolverines (Gulo gulo, [41]) and American mink [30].
The main objective of this study was to assess the extent of genetic structuring in striped skunks (Mephitis mephitis: Mephitidae) in an area where cases of the raccoon rabies variant were found in skunks and raccoons [42] in order to evaluate the permeability of landscape barriers (highway and rivers) to gene flow. The striped skunk is a medium-sized nocturnal North American mesocarnivore [43], which is often abundant in cities [44], [45] and is also abundant in farmsteads and cropfield edges where prey items are abundant [46], [47]. Home range size in this species is generally around 3 km2 [48]. It has been suggested that striped skunks in their native North American range originated from a common ancestor in the Texas-Mexico region and expanded north into several clades, one of which is present in northeastern North America, where our study took place [49]. Striped skunks are known to host several strains of rabies, a zoonotic disease of great concern for public health in North America [50], including the raccoon (Procyon lotor) variant of the rabies virus [51]. This rabies variant has been involved in one of the most important outbreaks of wildlife rabies in the United States [52]. In the last decade, rabid skunks have been found in Ontario and Québec, including in our study area [42]. Despite the close proximity of striped skunks to human infrastructure and the zoonotic risks involved, information is still lacking about the spatial organization and movement of this species. Identifying the determinants of the genetic structure in this species could provide insights into the potential of disease spread among striped skunk populations, which could then be used to predict the extent and most probable locations of epidemiologic outbreaks.
Specifically, we tested the hypothesis that geographic distance and landscape barriers (rivers and highways) are drivers of genetic structure in our study area. The purpose of our study is to determine if some landscape barriers can be targeted to improve efficiency of ORV zones. We hypothesized that isolation by distance (IBD) in the population would be detected, because daily movement rates of skunks are thought to be small (e.g., 1.25 km, [53]) and dispersal is typically <3 km ([54], but see [55] for a documented distance of 119 km in a marked individual). In comparison with average distances traveled by similar-size mesocarnivores, skunks can be regarded as relatively sedentary [56]. We also predicted that large rivers (physical barriers) and roads (behavioural avoidance and sources of mortality) would limit gene flow in skunks, as previously reported in other mesocarnivores (raccoons [24], [35] and badgers, Meles meles [10]). Finally, we hypothesized that males are more likely to disperse than females and thus, that sex-biased dispersal could be detected indirectly through differences in observed patterns of pairwise genetic relatedness in each sex. This study uses current genetic methods to provide useful information on the dispersal behavior in a lesser known rabies vector, the striped skunk.
Materials and Methods
Ethics Statement
All field operations were conducted by the personnel of the Ministère des Ressources Naturelles et de la Faune (MRNF) Québec government agency and its partners in a disease management perspective in areas of Québec where rabies was detected. Animal trapping and handling methods complied with the Agreement on International Humane Trapping Standards (Government of Canada, 1998). Biopsy sampling protocol was approved by the Canadian Council on Animal Care (protocol numbers are CPA-FAUNE 2009–12 and CPA-FAUNE 2010–29). Captured live animals were anesthetised by trained professionals under the supervision of a veterinarian.
Study Area and Sample Collection
Skunks were sampled over two years in a fragmented agricultural landscape in Southern Québec, Canada (45° 23′ N, 72° 43′ W), in an area of approximately 22 000 km2 (Figure 1). Tissue samples used in this study were collected by the MRNF and its partner agencies in 2009 and 2010 during the surveillance and control activities conducted against the raccoon variant of rabies, such as the recovery of roadkills for rabies testing and vaccination operations [42]. Each time an animal was recovered, its location was recorded using a handheld global positioning system. A skin biopsy was collected from the ear with a 2-mm punch for subsequent genetic analyses. Samples were stored in 95% ethanol until DNA extraction. Because many skunks were sampled over small areas due to control operations conducted for a specific zone (≈ 20 individuals/100 km2 zones), a random subset of individuals (up to 3 individuals/25 km2) was selected for each year to generate a balanced sample over the study area (Figure 1).
The location of sampled striped skunk (Mephitis mephitis) in this study is shown by a black star for 2009 (n = 148) sampling year and a grey circle for 2010 (n = 141) sampling year. Grey shapes are major bodies of water such as rivers and lakes in and around the study area. Thin black lines represent major highways found in the study area. St Lawrence River and the 5 selected rivers for our analyses (a: Châteauguay, b: Richelieu, c: Yamaska, d: Magog, e: Saint-François) are identified on the map. The city of Montréal is also identified.
Landscape Characterization
Major rivers and highways were mapped using aerial photographs of Southern Québec provided by the MRNF (Figure 1) with ArcGIS 9.2 (Environmental Systems Research Institute, Redlands, CA, USA). Major rivers were determined based on their mean water flow (m/sec) [57]. We selected rivers with a water flow greater than 0.6 m/s because they are known to maintain substantial discharge throughout the year, potentially limiting gene flow. Based on this characteristic, four rivers were retained for our analyses (rivers a, c, d and e in Figure 1). The Richelieu River (river b, Figure 1) was also considered because it was previously found to affect the genetic relatedness structure of female raccoons in this area [35]. Lakes in the course of each river were also taken into account. Highways were selected based on the definition provided by the Ministère des Transports du Québec, which defines major highways as being roads with high speed limit (100 km/h) and without crossroads (Figure 1; note that the highways considered here have no veterinary fences). To determine the number of highways and rivers separating each pair of individuals, we calculated how many times the shortest geographical distance between each pair of samples intersected with a section of a river or with a section of a highway using Hawth’s Tools (an extension of ArcGIS 9).
DNA Extraction and Genetic Analyses
DNA extraction was conducted using a salting out protocol as described in Chambers and Garant [58]. Microsatellite polymorphism was then analyzed at the following nine polymorphic loci developed specifically for striped skunks: Meph42-15, Meph22-16, Meph22-70, Meph42-73, Meme84, Meph22-14, Meme15, Meme75, Meph22-19 ([59], [60]; see Table S1 for details). DNA amplification was performed using GeneAmp System 9700 thermocyclers (Applied Biosystems, Foster, CA, USA). Further details on polymerase chain reaction (PCR) reagent volume, concentrations and amplification conditions for each locus are supplied in Tables S2 and S3. Genotyping was performed using an AB 3130 DNA sequencer (Applied Biosystems). For each sample, a volume of 1 µL of PCR product was added to 8.9 µL of Hi-Di Formamide and 0.1 µL of Genescan Liz 600 (Applied Biosystems). Allele size was scored using GeneMapper 4.0 (Applied Biosystems).
Molecular sexing was conducted using a protocol modified from Shaw et al. [61]. Amplification was performed in a 25 µl solution containing final concentrations of 0.3 µM of each primer (LGL331 and LGL335), 0.4 mM of nucleotide triphosphate, 0.9 mM of MgCl2, 2.5 µg of bovine serum albumin, 1x PCR buffer (50 mM KCl, 20 mM Tris HCl, pH 8.4), 1 unit of Taq polymerase (Life Technologies) and 25 ng of DNA. PCR products were visualized after a 10 minutes electrophoresis migration at 300 volts on 1% agarose gels in a sodium borate buffer (10 mM NaOH pH 8.5 adjusted with H3BO3) with ethidium bromide. A 100-bp DNA ladder (Life technologies) was used each time to standardize the migration.
Despite the high reliability of the method, sex could not be determined for 19% of the selected samples, possibly due to degraded DNA (some animals died several days before their recovery). Consequently, we had a smaller sample size of known-sex individuals (104 females and 129 males).
Microsatellite Polymorphism
We tested all loci for departure from Hardy-Weinberg equilibrium and calculated inbreeding coefficient (Fis) for each locus using the software Genepop 4.0 [62]. We also tested for linkage disequilibrium using the software Fstat 2.9.3.2 [63]. Finally, we used Cervus 3.0 [64], [65] to calculate the number of alleles, observed and expected heterozygosities, and to test for the presence of null alleles. Significance of these tests was assessed after Bonferroni correction [66].
Population Genetic Structure
To estimate the most likely number of genetic clusters in the sample, we used the Bayesian clustering software Structure 2.3.3 [67]. We performed the analyses using a model with admixture, separate admixture coefficients (α) for each genetic cluster, allele frequencies correlated (using allele frequency prior (λ) = 1.0) among genetic clusters, and without using prior information on sampling location. For each value of K (K being the number of genetic clusters considered: from 1 to 5), we ran 10 independent models with 500 000 iterations, plus a burn-in period of 100 000 iterations. Means of the ln-probabilities of all independent runs for a given K were then calculated. We ran these analyses for all individuals together and then for each sex separately.
Relatedness Genetic Structure
To estimate the effects of landscape features on pairwise genetic relatedness among individuals, we used Multiple Regressions on distance Matrices [68]. This method is an extension of Mantel tests [69] and partial Mantel tests [70] with more than two explanatory matrices, and we used permutations to calculate type 1 error probability values [71]. These analyses account for pseudo-replication in pairwise individual relationships and thus allow regression on distance matrices. We performed Multiple Regressions on distance Matrices analyses using the package Ecodist V1.2.2 [72] in R 2.11.1 (R Foundation for Statistical Computing, Vienna, Austria), with 10 000 permutations and Pearson’s correlation coefficient. Genetic distances among individuals were calculated using the pairwise genetic relatedness (rxy) estimator of Wang [73], with the program Spagedi V1.3 [74]. Relatedness estimators are measures of genetic similarity between pairs of individuals. In the analyses, we used 1- rxy to generate a pairwise genetic distance metric, as in Côté et al. [35]. Geographic distance (km) between each pair of individuals was measured using Hawth’s Tools to account for a possible pattern of IBD in our study system. Matrices of geographic distances (km), number of major rivers, number of highways and year of sampling (to assess temporal stability) between each pair of individuals were used both in separate models (univariate analyses) and in a single model where total variance explained was parted among the four factors (multivariate analyses). We also ran the analyses for each sex separately to test for differences between sexes. In each case, we applied jackknife resampling, removing one individual from each replicate, to estimate the standard error of model coefficients following Efron and Tibshirani [75]. This allowed the computation of 95% confidence intervals. In addition, it allowed testing whether slopes estimated for each sex in multivariate models were significantly different or not, using Student’s t test.
Results
Microsatellite Polymorphism
A total of 289 skunks were genotyped at nine microsatellites. Number of alleles identified per locus varied between 7.0 and 19.0 (mean = 11.9) and expected heterozygosity varied between 0.615 and 0.899 (mean = 0.807; see Table 1). We found neither a significant heterozygote deficit nor excess in our sample, and no evidence suggesting the presence of null alleles (lower than 5%, Table 1) or linkage disequilibrium (results not shown).
Population Genetic Structure
The results obtained with Structure indicated that the most likely number of genetic clusters was K = 1 (Figure 2), suggesting that striped skunks found in our study area were not subdivided in many distinct clusters. When sexes were analysed separately, Structure also provided evidence for K = 1 (results not shown), suggesting that the genetic structure within each sex was similar to that detected using the whole sample.
Mean and standard deviation of estimated ln-probabilities of data are presented for genetic clusters K = 1 to 5, calculated with the Structure software for striped skunks (Mephitis mephitis) in the study area in Southern Québec, Canada, in 2009 and 2010.
Relatedness Genetic Structure
Univariate regression analyses showed significant relationships between geographic distance, number of major rivers, number of highways and genetic distance among individuals (models 1, 2 and 3 in Table 2). However, multivariate regression analysis only recovered a significant relationship for rivers and a marginally non-significant relationship for highways, whereas the effect of geographic distance was no longer significant (model 5 in Table 2). This loss of significance observed here is likely due to collinearity between geographic distance and barrier variables (r = 0.705 with number of rivers and r = 0.451 with number of highways). No significant effect of the year of sampling was detected (models 4 and 5 in Table 2).
In the analyses performed for each sex separately, differences in the association between pairwise genetic relatedness and landscape features were observed between sexes. Univariate regression analyses (models 1, 2 and 3 in Table 3) again showed positive and significant relationships between geographic distance, number of major rivers, number of highways and genetic distance for females, but showed only non-significant relationships for males. In females, multivariate regression analyses also showed a significant and positive effect of major rivers and a marginally non-significant and positive effect of highways on genetic distance among individuals, but no effect of geographic distance (model 5 in Table 3). In males, multivariate regression analyses also showed that none of the landscape features considered constituted a significant predictor of the genetic distance among males (model 5 in Table 3). Despite a 20-fold difference, slopes were not statistically different between sexes (Table 3), which is a reflection of the large standard errors for males. Again, no significant effect of the year of sampling was detected (models 4 and 5 in Table 3).
Discussion
The goal of our study was to investigate the population genetic structure of the striped skunk in a heterogeneous landscape in Southern Québec, Canada. We hypothesised that geographic distance and landscape barriers, such as highways and major rivers, would create genetic structure for this species in our study area, and that structure would be greater for females than for males. Our results suggest the presence of a single genetic cluster at the spatial scale studied (22 000 km2). Using a pairwise genetic relatedness estimator at the individual level, however, we found that landscape features, such as highways and major rivers, influenced the structure of genetic relatedness between individuals and especially females. Our results thus support the contention that males disperse more than females in this species and would likely be more active vectors of rabies propagation.
Population Genetic Structure
Analyses performed with Structure suggested a single genetic cluster, indicating a low level of genetic differentiation over our study area. Similar results were obtained in two studies in the USA on this species. No genetic structure was found among groups of skunks in a 61.6 km2 study area in Texas [76], and a more recent study also failed to find significant genetic population structure over a larger area encompassing parts of North Dakota to Oklahoma (1,250 km2, [77]). Altogether, our results and previous evidence suggest a relatively high level of gene flow among groups of skunks which prevents population differentiation over a large scale. It should also be noted that a lack of genetic structure was reported for raccoon individuals sampled in southern Québec (over the same area as in our study – see [35]), suggesting that admixture among groups of individuals might be a common pattern for these two mesocarnivores in this area.
Relatedness Genetic Structure
At first, univariate regression analyses assessing the effect of geographic distance on pairwise genetic relatedness revealed significant patterns of isolation by distance (lower relatedness among individuals located further apart). However, when major rivers and highways were taken into account in multivariate analyses, the IBD signal was no longer detected. In cases where past or current barriers to gene flow are present in a study area, a relationship between geographic distance and genetic distance might potentially reflect the effect of barriers to gene flow rather than the simple effect of geographic distance (e.g., [58], [78]).
We also found that major rivers and, to some extent, highways affected genetic relatedness of striped skunk individuals sampled in Southern Québec and that this effect was detected in females but not in males. More specifically, female individuals located on opposite sides of these landscape elements were generally less related than individuals located on the same side. A similar effect was detected for raccoons separated by the Richelieu River in this area (river b on Figure 1; [35]). Individual raccoons that were separated by the Richelieu River were more distant genetically than individuals located on the same side and this effect was detected in females, but not in males [35]. Our results are also similar to those reported in a study of a badger population structure in England where the presence of a large river and motorway were shown to influence patterns of relatedness among individuals [10]. In this previous study, however, the possibly differential effect on both sexes was not considered.
Previous evidence, obtained from studies using live trapping and radio-tracking methods, suggested a greater dispersal in male than in female striped skunks ([55], but see also [54], [56]). Furthermore, in their study of skunks in Texas, Hansen et al. [76] reported that males within a given sampled group had lower average genetic similarity than females of the same group, also suggesting greater dispersal for males. Here, we found that genetic distance among males was not affected by the presence of highways and major rivers, also suggesting a stronger dispersal in males. Stronger male dispersal and female philopatry are thus also consistent with the patterns observed in the majority of mammals (reviewed in [38], [39]).
Studies of habitat selection have shown that skunks are often found near roads. For instance, Frey and Conover [48] showed that skunks tend to include roadsides in their home range more often than by chance and Hwang et al. [79] found that den sites were often closer to roads than to random sites. These studies suggest that striped skunks may often attempt to cross roads. In contrast to this presumption, our results suggest a marginally positive effect of the number of roads on female genetic distance. One possible explanation for these seemingly opposing results is that the study by Frey and Conover [48] was conducted in an area were most roads are secondary roads (Bear River Migratory Bird Refuge, United States) while here only highways (higher driving speed limit and more driving lanes) were considered in our analysis. Thus, while skunks could use areas along small roads to increase their foraging opportunities, they probably avoid crossing large highways. While this still remains to be properly tested, we suggest the existence of a threshold in the permeability of the roads under which striped skunks may attempt to cross them (e.g., width, noise, traffic flow).
Our study is the first to characterize fine-scale genetic differentiation and population structure in striped skunks over a large-spatial scale. Although we found no evidence of strong genetic structuring over the scale of our study area, our results indicate reduced dispersal across rivers and highways and suggest that the effects of these barriers mostly affect females. Our study also provides evidence for male-biased dispersal supporting the hypothesis of sex differences in dispersal in this species and thus, our results offer additional insights into the dispersal behaviour of the striped skunk.
From a disease management perspective, our results indicate that major roads and rivers are insufficient to halt or slow the spread of rabies in striped skunks. Using oral rabies vaccination over the landscape, regardless of the presence of barriers, is thus essential in an attempt to eradicate rabies.
Supporting Information
Table S1.
Marker name, GenBank accession number, primer sequences, fluorescence dye used (on the forward primer), repetition pattern and lengths of alleles for the nine microsatellite loci used in this study in Southern Québec, Canada, in 2009 and 2010 (1Dragoo et al. [59] and 2Munguia-Vega et al. [60]).
https://doi.org/10.1371/journal.pone.0049736.s001
(DOC)
Table S2.
Polymerase chain reaction reagent volume and concentrations (final volume of 10 µL per sample with 10 ng of DNA) for the nine microsatellite loci used in this study in Southern Québec, Canada, in 2009 and 2010 (Modified from Dragoo et al. [59] and Munguia-Vega et al. [60]).
https://doi.org/10.1371/journal.pone.0049736.s002
(DOC)
Table S3.
Amplification conditions for the nine microsatellite loci used in this study in Southern Québec, Canada, in 2009 and 2010 (Modified from Dragoo et al. [59] and Munguia-Vega et al. [60]).
https://doi.org/10.1371/journal.pone.0049736.s003
(DOC)
Acknowledgments
Thanks are given to all students, trainees, research assistants and coordinators who helped in this project. Special thanks are given to Hélène Presseault-Gauvin and Jennifer Chambers for their help with laboratory work. The Centre québécois sur la santé des animaux sauvages (especially Kathleen Brown) and the MRNF (especially Pierre Canac-Marquis and Frédérick Lelièvre) are also thanked for the tissue samples collected and provided for the genetic analyses conducted in this study. Finally, Paul Sunnucks, Eric Vander Wal and two anonymous reviewers offered valuable comments on the manuscript.
Author Contributions
Conceived and designed the experiments: BT DG JM FP. Performed the experiments: BT. Analyzed the data: BT SRP. Contributed reagents/materials/analysis tools: DG JM FP. Wrote the paper: BT.
References
- 1. Carroll SP, Hendry AP, Reznick DN, Fox CW (2007) Evolution on ecological time-scales. Funct Ecol 21: 387–393.
- 2. Garant D, Forde S, Hendry A (2007) The multifarious effects of dispersal and gene flow on contemporary adaptation. Funct Ecol 21: 434–443.
- 3.
Epperson BK (2003) Geographical genetics. New Jersey: Princeton University Press.
- 4. Manel S, Schwartz M, Luikart G, Taberlet P (2003) Landscape genetics: combining landscape ecology and population genetics. Trends Ecol Evol 18: 189–197.
- 5. Storfer A, Murphy MA, Evans JS, Goldberg CS, Robinson S, et al. (2007) Putting the ‘landscape’ in landscape genetics. Heredity 98: 128–142.
- 6. Funk WC, Blouin MS, Corn PS, Maxell BA, Pilliod DS, et al. (2005) Population structure of Columbia spotted frogs (Rana luteiventris) is strongly affected by the landscape. Mol Ecol 14: 483–496.
- 7. Keyghobadi N, Roland J, Strobeck C (2005) Genetic differentiation and gene flow among populations of the alpine butterfly, Parnassius smintheus, vary with landscape connectivity. Mol Ecol 14: 1897–1909.
- 8. Poissant J, Knight TW, Ferguson MM (2005) Nonequilibrium conditions following landscape rearrangement: the relative contribution of past and current hydrological landscapes on the genetic structure of a stream-dwelling fish. Mol Ecol 14: 1321–1331.
- 9. Zhu L, Zhan X, Meng T, Zhang S, Wei F (2010) Landscape features influence gene flow as measured by cost-distance and genetic analyses: a case study for giant pandas in the Daxiangling and Xiaoxiangling Mountains. BMC Genet 11: 1–11.
- 10. Frantz AC, Pope LC, Etherington TR, Wilson GJ, Burke T (2010) Using isolation-by-distance-based approaches to assess the barrier effect of linear landscape elements on badger (Meles meles) dispersal. Mol Ecol 19: 1663–1674.
- 11. Guillot G, Mortier F, Estoup A (2005) Geneland: a computer package for landscape genetics. Mol. Ecol. Notes 5: 712–715.
- 12. McRae BH, Beier P (2007) Circuit theory predicts gene flow in plant and animal populations. Proc. Natl. Acad. Sci. 104: 19885–19890.
- 13. Blanchong JA, Samuel MD, Scribner KT, Weckworth BV, Langenberg JA, et al. (2008) Landscape genetics and the spatial distribution of chronic wasting disease. Biol Lett 4: 130–133.
- 14. Porlier M, Bélisle M, Garant D (2009) Non-random distribution of individual genetic diversity along an environmental gradient. Philos Trans R Soc B Biol Sci 364: 1543–1554.
- 15. Goldberg CS, Waits LP (2010) Comparative landscape genetics of two pond-breeding amphibian species in a highly modified agricultural landscape. Mol Ecol 19: 3650–3663.
- 16. Selkoe KA, Watson JR, White C, Horin TB, Iacchei M, et al. (2010) Taking the chaos out of genetic patchiness: seascape genetics reveals ecological and oceanographic drivers of genetic patterns in three temperate reef species. Mol Ecol 19: 3708–3726.
- 17. Banks SC, Lindenmayer DB, Ward SJ, Taylor AC (2005) The effects of habitat fragmentation via forestry plantation establishment on spatial genotypic structure in the small marsupial carnivore, Antechinus agilis. Mol. Ecol. 14: 1667–1680.
- 18. Cushman SA, McKelvey KS, Hayden J, Schwartz MK (2006) Gene flow in complex landscapes: testing multiple hypotheses with causal modeling. Am. Nat. 168: 486–499.
- 19. Epps CW, Wehausen JD, Bleich VC, Torres SG, Brashares JS (2007) Optimizing dispersal and corridor models using landscape genetics. J. Appl. Ecol. 44: 714–724.
- 20. Shirk AJ, Wallin DO, Cushman SA, Rice CG, Warheit KI (2010) Inferring landscape effects on gene flow: a new model selection framework. Mol. Ecol. 19: 3603–3619.
- 21. Archie EA, Luikart G, Ezenwa VO (2009) Infecting epidemiology with genetics: a new frontier in disease ecology. Trends Ecol Evol 24: 21–30.
- 22. Cullingham CI, Merrill EH, Pybus MJ, Bollinger TK, Wilson GA, et al. (2011) Broad and fine-scale genetic analysis of white-tailed deer populations: estimating the relative risk of chronic wasting disease spread. Evol Appl 4: 116–131.
- 23. Russell CA, Real LA, Smith DL (2006) Spatial control of rabies on heterogeneous landscapes. PLoS ONE 1: 1–7.
- 24. Cullingham CI, Kyle CJ, Pond BA, Rees EE, White BN (2009) Differential permeability of rivers to raccoon gene flow corresponds to rabies incidence in Ontario, Canada. Mol Ecol 18: 43–53.
- 25. Rees E, Pond B, Cullingham C, Tinline R, Ball D, et al. (2008) Assessing a landscape barrier using genetic simulation modelling: implications for raccoon rabies management. Prev Vet Med 86: 107–123.
- 26. Rees E, Pond B, Cullingham C, Tinline R, Ball D, et al. (2009) Landscape modelling spatial bottlenecks: implications for raccoon rabies disease spread. Biol Lett 5: 387–390.
- 27. Rosatte R, Donovan D, Allan M, Howes L-A, Silver A, et al. (2001) Emergency response to raccoon rabies introduction into Ontario. J Wildl Dis 37: 265–279.
- 28. Rosatte R, MacInnes C, Williams RT, Williams O (1997) A proactive prevention strategy for raccoon rabies in Ontario, Canada. Wildl Soc Bull 25: 110–116.
- 29. Peterson MA, Denno RF (1998) The influence of dispersal and diet breadth on patterns of genetic isolation by distance in phytophagous insects. Am Nat 152: 428–446.
- 30. Zalewski A, Piertney SB, Zalewska H, Lambin X (2009) Landscape barriers reduce gene flow in an invasive carnivore: geographical and local genetic structure of American mink in Scotland. Mol Ecol 18: 1601–1615.
- 31. Broquet T, Ray N, Petit E, Fryxell JM, Burel F (2006) Genetic isolation by distance and landscape connectivity in the American marten (Martes americana). Landsc Ecol 21: 877–889.
- 32. Macdonald DW, Rushton S (2003) Modelling space use and dispersal of mammals in real landscapes: a tool for conservation. J Biogeogr 30: 607–620.
- 33. Coltman DW, Pilkington JG, Pemberton JM (2003) Fine-scale genetic structure in a free-living ungulate population. Mol Ecol 12: 733–742.
- 34. Foerster K, Valcu M, Johnsen A, Kempenaers B (2006) A spatial genetic structure and effects of relatedness on mate choice in a wild bird population. Mol Ecol 15: 4555–4567.
- 35. Côté H, Garant D, Robert K, Mainguy J, Pelletier F (2012) Linking population genetic structure and rabies spread in raccoons. Evol Appl 5: 393–404.
- 36. Lambin X, Yoccoz NG (1998) The impact of population kin-structure on nestling survival in Townsend’s voles, Microtus townsendii. J Anim Ecol 67: 1–16.
- 37. Bretman A, Rodrìguez-Munoz R, Walling C, Slate J, Tregenza T (2011) Fine-scale population structure, inbreeding risk and avoidance in a wild insect population. Mol Ecol 20: 3045–3055.
- 38. Handley LJL, Perrin N (2007) Advances in our understanding of mammalian sex-biased dispersal. Mol Ecol 16: 1559–1578.
- 39. Greenwood PJ (1980) Mating systems, philopatry and dispersal in birds and mammals. Anim Behav 28: 1140–1162.
- 40. Costello CM, Creel SR, Kalinowski ST, Vu NV, Quigley HB (2008) Sex-biased natal dispersal and inbreeding avoidance in American black bears as revealed by spatial genetic analyses. Mol Ecol 17: 4713–4723.
- 41. 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.
- 42. Rees EE, Bélanger D, Lelièvre F, Coté N, Lambert L (2011) Targeted surveillance of raccoon rabies in Québec, Canada. J Wildl Manage 75: 1406–1416.
- 43.
Rosatte R, Larivière S (2003) Skunks. In: Feldhammer G, Thompson B, Chapman J, editors. Wild mammals of North America: biology, management, and conservation. Baltimore: John Hopkins University Press, 692–707.
- 44. Broadfoot JD, Rosatte RC, O’Leary DT (2001) Raccoon and skunk population models for urban disease control planning in Ontario, Canada. Ecol Appl 11: 295–303.
- 45.
Rosatte RC, Power M, Macinnes C (1991) Ecology of urban skunks, raccoons and foxes in metropolitan Toronto. In: Adams L, Leedy D. Wildlife conservation in metropolitan environments. Columbia: National Institute for Urban Wildlife, 31–38.
- 46. Larivière S, Messier F (2000) Habitat selection and use of edges by striped skunks in the Canadian prairies. Can J Zool 78: 366–372.
- 47. Larivière S, Walton L, Messier F (1999) Selection by striped skunks (Mephitis mephitis) of farmsteads and buildings as denning sites. Am Midl Nat 142: 96–101.
- 48. Frey SN, Conover MR (2006) Habitat use by meso-predators in a corridor environment. J Wildl Manage 70: 1111–1118.
- 49. Barton HD, Wisely SM (2012) Phylogeography of striped skunks (Mephitis mephitis) in North America: Pleistocene dispersal and contemporary genetic structure. J Mammal 93: 38–51.
- 50. Charlton K, Webster W, Casey G, Rupprecht C (1988) Skunk rabies. Rev Infect Dis 10: S626–S628.
- 51. Guerra MA, Curns AT, Rupprecht CE, Hanlon CA, Krebs JW, et al. (2003) Skunk and raccoon rabies in the eastern United States: temporal and spatial analysis. Emerg Infect Dis 9: 1143–1150.
- 52. Rupprecht CE, Smith JS (1994) Raccoon rabies - the reemergence of a epizootic in a densely populated area. Semin Virol 5: 155–164.
- 53. Larivière S, Messier F (1998) Spatial organization of a prairie striped skunk population during the waterfowl nesting season. J Wildl Manage 62: 199–204.
- 54. Rosatte RC, Gunson J (1984) Dispersal and home range of striped skunks (Mephitis mephitis) in an area of population reduction in southern Alberta. Can Field-Nat 98: 315–319.
- 55. Sargeant A, Greenwood R, Piehl J, Bicknell W (1982) Recurrence, mortality, and dispersal of prairie striped skunks, Mephitis mephitis, and implications to rabies epizootiology. Can Field-Nat 96: 312–316.
- 56. Bjorge R, Gunson J, Samuel W (1981) Population characteristics and movements of striped skunks (Mephitis mephitis) in central Alberta. Can Field-Nat 95: 149–155.
- 57. Schulze K, Hunger M, Döll P (2005) Simulating river flow velocity on global scale. Adv Geosci 5: 133–136.
- 58. Chambers JL, Garant D (2010) Determinants of population genetic structure in eastern chipmunks (Tamias striatus): the role of landscape barriers and sex-biased dispersal. J Hered 101: 413–422.
- 59. Dragoo JW, Coan KE, Moore KA, Henke SE, Fleischer RC, et al. (2009) Polymorphic microsatellite markers for the striped skunk, Mephitis mephitis, and other mephitids. Mol Ecol Res 9: 383–385.
- 60. Munguia-Vega A, Rheude M, Culver M (2009) Ten new microsatellite loci for the striped skunk (Mephitis mephitis). Conserv Genet Resour 1: 437–439.
- 61. Shaw CN, Wilson PJ, White BN (2003) A reliable molecular method of gender determination for mammals. J Mammal 84: 123–128.
- 62. Raymond M, Rousset F (1995) GENEPOP (version 1.2): population genetics software for exact tests and ecumenicism. J Hered 86: 248–249.
- 63. Goudet J (1995) FSTAT (Version 1.2): a computer program to calculate F-statistics. J Hered 86: 485–486.
- 64. Kalinowski ST, Taper ML, Marshall TC (2007) Revising how the computer program CERVUS accommodates genotyping error increases success in paternity assignment. Mol Ecol 16: 1099–1106.
- 65. Marshall TC, Slate J, Kruuk LEB, Pemberton JM (1998) Statistical confidence for likelihood-based paternity inference in natural populations. Mol Ecol 7: 639–655.
- 66. Rice WR (1989) Analyzing tables of statistical tests. Evolution 43: 223–225.
- 67. Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155: 945–959.
- 68. Lichstein JW (2007) Multiple regression on distance matrices: A multivariate spatial analysis tool. Plant Ecol 188: 117–131.
- 69. Mantel N (1967) The detection of disease clustering and a generalized regression approach. Cancer Res 27: 209–220.
- 70. Smouse PE, Long JC, Sokal RR (1986) Multiple regression and correlation extensions of the Mantel test of matrix correspondence. Syst Zool 35: 627–632.
- 71. Legendre P, Lapointe F-J, Casgrain P (1994) Modeling brain evolution from behavior: a permutational regression approach. Evolution 48(5): 1487–1499.
- 72. Goslee SC, Urban DL (2007) The ecodist package for dissimilarity-based analysis of ecological data. J Stat Softw 22: 1–19.
- 73. Wang J (2002) An estimator for pairwise relatedness using molecular markers. Genetics 160: 1203–1215.
- 74. Hardy O, Vekemans X (2002) SPAGEDi: a versatile computer program to analyse spatial genetic structure at the individual or population levels. Mol Ecol Notes 2: 618–620.
- 75.
Efron B, Tibshirani R (1993) An introduction to the bootstrap. London: Chapman & Hall.
- 76. Hansen LA, Mathews NE, Hansen RW, Vander Lee BA, Lutz RS (2003) Genetic structure in striped skunks (Mephitis mephitis) on the Southern High Plains of Texas. West N Am Nat 63: 80–87.
- 77. Barton HD, Gregory AJ, Davis R, Hanlon CA, Wisely SM (2010) Contrasting landscape epidemiology of two sympatric rabies virus strains. Mol Ecol 19: 2725–2738.
- 78. Trénel P, Hansen MM, Normand S, Borchsenius F (2008) Landscape genetics, historical isolation and cross-Andean gene flow in the wax palm, Ceroxylon echinulatum (Arecaceae). Mol Ecol 17: 3528–3540.
- 79. Hwang YT, Lariviere S, Messier F (2007) Local- and landscape-level den selection of striped skunks on the Canadian prairies. Can J Zool 85: 33–39.