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Understanding how spatial genetic patterns respond to landscape change is crucial for advancing the emerging field of landscape genetics. We quantified the number of generations for new landscape barrier signatures to become detectable and for old signatures to disappear after barrier removal. We used spatially explicit, individual-based simulations to examine the ability of an individual-based statistic [Mantel's r using the proportion of shared alleles' statistic (Dps)] and population-based statistic (FST ) to detect barriers. We simulated a range of movement strategies including nearest neighbour dispersal, long-distance dispersal and panmixia. The lag time for the signal of a new barrier to become established is short using Mantel's r (1-15 generations). FST required approximately 200 generations to reach 50% of its equilibrium maximum, although G'ST performed much like Mantel's r. In strong contrast, FST and Mantel's r perform similarly following the removal of a barrier formerly dividing a population. Also, given neighbour mating and very short-distance dispersal strategies, historical discontinuities from more than 100 generations ago might still be detectable with either method. This suggests that historical events and landscapes could have long-term effects that confound inferences about the impacts of current landscape features on gene flow for species with very little long-distance dispersal. Nonetheless, populations of organisms with relatively large dispersal distances will lose the signal of a former barrier within less than 15 generations, suggesting that individual-based landscape genetic approaches can improve our ability to measure effects of existing landscape features on genetic structure and connectivity.

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Different analytical techniques used on the same data set may lead to different conclusions about the existence and strength of genetic structure. Therefore, reliable interpretation of the results from different methods depends on the efficacy and reliability of different statistical methods. In this paper, we evaluated the performance of multiple analytical methods to detect the presence of a linear barrier dividing populations. We were specifically interested in determining if simulation conditions, such as dispersal ability and genetic equilibrium, affect the power of different analytical methods for detecting barriers. We evaluated two boundary detection methods (Monmonier's algorithm and WOMBLING), two spatial Bayesian clustering methods (TESS and GENELAND), an aspatial clustering approach (STRUCTURE), and two recently developed, non-Bayesian clustering methods [PSMIX and discriminant analysis of principal components (DAPC)]. We found that clustering methods had higher success rates than boundary detection methods and also detected the barrier more quickly. All methods detected the barrier more quickly when dispersal was long distance in comparison to short-distance dispersal scenarios. Bayesian clustering methods performed best overall, both in terms of highest success rates and lowest time to barrier detection, with GENELAND showing the highest power. None of the methods suggested a continuous linear barrier when the data were generated under an isolation-by-distance (IBD) model. However, the clustering methods had higher potential for leading to incorrect barrier inferences under IBD unless strict criteria for successful barrier detection were implemented. Based on our findings and those of previous simulation studies, we discuss the utility of different methods for detecting linear barriers to gene flow.

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North American martens are forest dependent, influenced by human activity, and climate vulnerable. They have long been managed and harvested throughout their range as the American marten (Martes americana). Recent work has expanded evidence for the original description of two species in North America-M. americana and the Pacific Coast marten, M. caurina-but the geographic boundary between these groups has not been described in detail. From 2010 to 2016 we deployed 734 multi-taxa winter bait stations across a 53,474 km 2 study area spanning seven mountain ranges within the anticipated contact zone along the border of Canada and the United States. We collected marten hair samples and developed genotypes for 15 polymorphic microsatellite loci for 235 individuals, and 493 base-pair sequences of the mtDNA gene COI for 175 of those individuals. Both nuclear and mitochondrial genetic structure identified a sharp break across the Clark Fork Valley, United States with M. americana and M. caurina occurring north and south of the break, respectively. We estimated global effective population size (N e) for each mountain range, clinal genetic neighborhood sizes (NS), calculated observed (H o) and expected (H e) heterozygosity, fixation index (F ST) , and clinal measures of allelic richness (Ar), H o , and inbreeding coefficient (F IS). Despite substantial genetic structure, we detected hybridization along the fracture zone with both contemporary (nuclear DNA) and historic (mtDNA) gene flow. Marten populations in our study area are highly structured and the break across the fracture zone being the largest documented in North America (F ST range 0.21-0.34, mean = 0.27). With the exception of the Coeur d'Alene Mountains, marten were well distributed across higher elevation portions of our sampling area. Clinal NS values were variable suggesting substantial heterogeneity in marten density and movement. For both M. americana and

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