We investigated the relationships among landscape quality, gene flow, and population genetic structure of fishers (Martes pennanti) in ON, Canada. We used graph theory as an analytical framework considering each landscape as a network node. The 34 nodes were connected by 93 edges. Network structure was characterized by a higher level of clustering than expected by chance, a short mean path length connecting all pairs of nodes, and a resiliency to the loss of highly connected nodes. This suggests that alleles can be efficiently spread through the system and that extirpations and conservative harvest are not likely to affect their spread. Two measures of node centrality were negatively related to both the proportion of immigrants in a node and node snow depth. This suggests that central nodes are producers of emigrants, contain high-quality habitat (i.e., deep snow can make locomotion energetically costly) and that fishers were migrating from high to low quality habitat. A method of community detection on networks delineated five genetic clusters of nodes suggesting cryptic population structure. Our analyses showed that network models can provide system-level insight into the process of gene flow with implications for understanding how landscape alterations might affect population fitness and evolutionary potential.

Fishers (Martes pennanti) were extirpated from much of southern Ontario, Canada, prior to the 1950s. We hypothesised that the recent recolonization of this area originated from an expansion of the population in Algonquin Provincial Park, which historically served as a refuge for fishers. To test this hypothesis, we created a sampling lattice to encompass Algonquin and the surrounding area, and we collected contemporaneous DNA samples. We sampled fishers from each of 35 sites and genotyped them at 16 microsatellite loci. Using a Bayesian assignment approach, with no a priori geographic information, we inferred 5 discrete genetic populations and used genetic population assignment as a means to cluster sites together. We concluded that the Algonquin Park fisher population has not been a substantial source for recolonization and expansion, which has instead occurred from a number of remnant populations within Ontario, Quebec, and most recently from the Adirondacks in New York, USA. The genetic structure among sampling sites across the entire area revealed a pattern of isolation‐by‐distance (IBD). However, an examination of the distribution of genetic structure (FST/1‐ FST) at different distances showed higher rates of gene flow than predicted under a strict IBD model at small distances (40 km) within clusters and at larger distances up to 100 km among clusters. This pattern of genetic structure suggests increased migration and gene flow among expanding reproductive fronts.

Recent research shows that density dependence should result in predictable movements between habitats of different suitability, depending on whether population densities are increasing or decreasing. When population densities are increasing, habitats become filled in order of their suitability, resulting in a net flow from high suitability to low suitability. When populations decrease in density, the reverse can happen. These patterns suggest that genetic information can be used to infer habitat suitability since individual-based genetic assignment tests permit high resolution assessments of migration. We used replicated landscapes to study fishers (Martes pennanti ) during a population increase and predicted that there should be a net flow of individuals from areas of shallow to deep snow, since snow depth has previously been linked to fisher fitness. A total of 769 fishers were sampled from 35 different landscapes and profiled at 16 microsatellite loci. From assignment tests, we inferred five genetic populations. By assigning each of the 35 landscapes to one of these five populations, we were able to determine the proportion of immigrants to each. Consistent with our prediction, there was a positive relationship between the proportion of immigrants and snow depth. The best model of fisher habitat suitability was one with both snow depth and the proportion of coniferous forest in landscapes. Our findings suggest that where population trend is known, genetic information can be used to measure habitat suitability.

Recent research shows that density dependence should result in predictable movements between habitats of different suitability, depending on whether population densities are increasing or decreasing. When population densities are increasing, habitats become filled in order of their suitability, resulting in a net flow from high suitability to low suitability. When populations decrease in density, the reverse can happen. These patterns suggest that genetic information can be used to infer habitat suitability since individual-based genetic assignment tests permit high resolution assessments of migration. We used replicated landscapes to study fishers (Martes pennanti ) during a population increase and predicted that there should be a net flow of individuals from areas of shallow to deep snow, since snow depth has previously been linked to fisher fitness. A total of 769 fishers were sampled from 35 different landscapes and profiled at 16 microsatellite loci. From assignment tests, we inferred five genetic populations. By assigning each of the 35 landscapes to one of these five populations, we were able to determine the proportion of immigrants to each. Consistent with our prediction, there was a positive relationship between the proportion of immigrants and snow depth. The best model of fisher habitat suitability was one with both snow depth and the proportion of coniferous forest in landscapes. Our findings suggest that where population trend is known, genetic information can be used to measure habitat suitability.

BackgroundProbes on a microarray represent a frozen view of a genome and are quickly outdated when new sequencing studies extend our knowledge, resulting in significant measurement error when analyzing any microarray experiment. There are several bioinformatics approaches to improve probe assignments, but without in-house programming expertise, standardizing these custom array specifications as a usable file (e.g. as Affymetrix CDFs) is difficult, owing mostly to the complexity of the specification file format. However, without correctly standardized files there is a significant barrier for testing competing analysis approaches since this file is one of the required inputs for many commonly used algorithms. The need to test combinations of probe assignments and analysis algorithms led us to develop ArrayInitiative, a tool for creating and managing custom array specifications.ResultsArrayInitiative is a standalone, cross-platform, rich client desktop application for creating correctly formatted, custom versions of manufacturer-provided (default) array specifications, requiring only minimal knowledge of the array specification rules and file formats. Users can import default array specifications, import probe sequences for a default array specification, design and import a custom array specification, export any array specification to multiple output formats, export the probe sequences for any array specification and browse high-level information about the microarray, such as version and number of probes. The initial release of ArrayInitiative supports the Affymetrix 3' IVT expression arrays we currently analyze, but as an open source application, we hope that others will contribute modules for other platforms.ConclusionsArrayInitiative allows researchers to create new array specifications, in a standard format, based upon their own requirements. This makes it easier to test competing design and analysis strategies that depend on probe definitions. Since the custom array specifications are easily exported to the manufacturer's standard format, researchers can analyze these customized microarray experiments using established software tools, such as those available in Bioconductor.