Summary1. The ability to identify regions of high functional connectivity for multiple wildlife species is of conservation interest with respect to habitat management and corridor planning. We present a method that does not require independent, field-collected data, is insensitive to the placement of source and destination sites (nodes) for modeling connectivity, and does not require the selection of a focal species. 2. In the first step of our approach, we created a cost surface that represented permeability of the landscape to movement for a suite of species. We randomly selected nodes around the perimeter of the buffered study area and used circuit theory to connect pairs of nodes. When the buffer was removed, the resulting current density map represented, for each grid cell, the probability of use by moving animals. 3. We found that using nodes that were randomly located around the perimeter of the buffered study area was less biased by node placement than randomly selecting nodes within the study area. We also found that a buffer of ≥ 20% of the study area width was sufficient to remove the effects of node placement on current density. We tested our method by creating a map of connectivity in the Algonquin to Adirondack region in eastern North America, and we validated the map with independently collected data. We found that amphibians and reptiles were more likely to cross roads in areas of high current density, and fishers (Pekania [Martes] pennanti) used areas with high current density within their home ranges. 4. Our approach provides an efficient and cost effective method of predicting areas with relatively high landscape connectivity for multiple species..

BackgroundArtificial boundaries on a map occur when the map extent does not cover the entire area of study; edges on the map do not exist on the ground. These artificial boundaries might bias the results of animal dispersal models by creating artificial barriers to movement for model organisms where there are no barriers for real organisms. Here, we characterize the effects of artificial boundaries on calculations of landscape resistance to movement using circuit theory. We then propose and test a solution to artificially inflated resistance values whereby we place a buffer around the artificial boundary as a substitute for the true, but unknown, habitat.Methodology/Principal FindingsWe randomly assigned landscape resistance values to map cells in the buffer in proportion to their occurrence in the known map area. We used circuit theory to estimate landscape resistance to organism movement and gene flow, and compared the output across several scenarios: a habitat-quality map with artificial boundaries and no buffer, a map with a buffer composed of randomized habitat quality data, and a map with a buffer composed of the true habitat quality data. We tested the sensitivity of the randomized buffer to the possibility that the composition of the real but unknown buffer is biased toward high or low quality. We found that artificial boundaries result in an overestimate of landscape resistance.Conclusions/SignificanceArtificial map boundaries overestimate resistance values. We recommend the use of a buffer composed of randomized habitat data as a solution to this problem. We found that resistance estimated using the randomized buffer did not differ from estimates using the real data, even when the composition of the real data was varied. Our results may be relevant to those interested in employing Circuitscape software in landscape connectivity and landscape genetics studies.

A cost or resistance surface is a representation of a landscape's permeability to animal movement or gene flow and is a tool for measuring functional connectivity in landscape ecology and genetics studies. Parameterizing cost surfaces by assigning weights to different landscape elements has been challenging however, because true costs are rarely known; thus, expert opinion is often used to derive relative weights. Assigning weights would be made easier if the sensitivity of different landscape resistance estimates to relative costs was known. We carried out a sensitivity analysis of three methods to parameterize a cost surface and two models of landscape permeability: least cost path and effective resistance. We found two qualitatively different responses to varying cost weights: linear and asymptotic changes. The most sensitive models (i.e. those leading to linear change) were accumulated least cost and effective resistance estimates on a surface coded as resistance (i.e. where high-quality elements were held constant at a low-value, and low-quality elements were varied at higher values). All other cost surface scenarios led to asymptotic change. Developing a cost surface that produces a linear response of landscape resistance estimates to cost weight variation will improve the accuracy of functional connectivity estimates, especially when cost weights are selected through statistical model fitting procedures. On the other hand, for studies where cost weights are unknown and model selection is not being used, methods where resistance estimates vary asymptotically with cost weights may be more appropriate, because of their relative insensitivity to parameterization.

The amount and extent of dispersal can have a large effect on the evolutionary trajectory, dynamics and structure of populations. Thus, understanding patterns of genetic structure provide information about the needs and approaches for population management and species conservation. To date studies addressing the population structure of Canada lynx (Lynx canadensis) have been surprisingly equivocal, despite a large amount of research quantifying population cyclicity and synchrony and the species' species at-risk status in the contiguous United States and eastern provinces of Canada. Here we use 17 microsatellite loci to conduct a large-scale genetic structuring assessment for Canada lynx, including most of its geographic range from Alaska to Newfoundland. We found large differentiation between lynx populations on the island of Newfoundland and those on the mainland. Yet, contrary to previous studies we found little genetic differentiation (F ST , D est , R ST ) owing to the Rocky Mountains, but some evidence of a subtle gene flow restriction between Ontario and Manitoba as previously proposed to be the result of a climatic barrier. Bayesian clustering analysis, however, only suggested two genetic clusters, one consisting of lynx from Newfoundland, and the other consisting of lynx from the rest of the North American range. Because Canada lynx are harvested for fur across most of their range, our results are informative for effective management strategies (e.g., defining management units) aimed at ensuring long-term population connectivity and species persistence.

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.