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To control invasive species and prioritize limited resources, managers need information about population size to evaluate the current state of the problem, the trend in population growth through time, and to understand the potential magnitude of the problem in the absence of management actions. This information is critical for informing management actions and allocating resources. We used two national-scale data sets to estimate historical, current, and future potential population size of invasive wild pigs (Sus scrofa; hereafter wild pigs) in the United States. Between 1982 to present, the Southeastern Cooperative Wildlife Disease Study mapped the distribution of wild pigs in the United States. In addition, recent research has predicted potential population density of wild pigs across the United States by evaluating broad-scale landscape characteristics. We intersected these two data sets to estimate the population size of wild pigs in 1982, 1988, 2004, 2010, 2013, and 2016. In addition, we estimated potential population size if wild pigs were present at equilibrium conditions in all available habitat in each state. We demonstrate which states have experienced recent population growth of wild pigs and are predicted to experience the greatest population increase in the future without sufficient management actions and policy implementation. Regions in the western, northern, and northeastern United States contain no or few wild pig populations, but could potentially support large numbers of these animals if their populations become established. This information is useful in identifying regions at greatest risk if wild pigs become established, which can assist in prioritizing management actions aimed at controlling or eliminating this invasive species across broad to local scales.

Individual-based landscape genetic analyses provide empirically based models of gene flow. It would be valuable to verify the predictions of these models using independent data of a different type. Analyses using different data sources that produce consistent results provide strong support for the generality of the findings. Mating and dispersal movements are the mechanisms through which gene flow operates in animal populations. The best means to verify landscape genetic predictions would be to use movement data to independently predict landscape resistance. We used path-level, conditional logistic regression to predict landscape resistance for American black bear (Ursus americanus) in a landscape in which previous work predicted population connectivity using individual-based landscape genetics. We found consistent landscape factors influence genetic differentiation and movement path selection, with strong similarities between the predicted landscape resistance surfaces. Genetic differentiation in American black bear is driven by spring movement (mating and dispersal) in relation to residential development, roads, elevation and forest cover. Given the limited periods of the year when gene flow events primarily occur, models of landscape connectivity should carefully consider temporal changes in functional landscape resistance.

Background Reliable predictions of regional-scale population connectivity are needed to prioritize conservation actions. However, there have been few examples of regional connectivity models that are empirically derived and validated. The central goals of this paper were to (1) evaluate the effectiveness of factorial least cost path corridor mapping on an empirical resistance surface in reflecting the frequency of highway crossings by American black bear, (2) predict the location and predicted intensity of use of movement corridors for American black bear, and (3) identify where these corridors cross major highways and rank the intensity of these crossings. Results We used factorial least cost path modeling coupled with resistant kernel analysis to predict a network of movement corridors across a 30.2 million hectare analysis area in Montana and Idaho, USA. Factorial least cost path corridor mapping was associated with the locations of actual bear highway crossings. We identified corridor-highway intersections and ranked these based on corridor strength. We found that a major wildlife crossing overpass structure was located close to one of the most intense predicted corridors, and that the vast majority of the predicted corridor network was “protected” under federal management. However, narrow, linear corridors connecting the Greater Yellowstone Ecosystem to the rest of the analysis area had limited protection by federal ownership, making these additionally vulnerable to habitat loss and fragmentation. Conclusions Factorial least cost path modeling coupled with resistant kernel analysis provides detailed, synoptic information about connectivity across populations that vary in distribution and density in complex landscapes. Specifically, our results could be used to quantify the structure of the connectivity network, identify critical linkage nodes and core areas, map potential barriers and fracture zones, and prioritize locations for mitigation, restoration and conservation actions. Electronic supplementary material The online version of this article (doi:10.1186/2051-3933-1-12) contains supplementary material, which is available to authorized users.