Corridor-based approach with spatial cross-validation reveals scale-dependent effects of geographic distance, human footprint and canopy cover on grizzly bear genetic connectivity.

Understanding how human infrastructure and other landscape attributes affect genetic differentiation in animals is an important step for identifying and maintaining dispersal corridors for these species. We built upon recent advances in the field of landscape genetics by using an individual-based and multiscale approach to predict landscape-level genetic connectivity for grizzly bears (Ursus arctos) across ~100,000 km2 in Canada's southern Rocky Mountains. We used a genetic dataset with 1156 unique individuals genotyped at nine microsatellite loci to identify landscape characteristics that influence grizzly bear gene flow at multiple spatial scales and map predicted genetic connectivity through a matrix of rugged terrain, large protected areas, highways and a growing human footprint. Our corridor-based modelling approach used a machine learning algorithm that objectively parameterized landscape resistance, incorporated spatial cross validation and variable selection and explicitly accounted for isolation by distance. This approach avoided overfitting, discarded variables that did not improve model performance across withheld test datasets and spatial predictive capacity compared to random cross-validation. We found that across all spatial scales, geographic distance explained more variation in genetic differentiation in grizzly bears than landscape variables. Human footprint inhibited connectivity across all spatial scales, while open canopies inhibited connectivity at the broadest spatial scale. Our results highlight the negative effect of human footprint on genetic connectivity, provide strong evidence for using spatial cross-validation in landscape genetics analyses and show that multiscale analyses provide additional information on how landscape variables affect genetic differentiation.

Genetic connectivity for two bear species at wildlife crossing structures in Banff National Park.

Roads can fragment and isolate wildlife populations, which will eventually decrease genetic diversity within populations. Wildlife crossing structures may counteract these impacts, but most crossings are relatively new, and there is little evidence that they facilitate gene flow. We conducted a three-year research project in Banff National Park, Alberta, to evaluate the effectiveness of wildlife crossings to provide genetic connectivity. Our main objective was to determine how the Trans-Canada Highway and crossing structures along it affect gene flow in grizzly (Ursus arctos) and black bears (Ursus americanus). We compared genetic data generated from wildlife crossings with data collected from greater bear populations. We detected a genetic discontinuity at the highway in grizzly bears but not in black bears. We assigned grizzly bears that used crossings to populations north and south of the highway, providing evidence of bidirectional gene flow and genetic admixture. Parentage tests showed that 47% of black bears and 27% of grizzly bears that used crossings successfully bred, including multiple males and females of both species. Differentiating between dispersal and gene flow is difficult, but we documented gene flow by showing migration, reproduction and genetic admixture. We conclude that wildlife crossings allow sufficient gene flow to prevent genetic isolation.

Demographic connectivity for ursid populations at wildlife crossing structures in Banff National Park.

Wildlife crossing structures are one solution to mitigating the fragmentation of wildlife populations caused by roads, but their effectiveness in providing connectivity has only been superficially evaluated. Hundreds of grizzly (Ursus arctos) and black bear (Ursus americanus) passages through under and overpasses have been recorded in Banff National Park, Alberta, Canada. However, the ability of crossing structures to allow individual and population-level movements across road networks remains unknown. In April 2006, we initiated a 3-year investigation into whether crossing structures provide demographic connectivity for grizzly and black bears in Banff National Park. We collected hair with multiple noninvasive methods to obtain genetic samples from grizzly and black bears around the Bow Valley. Our objectives were to determine the number of male and female grizzly and black bears that use crossing structures; examine spatial and temporal patterns of crossings; and estimate the proportions of grizzly and black bear populations in the Bow Valley that use crossing structures. Fifteen grizzly (7 female, 8 male) and 17 black bears (8 female, 9 male) used wildlife crossing structures. The number of individuals detected at wildlife crossing structures was highly correlated with the number of passages in space and time. Grizzly bears used open crossing structures (e.g., overpasses) more often than constricted crossings (e.g., culverts). Peak use of crossing structures for both bear species occurred in July, when high rates of foraging activity coincide with mating season. We compared the number of bears that used crossings with estimates of population abundance from a related study and determined that substantial percentages of grizzly (15.0% in 2006, 19.8% in 2008) and black bear (17.6% in 2006, 11.0% in 2008) populations used crossing structures. On the basis of our results, we concluded wildlife crossing structures provide demographic connectivity for bear populations in Banff National Park.

A Comparison of Grizzly Bear Demographic Parameters Estimated from Non-Spatial and Spatial Open Population Capture-Recapture Models.

Capture-recapture studies are frequently used to monitor the status and trends of wildlife populations. Detection histories from individual animals are used to estimate probability of detection and abundance or density. The accuracy of abundance and density estimates depends on the ability to model factors affecting detection probability. Non-spatial capture-recapture models have recently evolved into spatial capture-recapture models that directly include the effect of distances between an animal's home range centre and trap locations on detection probability. Most studies comparing non-spatial and spatial capture-recapture biases focussed on single year models and no studies have compared the accuracy of demographic parameter estimates from open population models. We applied open population non-spatial and spatial capture-recapture models to three years of grizzly bear DNA-based data from Banff National Park and simulated data sets. The two models produced similar estimates of grizzly bear apparent survival, per capita recruitment, and population growth rates but the spatial capture-recapture models had better fit. Simulations showed that spatial capture-recapture models produced more accurate parameter estimates with better credible interval coverage than non-spatial capture-recapture models. Non-spatial capture-recapture models produced negatively biased estimates of apparent survival and positively biased estimates of per capita recruitment. The spatial capture-recapture grizzly bear population growth rates and 95% highest posterior density averaged across the three years were 0.925 (0.786-1.071) for females, 0.844 (0.703-0.975) for males, and 0.882 (0.779-0.981) for females and males combined. The non-spatial capture-recapture population growth rates were 0.894 (0.758-1.024) for females, 0.825 (0.700-0.948) for males, and 0.863 (0.771-0.957) for both sexes. The combination of low densities, low reproductive rates, and predominantly negative population growth rates suggest that Banff National Park's population of grizzly bears requires continued conservation-oriented management actions.

Stable isotopes reveal rail-associated behavior in a threatened carnivore.

Human-wildlife conflict is a leading cause of adult mortality for large carnivores worldwide. Train collision is the primary cause of mortality for threatened grizzly bears (Ursus arctos) in Banff National Park. We investigated the use of stable isotope analysis as a tool for identifying bears that use the railway in Banff. Rail-associated bears had higher δ(15)N and δ(34)S values than bears sampled away from the rail, but similar δ(13)C values. Because elevated δ(15)N values are indicative of higher animal protein consumption, rail-associated bears likely preyed on ungulates that foraged along the rail or scavenged on train-killed animals. The higher δ(34)S values in bear hair could have resulted from bears consuming sulfur pellets spilled on the rail or through the uptake of sulfur in the plants bears or animals consumed. Similar δ(13)C values suggest that the two types of bears had generally similar plant-based diets. Results from this study suggest that stable isotopes analysis could be used as a non-invasive, affordable, and efficient technique to identify and monitor bears that forage on the railway in Banff and potentially other transportation corridors worldwide.

Estimating grizzly and black bear population abundance and trend in Banff National Park using noninvasive genetic sampling.

We evaluated the potential of two noninvasive genetic sampling methods, hair traps and bear rub surveys, to estimate population abundance and trend of grizzly (Ursus arctos) and black bear (U. americanus) populations in Banff National Park, Alberta, Canada. Using Huggins closed population mark-recapture models, we obtained the first precise abundance estimates for grizzly bears (N= 73.5, 95% CI = 64-94 in 2006; N= 50.4, 95% CI = 49-59 in 2008) and black bears (N= 62.6, 95% CI = 51-89 in 2006; N= 81.8, 95% CI = 72-102 in 2008) in the Bow Valley. Hair traps had high detection rates for female grizzlies, and male and female black bears, but extremely low detection rates for male grizzlies. Conversely, bear rubs had high detection rates for male and female grizzlies, but low rates for black bears. We estimated realized population growth rates, lambda, for grizzly bear males (λ= 0.93, 95% CI = 0.74-1.17) and females (λ= 0.90, 95% CI = 0.67-1.20) using Pradel open population models with three years of bear rub data. Lambda estimates are supported by abundance estimates from combined hair trap/bear rub closed population models and are consistent with a system that is likely driven by high levels of human-caused mortality. Our results suggest that bear rub surveys would provide an efficient and powerful means to inventory and monitor grizzly bear populations in the Central Canadian Rocky Mountains.