Integrated species distribution models reveal spatiotemporal patterns of human-wildlife conflict

Applying a Random Encounter Model to Estimate the Asiatic Black Bear (Ursus thibetanus) Density from Camera Traps in the Hindu Raj Mountains, Pakistan

Estimating the population density of vulnerable species, such as the elusive and nocturnal Asiatic black bear (Ursus thibetanus), is essential for wildlife conservation and management. We used camera traps and a Random Encounter Model (REM) to estimate the population density of U. thibetanus during the autumn and winter seasons in the Hindu Raj Mountains. We installed 23 camera traps from October to December 2020 and acquired 66 independent pictures of Asiatic black bears over 428 trap nights. Our results showed that the bears preferred lowland areas with the presence of Quercus spp. We estimated, using the REM, a population density of U. thibetanus of 1.875 (standard error = 0.185) per square kilometer, which is significantly higher than that in other habitats. Our results showed that during autumn and winter, the bear population density tends to concentrate at lower elevations. Forest cover showed a positive correlation with the rates of bear encounters unlike the Euclidean distance to human settlements, altitude, and aspect variables. The approaches used here are cost-effective for estimating the population density of rare and vulnerable species such as U. thibetanus, and can be used to estimate their population density in Pakistan. Population density estimation can identify areas where the bears live and human-bear conflicts occurred and use this information in future wildlife management plans.

Data-integration of opportunistic species observations into hierarchical modeling frameworks improves spatial predictions for urban red squirrels

The prevailing trend of increasing urbanization and habitat fragmentation makes knowledge of species’ habitat requirements and distribution a crucial factor in conservation and urban planning. Species distribution models (SDMs) offer powerful toolboxes for discriminating the underlying environmental factors driving habitat suitability. Nevertheless, challenges in SDMs emerge if multiple data sets - often sampled with different intention and therefore sampling scheme – can complement each other and increase predictive accuracy. Here, we investigate the potential of using recent data integration techniques to model potential habitat and movement corridors for Eurasian red squirrels (Sciurus vulgaris), in an urban area. We constructed hierarchical models integrating data sets of different quality stemming from unstructured on one side and semi-structured wildlife observation campaigns on the other side in a combined likelihood approach and compared the results to modeling techniques based on only one data source - wherein all models were fit with the same selection of environmental variables. Our study highlights the increasing importance of considering multiple data sets for SDMs to enhance their predictive performance. We finally used Circuitscape (version 4.0.5) on the most robust SDM to delineate suitable movement corridors for red squirrels as a basis for planning road mortality mitigation measures. Our results indicate that even though red squirrels are common, urban habitats are rather small and partially lack connectivity along natural connectivity corridors in Berlin. Thus, additional fragmentation could bring the species closer to its limit to persist in urban environments, where our results can act as a template for conservation and management implications.