Conservation planning using resource selection models: altered selection in the presence of human activity changes spatial prediction of resource use

Abstract

Managing wildlife populations in areas subject to human activity is an increasingly prominent challenge. Estimating resource selection functions for species of conservation concern and developing spatially explicit maps predicting animal use across landscapes is a powerful tool for minimizing negative impacts and enhancing positive influences of human activities. However, if animals modify their selection of resources in response to humans, application of spatially explicit conservation tools based on resource selection among animals exposed to high levels of human activity risks uncertainty in the performance of such tools. This could lead to ineffective conservation action and wasted conservation dollars. To evaluate the magnitude of differences between spatial predictions based on animals exposed to different levels of human activity and develop reliable conservation tools, we used the treatment/control concept and Bayesian hierarchical discrete choice methods to model day time resource selection by female elk in a natural gas field and in areas adjacent to the gas field during winter. We found that female elk showed strong variation in resource selection patterns among years, tended to avoid roads and natural gas wells and consistently showed stronger selection for security cover, steeper slopes and greater distance to edge habitats within the gas field relative to outside of the gas field. Predictive probability of use maps based on ‘within gas field’ models classified probability of use differently in 10–55% of grid cells relative to outside of the gas field models depending on year. Conservation research and applications should consider that models based on resource selection data collected from animals subjected to human activity may not elucidate innate resource selection patterns and therefore may result in reduced effectiveness of management actions.