Can we predict visitor movement? Using step selection function analysis to map high probability camping areas in a remote Alaskan wilderness

Abstract

Recreational use of parks and protected areas (PPAs) can have significant impacts to resources that can be difficult to mitigate. Cost-effective planning tools are needed to help managers anticipate where recreation disturbance is likely to occur. Tools from wildlife ecology may be helpful in meeting this information need, especially for large, remote PPA that experience sporadic use. We employed step selection function (SSF) analysis as a novel and flexible tool to identify areas with high probabilities of recreational disturbance. We fit SSFs to model environmental and experiential factors associated with campsite selection among backcountry kayakers in two regions of Glacier Bay National Park and Preserve (GLBA) in Alaska, USA. We hypothesized campers would select sites that offer bare, flat ground, are close to freshwater sources, and maximize experience-related attributes of glacier and scenic viewing while minimizing exposure to large vessels. Campers in both regions selected sites offering a combination of experiential and environmental site characteristics; however, not all factors were significant in the best fitting models. Site selection probabilities increased with increases in bare, flat ground and as distance to the nearest glacier decreased. In one region, selection probability also increased with increases in scenic viewshed. Using modeled relationships, we generated predictive maps of relative selection strength to locate areas within GLBA with high potential for camping based on site characteristics. Our results can be used to prioritize monitoring, target restoration efforts, or implement management actions to prevent camping-related resource impacts.