Mapping recreation and tourism use across grizzly bear recovery areas using social network data and maximum entropy modelling

• Main Authors: Goodbody, Tristan R.H., Coops, Nicholas C., Srivastava, Vivek, Parsons, Bethany, Kearney, Sean P., Rickbeil, Gregory J.M., Stenhouse, Gordon B.
• Format: Article in Journal/Newspaper
• Language: unknown
• Subjects: Canada; Ursus arctos
• Online Access: http://www.sciencedirect.com/science/article/pii/S0304380020304427

Understanding biodiversity pressures associated with recreation and tourism is a major challenge for conservation planning and landscape management. While estimates of landscape use are often collected using mechanisms such as park entry fees and traffic density estimates, these data do not provide substantial detail about the spatial location or intensity of recreation and tourism across biodiversity management areas. To better predict patterns of recreation and tourism likelihood to support conservation planning, we used social network data from Facebook(™), Flickr(™), Google(™), Strava(™), and Wikilocs(™) along with a suite of remote-sensing-derived environmental covariates in a maximum entropy (MaxEnt) presence-only modelling framework. Social network samples were compiled and processed to reduce sampling bias and spatial autocorrelation. Road access, climate data, and remote sensing covariates describing vegetation greenness, disturbance, topography, and moisture were used as predictor variables in the MaxEnt modelling framework. Our focus site was a grizzly bear (Ursus arctos) management area in west-central Alberta, Canada. Individual models were developed for each social network dataset, as well as a combined model including all the samples . Mean cross-validated AUC, partial ROC, and true skill statistics (TSS) were used to evaluate model accuracy. Results indicated that the covariates proposed were able to best model Strava and Wikilocs activity (TSS = 0.69 and 0.50, respectively), while samples from Flickr or the combination of all social networks were least accurate (TSS = 0.32). The “access” covariate was most important for MaxEnt training gain across a number of social network models, highlighting the importance of access for recreation and tourism likelihood. The summer heat moisture index and normalized burn ratio were also useful spatial covariates in many predictions. Recreation and tourism likelihood maps were combined with grizzly bear telemetry data to examine how recreation and tourism may ...