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

Tristan R.H. Goodbody, Nicholas C. Coops, Vivek Srivastava, Bethany Parsons, Sean P. Kearney, Gregory J.M. Rickbeil and Gordon B. Stenhouse

Ecological Modelling, 2021, vol. 440, issue C

Abstract: 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 affect grizzly bear behaviour. All social network models found a similar influence on grizzly bear behaviour, with increasing recreation and tourism use resulting in decreased foraging behaviour and increased rapid movement, suggesting that the models developed here are useful tools for predicting grizzly bear behaviour and planning conservation strategies for the species.

Keywords: Social network; Grizzly bear; Maximum entropy models (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0304380020304427
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:440:y:2021:i:c:s0304380020304427

DOI: 10.1016/j.ecolmodel.2020.109377

Access Statistics for this article

Ecological Modelling is currently edited by Brian D. Fath

More articles in Ecological Modelling from Elsevier
Bibliographic data for series maintained by Catherine Liu ().