Comparing citizen science and professional data to evaluate extrapolated mountain goat distribution models

Abstract

Citizen science provides a prime opportunity for wildlife managers to obtain low-cost data recorded by volunteers to evaluate species distribution models and address research objectives. Using mountain goat (Oreamnos americanus) location data collected through aerial surveys by professionals, ground surveys by professionals, and ground surveys by volunteers, we evaluated two mountain goat distribution models extrapolated across Waterton-Glacier International Peace Park. In addition, we compared mountain goat location data by observer and survey type to determine whether there were differences that affected extrapolated model evaluation. We found that all dataset types compared similarly to both mountain goat models. A mountain goat occupancy model developed in the Greater Yellowstone Area (GYA) was the most informative in describing mountain goat locations. We compared Spearman-rank correlations (rs) for occupancy probability bin ranks in the GYA model extrapolation and area-adjusted frequencies of mountain goat locations, and we found that all datasets had a positive correlation, indicating the model had useful predictive ability. Aerial observations had a slightly greater Spearman-rank correlation (rs = 0.964), followed by the professional ground surveys (rs = 0.946), and volunteer ground datasets (rs = 0.898). These results suggest that with effective protocol development and volunteer training, biologists can use mountain goat location data collected by volunteers to evaluate extrapolated models. We recommend that future efforts should apply this approach to other wildlife species and explore development of wildlife distribution models using citizen science.