Citizen Science Provides Accurate, Reliable Data for Mapping White-Tailed Ptarmigan Distributions on Vancouver Island

Wildlife in alpine ecosystems can be elusive and difficult to survey, yet knowledge of their distributions is critical as these habitats disappear due to climate change. Opportunistic citizen science observations submitted by... [ view full abstract ]

Wildlife in alpine ecosystems can be elusive and difficult to survey, yet knowledge of their distributions is critical as these habitats disappear due to climate change. Opportunistic citizen science observations submitted by hikers in remote alpine regions can be valuable, as coverage is often extensive compared to scientific field surveys. However, many researchers believe citizen science data contains bias and should be used only to supplement systematic surveys by professional scientists. Here, we compare the performance of five statistical models and an ensemble model to predict the distribution of the Vancouver Island White-tailed Ptarmigan (Lagopus leucura saxatilis) based on two datasets: (1) field survey observations from radio-telemetry and call-playbacks, and (2) opportunistic citizen science observations submitted by hikers. The citizen science program was formed by the University of British Columbia’s Centre for Alpine Studies and the Strathcona Wilderness Institute, and has resulted in >400 confirmed ptarmigan locations since 1995.

Predictions of suitable ptarmigan habitat on Vancouver Island varied from 370-1,039 km2 based on field survey observations and from 404-1,354 km2 based on public observations. Distribution maps of suitable habitat differed very little between the field survey and citizen science datasets for any given model, implying that opportunistically collected data predicts habitat suitability in a manner consistent with more time and cost-intensive field survey data. All models had fair accuracy (kappa > 0.45) when tested on the alternative dataset, but Generalized Linear Models and Generalized Additive Models over-predicted ptarmigan occurrence, had the lowest accuracy, and were most sensitive to the type of response data used. Accuracy of all other modeling techniques differed little between the datasets. Such similarity across datasets is encouraging for the continued use of citizen science programs for monitoring elusive species, which can save both time and expense while involving and educating the public.