Volume 181, January 2015, Pages 162–172
Citizen science and field survey observations provide comparable results for mapping Vancouver Island White-tailed Ptarmigan (Lagopus leucura saxatilis) distributions
Highlights
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- Field surveys to monitor rare species in remote alpine habitats are expensive.
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- Citizen science ptarmigan observations cover a larger scale than field observations.
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- Field survey and citizen science models were similar in accuracy.
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- Machine learning and ensemble model predictions differed little between datasets.
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- Citizen science data can be used as a stand-alone tool for monitoring ptarmigan.
Abstract
Wildlife
in alpine ecosystems can be elusive and difficult to survey, yet
knowledge of their distributions is critical as these habitats are
threatened by climate change. Opportunistic “citizen science”
observations submitted by hikers in remote alpine regions can be
valuable, as coverage can be extensive compared to scientific field
surveys. Here, we compare the performance of two regression and three
machine learning statistical modeling approaches and an ensemble model
to predict the distribution of the Vancouver Island subspecies of
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. Predictions of suitable habitat for the Vancouver
Island subspecies varied from 370 to 1039 km2 based on field survey observations and from 404 to 1354 km2
based on public observations. All models had fair accuracy
(kappa > 0.45) when tested on an independent dataset, but Generalized
Linear Models and Generalized Additive Models tended to over-predict
ptarmigan occurrence, had the lowest accuracy, and were most sensitive
to the type of response data used. All the machine learning modeling
techniques differed little between the datasets. These comparable
results are encouraging for the continued use of citizen science
monitoring programs, which can save both time and expense while
involving and educating the public about threatened species. We advocate
the use of opportunistic citizen science data and machine learning
modeling techniques (Random Forest, Boosted Regression Trees, and
Maxent) for predicting alpine vertebrate species distributions.
Keywords
- Alpine;
- British Columbia;
- Citizen science;
- Species distribution modeling;
- Vancouver Island;
- White-tailed Ptarmigan
- Corresponding author. Tel.:
+1 604 802 1675.
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