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Existing caribou habitat and demographic models are poorly suited for Ring of Fire impact assessment: A roadmap for improving the usefulness, transparency, and availability of models for conservation

Dyson, M. E.; Endicott, S.; Simpkins, C.; Turner, J. W.; Avery-Gomm, S.; Johnson, C. A.; Leblond, M.; Neilson, E.; Rempel, R. S.; Wiebe, P.; Baltzer, J. L.; Stewart, F. E. C.; Hughes, J.

2022-06-03 ecology
10.1101/2022.06.01.494350 bioRxiv
Show abstract

Decision making in conservation science often relies on the best available information. This may include using models that were not designed for purpose and are not accompanied by an assessment of limitations. To begin addressing these issues, we sought to reproduce, and evaluate the suitability of, the best available models for predicting impacts of proposed mining on boreal woodland caribou (Rangifer tarandus caribou) resource selection and demography in northern Ontario. We then evaluated their suitability for projecting the impacts of development in the Ring of Fire region. To aid in accessibility, we developed an R package for data preparation, analyses of resource selection, and demographic parameters. We found existing models were either ill suited, or lacking, for ongoing regional planning. The specificity of the regional resource selection model limited its usefulness for predicting impacts of development, and the high variability across caribou ranges limited the usefulness of a national aspatial demographic model for predicting range-specific impacts. Variability in model coefficients across caribou ranges suggests selection responses vary with habitat availability (i.e. a functional response) while demographic responses continue to decline with increasing disturbance. Models designed for forecasting that are continuously updated by range-specific demographic and habitat information, are required to better inform conservation decisions and ongoing policy and planning practices in the Ring of Fire region.

Published in Ecology and Evolution (predicted rank #12) · training set

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