Where to Begin? A Flexible Framework to Prioritize Caribou Habitat Restoration

Melanie Dickie
Caroline Bampfylde
Michael Cody
Kendal Benesh
Mandy Kellner
Michelle McLellan
Stan Boutin
Robert Serrouya
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This resource is available on an external database and may require a paid subscription to access it. It is included on the CCLM to support our goal of capturing and sharing the breadth of all available knowledge pertaining to Boreal Caribou, Wetlands, and Land Management.

Habitat loss is a leading threat to many species at risk, and as such, the need for habitat restoration is widespread. In the boreal forests of Western Canada, habitat restoration is a key habitat management action needed to achieve self-sustaining populations of woodland caribou, a federally Threatened species in decline. Hundreds of thousands of kilometers of linear features were created during the exploration or extraction of oil and gas that are no longer used, yet natural regeneration remains stagnated. Only a fraction of these linear features are restored each year, sparking the need for managers to prioritize efforts. We developed an algorithm to prioritize habitat restoration and demonstrate how it can be used to predict and monitor progress towards restoration goals. Our approach is based on the idea of maximizing the gain in unaltered caribou habitat per unit cost, while allowing for the inclusion of different goals, costs, and weighting criteria. Our algorithm ranked landscape units into five zones of restoration priority. The largest gain in unaltered habitat occurred following restoration of the highest priority zones, with diminishing returns as restoration proceeded. None of the caribou ranges reached habitat management targets when not considering restoration within energy project boundaries, even after all candidate linear features were restored. Our results highlight the need for ambitious, coordinated restoration, and the need for improved land-use planning to minimize alteration within caribou range. We demonstrate the flexibility of our algorithm by applying the framework to a case study in a mountain ecosystem.