Trees for tomorrow: an evaluation framework to assess potential candidates for assisted migration to Manitoba’s forests

Andrew Park (), Carolyn Talbot and Ryan Smith
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Andrew Park: University of Winnipeg
Carolyn Talbot: University of Winnipeg
Ryan Smith: University of Winnipeg

Climatic Change, 2018, vol. 148, issue 4, No 11, 606 pages

Abstract: Abstract Forest managers are beginning to experiment with assisted migration (AM), the intentional movement of organisms to areas outside their historic range, as a pre-emptive adaptation to climate change. To date, AM studies have focused on species conservation, while AM in forestry has received little attention. Using Manitoba, Canada, as our study area, we developed a two-stage framework to evaluate North American tree species as AM candidates. Little’s (1971) range maps were used to characterize climatic ranges for 87 species, and GCM projections under RCP8.5 estimated potential future tree distributions for 2011–2040, 2041–2070, and 2071–2100. Traits for the resulting 26 candidate species were evaluated in eight categories, each divided into several response factors, to investigate management potential, adaptation and interspecific interactions, vulnerability to pests, diseases and natural disturbance, and range of soil conditions tolerated. Multivariate analyses were used to classify species into groups characterized by different combinations of management potential, tolerance for climate extremes, and relative vulnerability to disturbances, insects, and disease. These groupings could be used by managers in a variety of applications—commercial forestry, urban forests, or restoration—as an initial selection filter for AM candidates. Separate uncertainty scores in each category should allow users to independently judge the quality of information contributing to a given category. Although our framework was regionally focused, it could be readily adapted to selecting AM candidates elsewhere. We recommend that the framework be further field tested among different practitioners, modifying, editing, and adding to the list of categories and factors, as needed.

Date: 2018
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DOI: 10.1007/s10584-018-2201-7

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