A multistage decision support framework to guide tree species management under climate change via habitat suitability and colonization models, and a knowledge-based scoring system
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No single model can capture the complex species range dynamics under changing climates—hence the need for a combination approach that addresses management concerns.
A multistage approach is illustrated to manage forested landscapes under climate change. We combine a tree species habitat model—DISTRIB II, a species colonization model—SHIFT, and knowledge-based scoring system—MODFACs, to illustrate a decision support framework.
Using shortleaf pine (Pinus echinata) and sugar maple (Acer saccharum) as examples, we project suitable habitats under two future climate change scenarios (harsh, Hadley RCP8.5 and mild CCSM RCP4.5 at ~2100) at a resolution of 10 km and assess the colonization likelihood of the projected suitable habitats at a 1 km resolution; and score biological and disturbance factors for interpreting modeled outcomes.
Shortleaf pine shows increased habitat northward by 2100, especially under the harsh scenario of climate change, and with higher possibility of natural migration confined to a narrow region close to the current species range boundary. Sugar maple shows decreased habitat and has negligible possibility of migration within the US due to a large portion of its range being north of the US border. Combination of suitable habitats with colonization likelihoods also allows for identification of potential locations appropriate for assisted migration, should that be deemed feasible.
The combination of these multiple components using diverse approaches leads to tools and products that may help managers make management decisions in the face of a changing climate.
KeywordsDecision support system Tree habitat suitability model Tree species migration model Tree species distribution model Forest management Climate change
Thanks to the Northern Research Station, USDA Forest Service, for funding, and external reviewers Maria Janowiak and Laura Leites for their help in improving the manuscript. We declare no conflict of interest to the best of our knowledge.
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