Linking population genetics and tree height growth models to predict impacts of climate change on forest production

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Changes to forest growth models used widely in global change research and sustainable forest management are needed to account for expected climate change impacts. We provide a new approach that dynamically merges height–age functions prevalent in forest growth models with transfer functions prevalent in population adaptation research to better represent changes to forest productivity as climates gradually change. Our simulations with data from an extensive provenance test of lodgepole pine (Pinus contorta) in British Columbia, Canada, suggest that climate change will reduce production in lodgepole pine forests established today by at least 7–13% at the end of this century – considerably less than most predictions based solely on transfer or response functions, which do not integrate impacts as climate gradually changes. This work illustrates the need for forest productivity models to consider the changing climate in which a population is growing relative to the static climate of its origin. It also demonstrates the value of long-term provenance trials in assessing the dynamic impact of climate change on forest productivity, and serves as an example of how provenance trials may be exploited in other forest productivity models or other research fields to assess plant responses to climate.

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