Multi-model comparison on the effects of climate change on tree species in the eastern U.S.: results from an enhanced niche model and process-based ecosystem and landscape models
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Species distribution models (SDM) establish statistical relationships between the current distribution of species and key attributes whereas process-based models simulate ecosystem and tree species dynamics based on representations of physical and biological processes. TreeAtlas, which uses DISTRIB SDM, and Linkages and LANDIS PRO, process-based ecosystem and landscape models, respectively, were used concurrently on four regional climate change assessments in the eastern Unites States.
We compared predictions for 30 species from TreeAtlas, Linkages, and LANDIS PRO, using two climate change scenarios on four regions, to derive a more robust assessment of species change in response to climate change.
We calculated the ratio of future importance or biomass to current for each species, then compared agreement among models by species, region, and climate scenario using change classes, an ordinal agreement score, spearman rank correlations, and model averaged change ratios.
Comparisons indicated high agreement for many species, especially northern species modeled to lose habitat. TreeAtlas and Linkages agreed the most but each also agreed with many species outputs from LANDIS PRO, particularly when succession within LANDIS PRO was simulated to 2300. A geographic analysis showed that a simple difference (in latitude degrees) of the weighted mean center of a species distribution versus the geographic center of the region of interest provides an initial estimate for the species’ potential to gain, lose, or remain stable under climate change.
This analysis of multiple models provides a useful approach to compare among disparate models and a more consistent interpretation of the future for use in vulnerability assessments and adaptation planning.
KeywordsClimate change Eastern United States Multi-model comparison TreeAtlas DISTRIB LANDIS PRO Linkages Forests
Many people are to be thanked, for we authors are dependent on data collected by others! Forest inventory data are paramount, as are historic and potential future climate data along with environmental data, all geographically referenced. We thank Eric Gustafson and journal reviewers for reviewing the manuscript. This project was funded by the USDA Forest Service Northern Research Station and Eastern Region, the United States Geological Survey Northeast Climate Science Center, and the University of Missouri-Columbia. Its contents are solely the responsibility of the authors and do not necessarily represent views of the funding agencies. This manuscript is submitted for publication with the understanding that the United States Government is authorized to reproduce and distribute reprints for governmental purposes.
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