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Forecasts from Multiple Models Provides more Reliable Results

Photo of Numbers indicate the future:current ratio, while colors represent the change class, where red=large decrease (future:current ratio 0.5 & 0.2 & 1.2 & 2.0). Louis Iverson, U.S. Department of Agriculture Forest Service.Numbers indicate the future:current ratio, while colors represent the change class, where red=large decrease (future:current ratio 0.5 & 0.2 & 1.2 & 2.0). Louis Iverson, U.S. Department of Agriculture Forest Service.Snapshot : Using multiple models instead of a single model allows researchers to develop more reliable forecasts of future forest change.

Principal Investigators(s) :
Iverson, LouisThompson, Frank R.
Research Location : Eastern United States
Research Station : Northern Research Station (NRS)
Year : 2016
Highlight ID : 1074

Summary

Modeling of potential changes in tree species from climate change has expanded and matured over the past decade resulting in multiple approaches based on empirical modeling of species habitat and process based simulation of ecosystem and landscape change. Forest Service scientists and collaborators compared predictions for 30 species from three models (TreeAtlas, Linkages, and LANDIS PRO) applied to four regions in the eastern United States (Central Hardwoods, Central Appalachians, Mid-Atlantic, and New England) under two climate change scenarios (a harsh [GFDL] and a mild [PCM] scenario) to derive a more robust assessment of species change in response to climate change. Scientists compared agreement among models in the ratio of future importance of biomass or current importance for each species by region and climate scenario. Comparisons indicate high agreement for many species such as northern species (i.e., sugar maple, red and black spruce) predicted to lose habitat and decrease in biomass while some oaks and southern pines are predicted to gain habitat and increase in biomass. 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. This study suggests that where multiple models provided consistent predictions, we have a more reliable forecast of future forest changes for use in vulnerability assessments and adaptation planning.

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