The limits of the predictability of near-term changes in forest biomass: a case study in boreal North America

Climate change is driving large changes in forest ecosystems around the globe. Over the last 70 years, anthropogenic climate change has caused the boreal forest zone to warm at a much faster rate than most other terrestrial biomes. It is likely that increases in temperature will drive significant changes to forest productivity, mortality and recruitment. Some of these changes will occur rapidly while others are expected to unfold gradually. For example, tree-level productivity can decline decades before mortality and associated landscape-level changes occur. The ability to predict these changes would be invaluable to both researchers and land managers, as it would allow time to address or mitigate impending changes to forests and their ecosystem services. There is evidence that specific satellite-based vegetation indices can be used to predict both increases in site-level productivity and to provide early warning signals of tree mortality. When these remotely sensed data are used in combination with site and climate characteristics, as well as an understanding of how productivity dynamics relate to tree mortality, they may be used to develop tools that can be used to predict future changes in biomass. In this study, we present a modeling framework to predict changes in forest biomass at sites across Canada and Alaska. We integrate several long-term satellite vegetation indices along with soil characteristics, long-term climate variables and species composition. A extreme gradient boosted models, trained on over 10,000 forest inventory plots with repeat measurements covering a wide range of forest types across boreal North America. We present results of our predictive models, cross-validated using a withheld subset of the forest inventory plots. Using these models we investigate the limits of prediction of future biomass change. The models developed by this study has the potential to improve our understanding of long-term and large-scale drivers and changes in boreal forest biomass as well as to provide scientific and management communities with a novel tool for monitoring.