The predictability of near-term forest biomass change in boreal North America

Higher stem density, winter temperatures, and the presence of temperate tree species in forest plots were positively associated with AGB change, whereas greater initial biomass, continentality (difference between mean summer and winter temperatures), prevalence of black spruce (Picea mariana), summer precipitation, and early warning metrics from long-term remote sensing time series were negatively associated with AGB change.

Across the domain, we predict nondisturbance-induced declines in AGB at 23% of sites by 2030. The approach developed here can be used to estimate near-future forest biomass in boreal North America and inform relevant management decisions. Our study also highlights the power of machine learning multi-model ensembles when trained on a large volume of forest inventory plots, which could be applied to other regions with adequate plot density and spatial coverage.