Aim To develop and test a simple climate-based ecophysiological model of above-ground biomass – an approach that can be applied directly to predicting the effects of climate change on forest carbon stores.
Location Humid lowland forests world-wide.
Methods We developed a new approach to modelling the aboveground biomass of old-growth forest (AGBmax) based on the influences of temperature on gross primary productivity (GPP) and what we call total maintenance cost (TMC), which includes autotrophic respiration as well as leaf, stem and other plant construction required to maintain biomass. We parameterized the models with measured carbon fluxes and tested them by comparing predicted AGBmax with measured AGB for another 109 old-growth sites.
Results Our models explained 57% of the variation in GPP across 95 sites and 79% of the variation in TMC across 17 sites. According to the best-fit models, the ratio of GPP to maintenance cost per unit biomass (MCB) peaks at 16.5 °C, indicating that this is the air temperature leading to the highest possible AGBmax when temperatures are constant. Seasonal temperature variation generally reduces predicted AGBmax, and thus maritime temperate climates are predicted to have the highest AGBmax. The shift in temperatures from temperate maritime to tropical climates increases MCB more than GPP, and thus decreases AGBmax. Overall, our model explains exactly 50% of the variation in AGB among humid lowland old-growth forests.
Main conclusions Temperature plays an important role in explaining global variation in biomass among humid lowland old-growth forests, a role that can be understood in terms of the dual effects of temperature on GPP and TMC. Our simple model captures these influences, and could be an important tool for predicting the effects of climate change on forest carbon stores.