Testing for changes in biomass dynamics in large-scale forest datasets

Abstract

Tropical forest responses to climate and atmospheric change are critical to the future of the global carbon budget. Recent studies have reported increases in estimated above-ground biomass (EAGB) stocks, productivity, and mortality in old-growth tropical forests. These increases could reflect a shift in forest functioning due to global change and/or long-lasting recovery from past disturbance. We introduce a novel approach to disentangle the relative contributions of these mechanisms by decomposing changes in whole-plot biomass fluxes into contributions from changes in the distribution of gap-successional stages and changes in fluxes for a given stage. Using 30 years of forest dynamic data at Barro Colorado Island (BCI), Panama, we investigated temporal variation in EAGB fluxes as a function of initial EAGB in 10x10m quadrats. Productivity and mortality fluxes both increased strongly with initial quadrat EAGB. The distribution of EAGB (and thus initial EAGB) across quadrats hardly varied over 30 years (and 7 censuses). EAGB fluxes as a function of initial EAGB varied strongly and significantly among census intervals, with notably higher productivity in 1985-1990 associated with recovery from the 1982-83 El Niño event. Variation in whole-plot fluxes among census intervals was explained overwhelmingly by variation in fluxes as a function of initial EAGB, with essentially no contribution from changes in initial EAGB distributions. The high observed temporal variation in productivity and mortality suggests that this forest is very sensitive to climate variability. There was no consistent long-term trend in productivity, mortality, or biomass in this forest over 30 years, although the temporal variability in productivity and mortality was so strong that it could well mask a substantial trend. Accurate prediction of future tropical forest carbon budgets will require accounting for disturbance-recovery dynamics and understanding temporal variability in productivity and mortality.