Biogeographic pattern of living vegetation carbon turnover time in mature forests across continents

Aim: Theoretically, woody biomass turnover time (tau) quantified using outflux (i.e. tree mortality) predicts biomass dynamics better than using influx (i.e. productivity). This study aims at using forest inventory data to empirically test the outflux approach and generate a spatially explicit understanding of woody tau in mature forests. We further compared woody tau estimates with dynamic global vegetation models (DGVMs) and with a data assimilation product of C stocks and fluxes-CARDAMOM. Location: Continents. Time Period: Historic from 1951 to 2018. Major Taxa Studied: Trees and forests. Methods: We compared the approaches of using outflux versus influx for estimating woody tau and predicting biomass accumulation rates. We investigated abiotic and biotic drivers of spatial woody tau and generated a spatially explicit map of woody tau at a 0.25-degree resolution across continents using machine learning. We further examined whether six DGVMs and CARDAMOM generally captured the observational pattern of woody tau. Results: Woody tau quantified by the outflux approach better (with R-2 0.4-0.5) predicted the biomass accumulation rates than the influx approach (with R-2 0.1-0.4) across continents. We found large spatial variations of woody tau for mature forests, with highest values in temperate forests (98.8 +/- 2.6 y) followed by boreal forests (73.9 +/- 3.6 y) and tropical forests. The map of woody tau extrapolated from plot data showed higher values in wetter eastern and pacific coast USA, Africa and eastern Amazon. Climate (temperature and aridity index) and vegetation structure (tree density and forest age) were the dominant drivers of woody tau across continents. The highest woody tau in temperate forests was not captured by either DGVMs or CARDAMOM. Main Conclusions: Our study empirically demonstrated the preference of using outflux over influx to estimate woody tau for predicting biomass accumulation rates. The spatially explicit map of woody tau and the underlying drivers provide valuable information to improve the representation of forest demography and carbon turnover processes in DGVMs.