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

Aim: Theoretically, woody biomass turnover time (τ) quantified using outflux (i.e., tree mortality) predicts biomass dynamics better using influx (i.e., productivity). This study aims at using forest inventory data to empirically test whether woody τ estimated using outflux is a better predictor of biomass dynamics and generate a spatially explicit understanding of woody τ in mature forests. Location: Continents Time period: Historic from 1951 to 2018 Major taxa studied: Trees and Forests Methods: We compared the approaches of using outflux vs. influx for estimating woody τ and predicting biomass accumulation rates. We investigated abiotic and biotic drivers of spatial woody τ and generated a spatially explicit map of woody τ at a 0.25-degree resolution across continents using machine learning. We further examined whether the six dynamic global vegetation models (DGVMs) and the data assimilated product generally captured the observational pattern of woody τ. Results: Woody τ quantified by the outflux approach more accurately predicted the biomass accumulation rates, thus empirically demonstrating the validity of this approach. We found large spatial variations of woody τ for mature forests, with highest values in temperate forests (98.8 ± 2.6 y) followed by boreal forests (in North America), and then tropical forests. The map of woody τ 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, competition and forest age) were the dominant drivers of woody τ across continents. The highest woody τ in temperate forests were not captured by either DGVMs or the data assimilated product.