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

Abstract

Aim: Theoretically, woody biomass turnover time ((Figure presented.)) 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 (Figure presented.) in mature forests. We further compared woody (Figure presented.) 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 (Figure presented.) and predicting biomass accumulation rates. We investigated abiotic and biotic drivers of spatial woody (Figure presented.) and generated a spatially explicit map of woody (Figure presented.) 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 (Figure presented.). Results: Woody (Figure presented.) quantified by the outflux approach better (with R2 0.4–0.5) predicted the biomass accumulation rates than the influx approach (with R2 0.1–0.4) across continents. We found large spatial variations of woody (Figure presented.) 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 (Figure presented.) 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 (Figure presented.) across continents. The highest woody (Figure presented.) 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 (Figure presented.) for predicting biomass accumulation rates. The spatially explicit map of woody (Figure presented.) and the underlying drivers provide valuable information to improve the representation of forest demography and carbon turnover processes in DGVMs.