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

Aim Theoretically, woody biomass turnover time ( τ $$ \tau ) quantified using outflux (i.e. tree mortality) predicts dynamics better than influx productivity). This study aims at forest inventory data to empirically test the approach and generate a spatially explicit understanding of in mature forests. We further compared estimates with dynamic global vegetation models (DGVMs) assimilation product C stocks fluxes—CARDAMOM. Location Continents. Time Period Historic from 1951 2018. Major Taxa Studied Trees Methods approaches versus for estimating predicting accumulation rates. investigated abiotic biotic drivers spatial generated map 0.25-degree resolution across continents machine learning. examined whether six DGVMs CARDAMOM generally captured observational pattern . Results Woody by (with R2 0.4–0.5) predicted rates 0.1–0.4) continents. found large variations forests, highest values temperate forests (98.8 ± 2.6 y) followed boreal (73.9 3.6 tropical The extrapolated plot showed higher wetter eastern pacific coast USA, Africa Amazon. Climate (temperature aridity index) structure (tree density age) were dominant was not either or CARDAMOM. Main Conclusions Our demonstrated preference over estimate underlying provide valuable information improve representation demography carbon processes DGVMs.