Data for: 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 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 τ in mature forests. We further
compared woody τ 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 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 six DGVMs and CARDAMOM generally captured the observational
pattern of woody τ. Results: Woody τ 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 τ 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
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 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 CARDAMOM. Main conclusions: Our study empirically demonstrated
the preference of using outflux over influx to estimate woody
τ for predicting biomass accumulation rates. The spatially
explicit map of woody τ and the underlying drivers provide
valuable information to improve the  representation of forest
demography and carbon turnover processes in DGVMs.