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 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 $\tau$ 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.