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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 versus 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 was 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. [ABSTRACT FROM AUTHOR]

Aim: This study aimed to infer the allocation of belowground net primary productivity (BNPP) to sequential soil depths down to 2 m across the globe at a 1 km resolution and assess underlying environmental drivers. Location: Global. Time Period: Contemporary (1932–2017). Major Taxa Studied: Terrestrial plants. Methods: Global datasets including field net primary production (NPP, i.e., the difference between plant assimilated and respired carbon) from 725 soil profiles, root biomass and its depth distribution from 559 soil profiles were compiled and used to infer the depth distribution of BNPP across the globe and digitally map depth‐resolved BNPP globally at 1 km resolution. Drivers of the depth distribution of BNPP were evaluated using machine learning‐based models. Results: Global average BNPP allocated to the 0–20 cm soil layer is estimated to be 1.1 Mg C ha−1 yr−1, accounting for ~60% of total BNPP. Across the globe, the depth distribution of BNPP varies largely, and more BNPP is allocated to deeper layers in hotter and drier regions. Edaphic, climatic and topographic properties (in order of importance) explain >80% of such variability; and the direction and magnitude of the influence of individual properties are soil depth‐ and biome‐dependent. Main Conclusions: The findings suggest that mean annual temperature and precipitation are the two most important factors regulating BNPP across the globe. Soil properties such as soil actual evaporation and total nitrogen also play a vital role in regulating the depth distribution of BNPP. The maps of BNPP provide global benchmarks of depth‐resolved BNPP for the prediction of whole‐profile soil carbon dynamics across biomes. [ABSTRACT FROM AUTHOR]

Aim: Fresh carbon (C) inputs to the soil can have important consequences for the decomposition rates of soil organic matter (priming effect), thereby impacting the delicate global C balance at the soil–atmosphere interface. Yet, the environmental factors that control soil priming effect intensity remain poorly understood at a global scale. Location: Global. Time period: 1980–2020. Major taxa studied: Soil priming effect intensity. Methods: We conducted a global dataset of CO2 effluxes in 711 pairwise soils with 13C or 14C simple C sources inputs and without C inputs from incubation experiments in which isotope‐labelled C was used to quantify fresh C‐induced rather than exudate‐induced priming. Results: Soil priming effect intensity is predominantly positive. Soil texture and C content were identified as the most important factors associated with priming effects, with sandy soils from tropical and mid‐latitudes supporting the highest soil priming effect intensity, and soils with greater C content and fine textures from high latitudes maintaining the lowest soil priming effects. The negative association between C content and soil priming effect intensity was also indirectly driven by changing mean annual temperature, net primary productivity, and fungi : bacteria ratio. Using this information, we generated a global map of soil priming effect intensity, and found that the priming was lower at high latitudes and higher at lower latitudes. Main conclusions: Global patterns of soil priming effect intensity can be predicted using environmental data, with soil texture and C content playing a predominant role in explaining in priming effects. These effects were also indirectly driven by climate, vegetation and soil microbial properties. We present the first global atlas of soil priming effect intensity and advance our knowledge on the potential mechanisms underlying soil priming effect intensity, which are integral to improving the climate change and soil C dynamics components of Earth System models. [ABSTRACT FROM AUTHOR]

Aim: The carbon sink in tropical forests is a highly uncertain component of the global carbon budget. An understanding of the processes controlling this sink requires better quantification of carbon allocation, stocks and turnover times. Location: Tropical forests. Time period: 2010–2017. Major taxa studied: Tropical forest ecosystem. Methods: We develop a novel data assimilation system using satellite‐based annual above‐ground biomass derived from L‐band vegetation optical depth with 25 km × 25 km grid spacing, together with leaf area, to constrain the 25 km × 25 km carbon allocation patterns of net primary productivity (NPP) into the wood, leaf and root pools, and their turnover times. Our average data‐driven estimates of these variables are broadly consistent with independent ground‐based estimates of NPP allocation and wood turnover from forest inventory plots. Results: In tropical forest, on average, the NPP allocation into wood (0.30 ± 0.04) is significantly higher than that into leaves (0.24 ± 0.07). From the wet to dry tropics, forest NPP allocation into both wood and leaves declines slightly. The turnover times of forest leaf pools exhibit little spatial variation, whereas the turnover times of wood pools in Africa (median and interquartile range: 50.1‐4.0+5.5 years) are slightly longer than those in South America (48.2‐3.3+4.0 years) and Southeast Asia (48.3‐3.1+5.4 years). Our datasets reveal emergent trade‐offs across climatic and vegetation gradients between growth and life span/turnover for both wood and leaves. The spatial gradients of NPP allocation to wood/leaves are associated with canopy height, adjusted by climate condition and nutrient acquisition. The spatial gradients of wood and leaf turnover times are influenced mainly by climate and leaf characteristics. Main conclusions: Our data‐driven estimates of carbon allocation and turnover times provide a basis for more detailed exploration of these mechanisms in field studies. This highlights that improved model representation of carbon allocation and turnover is necessary for more accurate prediction of future carbon dynamics. [ABSTRACT FROM AUTHOR]