%AGeorgiou, Katerina%AGeorgiou, Katerina%AMalhotra, Avni%AMalhotra, Avni%AWieder, William%AWieder, William%AEnnis, Jacqueline%AEnnis, Jacqueline%AHartman, Melannie%AHartman, Melannie%ASulman, Benjamin%ASulman, Benjamin%ABerhe, Asmeret%ABerhe, Asmeret%AGrandy, A.%AGrandy, A.%AKyker-Snowman, Emily%AKyker-Snowman, Emily%ALajtha, Kate%ALajtha, Kate%AMoore, Jessica%AMoore, Jessica%APierson, Derek%APierson, Derek%AJackson, Robert%AJackson, Robert%BJournal Name: Biogeochemistry; Journal Volume: 156; Journal Issue: 1; Related Information: CHORUS Timestamp: 2021-09-20 05:33:53 %D2021%ISpringer Science + Business Media %JJournal Name: Biogeochemistry; Journal Volume: 156; Journal Issue: 1; Related Information: CHORUS Timestamp: 2021-09-20 05:33:53 %K %MOSTI ID: 10275995 %PMedium: X %TDivergent controls of soil organic carbon between observations and process-based models %X
The storage and cycling of soil organic carbon (SOC) are governed by multiple co-varying factors, including climate, plant productivity, edaphic properties, and disturbance history. Yet, it remains unclear which of these factors are the dominant predictors of observed SOC stocks, globally and within biomes, and how the role of these predictors varies between observations and process-based models. Here we use global observations and an ensemble of soil biogeochemical models to quantify the emergent importance of key state factors – namely, mean annual temperature, net primary productivity, and soil mineralogy – in explaining biome- to global-scale variation in SOC stocks. We use a machine-learning approach to disentangle the role of covariates and elucidate individual relationships with SOC, without imposing expected relationships