Leveraging model-generated hypotheses and cross-network observations to understand biome- to global-scale controls on soil organic matter stocks

The storage and cycling of soil organic carbon (SOC) are governed by several 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. Historically, soil and ecosystem model development has focused on the role of climate and plant productivity in regulating SOC storage. Emerging theories, however, place a growing emphasis on litter quality and mineralogical properties in controlling the long-term persistence of SOC. Application of these evolving theories in mathematical models generate alternative hypotheses about the relative importance of key state factors and their interactions. Here we use cross-network observations and an ensemble of soil biogeochemical models to quantify the relative importance of key state factors - namely, mean annual temperature, mean annual precipitation, net primary productivity, plant litter chemistry, and soil texture - 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 apriori. While we observe a non-linear decrease in SOC with increasing temperature and a linear increase in SOC with increasing clay and silt content, the magnitude and degree of non-linearity varies substantially among models and observations. We also note a threshold-increase in SOC with increasing precipitation in our observational dataset, though this behavior is not as apparent across models. Elucidating the role of key SOC controls, and the discrepancies between models and cross-network observations, globally and across biomes, is essential for improving and validating process representations in soil and ecosystem models for predictions under novel future conditions.