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Abstract Aluminum (Al)‐bearing and iron (Fe)‐bearing minerals, especially short‐range‐ordered (SRO) phases, are thought to protect soil organic C (SOC). However, it remains methodologically challenging to assess the influence of Al vs. Fe minerals or metal complexes. Whereas SRO Al and Fe phases share some properties, Al dissolved by oxalate (Al_ox) often correlates stronger with SOC than Fe dissolved by oxalate (Fe_ox) or citrate–dithionite (Fe_cd). To further evaluate these relationships, we analyzed a large North American soil dataset from the National Ecological Observatory Network. A strong relationship between Al_oxand SOC (and weaker Fe_ox‐SOC relationship) persisted even after excluding soils rich in SRO minerals (Andisols and Spodosols). Al dissolved by oxalate was strongly correlated with citrate–dithionite‐extractable Al (Al_cd; slope = 0.92,R² = .69), and discrepancies could be explained (R² = .87) by greater dissolution of Al‐substituted Fe phases by citrate–dithionite and greater dissolution of aluminosilicates by oxalate. Aluminum dissolved by oxalate and Al_cdwere both strong SOC predictors despite their differing relationships with silicon (Si). Al dissolved by oxalate and Si_oxstrongly covaried (R² = .79), but Al_cdwas inconsistently related to Si_cd(R² = .18). Similar relationships of Al_oxand Al_cdwith SOC, despite differences in minerals extracted by oxalate and citrate–dithionite, suggest that Al‐OC complexes (as opposed to aluminosilicate or iron‐bearing minerals) were the best SOC predictor. This raises important questions: do Al‐OC complexes indicate protection from decomposition or simply reflect greater intensity of mineral weathering by organic acids; and, if the latter, then perhaps SOC input is driving Al_oxand SOC correlations rather than Al phase composition or abundance. 

Abstract Previous studies found conflicting results on the importance of temperature and precipitation versus geochemical variables for predicting soil organic carbon (SOC) concentrations and trends with depth, and most utilized linear statistical models. To reconcile the controversy, we used data from 2574 mineral horizons from 675 pits from National Ecological Observatory Network sites across North America, typically collected to 1 m depth. Climate was a fundamental predictor of SOC and played similarly important roles as some geochemical predictors. Yet, this only emerged in the generalized additive mixed model and random forest model and was obscured in the linear mixed model. Relationships between water availability and SOC were strongest in very dry ecosystems and SOC increased most strongly at mean annual temperature < 0°C. In all models, depth, oxalate‐extractable Al (Al_ox), pH, and exchangeable calcium plus exchangeable magnesium were important while silt + clay, oxalate‐extractable Fe (Fe_ox), and vegetation type were weaker predictors. Climate and pH were independently related to SOC and also interacted with geochemical composition: Fe_oxand Al_oxrelated more strongly to SOC in wet or cold climates. Most predictors had nonlinear threshold relationships with SOC, and a saturating response to increasing reactive metals indicates soils where SOC might be limited by C inputs. We observed a mostly constant relative importance of geochemical and climate predictors of SOC with increasing depth, challenging previous statements. Overall, our findings challenge the notion that climate is redundant after accounting for geochemistry and demonstrate that considering their nonlinearities and interactions improves spatial predictions of SOC. 

Abstract Carbon (C) emission and uptake due to land use and land cover change (LULCC) are the most uncertain term in the global carbon budget primarily due to limited LULCC data and inadequate model capability (e.g., underrepresented agricultural managements). We take the commonly used FAOSTAT‐based global Land Use Harmonization data (LUH2) and a new high‐resolution multisource harmonized national LULCC database (YLmap) to drive a land ecosystem model (DLEM) in the conterminous United States. We found that recent cropland abandonment and forest recovery may have been overestimated in the LUH2 data derived from national statistics, causing previously reported C emissions from land use have been underestimated due to the definition of cropland and aggregated LULCC signals at coarse resolution. This overestimation leads to a strong C sink (30.3 ± 2.5 Tg C/year) in model simulations driven by LUH2 in the United States during the 1980–2016 period, while we find a moderate C source (13.6 ± 3.5 Tg C/year) when using YLmap. This divergence implies that previous C budget analyses based on the global LUH2 dataset have underestimated C emission in the United States owing to the delineation of suitable cropland and aggregated land conversion signals at coarse resolution which YLmap overcomes. Thus, to obtain more accurate quantification of LULCC‐induced C emission and better serve global C budget accounting, it is urgently needed to develop fine‐scale country‐specific LULCC data to characterize the details of land conversion. 

We used incubations of soil and stable isotope measurements to measure lignin, litter, and SOC decomposition over an 18-month lab incubation and assessed their relationships with geochemical, microbial, N-related and climatic factors across 156 mineral soils collected from 20 National Ecological Observatory Network (NEON) sites, which span broad biophysical gradients (climate, soil, and vegetation type) across North America. The soils were collected in 2019. Lignin decomposition and biogeochemical variables were also measured in an approximately 12-month field incubation. 

We compiled National Ecological Observatory Network (NEON) datasets related to the initial distributed soil sampling effort and subsetted them (removed samples with missing values for certain variables, and several samples with extreme values) for use in statistical analyses to describe relationships between soil organic carbon (SOC) and metals measured in several soil chemical extractions. The NEON provisional data products we used were DP1.10047.001 and DP1.10008.001, which were subsequently combined by NEON as a single data product DP1.10047.001, “Soil physical and chemical properties, distributed initial characterization”. These datasets were used for the analyses reported in a manuscript by Hall and Thompson (2021) in the Soil Science Society of America Journal. 

We incubated 10 forest soils (collected from sites across North America, including the Luquillo LTER/CZO) in the laboratory for over two years to quantify the decomposition of carbon derived from added litter and lignin, as well as from extant soil organic matter. Each soil was subjected to two substrate addition treatments: a) litter derived from a C4 grass precipitated with 13C-enriched lignin, or the same C4 grass litter was precipitated with natural-abundance lignin. The concentrations and delta13C composition of carbon dioxide produced from each soil were measured periodically over time and partitioned into sources (soil organic matter, litter, and added lignin) using isotope mixing models. The methods and results are described in detail by a manuscript in Ecology (Hall et al., 2020). 

To understand controls on soil organic matter chemical composition across North America, we collected 13C NMR spectra and conducted and synthesized additional biogeochemical measurements from NEON Megapit soil samples as well as additional samples (total n = 42). This dataset supports the findings described in the associated manuscript by Hall, Ye et al. (2020).