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Abstract Lignin is an abundant and complex plant polymer that may limit litter decomposition, yet lignin is sometimes a minor constituent of soil organic carbon (SOC). Accounting for diversity in soil characteristics might reconcile this apparent contradiction. Tracking decomposition of a lignin/litter mixture and SOC across different North American mineral soils using lab and field incubations, here we show that cumulative lignin decomposition varies 18-fold among soils and is strongly correlated with bulk litter decomposition, but not SOC decomposition. Climate legacy predicts decomposition in the lab, and impacts of nitrogen availability are minor compared with geochemical and microbial properties. Lignin decomposition increases with some metals and fungal taxa, whereas SOC decomposition decreases with metals and is weakly related with fungi. Decoupling of lignin and SOC decomposition and their contrasting biogeochemical drivers indicate that lignin is not necessarily a bottleneck for SOC decomposition and can explain variable contributions of lignin to SOC among ecosystems. 

Molecular simulations are a powerful tool in the study of crystallization and polymorphic transitions yielding detailed information of transformation mechanisms with high spatiotemporal resolution. How- ever, characterizing various crystalline and amorphous phases as well as sampling nucleation events and structural transitions remain extremely challenging tasks. The integration of machine learning with molecular simulations has the potential of unprecedented advancement in the area of crystal nucleation and growth. In this article, we discuss recent progress in the analysis and sampling of structural trans- formations aided by machine learning and the resulting potential future directions opening in this area. 

Nitrogen (N) is a key limiting nutrient in terrestrial ecosystems, but there remain critical gaps in our ability to predict and model controls on soil N cycling. This may be in part due to lack of standardized sampling across broad spatial–temporal scales. Here, we introduce a continentally distributed, publicly available data set collected by the National Ecological Observatory Network (NEON) that can help fill these gaps. First, we detail the sampling design and methods used to collect and analyze soil inorganic N pool and net flux rate data from 47 terrestrial sites. We address methodological challenges in generating a standardized data set, even for a network using uniform protocols. Then, we evaluate sources of variation within the sampling design and compare measured net N mineralization to simulated fluxes from the Community Earth System Model 2 (CESM2). We observed wide spatiotemporal variation in inorganic N pool sizes and net transformation rates. Site explained the most variation in NEON’s stratified sampling design, followed by plots within sites. Organic horizons had larger pools and net N transformation rates than mineral horizons on a sample weight basis. The majority of sites showed some degree of seasonality in N dynamics, but overall these temporal patterns were not matched by CESM2, leading to poor correspondence between observed and modeled data. Looking forward, these data can reveal new insights into controls on soil N cycling, especially in the context of other environmental data sets provided by NEON, and should be leveraged to improve predictive modeling of the soil N cycle.

 

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. 

Confidence in model estimates of soil CO₂flux depends on assumptions regarding fundamental mechanisms that control the decomposition of litter and soil organic carbon (SOC). Multiple hypotheses have been proposed to explain the role of lignin, an abundant and complex biopolymer that may limit decomposition. We tested competing mechanisms using data‐model fusion with modified versions of the CN‐SIM model and a 571‐day laboratory incubation dataset where decomposition of litter, lignin, and SOC was measured across 80 soil samples from the National Ecological Observatory Network. We found that lignin decomposition consistently decreased over time in 65 samples, whereas in the other 15 samples, lignin decomposition subsequently increased. These “lagged‐peak” samples can be predicted by low soil pH, high extractable Mn, and fungal community composition as measured by ITS PC2 (the second principal component of an ordination of fungal ITS amplicon sequences). The highest‐performing model incorporated soil biogeochemical factors and daily dynamics of substrate availability (labile bulk litter:lignin) that jointly represented two hypotheses (C substrate limitation and co‐metabolism) previously thought to influence lignin decomposition. In contrast, models representing either hypothesis alone were biased and underestimated cumulative decomposition. Our findings reconcile competing hypotheses of lignin decomposition and suggest the need to precisely represent the role of lignin and consider soil metal and fungal characteristics to accurately estimate decomposition in Earth‐system models.

 

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.