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There is growing interest in enhancing soil carbon sequestration (SCS) as a climate mitigation strategy, including neutralizing atmospheric emissions from fossil‐fuel combustion through the development of soil carbon offset markets. Several studies have focused on refining estimates of the magnitude of potential SCS or on developing methods for soil carbon quantification in markets. We call on scientists and policy makers to resist assimilating soils into carbon offset markets due to not only fundamental flaws in the logic of these markets to reach climate neutrality but also environmental justice concerns. Here, we first highlight how carbon offset markets rely on an inappropriate substitution of inert fossil carbon with dynamic stocks of soil carbon. We then note the failure of these markets to account for intersecting anthropogenic perturbations to the carbon cycle, including the soil carbon debt and ongoing agricultural emissions. Next, we invite scientists to consider soil functions beyond productivity and profitability. Finally, we describe and support historical opposition to offset markets by environmental justice advocates. We encourage scientists to consider how their research and communications can promote diverse soil functions and just climate‐change mitigation. [ABSTRACT FROM AUTHOR]

Advancements in microbially explicit ecosystem models incorporate increasingly accurate representations of microbial physiology and enzyme‐mediated depolymerization of soil organic matter in predicting biogeochemical responses to global change. However, a major challenge with model structural improvements is the requirement for additional parameters, which are often poorly constrained sources of uncertainty. Furthermore, it is often unclear how to best focus data collection efforts toward reducing model uncertainty. Here, we use Dual Arrhenius Michaelis‐Menten Microbial Carbon and Nitrogen Physiology, a microbially mediated, coupled soil C and N cycling model, as a tool to explore the influence of microbial physiological and enzyme kinetic parameters on model estimates. We first quantify the potential for constraining model parameters using empirical measurements of soil respiration. We then use simulated data to identify which additional sources of data collection from the field would provide the greatest impact for constraining model estimates. We find that modeled soil C and N pools and fluxes are disproportionately sensitive to only a few parameters (e.g., activation energies and microbial CUE), while others exert less influence (e.g., Michaelis‐Menten half‐saturation constants). While some parameters can be constrained by the available data on heterotrophic respiration, the collection of additional data on dissolved organic C and N pools in the soil is identified as a high‐priority data need. Improving our ability to model the interactions of soil microbial physiology, soil chemistry, enzyme activities, and environmental factors on C and N cycling will require closely considering model uncertainties and prioritizing future data collection opportunities based on their impact on model performance. Plain Language Summary: Soil biogeochemical models have increasingly realistic representations of microbial processes; however, model outputs are associated with multiple types of uncertainty. We quantify the uncertainty associated with specific parameters in Dual Arrhenius Michaelis‐Menten Microbial Carbon and Nitrogen Physiology, a microbial coupled carbon and nitrogen cycle model. Reducing uncertainty in model outputs can be achieved through collecting additional data. We use existing and simulated data to identify which types of data would be most useful in reducing parameter uncertainty for this model, which may serve as an example for other microbial soil biogeochemical models. Key Points: Model outputs are highly sensitive to certain enzyme rate parameters (e.g., activation energies), and less sensitive to other parametersRespiration data helped constrain enzyme activation energies and carbon use efficiency; dissolved organic CN data would be a high priorityThis approach can reduce parameter uncertainty in biogeochemical models by tailoring data collection to reduce model uncertainty [ABSTRACT FROM AUTHOR]

Microbial processes play a central role in controlling the availability of N in temperate forests. While bacteria, archaea, and fungi account for major inputs, transformations, and exports of N in soil, relationships between microbial community structure and N cycle fluxes have been difficult to detect and characterize. Several studies have reported differences in N cycling based on mycorrhizal type in temperate forests, but associated differences in N cycling genes underlying these fluxes are not well-understood. We explored how rates of soil N cycle fluxes vary across gradients of mycorrhizal abundance (hereafter "mycorrhizal gradients") at four temperate forest sites in Massachusetts and Indiana, USA. We paired measurements of N-fixation, net nitrification, and denitrification rates with gene abundance data for specific bacterial functional groups associated with each process. We find that the availability of NO3 and rates of N-fixation, net nitrification, and denitrification are reduced in stands dominated by trees associated with ECM fungi. On average, rates of N-fixation and denitrification in stands dominated by trees associated with arbuscular mycorrhizal fungi were more than double the corresponding rates in stands dominated by trees associated with ectomycorrhizal fungi. Despite the structuring of flux rates across the mycorrhizal gradients, we did not find concomitant shifts in the abundances of N-cycling bacterial genes, and gene abundances were not correlated with process rates. Given that AM-associating trees are replacing ECM-associating trees throughout much of the eastern US, our results suggest that shifts in mycorrhizal dominance may accelerate N cycling independent of changes in the relative abundance of N cycling bacteria, with consequences for forest productivity and N retention. [ABSTRACT FROM AUTHOR]

Given that many pollinators have exhibited dramatic declines related to habitat destruction, an improved understanding of pollinator resource collection across human-altered landscapes is essential to conservation efforts. Despite the importance of bumble bees (Bombus spp.) as global pollinators, little is known regarding how pollen collection patterns vary between individuals, colonies, and landscapes. In this study, Vosnesensky bumble bees (Bombus vosnesenskii Radoszkowski) were collected from a range of human-altered and natural landscapes in northern California. Extensive vegetation surveys and Geographic Information System (GIS)-based habitat classifications were conducted at each site, bees were genotyped to identify colony mates, and pollen loads were examined to identify visited plants. In contrast to predictions based on strong competitive interactions, pollen load composition was significantly more similar for bees captured in a shared study region compared with bees throughout the research area but was not significantly more similar for colony mates. Preference analyses revealed that pollen loads were not composed of the most abundant plant species per study region. The majority of ranked pollen preference lists were significantly correlated for pairwise comparisons of colony mates and individuals within a study region, whereas the majority of pairwise comparisons of ranked pollen preference lists between individuals located at separate study regions were uncorrelated. Results suggest that pollen load composition and foraging preferences are similar for bees throughout a shared landscape regardless of colony membership. The importance of native plant species in pollen collection is illustrated through preference analyses, and we suggest prioritization of specific rare native plant species for enhanced bumble bee pollen collection. [ABSTRACT FROM AUTHOR]

Respiration by soil bacteria and fungi is one of the largest fluxes of carbon (C) from the land surface. Although this flux is a direct product of microbial metabolism, controls over metabolism and their responses to global change are a major uncertainty in the global C cycle. Here, we explore an in silico approach to predict bacterial C-use efficiency (CUE) for over 200 species using genome-specific constraint-based metabolic modeling. We find that potential CUE averages 0.62 ± 0.17 with a range of 0.22 to 0.98 across taxa and phylogenetic structuring at the subphylum levels. Potential CUE is negatively correlated with genome size, while taxa with larger genomes are able to access a wider variety of C substrates. Incorporating the range of CUE values reported here into a next-generation model of soil biogeochemistry suggests that these differences in physiology across microbial taxa can feed back on soil-C cycling. Microbial respiration releases carbon from the soil. Here, the authors estimate bacterial carbon use efficiency in soils for over 200 species using constraint-based modeling, incorporate the values into an ecosystem model, and find that shifts in community composition may impact carbon storage. [ABSTRACT FROM AUTHOR]