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ABSTRACT Rapid climate change in the Arctic is altering microbial structure and function, with important consequences for the global ecosystem. Emerging evidence suggests organisms in higher trophic levels may also influence microbial communities, but whether warming alters these effects is unclear. Wolf spiders are dominant Arctic predators whose densities are expected to increase with warming. These predators have temperature-dependent effects on decomposition via their consumption of fungal-feeding detritivores, suggesting they may indirectly affect the microbial structure as well. To address this, we used a fully factorial mesocosm experiment to test the effects of wolf spider density and warming on litter microbial structure in Arctic tundra. We deployed replicate litter bags at the surface and belowground in the organic soil profile and analyzed the litter for bacterial and fungal community structure, mass loss, and nutrient characteristics after 2 and 14 months. We found there were significant interactive effects of wolf spider density and warming on fungal but not bacterial communities. Specifically, higher wolf spider densities caused greater fungal diversity under ambient temperature but lower fungal diversity under warming at the soil surface. We also observed interactive treatment effects on fungal composition belowground. Wolf spider density influenced surface bacterial composition, but the effects did not change with warming. These findings suggest a widespread predator can have indirect, cascading effects on litter microbes and that effects on fungi specifically shift under future expected levels of warming. Overall, our study highlights that trophic interactions may play important, albeit overlooked, roles in driving microbial responses to warming in Arctic terrestrial ecosystems. IMPORTANCEThe Arctic contains nearly half of the global pool of soil organic carbon and is one of the fastest warming regions on the planet. Accelerated decomposition of soil organic carbon due to warming could cause positive feedbacks to climate change through increased greenhouse gas emissions; thus, changes in ecological dynamics in this region are of global relevance. Microbial structure is an important driver of decomposition and is affected by both abiotic and biotic conditions. Yet how activities of soil-dwelling organisms in higher trophic levels influence microbial structure and function is unclear. In this study, we demonstrate that predicted changes in abundances of a dominant predator and warming interactively affect the structure of litter-dwelling fungal communities in the Arctic. These findings suggest predators may have widespread, indirect cascading effects on microbial communities, which could influence ecosystem responses to future climate change. 

Abstract. Soil organic matter (SOM) dynamics in ecosystem-scale biogeochemical modelshave traditionally been simulated as immeasurable fluxes between conceptuallydefined pools. This greatly limits how empirical data can be used to improvemodel performance and reduce the uncertainty associated with theirpredictions of carbon (C) cycling. Recent advances in our understanding ofthe biogeochemical processes that govern SOM formation and persistence demanda new mathematical model with a structure built around key mechanisms andbiogeochemically relevant pools. Here, we present one approach that aims toaddress this need. Our new model (MEMS v1.0) is developed from the MicrobialEfficiency-Matrix Stabilization framework, which emphasizes the importance oflinking the chemistry of organic matter inputs with efficiency of microbialprocessing and ultimately with the soil mineral matrix, when studying SOMformation and stabilization. Building on this framework, MEMS v1.0 is alsocapable of simulating the concept of C saturation and representsdecomposition processes and mechanisms of physico-chemical stabilization todefine SOM formation into four primary fractions. After describing the modelin detail, we optimize four key parameters identified through avariance-based sensitivity analysis. Optimization employed soil fractionationdata from 154 sites with diverse environmental conditions, directly equatingmineral-associated organic matter and particulate organic matter fractionswith corresponding model pools. Finally, model performance was evaluatedusing total topsoil (0–20 cm) C data from 8192 forest and grassland sitesacross Europe. Despite the relative simplicity of the model, it was able toaccurately capture general trends in soil C stocks across extensive gradientsof temperature, precipitation, annual C inputs and soil texture. The novelapproach that MEMS v1.0 takes to simulate SOM dynamics has the potential toimprove our forecasts of how soils respond to management and environmentalperturbation. Ensuring these forecasts are accurate is key to effectivelyinforming policy that can address the sustainability of ecosystem servicesand help mitigate climate change.