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Wood decomposition is regulated by multiple controls, including climate and wood traits, that vary at local to regional scales. Yet decomposition rates differ dramatically when these controls do not. Fungal community dynamics are often invoked to explain these differences, suggesting that knowledge of ecosystem properties that influence fungal communities will improve understanding and projection of wood decomposition. We hypothesize that deadwood inputs decompose faster in forests with higher stocks of downed coarse woody material (CWM) because CWM is a resource from which lignocellulolytic fungi rapidly colonize new inputs. To test this hypothesis, we measure decomposition of 1,116 pieces of fine woody material (FWM) of five species, incubated for 13 to 49 months at five locations spanning 10°-latitude in eastern U.S. forest. We place FWM pieces near and far from CWM across observational transects and experimental common gardens. Soil temperature positively affects location-level mean decomposition rates, but these among-location differences are smaller than within-location variation in decomposition. Some of this variability is caused by CWM, where FWM pieces next to CWM decompose more rapidly. These effects are greater with time of incubation and lower initial wood density of FWM. The effect size of CWM is of the same relative magnitude as for the known controls of temperature, deadwood density and diameter. Abundance data for CWM is available for many forests and hence may be an ecosystem variable amenable for inclusion in decomposition models. Our findings suggest that conservation efforts to rebuild depleted CWM stocks in temperate forests may accelerate decomposition of fresh deadwood inputs. Please see the associated manuscript for the Methods. All files are in .txt or .csv format and so can be opened with common, open-source software. The file named 'README_BradfordetalCWMproximity.txt' describes the uploaded files. 

Soil organic matter (SOM) stocks, decom- position and persistence are largely the product of controls that act locally. Yet the controls are shaped and interact at multiple spatiotemporal scales, from which macrosystem patterns in SOM emerge. Theory on SOM turnover recognizes the resulting spatial and temporal conditionality in the effect sizes of controls that play out across macrosystems, and couples them through evolutionary and community assembly pro- cesses. For example, climate history shapes plant functional traits, which in turn interact with contem- porary climate to influence SOM dynamics. Selection and assembly also shape the functional traits of soil decomposer communities, but it is less clear how in turn these traits influence temporal macrosystem patterns in SOM turnover. Here, we review evidence that establishes the expectation that selection and assembly should generate decomposer communities across macrosystems that have distinct functional effects on SOM dynamics. Representation of this knowledge in soil biogeochemical models affects the magnitude and direction of projected SOM responses under global change. Yet there is high uncertainty and low confidence in these projections. To address these issues, we make the case that a coordinated set of empirical practices are required which necessitate (1) greater use of statistical approaches in biogeochem- istry that are suited to causative inference; (2) long- term, macrosystem-scale, observational and experi- mental networks to reveal conditionality in effect sizes, and embedded correlation, in controls on SOM turnover; and (3) use of multiple measurement grains to capture local- and macroscale variation in controls and outcomes, to avoid obscuring causative understanding through data aggregation. When employed together, along with process-based models to synthesize knowledge and guide further empirical work, we believe these practices will rapidly advance understanding of microbial controls on SOM and improve carbon cycle projections that guide policies on climate adaptation and mitigation.