Fungal:bacterial biomass balance links environmental gradients to soil respiration across a forest-to-marsh transition

Abstract

Soil microbes regulate whether carbon is retained in soils or returned to the atmosphere through respiration, but the extent to which microbial community characteristics improve the prediction of heterotrophic soil CO2 production beyond predictions by environmental controls remains unclear. We tested this across a topographically structured forest-to-marsh gradient in coastal Oregon by measuring heterotrophic soil respiration, soil physicochemical properties, PLFA-based microbial biomass, metagenomic taxonomic composition, and functional gene-based trait indicators. Across the gradient, soil moisture increased from forest to marsh, while mineral soil and organic matter C:N decreased. These environmental shifts were accompanied by strong but uneven microbial responses: total microbial, fungal, and bacterial biomass declined from forest to marsh, taxonomic composition showed the strongest structuring by environmental conditions, and functional gene-based indicators showed mixed relationships with the gradient. Environmental model comparisons identified soil moisture and organic layer C as the strongest baseline predictors of respiration. Among microbial descriptors, only a small subset improved respiration prediction beyond this environmental baseline. The fungal:bacterial (F:B) biomass ratio produced the largest increase in model fit and the greatest reduction in AICc, whereas the best taxonomic and functional-gene predictors yielded more minor gains. Our results showed that the microbial descriptors most responsive to environmental gradients were not the ones most useful for predicting soil respiration; instead, a relatively simple biomass-partitioning metric captured respiration-relevant microbial variation more effectively than finer taxonomic and genomic descriptors. This suggests that F:B ratio may be especially useful for representing respiration-relevant microbial variation in local landscape-scale studies and for future carbon cycle models applied across heterogeneous ecosystem transition zones.