Wed, Aug 04, 2021:On Demand
Background/Question/Methods
Fine root litter accounts for roughly half of litter inputs to forest soils and is increasingly recognized as a primary source of soil organic matter, yet the ecological factors that regulate fine root decomposition remain a key uncertainty in our understanding of the terrestrial carbon cycle. There is growing evidence that microbial community composition is an important control over litter decomposition, and that competitive interactions may directly modify the physiological processes of fungi that regulate decomposition. Thus, we predicted these competitive interactions explain greater variation in fine root decomposition than environmental factors. We established a litterbag field study encompassing 72 plots evenly distributed among 12 northern hardwood forest ecosystems in northern Lower Michigan to evaluate the relative roles of environmental factors and fungal community characteristics in regulating fine root decomposition. We measured key environmental factors, including soil pH, inorganic nitrogen availability, and hourly values for soil temperature and volumetric water content. We are currently characterizing fungal community composition in decaying fine root litter using high throughput amplicon sequencing of the ITS2 region of the fungal ribosomal internal transcribed region.
Results/Conclusions We performed a generalized additive mixed effects model (GAMM) to evaluate fine root decomposition (% mass loss after 13 months of decomposition) as a function of environmental variables while explicitly accounting for spatial autocorrelation among plots (n = 72). We found that fine root mass loss was positively related to inorganic N availability (P = 0.045), negatively related to both mean volumetric water content (P = 0.008) and soil temperature (P = 0.006), and was not related to soil pH (P > 0.1). However, these environmental factors only accounted for 27% of the variation in fine root decomposition after accounting for geographic distance among plots, suggesting there are other ecological processes regulating the decomposition of this important litter source. To determine if competitive interactions among fungi account for a significant portion of residual variation in fine root decomposition, we will quantify negative species co-occurrences not explained by shared environmental preferences using Bayesian Markov chain Monte Carlo models. There is evidence that the effect of competitive interactions on fungal community activity scales with competition strength. Thus, we will quantify a community-weighted competition strength based on these co-occurrences (putative competitive interactions) and incorporate these values into the GAMM as an additional predictor.
Results/Conclusions We performed a generalized additive mixed effects model (GAMM) to evaluate fine root decomposition (% mass loss after 13 months of decomposition) as a function of environmental variables while explicitly accounting for spatial autocorrelation among plots (n = 72). We found that fine root mass loss was positively related to inorganic N availability (P = 0.045), negatively related to both mean volumetric water content (P = 0.008) and soil temperature (P = 0.006), and was not related to soil pH (P > 0.1). However, these environmental factors only accounted for 27% of the variation in fine root decomposition after accounting for geographic distance among plots, suggesting there are other ecological processes regulating the decomposition of this important litter source. To determine if competitive interactions among fungi account for a significant portion of residual variation in fine root decomposition, we will quantify negative species co-occurrences not explained by shared environmental preferences using Bayesian Markov chain Monte Carlo models. There is evidence that the effect of competitive interactions on fungal community activity scales with competition strength. Thus, we will quantify a community-weighted competition strength based on these co-occurrences (putative competitive interactions) and incorporate these values into the GAMM as an additional predictor.