Chemical and microbial diversity covary in fresh water to influence ecosystem functioning

Abstract

Invisible to the naked eye lies a tremendous diversity of organic molecules and organisms that make major contributions to important biogeochemical cycles. However, how the diversity and composition of these two communities are interlinked remains poorly characterized in fresh waters, despite the potential for chemical and microbial diversity to promote one another. Here we exploited gradients in chemodiversity within a common microbial pool to test how chemical and biological diversity covary and characterized the implications for ecosystem functioning. We found that both chemodiversity and genes associated with organic matter decomposition increased as more plant litterfall accumulated in experimental lake sediments, consistent with scenarios of future environmental change. Chemical and microbial diversity were also positively correlated, with dissolved organic matter having stronger effects on microbes than vice versa. Under our experimental scenarios that increased sediment organic matter from 5 to 25% or darkened overlying waters by 2.5 times, the resulting increases in chemodiversity could increase greenhouse gas concentrations in lake sediments by an average of 1.5 to 2.7 times, when all of the other effects of litterfall and water color were considered. Our results open a major new avenue for research in aquatic ecosystems by exposing connections between chemical and microbial diversity and their implications for the global carbon cycle in greater detail than ever before.

Keywords: carbon cycling; chemical diversity; fresh waters; microbial diversity.

Fig. 1.

Microbial and chemical diversity were positively correlated in sediments of a light- and dark-colored lake. (A) Effective number of microbial taxa (S_E) and molecular formulas (F_E) across 25 sediment mesocosms in a light and a dark lake. Marker gene sequences were clustered into operational taxonomic units (OTUs) based on their similarity to delineate microbial taxa. (B) All pairwise combinations across the 25 mesocosms in the Bray–Curtis similarity index for communities of OTUs and mixtures of molecular formulas. Values approaching 0 indicate two mesocosms do not share any species, whereas values of 1 indicate identical composition. Solid lines show line of best fit. r = 0.60 and 0.25 in A and B, respectively.

Fig. 2.

Sediments had higher concentrations of greenhouse gases with increasing chemodiversity. Partial residuals from regressions predicting log-transformed (A) CO₂ and (B) CH₄ concentration across 25 sediment mesocosms in a light and a dark lake. Solid lines are mean model fit (±95% CI) at the mean of other variables. Model R² = 0.85 and 0.65 in A and B, respectively. Partial residuals visualize the relationship between chemodiversity and the greenhouse gases, when accounting for all of the other predictors in the multiple regression models. They were calculated in each model by adding the product of observed chemodiversity and its estimated effect to the difference between observed and predicted response values (91).