Microbiome Damköhler number predicts lotic microbial community succession in river networks

TL;DR

This paper introduces a new metric called the microbiome Damköhler number to predict how microbial communities change in river networks based on water flow and growth rates.

Contribution

The novel contribution is the application of the Damköhler number from chemical engineering to predict microbial community assembly in rivers.

Findings

Microbial diversity correlates more strongly with the Damköhler number than with season, basin identity, or water chemistry.

The Nearest Taxon Index increases with higher Dam values, indicating stronger environmental filtering of microbial communities.

Phylogenetic diversity declines with increasing Dam, likely due to the loss of non-growing, dispersed taxa.

Abstract

River microbiomes are dynamic contributors to ecosystem function and sensitive indicators of hydrologic processes, yet predictive frameworks for their assembly across drainage networks remain limited. We introduce the microbiome Damköhler number (Dam), a simple, mechanistic metric that captures the balance between microbial transport and growth. Adapted from chemical engineering, Dam is defined as the ratio of microbial travel time to microbial community doubling time. Travel time was estimated from mean water residence time and doubling time from temperature-dependent growth rates. We tested this framework using microbiome datasets from four similarly sized US rivers with contrasting hydrology and temperature regimes. Microbial diversity correlated more strongly with Dam than with basin identity, season, or water chemistry. Nearest Taxon Index (NTI) was consistently positive, indicating that environmental filtering controlled community assembly. Moreover, NTI increased with Dam, suggesting stronger selection with longer travel times and shorter doubling times. Faith’s phylogenetic diversity declined with Dam, likely reflecting the progressive loss of phylogenetically diverse, non-growing taxa dispersed from surrounding landscapes. To evaluate predictive power, we fit generalized dissimilarity models and found that Dam explained the largest share of phylogenetic turnover and substantially improved out-of-watershed predictions compared to models without Dam. These results suggest that river microbiome succession is governed by the interplay between hydrologic transport and growth timescales. Because Dam can be calculated from residence time and temperature, it provides a scalable, mechanistic predictor of microbiome structure in river networks and offers a unifying framework for forecasting microbial community assembly in flowing waters.

Graphical Abstract

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