ArticleThe ISME journal2025
Hydrological regime of a continental river system predicts bacterial macroecological patterns.
Article in The ISME journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Modeling bacterial dynamics in large river systems is crucial for predicting continental-scale ecosystem functioning under anthropogenic pressures. Although the River Continuum and Metacommunity concepts have provided theoretical frameworks, quantitative parameters necessary for microbial macroecological models remain scarce. Here, we present results from two whole-river surveys, conducted six years apart along 2600 km of the Danube River. Using bacterial secondary production, cell counts, and 16S ribosomal RNA (rRNA) gene amplicon sequencing, we quantified carbon, cell, phylotype, and diversity turnover along the river. Carbon incorporation per cell declined with water travel time by 6000-21 000 atoms per hour. Bacterial cells multiplied every eight days, resulting in four to six doublings during downstream transport. Growth responses at the level of individual phylotypes differed up to a hundredfold from these bulk community estimates. Bacterial diversity dynamics were dominated by phylotype turnover rather than phylotype loss. Turnover ranged from 0.92 to 0.96 along the river, indicating an almost complete replacement of phylotypes with 2%-11% of headwater-associated amplicon sequence variants (ASVs) persisting under base-flow conditions. Richness declined gradually downstream at a rate of ~0.13 ASVs per hour. Variations in bacterial secondary production, cell abundance, and observed ASVs were best explained by models combining hydrological and water quality parameters, whereas beta diversity followed a gradual development primarily structured by water travel time. Together, these results identify water travel time as the key integrative parameter governing microbial macroecological dynamics along large rivers, with environmental conditions fine-tuning local responses. These models can help predict changes in microbial diversity and functioning under anthropogenic alterations.
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