Biogeographical Distribution of River Microbial Communities in Atlantic Catchments

Abstract

Microbes inhabit virtually all river ecosystems, influencing energy flow and playing a key role in global sustainability and climate change. Yet, there is uncertainty about how various taxonomic groups respond to large-scale factors in river networks. We analysed microbial community richness and composition across six European Atlantic catchments using environmental DNA sequencing. Our findings reveal different drivers for diversity and composition: land use is pivotal for eukaryotes, while climate and geology are crucial for prokaryotes. A strong regional influence shapes these communities, with warmer, drier regions (Portugal and France) differing from cooler, wetter ones (Northern Spain, Ireland and the United Kingdom). These patterns suggest potential indicators for global change, such as taxa resistant to temperature increases and water scarcity, or those sensitive to land use changes.

Keywords: Atlantic landscapes; eDNA; eukaryotes; freshwater microbial communities; land use; prokaryotes.

FIGURE 1

Map of river networks, study area boundaries and sampling sites along the Atlantic catchments: (a) Carlingford Lough (Ireland and the United Kingdom), (b) Couesnon (France), (c) Pas, Miera and Asón (Spain) and (d) Paiva (Portugal).

FIGURE 2

Overview of the prokaryote (V4 16S rDNA) and eukaryote (V7 18S rDNA) datasets in the Atlantic catchments. Sequence size rarefaction curve for each of the analysed groups (a–d) and treemaps representing the diversity (size of rectangles) of the most abundant phyla per taxonomical group ((e) prokaryotes, (f) fungi, (g) heterotrophic protists and (h) algae). Panel e—A: Acidobacteria; C: Cyanobacteria; Ch: Chloroflexi; E: Euryarchaeota; P: Planctomycetes; V: Verrucomicrobia. Panel f—M: Mucoromycota; P: Peronosporomycetes; Zoopag: Zoopagomycota. Panel g—Labyrinthulo: Labyrinthulomycetes; M: Mast‐3. Panel h—Cryptop: Cryptophyta.

FIGURE 3

(a–d) Variation in rarefied richness (expressed as number of ASVs) across catchments. Letters denote statistically homogeneous groups based on ANOVA. (e–j) Rarefied richness correlations (Pearson) between RMC groups.

FIGURE 4

(a–d) Redundancy analysis (RDA) between microbial communities (prokaryotes; fungi; heterotrophic protists and algae) at highest level of taxonomic resolution possible and environmental variables. For each of the analysed communities, environmental variables were selected via redundancy analysis followed by forward selection separately per group of variables: (1) climatic‐geological (red), (2) topographical (black) and (3) LULC (grey). Then, the selected variables were used for final redundancy analysis. See Table S1 for environmental variable codes. (e–j) Correlations of community composition (Mantel tests based on Bray Curtis similarity).

FIGURE 5

(a–d) Variation partitioning results denoting the percentage of unique explained variation per group (single effects) of environmental variables (climatic‐geological, topographical and LULC) for RMC richness and composition. (e–h) Percentage of shared variation (interaction effects) of the three groups of variables for RMC richness and composition.

FIGURE 6

Correlation heatmaps showing the Pearson correlation between the significant environmental variables based on RDAs and the top 30 most abundant taxa per taxonomical group: (a) prokaryotes, (b) fungi, (c) heterotrophic protist and (d) algae. Column dendrograms denotes correlation between environmental variables and row dendrograms denote taxa correlations. Taxa prevalence (Prev.) and relative abundance (Abun.) is also shown per taxonomical group. The first division of row dendrograms split the taxa in two groups: red rectangles denote communities more abundant in warmer catchments (Couesnon and Paiva) while blue rectangles denote communities more abundant in catchments with lower average temperature and regular year rainfall (Spanish catchments and Carlingford Lough). See Table S1 for environmental variable codes.