Unravelling large-scale patterns and drivers of biodiversity in dry rivers

Abstract

More than half of the world's rivers dry up periodically, but our understanding of the biological communities in dry riverbeds remains limited. Specifically, the roles of dispersal, environmental filtering and biotic interactions in driving biodiversity in dry rivers are poorly understood. Here, we conduct a large-scale coordinated survey of patterns and drivers of biodiversity in dry riverbeds. We focus on eight major taxa, including microorganisms, invertebrates and plants: Algae, Archaea, Bacteria, Fungi, Protozoa, Arthropods, Nematodes and Streptophyta. We use environmental DNA metabarcoding to assess biodiversity in dry sediments collected over a 1-year period from 84 non-perennial rivers across 19 countries on four continents. Both direct factors, such as nutrient and carbon availability, and indirect factors such as climate influence the local biodiversity of most taxa. Limited resource availability and prolonged dry phases favor oligotrophic microbial taxa. Co-variation among taxa, particularly Bacteria, Fungi, Algae and Protozoa, explain more spatial variation in community composition than dispersal or environmental gradients. This finding suggests that biotic interactions or unmeasured ecological and evolutionary factors may strongly influence communities during dry phases, altering biodiversity responses to global changes.

Fig. 1. Location of 84 dry-riverbed sampling sites and their Köppen climate class.

The inset illustrates site locations within the most densely sampled area.

Fig. 2. Composition of Bacteria and Archaea communities in dry riverbed sediments (n = 84).

Read proportions (square root scale) correspond to the relative abundance of each taxon per sample and were estimated using the 16S marker dataset rarefied to 2311 reads per sample. The vertical bold line within the box represents the median. The upper and lower limits of the box represent the 75th and 25th percentiles. Horizontal dotted lines indicate the range of observed values within 1.5× the interquartile range from the 75th and 25th percentiles. Values outside this range were considered outliers and are indicated as points. Source data are provided as a Source Data file.

Fig. 3. Composition of eukaryotic communities in dry riverbed sediments (n = 76).

Read proportions (square root scale) correspond to the relative abundances of each taxon per sample and were estimated using the 18S marker dataset rarefied to 15624 reads per sample. Further details are provided in the Fig. 2 legend. Source data are provided as a Source Data file.

Fig. 4. Influence of environmental variables on the bacterial copiotrophic:oligotrophic ratio.

Partial dependence plots of the relative abundance of bacterial copiotrophic and oligotrophic operational taxonomic units (OTUs), indicating the contribution of five predictor variables to log₁₀(copiotrophic:oligotrophic [copio:oligo] ratio +1) as a function of the predictors (i.e. when the other contributing predictors are held at their mean). Hash marks on x axes indicate the deciles of the predictor variables. Predictors are shown in order of decreasing importance (%IncMse as defined in Table 1).

Fig. 5. Partial correlation network between the beta-diversity of the eight major taxa contributing to dry-riverbed biodiversity and climatic, physicochemical, land-use, and spatial distances between sites, as inferred from a graphical lasso method.

Each node represents the turnover component of beta-diversity (calculated using the Bray-Curtis index) of a taxon (yellow) or a spatial (blue), climatic (orange), or environmental (green) distance. Line width is proportional to the partial correlations (indicated on each line) between pairs of nodes.