Abstract
Freshwater biodiversity is declining at a pace that outstrips the capacity of existing monitoring approaches both in temporal and spatial dimensions, highlighting the urgent need for rapid and scalable assessment and attribution of biodiversity states and changes. Here we present a global assessment and unified analysis of riverine fish biodiversity using environmental DNA collected from 1,818 sites across 113 river systems spanning 5 continents. We quantified species richness, functional redundancy, phylogenetic diversity and genetic sequence diversity, and related them to drainage characteristics. Our results showed that environmental DNA effectively captured global patterns of multi-faceted riverine fish biodiversity and disentangled the roles of climate and different aspects of human activities in shaping biodiversity–area relationships. The accumulation of biodiversity with catchment area was consistently enhanced in warmer climates, while human activities weakened this scaling. Further, species richness, functional and genetic sequence diversity exhibited stronger negative responses to human activities in larger catchments. In contrast, phylogenetic diversity was negatively affected by human activities, yet this effect diminished as catchment area increased, highlighting the facet-dependent nature of biodiversity responses to environmental gradients and catchment characteristics. Our findings demonstrate the power of environmental-DNA-based datasets for harmonized, multi-faceted biodiversity assessments, offering a scalable approach for detecting and attributing biodiversity change and informing conservation strategies under accelerating global change.
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Modelling environmental DNA transport in rivers reveals highly resolved spatio-temporal biodiversity patterns
Comparison between eDNA and traditional morphological methods for fish diversity monitoring in rivers
Data availability
The unified and standardized dataset for global riverine fish eDNA meta-analysis is available via Dryad at https://doi.org/10.5061/dryad.gf1vhhn2h (ref. 86). The minimum dataset required to interpret, verify and extend this study is available via GitHub at https://github.com/Laura61616/GRiversFisheDNA_Main. Source data are provided with this paper.
Code availability
All custom code used for data processing, analysis and figure generation is publicly available via GitHub at https://github.com/Laura61616/GRiversFisheDNA_Main.
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Acknowledgements
We thank the many people involved in the field and laboratory work of the respective studies we based our reanalysis on, and all researchers who provided comments and information on the published dataset. The views expressed in this article are those of the authors’ and do not necessarily represent the views or policies of the US Environmental Protection Agency.
Funding
X.Z. acknowledges support from the National Key Research and Development Program of China (grant number 2022YFC32021001). Y.Z. acknowledges support from the National Natural Science Foundation of China (grant number 42507382). F.A. acknowledges support from the Swiss National Science Foundation (grant numbers 310030_197410 and 31003A_173074). The other authors received no specific funding for this work.
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Y.Z. and F.A. conceived and led the study. Y.Z. collected and analysed the datasets, and wrote a first draft of the paper under the supervision of F.A. and X.Z. Heng Zhang helped with the delineation of river catchments and extraction of drainage characteristics. All other authors contributed data and commented on drafts of the paper.
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X.Z. directs a translation project at Nanjing University that develops apparatus for routine eDNA biomonitoring. The other authors declare no competing interests.
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Extended data
Extended Data Fig. 1 Validation of gamma biodiversity metrics.
a, Relationship between original individually-reported species richness for each river catchment and original recorded species numbers from historical data at the corresponding river basin scale. b, Comparison of ASV-based MNTD with tree-based MNTD derived from a global freshwater fish phylogeny. c, Comparison of genetic sequence diversity estimated from within-species sequence variation with barcode-based genetic sequence diversity derived from COI sequences in NCBI. All relationships were modelled using generalized linear mixed models, and significance was assessed using two-sided Wald tests. Shaded areas indicate 95% confidence intervals. The exact p values, standard errors and model coefficients are reported in Extended Data Table 1.
Source data
Extended Data Fig. 2 Comparison of genetic sequence diversity estimated from within-species ASV sequence variation and barcode-based genetic sequence diversity.
a,b, Comparison of gamma-level genetic sequence diversity derived from ASV sequences with barcode-based genetic sequence diversity calculated from 12S (a) and 16S (b) marker sequences. c, d, Comparison of alpha-level genetic sequence diversity based on ASV sequences with corresponding barcode-based estimates from 12S (c) and 16S (d) markers. All relationships were evaluated using generalized linear mixed models, and significance was assessed using two-sided Wald tests. Shaded areas represent 95% confidence intervals. The exact p values, standard errors and model coefficients are reported in Extended Data Table 1.
Source data
Extended Data Fig. 3 Relationships between temperature and the slope of biodiversity responses to catchment area across four biodiversity facets.
Panels show species richness (a), functional redundancy (b), MNTD (c) and genetic sequence diversity (d). Slopes were estimated from 1,000 generalized linear mixed model fits using 70% downsampling. Solid lines represent Unified biodiversity metrics; dashed lines represent Original individually-reported or conventional metrics. Shaded areas show 95% confidence intervals, and p values reflect the proportion of reshuffled effects exceeding the median estimate. For p < 0.001, this indicates that none of the 1000 reshuffling iterations resulted in an estimated slope exceeding the median of the downsampling distribution. For box plots, the centre line indicates the median, the box bounds indicate the 25th and 75th percentiles, and the whiskers extend to the most extreme data points within 1.5 × the interquartile range.
Source data
Extended Data Fig. 4 Relationships between nighttime lights and the slope of biodiversity responses to catchment area across four biodiversity facets.
Panels show species richness (a), functional redundancy (b), MNTD (c) and genetic sequence diversity (d). Slopes were estimated from 1,000 generalized linear mixed model fits using 70% downsampling. Solid lines represent Unified biodiversity metrics; dashed lines represent Original individually-reported or conventional metrics. Shaded areas show 95% confidence intervals, and p values reflect the proportion of reshuffled effects exceeding the median estimate. For p < 0.001, this indicates that none of the 1000 reshuffling iterations resulted in an estimated slope exceeding the median of the downsampling distribution. For box plots, the centre line indicates the median, the box bounds indicate the 25th and 75th percentiles, and the whiskers extend to the most extreme data points within 1.5 × the interquartile range.
Source data
Extended Data Fig. 5 Area effects on diversity–nighttime lights slopes estimated from downsampling and reshuffling procedures.
a–d, Distributions of area effects on the diversity–nighttime lights slope across four biodiversity facets: (a) species richness, (b) functional redundancy, (c) phylogenetic diversity (MNTD), and (d) genetic sequence diversity. For each facet, effects were estimated from 1,000 generalized linear mixed model fits using 70% downsampling (left violin plots) and 1,000 reshuffled replicates (right violin plots). Unified biodiversity metrics are shown on the left of each panel; Original individually-reported or conventional metrics are shown on the right. p values indicate the proportion of reshuffled estimates that exceed the median effect size derived from the downsampling procedure. Boxplots within violins represent interquartile ranges and medians of the estimated effects. For p < 0.001, this indicates that none of the 1000 reshuffling iterations resulted in an estimated slope exceeding the median of the downsampling distribution. For box plots, the centre line indicates the median, the box bounds indicate the 25th and 75th percentiles, and the whiskers extend to the most extreme data points within 1.5 × the interquartile range.
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Zhang, Y., Zhang, H., Akashi, H. et al. A unified analysis of global riverine eDNA reveals common associations of fish biodiversity with drainage characteristics.
Nat Ecol Evol (2026). https://doi.org/10.1038/s41559-026-03106-1
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DOI: https://doi.org/10.1038/s41559-026-03106-1
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