Abstract
Humans impact terrestrial, marine and freshwater ecosystems, yet many broad-scale studies have found no systematic, negative biodiversity changes (for example, decreasing abundance or taxon richness). Here we show that mixed biodiversity responses may arise because community metrics show variable responses to anthropogenic impacts across broad spatial scales. We first quantified temporal trends in anthropogenic impacts for 1,365 riverine invertebrate communities from 23 European countries, based on similarity to least-impacted reference communities. Reference comparisons provide necessary, but often missing, baselines for evaluating whether communities are negatively impacted or have improved (less or more similar, respectively). We then determined whether changing impacts were consistently reflected in metrics of community abundance, taxon richness, evenness and composition. Invertebrate communities improved, that is, became more similar to reference conditions, from 1992 until the 2010s, after which improvements plateaued. Improvements were generally reflected by higher taxon richness, providing evidence that certain community metrics can broadly indicate anthropogenic impacts. However, richness responses were highly variable among sites, and we found no consistent responses in community abundance, evenness or composition. These findings suggest that, without sufficient data and careful metric selection, many common community metrics cannot reliably reflect anthropogenic impacts, helping explain the prevalence of mixed biodiversity trends.
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Data availability
All community metrics, biomonitoring indices and ecological quality data needed to reproduce our analyses are publicly available from Figshare at https://doi.org/10.6084/m9.figshare.24486769.
Code availability
All code used for our analyses is available upon request.
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Acknowledgements
We thank J. England for assistance with calculating ecological quality and the biomonitoring indices in the UK. Funding for authors, data collection and processing was provided by the European Union Horizon 2020 project eLTER PLUS (grant number 871128). F.A. was supported by the Swiss National Science Foundation (grant numbers 310030_197410 and 31003A_173074) and the University of Zurich Research Priority Program Global Change and Biodiversity. J.B. and M.A.-C. were funded by the European Commission, under the L‘Instrument Financier pour l’Environnement (LIFE) Nature and Biodiversity program, as part of the project LIFE-DIVAQUA (LIFE18 NAT/ES/000121) and also by the project ‘WATERLANDS’ (PID2019-107085RB-I00) funded by the Ministerio de Ciencia, Innovación y Universidades (MCIN) and Agencia Estatal de Investigación (AEI; MCIN/AEI/10.13039/501100011033/ and by the European Regional Development Fund (ERDF) ‘A way of making Europe’. N.J.B. and V.P. were supported by the Lithuanian Environmental Protection Agency (https://aaa.lrv.lt/) who collected the data and were funded by the Lithuanian Research Council (project number S-PD-22-72). J.H. was supported by the Academy of Finland (grant number 331957). S.C.J. acknowledges funding by the Leibniz Competition project Freshwater Megafauna Futures and the German Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung or BMBF; 033W034A). A.L. acknowledges funding by the Spanish Ministry of Science and Innovation (PID2020-115830GB-100). P.P., M.P. and M.S. were supported by the Czech Science Foundation (GA23-05268S and P505-20-17305S) and thank the Czech Hydrometeorological Institute and the state enterprises Povodí for the data used to calculate ecological quality metrics from the Czech surface water monitoring program. H.T. was supported by the Estonian Research Council (number PRG1266) and by the Estonian national program ‘Humanitarian and natural science collections’. M.J.F. acknowledges the support of Fundação para a Ciência e Tecnologia, Portugal, through the projects UIDB/04292/2020 and UIDP/04292/2020 granted to the Marine and Environmental Sciences Centre, LA/P/0069/2020 granted to the Associate Laboratory Aquatic Research Network (ARNET), and a Call Estímulo ao Emprego Científico (CEEC) contract.
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J.S.S. and P.H. conceived the study. E.A.R.W. cleaned the data, and J.S.S. performed the analyses. J.S.S. and P.H. wrote the majority of the manuscript. F.A., M.A.-C., J.A., N.J.B., L. Barešová, J.B., L. Bonacina, N.B., M.C.-A., Z.C., E.d.E., A.D., G.D., T.E.E., V.E., M.J.F., M. Ferréol, M. Floury, M.A.E.F., R.F., P.L.M.G., J.H., D.H., K.-L.H., S.C.J., R.K.J., L.K., B.K., L.L., A.L., P.L., A.W.L., B.G.M., T.M., D.O., R.P., V.P., P.P., F.P., M.P., J.J.R., R.B.S., A.S.-K., A. Scotti, A. Skuja, M.S., R.S., H.T., V.T., I.T., R.V., G. Várbíró, G. Velle, R.C.M.V. and S.V. provided invertebrate data or contributed to calculating ecological quality values for their respective countries, and all edited the manuscript.
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A. Scotti is affiliated with APEM, which is an environmental consultancy company, although they provided no funding for this study. The other authors declare no competing interests.
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Extended data
Extended Data Fig. 1 Year-to-year changes in ecological quality.
Differences in the predicted (a) EQRs and (b) EQCs between each year and the previous year during 1993–2019. For example, the 1993 values are the absolute differences in the predicted EQRs/EQCs between 1992 and 1993. Thus, values closer to 0 indicate less change between successive years. Predicted values for the EQRs and EQCs were obtained from their respective generalized additive mixed models (that is, the fitted relationships in Fig. 2).
Extended Data Fig. 2 Country-scale links between ecological quality, community metrics, and biomonitoring indices.
Redundancy Analyses (RDAs) of the relationship between the Ecological Quality Ratios (EQRs; black arrows) and the community metrics and biomonitoring indices for (a) Finland (FI), (b) Ireland (IE), (c) Luxembourg (LU), (d) Norway (NO), and (e) the United Kingdom (UK). The community metrics comprise abundance (Nind), richness (Ntaxa), evenness (EvPie), Shannon diversity (H), and temporal turnover between consecutive years (TurnY) and compared to the first year (Turn1). The biomonitoring indices comprise the total abundance (EPTind), proportion (EPT%), and richness (EPTtaxa) of Ephemeroptera, Plecoptera, and Trichoptera, in addition to the Community Temperature Index (CTI), the proportion of littoral taxa (Plit), and the Rhithron feeding type index (RETI; all indices are described in the Methods and Extended Data Table 1). Metrics and indices are coloured from orange to blue based on their loadings on RDA axis 1, with blues indicating stronger relationships to ecological quality.
Extended Data Fig. 3 Site-scale links between ecological quality and biomonitoring indices.
Relationship between the temporal slope of the Ecological Quality Ratio (EQR) at each site and the slope of (a) the richness of Ephemeroptera, Plecoptera, and Trichoptera (EPTtaxa), (b) EPT abundance (EPTind), (c) the proportion of EPT taxa (EPT%), (d) the Average Score Per Taxon (ASPT) index, (e) the Community Temperature Index (CTI), (f) the proportion of littoral taxa (Plit), (g) the Saprobic Index (SI), and (h) the Rhithron feeding type index (RETI). Sites are coloured by country and sites with matching ecological quality and biodiversity trends are in the gray shaded areas, whereas opposing relationships are in the white areas.
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Sinclair, J.S., Welti, E.A.R., Altermatt, F. et al. Multi-decadal improvements in the ecological quality of European rivers are not consistently reflected in biodiversity metrics. Nat Ecol Evol 8, 430–441 (2024). https://doi.org/10.1038/s41559-023-02305-4
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DOI: https://doi.org/10.1038/s41559-023-02305-4
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