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Diverging fish biodiversity trends in cold and warm rivers and streams

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Abstract

Worldwide, freshwater systems contain more than 18,000 fish species1,2,3, which are critical to the functioning of these ecosystems4 and are vital cultural and economic resources to humans5,6,7; despite this value, fish biodiversity is at risk globally8,9. In the USA, leading threats to fish communities in rivers and streams include climate change and invasive fish introductions and game fish stocking by humans10,11,12,13,14. Here we harmonized US federal biomonitoring datasets with 389 species spanning 27 years (1993–2019) and 2,992 sites to analyse trends in fish biodiversity. In cold streams (past summer stream temperatures below 15.4 °C), fish abundance and richness declined by 53.4% and 32% over 27 years, respectively, and uniqueness increased. Periodic (large-bodied, late-maturing) fishes increased, and opportunists (small-bodied, short generation time, ‘r-selected’) decreased, possibly due to proliferation of native or introduced game fishes. In warm streams (stream temperatures greater than 23.8 °C), fish abundance and richness increased by 70.5% and 15.6% over 27 years, respectively, and communities homogenized. Small opportunistic fishes replaced large periodic fishes. Intermediate streams (stream temperatures 15.4–23.8 °C), representing the average stream, had minimal changes in fish biodiversity through time. Interactions between warming and introduced fish were associated with increased rates of degradation to local fish biodiversity. Given the magnitude of these changes in a relatively short time span, there is an urgent need to curb degradation of fish biodiversity caused by fish introductions and warming water temperatures.

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Fig. 1: Conceptual model of central questions evaluated.
Fig. 2: Establishing past temperature regimes.
Fig. 3: Diverging trends in biodiversity metrics in cold and warm streams.
Fig. 4: Temporal changes in abundance-weighted life history strategies of assemblages.
Fig. 5: Changes in stream temperatures and introduced game abundance linked to changes in fish biodiversity metrics.

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Data availability

Data were gathered using BioData (USGS) or US EPA NRSA. Raw fish biomonitoring datasets are publicly available from the USGS (https://apps.usgs.gov/biodata/) and EPA (https://www.epa.gov/national-aquatic-resource-surveys/data-national-aquatic-resource-surveys). Cleaned fish biodiversity datasets were generated by the finsyncR R package v.1.0.0. Fish traits and life history strategies were generated from the FishLife R package v.3.1.0. Stream temperature data were provided by the USGS. Conductivity data was gathered with the EPATADA R package v.0.0.1. Watershed-level characteristics for stream segments were gathered by StreamCat with the StreamCatTools R package v.0.3.0. Watershed air temperature data were gathered from PRISM using the prism R package 0.2.3. HUC designations were generated by the nhdplusTools R package v.1.3.1. Sub-basin-level native status of fish species was generated by the USGS Non-indigenous Aquatic Species database (https://doi.org/10.5066/P9C4N10N). All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Data. Datasets for analyses and the generation of figures are available at Figshare (https://doi.org/10.6084/m9.figshare.28049777)92 and GitHub (https://github.com/StreamData/StreamFishBiodiversityChange).

Code availability

All code for analyses and the generation of figures is available at Figshare (https://doi.org/10.6084/m9.figshare.28049777)92 and GitHub (https://github.com/StreamData/StreamFishBiodiversityChange).

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Acknowledgements

We appreciate initial conversations with D. Peck, which led to the formulation of the general research direction. Thank you to J. Ebersole, L. Yuan and J. Stevenson for their feedback on this manuscript. This work was conducted as part of the Analyses of Contaminant Effects in Freshwater Systems: Synthesizing Abiotic and Biotic Stream Datasets for Long-Term Ecological Research Working Group supported by the John Wesley Powell Center for Analysis and Synthesis, funded by the US Geological Survey. Any use of trade, firm or product names is for descriptive purposes only and does not imply endorsement by the US Government. The findings and conclusions here are those of the authors and do not necessarily represent the views or policies of the US Environmental Protection Agency.

Author information

Authors and Affiliations

  1. Center for Computational Toxicology and Exposure, Office of Research and Development, United States Environmental Protection Agency, Duluth, MN, USA

    Samantha L. Rumschlag, Joel Hoffman, Ryan Lepak & Michael B. Mahon

  2. Oak Ridge Institute for Science and Education, Oak Ridge, TN, USA

    Brian Gallagher

  3. Center for Public Health and Environmental Assessment, Office of Research and Development, United States Environmental Protection Agency, Corvallis, OR, USA

    Ryan Hill, Darin Kopp & Michael Dumelle

  4. Ecotoxicology, Research Centre One Health Ruhr, University Alliance Ruhr, Bochum, Germany

    Ralf B. Schäfer

  5. Faculty of Biology, University Duisburg-Essen, Essen, Germany

    Ralf B. Schäfer

  6. Wyoming-Montana Water Science Center, US Geological Survey, Helena, MT, USA

    Travis S. Schmidt

  7. Eastern Ecological Science Center, US Geological Survey, Kearneysville, WV, USA

    Taylor Woods

  8. Department of Biological Sciences, Environmental Change Initiative, and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA

    Jason R. Rohr

  9. Research Unit of Environmental and Evolutionary Biology, naXys, ILEE, University of Namur, Namur, Belgium

    Frederik De Laender

  10. Department of Biology, Duke University, Durham, NC, USA

    Jonathan Behrens

  11. Office of Chemical Safety and Pollution Prevention, United States Environmental Protection Agency, Washington, DC, USA

    Devin K. Jones

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Contributions

Conceptualization: S.L.R., B.G., R.H., R.B.S., J.R.R., F.D.L., J.H. and M.B.M. Methodology: S.L.R., B.G., R.H., R.B.S., T.S.S., T.W., D.K., M.D., J.R.R., F.D.L., J.H., J.B. and M.B.M. Validation: R.H. and M.B.M. Formal analysis: S.L.R., R.H., D.K. and M.B.M. Data curation: S.L.R., M.B.M., R.H., D.K., and J.B. Writing—original draft: S.L.R., M.B.M. and B.G. Writing—reviewing and editing: S.L.R., B.G., R.H., R.B.S, T.S.S., T.W., D.K., M.D., J.R.R., F.D.L., J.H., J.B., R.L., D.K.J. and M.B.M. Visualization: S.L.R., M.B.M., R.H., D.K. and T.W. Supervision: S.L.R., M.B.M. and R.H. Project administration: S.L.R. Funding acquisition: D.K.J., T.S.S. and J.R.R.

Corresponding authors

Correspondence to Samantha L. Rumschlag or Michael B. Mahon.

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The authors declare no competing interests.

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Nature thanks Phaedra Budy, Ian Vaughan and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Diagram showing size of initial dataset and data cleaning steps to final dataset for each dataset.

Samples returned from finsyncR were limited to wadeable streams. *Outlier effort was defined as less than three minutes or greater than 150 min spent electroshocking as well as less than 10 meters or greater than 4000 meters reach length fished, which applies specifically to the filtering of data for abundance. Outlier past temperature regime temperatures were defined as <7 °C. CPUE is catch per unit effort, a standardized measure of abundance.

Extended Data Fig. 2 Distribution of environmental covariates through time.

The relative frequency of past stream temperature regimes (A), predicted wetted width of streams (B), and conductivity (C) as well as the coverage of stream orders (D) and dominant land use class (E) is relatively consistent through time. This consistency in environmental covariates across years indicates that the results of our present analyses are not an artifact of site turnover year-to-year. In (A, B, C), the grey box indicates the average annual range of these variables, which highlights that streams sampled in most years capture the typical range of values observed across time. The relatively consistent distribution and range of environmental covariates captured at the sampling locations across years provides strong reassurances that a similar population of streams were sampled through time.

Extended Data Fig. 3 Stream temperature data and model performance.

A) Map of monthly stream temperature monitoring sites from the US Geological Survey58. Points are colored according to the number of monthly summer stream temperature observations available from July and August during 1999–2008, after screening for data quality and availability. B) Plot of observed mean summer (July and August, 1999–2008) stream temperature versus predictions from the final spatial linear mixed effects model for each year and site. Note that points with red shading denote coordinates with a higher density of observations and are mostly clustered near the 1:1 line (dashed black line). Model evaluation calculated a predictive R2 equal to 0.89 for mean yearly summer temperatures. Regression lines and associated error (95% confidence intervals) represent the 10%, 25%, 50%, 75%, and 90% quantile regression lines. Quantile regression implies a tendency for the steam temperature model to be more precise in predicting higher stream temperatures compared to lower stream temperature.

Extended Data Fig. 4 Comparisons between spatial and non-spatial linear models of biodiversity endpoints.

This figure is a replicate of Fig. 3 in the main text with the addition of a spatial model of the whole community. We fit a spatial linear mixed effects model that allowed spatial dependence among sites to influence both the estimation of fixed effect and covariance parameters. Spatial models were fit with an exponential spatial covariance structure and the same fixed and random effects as the non-spatial model. Results indicate little-to-no change in the mean predictions of temporal biodiversity trends or their errors. Importantly, the interpretation of results do not change according to if a spatial or non-spatial models are used. Mean estimates are represented by symbols with error bars indicating ± 75 and 95% confidence intervals.

Extended Data Fig. 5 Linear display of trends in biodiversity metrics.

This figure is a complement to Fig. 3 in the main text to show intercepts and slopes of trends in biodiversity metrics. Lines represent model predicted trend with 95% confidence interval bands. Statistical output provided in Table S3. Panels A), D), G), J), M), and N) correspond to trends for whole communities. B), E), H), and K) correspond to trends for the local, non-game fish assemblage subsets. C), F), I) and L) correspond to trends for the introduced, game fish assemblage subsets. Significance of trends was determined by two-sided chi-square tests. Temporal trends of M) non-rarefied richness (χ2(1) = 3.39, p = 0.066) did not vary by past temperature regime, but N) evenness (χ2(1) = 7.917, p = 0.005) did vary by past temperature regime. In warm streams, non-rarefied richness nonsignificantly (Z = 1.804, p = 0.071) and evenness significantly (Z = 1.964, p = 0.049) increased. In intermediate streams, non-rarefied richness (Z = −0.424, p = 0.671) and evenness (Z = −0.395, p = 0.693) trends were not significantly different from zero. In cold streams, non-rarefied richness nonsignificantly (Z = −1.772, p = 0.077) and evenness significantly (Z = −2.556, p = 0.011) decreased. By comparing changes relative abundance, rarefied richness, non-rarefied richness, and evenness we can evaluate causes of changes in rarefied richness.

Extended Data Fig. 6 Associations between life history continuums and fish characteristics.

A) Local and introduced fish species do not differ in their life history strategies (opportunistic: χ2(1) = 0.377, p = 0.539; equilibrium: χ2(1) = 0.142, p = 0.707; periodic: χ2(1) = 0.583, p = 0.445). B) Non-game species tend to be opportunistic, and game speies tend to be periodic (opportunistic: χ2(1) = 25.843, p = 0.0000003; equilibrium: χ2(1) = 0.276, p = 0.600; periodic: χ2(1) = 16.525, p = 0.0005). Significance was determined by two-sided chi-square tests. Points and errors in A) and B) are model-estimates and 95% confidence intervals based on a beta distribution, holding game/non-game and local/introduced statuses at their proportional values, respectively. C) The opportunistic continuum is correlated with fish temperature preference. No relationship exists between the periodic continuum and temperature. In C), lines represent simple linear model with 95% confidence interval bands. For all, n = 389 independent fish species.

Extended Data Fig. 7 Distribution of stream temperatures and assemblage thermal preference in the first and final five years of the dataset.

A) Predicted stream temperatures for all streams in the first (1993–1997) and final five years (2014–2019) of the dataset. Coloration of background matches breakpoints of stream temperatures in Fig. 1. Assemblage thermal preference B) weighted by relative abundance and C) all species weighted equally for organisms sampled in the first and final five years of the dataset. In all panels, solid lines and filled circles are the kernel density estimate and means. Error bars of means are standard deviations. For A) for early and late timeseries, n = 2992 unique sampling locations across 5 years. For B) and C), n = 276 for early samples (filled circle, solid line) and n = 1172 for late samples (open circle, dashed line).

Extended Data Fig. 8 Predicted site temporal trends based on historic summer temperature.

Overall, the locations of increases and decreases in biodiversity endpoints is correlated with locations of cold and warm streams (Fig. 2c). This pattern emphasizes that the variation in biodiversity trends is associated with past stream temperature.

Extended Data Fig. 9 Regional occupancy of fish families through time.

A) Panels are arranged by the approximate spatial location of HUC2s (hydrological unit code) and colored by region. B) Statistical significance was evaluated by the overlap of the 95% confidence interval with zero. Points represent model-estimates for fish family trends within a given region. Fish families with no significant trends were excluded. Two families with relatively consistent occupancy trends across regions are Salmonidae (salmonids) and Centrarchidae (sunfish), which are actively managed in many regions. Salmonids are increasing in the southwest (HUCs 15, 18), southeast (HUCs 3, 6), and parts of the Midwest and interior (HUCs 7, 9, 11). In contrast, centrarchids are decreasing in western (HUCs 15, 17, 18), Midwest and interior (HUCs 4, 7, 9,11), and northeast (HUC 1) regions. Centrarchids are increasing in southcentral regions (HUCs 8, 12, 13). There are also additional consistent responses of families. Cyprinidae (carps and minnows) are decreasing in 10 of 10 HUCs. Catostomidae (suckers) are decreasing in 12 of 12 HUCs. Ictaluridae (catfishes) are decreasing in 6 of 8 HUCs. Consistent regional patterns also exist. Few families change in the Xeric region, which is species poor, and the Tennessee drainage (HUC 6), which is species rich. Many families are decreasing in the Midwest (23/27 trends, 85%). Increasing trends are common in the Coastal plains (11/16 trends, 69%). N = 165,714 unique species occurrences (e.g., 2 of 5 sites occupied) at the region-level, replicated by sampling agency and collection year. Credit: US Geological Survey, National Geospatial Technical Operations Center, 2016, USGS National Watershed Boundary Dataset (WBD) Downloadable Data Collection - National Geospatial Data Asset (NGDA) Watershed Boundary Dataset (WBD): US Geological Survey.

Extended Data Fig. 10 Evaluation of the effects of land use on biodiversity trends.

A) Trends in abundance (catch per unit effort) varied with land use. Abundance did not change in agriculture, grassland/shrub, and urban streams, while abundance increased in forest/wetland streams. For all other biodiversity metrics, land use influenced the intercept but not the slope of temporal trends. B) Rarefied richness was lowest in urban streams compared to all others. C) Site uniqueness is greater in forest/wetland and grassland/shrub streams compared to agricultural streams, and other land use types are not different from each other. D) Functional diversity is greater in agricultural streams compared to forest/wetland and grassland/shrub streams, and other land use types are not different from each other. All pairwise comparisons can be found in Table S7. In A), lines represent model predicted trend with 95% CI bands. Points and errors in B), C), and D) are model-estimates and 95% CIs, holding covariates at their proportional values. For A), n = 4240; for B), n = 3784; for C), n = 4491; and for D), n = 4482. Sample sized varied by endpoint, because of differences in available sampling information, minimum sample size, and number of unique species.

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Rumschlag, S.L., Gallagher, B., Hill, R. et al. Diverging fish biodiversity trends in cold and warm rivers and streams. Nature (2025). https://doi.org/10.1038/s41586-025-09556-0

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