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  • Review Article
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Extreme events and river biodiversity under climate change

Nature Reviews Biodiversity volume 2, pages 150–169 (2026)Cite this article

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Abstract

Extreme climatic events (ECEs) including floods, droughts and heatwaves are increasing in severity and frequency, fundamentally reshaping riverine ecosystems. In this Review, we synthesize global evidence of the impacts of ECEs on riverine biodiversity, revealing widespread and often compounding threats. ECEs affect biodiversity in diverse ways across scales; they can erode genetic diversity, alter community composition, reduce ecosystem function and disrupt population and community synchrony across the wider river meta-network. ECEs can also amplify the impact of, and be amplified by, other global stressors, and ECEs that occur in tandem or sequentially (compound events) have potentially strong but poorly understood biodiversity impacts. Several promising statistical and mechanistic modelling frameworks now enable prediction of the impacts of ECEs under non-stationary conditions. To adequately prepare for increasing and compounding ECEs, management strategies must shift from local, reactive interventions to catchment-scale, resilience-focused approaches. Top future research priorities include high-frequency and coordinated long-term monitoring, understanding legacies and biophysical feedbacks from extremes and deconstructing the impacts of compounding events. Our synthesis provides a roadmap for advancing science and practice to confront the ecological challenges posed by an increasingly extreme future.

Key points

  • Extreme climatic events (ECEs) erode river biodiversity across organizational levels — from genes to ecosystems — through selective mortality, with evolutionary and ecological consequences that permeate across river networks. Network connectivity shapes river resilience: the hierarchical and connected structure of river networks allows the impact of ECEs to propagate across systems, while also providing pathways that support biodiversity recovery following disturbance.

  • The impacts of ECEs in rivers are often exacerbated by underlying or interacting stressors, such as land-use change or pollution, and can intensify, or be intensified by, biological invasions. Recovery from ECEs can vary widely from weeks to multiple years, if ever, depending on the specifics of the event and the biodiversity metric of interest.

  • Compound events can impose disproportionately large ecological impacts, but limited research to date highlights this as an emerging frontier of research.

  • Predictive tools are improving, with distributional regression approaches and mechanistic or hybrid models enabling forecasting of changing extremes and their ecological consequences at scale. To adequately prepare for increasing and compounding ECEs, management strategies must shift from local, reactive interventions to catchment-scale, resilience-focused approaches that are anticipatory.

  • Promising future avenues for research and application include advancing the implementation of high-resolution, high-frequency monitoring programmes; continued and coordinated long-term research programmes; broadening the collective understanding of compounding impacts; building knowledge of legacy effects; and studying the biophysical feedbacks between ECEs, river flow regimes, geomorphological dynamics and biodiversity resilience.

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Fig. 1: Future probability of extreme events.
Fig. 2: Extreme events and the ways they affect river biodiversity.
Fig. 3: Global extreme climatic event impacts on riverine biodiversity.
Fig. 4: Context-dependent effects of extreme changes in water flow and temperature on species richness.
Fig. 5: Relationship between ecological mechanisms, iterative forecast cycles and adaptive management.

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Acknowledgements

J.D.T. is supported by a Rutherford Discovery Fellowship administered by the Royal Society Te Apārangi (RDF-18-UOC-007), Te Pūnaha Matatini, a Centre of Research Excellence funded by the Tertiary Education Commission, New Zealand. J.D.T. and J.M. acknowledge funding from the Ministry of Business, Innovation and Employment (Fish Futures: preparing for novel freshwater ecosystems; CAWX2101). J.D.O. is supported by the Richard C. and Lois M. Worthington Endowed Professor in Fisheries Management from the School of Aquatic and Fishery Sciences, University of Washington. T.D. was supported through the DRYvER project, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 869226. T.S. is supported by a productivity grant (309496/2021-7) administered by the Brazilian National Council for Scientific and Technological Development (CNPq). T.S. acknowledges funding from the São Paulo Research Foundation (FAPESP; 2021/00619-7 and 2021/10639-5). J.T.-J. acknowledges funding from the Royal Society Te Apārangi (Fresh ideas for water economics and policy - VUW2006) and Te Pūnaha Matatini, a Centre of Research Excellence funded by the Tertiary Education Commission, New Zealand.

Author information

Authors and Affiliations

  1. School of Biological Sciences, University of Canterbury, Christchurch, New Zealand

    Jonathan D. Tonkin, Tadeu Siqueira & Julian Merder

  2. Te Pūnaha Matatini Centre of Research Excellence, University of Canterbury, Christchurch, New Zealand

    Jonathan D. Tonkin & Julia Talbot-Jones

  3. Department of Biodiversity, Institute of Biosciences, São Paulo State University (UNESP), Rio Claro, Brazil

    Tadeu Siqueira

  4. Centre for Research on Biodiversity Dynamics and Climate Change, Institute of Biosciences, São Paulo State University (UNESP), Rio Claro, Brazil

    Tadeu Siqueira

  5. INRAE, UR RiverLy, Centre Lyon-Grenoble Auvergne-Rhône-Alpes, Villeurbanne, France

    Thibault Datry

  6. Department of Biology and Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, USA

    N. LeRoy Poff

  7. Centre for Applied Water Science, University of Canberra, Canberra, Australian Capital Territory, Australia

    N. LeRoy Poff

  8. School of Government, Te Herenga Waka — Victoria University of Wellington, Wellington, New Zealand

    Julia Talbot-Jones

  9. School of Aquatic and Fishery Sciences, University of Washington, Seattle, WA, USA

    Julian D. Olden

Authors
  1. Jonathan D. Tonkin
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  2. Tadeu Siqueira
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  3. Julian Merder
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  4. Thibault Datry
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  5. N. LeRoy Poff
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  7. Julian D. Olden
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Contributions

J.D.T., T.S. and J.D.O. conceptualized the main ideas. J.D.T. led the writing of the manuscript, with all authors leading sections of the first draft and contributing to revisions. J.D.T. and J.M. produced the figures.

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Correspondence to Jonathan D. Tonkin.

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Nature Reviews Biodiversity thanks Juan David González-Trujillo and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Glossary

Compound event

An extreme climate event in which multiple climate drivers or hazards occur simultaneously or sequentially, interacting to produce impacts that are greater than would be expected from each event acting alone.

Conditional distribution

A conditional distribution characterizes the probability distribution of a response variable given fixed values of one or more covariates (explanatory variables).

Dendritic

A hierarchical, tree-like branching network formed by repeated bifurcation, characteristic of river systems.

Distribution

A probability distribution specifies the probabilities of all possible values of a random variable and reflects key features such as central tendency, variability, skewness and tail shape.

Distributional regression

Methods that model the conditional distribution of an outcome, allowing parameters such as mean, variance, skewness and tail heaviness to depend on explanatory variables, rather than just the mean.

Generalized Additive Models for Location, Scale and Shape (GAMLSS)

A widely used framework for distributional regression that can incorporate nonlinear relationships through smoothing splines and other machine-learning architectures, enabling the prediction of central tendency, quantiles and exceedance probabilities of thresholds, which are essential for assessing variability, extreme event risk and magnitude.

Generalized extreme value distribution

A family of continuous distributions encompassing the Gumbel, Fréchet and Weibull type distributions, often used to approximate the tails of other distributions, or when modelling the maxima or minima sampled within specific spatiotemporal intervals.

Hybrid statistical–mechanistic model

A model that combines elements of both statistical and mechanistic models, including those that estimate parameters within mechanistic frameworks directly from observed data.

Hydrological connectivity

Water-mediated transfer of matter, energy or organisms between components of a hydrological system, linking habitats and influencing nutrient fluxes, sediment transport, ecological interactions and pollutant spread.

Mechanistic models

Parametric models grounded in hypothesized, anticipated or known functional relationships among underlying biological, chemical or physical processes. Also known as process-oriented or process-based models.

Meta-system

Sets of spatially structured ecological units — including metapopulations, metacommunities and meta-ecosystems — that interact through the movement of organisms, energy or materials across space.

Near-term iterative forecast

A near-horizon to medium-horizon prediction framework in which forecasts are repeatedly updated as new observations become available, making it particularly useful for dynamic systems in which conditions change rapidly.

Perennial

A perennial stream maintains continuous flow throughout the year under normal climatic conditions and is not subject to seasonal drying.

Quantile regression

A framework that models conditional quantiles of a response variable given explanatory variables, rather than focusing solely on the conditional mean.

RAD framework

A decision-making framework that helps resource managers to develop strategies for responding to socio-ecological changes, including those caused by climate change, by resisting, accepting or directing.

Refuge

An area where organisms find protection from adverse conditions, such as predation or extreme environmental factors, allowing them to persist when surrounding habitats are inhospitable.

Resilience

The ability of a system to absorb shocks and recover to a similar form and function.

Stationary

Stationarity signifies that the statistical properties of a time series, such as mean, variance and covariance, remain constant over time; an assumption increasingly challenged in hydrodynamic systems under climate change (in other words, non-stationarity).

Tail heaviness

How quickly probabilities decline in the tails of a distribution, with heavy-tailed distributions (for example, Cauchy) being more likely to produce extreme values than light-tailed ones (for example, normal).

Tails

The tails of a statistical distribution are the regions at both extremes, corresponding to the smallest and largest values.

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Tonkin, J.D., Siqueira, T., Merder, J. et al. Extreme events and river biodiversity under climate change. Nat. Rev. Biodivers. 2, 150–169 (2026). https://doi.org/10.1038/s44358-026-00131-7

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