One-quarter of freshwater fauna threatened with extinction
Nature (2025)
Surrogacy estimation analyses within two conservation strategies
To evaluate the degree to which conservation of threatened amphibians, birds, mammals and reptiles (individually or combined) serve as surrogates for conservation of threatened freshwater decapods, fishes and odonates (individually or combined), we calculated the SAI of surrogate effectiveness33. A surrogate is selected as a representative of conservation planning, simplifying the process of monitoring and conserving biodiversity. Its effectiveness hinges on how well the surrogate can reflect the presence, abundance and diversity of species in a given area. Here we used species accumulation curves to measure this effectiveness, by comparing the species accumulation curves of surrogates with those of the target group.
We performed the analyses on two main global conservation strategies: (a) maximizing rarity-weighted richness (that is, the aggregate importance of each grid cell to the species occurring there) of threatened species, and (b) maximizing inclusion of the most range-restricted threatened species. The first strategy prioritizes areas containing many threatened species with highly restricted ranges globally, whereas the second prioritizes essential areas for the most globally range-restricted threatened species.
We implemented both conservation strategies within the spatial conservation planning software Zonation80 and the R81 package ‘zonator’82, using the additive benefit function (ABF) and the core-area zonation (CAZ) algorithms for strategy a and strategy b, respectively. The algorithm for the ABF (strategy a) focuses on ranking areas by the sum of the proportion of the overall range size of all species found within a specific grid cell (that is, a quantity similar to weighted species endemism and endemism richness). The grid cells that contain many species occurring exclusively in that cell or in only a few other cells are given the highest priority. In the CAZ algorithm (strategy b), areas are prioritized based on the maximum proportion of the global range size of all species within a specific grid cell. The algorithm assigns the highest priority to cells that contain the greatest proportions of the ranges of the most range-restricted species.
We estimated optimal, surrogate and random curves based on multiple target species-surrogate species combinations. We used 100 sets of random terrestrial grid-cell sequences to generate 95% confidence intervals around a median random curve. We ran five iterations of each spatial prioritization algorithm for each taxonomic group, and optimal and surrogate curves were summarized using the median and 95% confidence intervals across the five iterations.
We derived the SAI of surrogate effectiveness83, which quantifies the rate of inclusion of target biodiversity units across areas selected optimally based on the targets themselves, based on surrogate diversity, or at random, as
where s is the area under the surrogate curve, r is the area under the random curve, and o is the area under the optimal curve. If SAI = 1, the optimal and the surrogate curves coincide (perfect surrogacy); if SAI is between 1 and 0, the surrogate curve is above the random curve (positive surrogacy); if SAI = 0, the surrogate and random curves are the same (no surrogacy); and if SAI < 0, the surrogate curve is below the random curve (negative surrogacy). We used the following descriptors to define SAI performance: 0.01–0.19 as very poor, 0.20–0.39 as poor, 0.40–0.59 as reasonable, 0.60–0.79 as good, and 0.80–0.99 as very good. It should be noted that if SAI = 0.5, for example, this does not mean that 50% of targets are represented and 50% of targets are not represented. For each SAI, we reported the median and 95% confidence intervals based on the five target and surrogate curve iterations and 100 random curve iterations.
In addition, we evaluated whether prioritizing for two widely used hydrological variables (water stress as a measure of water quantity, and eutrophication (nitrate–nitrite) as a measure of water quality) are effective surrogacy strategies for conservation of threatened freshwater species. We used the SAI to evaluate the ability of both variables to identify areas that most efficiently represent threatened freshwater species, again harnessing strategies for both maximizing rarity-weighted richness (ABF) and maximizing inclusion range-restricted species (CAZ). Once again, we used Zonation80 to generate the complementarity-based ranking of conservation values of the target, with the respective algorithms, over the landscape of interest. To generate the rank order, we used (1) the baseline water stress layer from the Aqueduct Water Risk Atlas, which measures the ratio of total water demand (for example, domestic, industrial, irrigation, and livestock consumptive and non-consumptive uses) to available renewable surface and groundwater supplies84,85, and (2) the baseline nitrogen layer from the World Bank catalogue, which provides global predictions of nitrate–nitrite levels86. The water stress layer was considered a proxy of a baseline level of water demand compared with available renewable water and groundwater, as used in setting science-based targets for freshwater8. Nitrogen levels in water around the world are highly correlated with population density, sanitation practices and agricultural activities. Here the nitrogen layer was predicted globally and provides valuable information about nitrogen concentrations in areas where no previous observations have been made.
We rasterized the baseline water stress and the nitrogen layers to a 0.5 × 0.5 latitude–longitude grids (approximately 50-km resolution; WGS84) to match the species rasters. For the water stress analysis, we excluded cells with missing water stress data across the world’s land (12% of cells excluded). We found that 44% of the world’s cells with water stress data had no threatened freshwater species, but these cells were still included in the analysis. For the nitrogen levels analysis, we excluded cells missing nitrogen data across the world’s land, which accounted for 16% of the cells. Among the remaining cells with nitrogen data, 52% had no threatened freshwater species, but again these were retained in the analysis. Before constructing the curves, we organized sites (grid cells) in the species matrix from those with high abiotic values to low abiotic values for ranking cells. We used 100 sets of random terrestrial grid-cell sequences to generate 95% confidence intervals around a median random curve. We generated five random terrestrial grid cell sequences for constructing the surrogate curves, so we randomly changed the rank order only between those cells that have the same values.
To evaluate the degree to which conservation of threatened amphibians, birds, mammals and reptiles (individually or combined) serve as surrogates for conservation of threatened freshwater decapods, fishes and odonates (individually or combined), we calculated the SAI of surrogate effectiveness33. A surrogate is selected as a representative of conservation planning, simplifying the process of monitoring and conserving biodiversity. Its effectiveness hinges on how well the surrogate can reflect the presence, abundance and diversity of species in a given area. Here we used species accumulation curves to measure this effectiveness, by comparing the species accumulation curves of surrogates with those of the target group.
We performed the analyses on two main global conservation strategies: (a) maximizing rarity-weighted richness (that is, the aggregate importance of each grid cell to the species occurring there) of threatened species, and (b) maximizing inclusion of the most range-restricted threatened species. The first strategy prioritizes areas containing many threatened species with highly restricted ranges globally, whereas the second prioritizes essential areas for the most globally range-restricted threatened species.
We implemented both conservation strategies within the spatial conservation planning software Zonation80 and the R81 package ‘zonator’82, using the additive benefit function (ABF) and the core-area zonation (CAZ) algorithms for strategy a and strategy b, respectively. The algorithm for the ABF (strategy a) focuses on ranking areas by the sum of the proportion of the overall range size of all species found within a specific grid cell (that is, a quantity similar to weighted species endemism and endemism richness). The grid cells that contain many species occurring exclusively in that cell or in only a few other cells are given the highest priority. In the CAZ algorithm (strategy b), areas are prioritized based on the maximum proportion of the global range size of all species within a specific grid cell. The algorithm assigns the highest priority to cells that contain the greatest proportions of the ranges of the most range-restricted species.
We estimated optimal, surrogate and random curves based on multiple target species-surrogate species combinations. We used 100 sets of random terrestrial grid-cell sequences to generate 95% confidence intervals around a median random curve. We ran five iterations of each spatial prioritization algorithm for each taxonomic group, and optimal and surrogate curves were summarized using the median and 95% confidence intervals across the five iterations.
We derived the SAI of surrogate effectiveness83, which quantifies the rate of inclusion of target biodiversity units across areas selected optimally based on the targets themselves, based on surrogate diversity, or at random, as
where s is the area under the surrogate curve, r is the area under the random curve, and o is the area under the optimal curve. If SAI = 1, the optimal and the surrogate curves coincide (perfect surrogacy); if SAI is between 1 and 0, the surrogate curve is above the random curve (positive surrogacy); if SAI = 0, the surrogate and random curves are the same (no surrogacy); and if SAI < 0, the surrogate curve is below the random curve (negative surrogacy). We used the following descriptors to define SAI performance: 0.01–0.19 as very poor, 0.20–0.39 as poor, 0.40–0.59 as reasonable, 0.60–0.79 as good, and 0.80–0.99 as very good. It should be noted that if SAI = 0.5, for example, this does not mean that 50% of targets are represented and 50% of targets are not represented. For each SAI, we reported the median and 95% confidence intervals based on the five target and surrogate curve iterations and 100 random curve iterations.
In addition, we evaluated whether prioritizing for two widely used hydrological variables (water stress as a measure of water quantity, and eutrophication (nitrate–nitrite) as a measure of water quality) are effective surrogacy strategies for conservation of threatened freshwater species. We used the SAI to evaluate the ability of both variables to identify areas that most efficiently represent threatened freshwater species, again harnessing strategies for both maximizing rarity-weighted richness (ABF) and maximizing inclusion range-restricted species (CAZ). Once again, we used Zonation80 to generate the complementarity-based ranking of conservation values of the target, with the respective algorithms, over the landscape of interest. To generate the rank order, we used (1) the baseline water stress layer from the Aqueduct Water Risk Atlas, which measures the ratio of total water demand (for example, domestic, industrial, irrigation, and livestock consumptive and non-consumptive uses) to available renewable surface and groundwater supplies84,85, and (2) the baseline nitrogen layer from the World Bank catalogue, which provides global predictions of nitrate–nitrite levels86. The water stress layer was considered a proxy of a baseline level of water demand compared with available renewable water and groundwater, as used in setting science-based targets for freshwater8. Nitrogen levels in water around the world are highly correlated with population density, sanitation practices and agricultural activities. Here the nitrogen layer was predicted globally and provides valuable information about nitrogen concentrations in areas where no previous observations have been made.
We rasterized the baseline water stress and the nitrogen layers to a 0.5 × 0.5 latitude–longitude grids (approximately 50-km resolution; WGS84) to match the species rasters. For the water stress analysis, we excluded cells with missing water stress data across the world’s land (12% of cells excluded). We found that 44% of the world’s cells with water stress data had no threatened freshwater species, but these cells were still included in the analysis. For the nitrogen levels analysis, we excluded cells missing nitrogen data across the world’s land, which accounted for 16% of the cells. Among the remaining cells with nitrogen data, 52% had no threatened freshwater species, but again these were retained in the analysis. Before constructing the curves, we organized sites (grid cells) in the species matrix from those with high abiotic values to low abiotic values for ranking cells. We used 100 sets of random terrestrial grid-cell sequences to generate 95% confidence intervals around a median random curve. We generated five random terrestrial grid cell sequences for constructing the surrogate curves, so we randomly changed the rank order only between those cells that have the same values.
Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
Data availability
Taxonomic data for freshwater fishes are available from Eschmeyer’s Catalog of Fishes (http://researcharchive.calacademy.org/research/ichthyology/catalog/fishcatmain.asp) and for odonates from the World Odonata List (https://www.pugetsound.edu/slater-museum-natural-history-0/biodiversity-resources/insects/dragonflies/world-odonata-list). All IUCN Red List assessment data are publicly available on the IUCN Red List of Threatened Species website (www.iucnredlist.org). Occasionally, where a species may be under threat from over-collection, sensitive spatial data are not publicly available. All tabular and spatial data used in the analyses (‘One-quarter of freshwater fauna threatened with extinction’) are available (https://www.iucnredlist.org/resources/data-repository). Baseline water stress data (‘Aqueduct water stress projections data’) are available from the Aqueduct Water Risk Atlas (https://www.wri.org/data/aqueduct-water-stress-projections-data). Baseline nitrogen data (‘Global — nitrate–nitrite in surface water’) are available from the World Bank catalogue (https://datacatalog.worldbank.org/search/dataset/0038385/Global–Nitrate-nitrite-in-Surface-Water). Source data are provided with this paper.
Taxonomic data for freshwater fishes are available from Eschmeyer’s Catalog of Fishes (http://researcharchive.calacademy.org/research/ichthyology/catalog/fishcatmain.asp) and for odonates from the World Odonata List (https://www.pugetsound.edu/slater-museum-natural-history-0/biodiversity-resources/insects/dragonflies/world-odonata-list). All IUCN Red List assessment data are publicly available on the IUCN Red List of Threatened Species website (www.iucnredlist.org). Occasionally, where a species may be under threat from over-collection, sensitive spatial data are not publicly available. All tabular and spatial data used in the analyses (‘One-quarter of freshwater fauna threatened with extinction’) are available (https://www.iucnredlist.org/resources/data-repository). Baseline water stress data (‘Aqueduct water stress projections data’) are available from the Aqueduct Water Risk Atlas (https://www.wri.org/data/aqueduct-water-stress-projections-data). Baseline nitrogen data (‘Global — nitrate–nitrite in surface water’) are available from the World Bank catalogue (https://datacatalog.worldbank.org/search/dataset/0038385/Global–Nitrate-nitrite-in-Surface-Water). Source data are provided with this paper.
Code availability
The code used for the surrogacy analyses is available at Zenodo87(https://doi.org/10.5281/zenodo.10286099). No code was used for the chi-squared tests, which were performed in Microsoft Excel.
The code used for the surrogacy analyses is available at Zenodo87(https://doi.org/10.5281/zenodo.10286099). No code was used for the chi-squared tests, which were performed in Microsoft Excel.
References
Strayer, D. L. & Dudgeon, D. Freshwater biodiversity conservation: recent progress and future challenges. J. North Am. Benthol. Soc. 29, 344–358 (2010).
Lynch, A. J. et al. People need freshwater biodiversity. WIREs Water 10, e1633 (2023).
Dudgeon, D. Multiple threats imperil freshwater biodiversity in the Anthropocene. Curr. Biol. 29, R960–R967 (2019).
Mair, L. et al. A metric for spatially explicit contributions to science-based species targets. Nat. Ecol. Evol. 5, 836–844 (2021).
Hoffmann, M. et al. The impact of conservation on the status of the world’s vertebrates. Science 330, 1503–1509 (2010).
IPBES. Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Serviceshttps://doi.org/10.5281/ZENODO.3831673 (IPBES, 2019)
GEF. Policy & guidelines on system for transparent allocation of resources (STAR). STAR Policy (GA/PL/01) & Guidelines (GA/GN/01). GEFhttps://www.thegef.org/sites/default/files/documents/STAR_Policy_Guidelines.pdf(2018).
Science Based Targets Network. Technical guidance: step 3 freshwater: measure, set & disclose. Science Based Targets Network https://sciencebasedtargetsnetwork.org/wp-content/uploads/2023/05/Technical-Guidance-2023-Step3-Freshwater-v1.pdf (2023).
Global Compact. CEO Water Mandate: corporate water disclosure guidelines — toward a common approach to reporting water issues. CEO Water Mandatehttps://ceowatermandate.org/disclosure/download/ (2014).
GRI. The global standards for sustainability impacts. Global Reportinghttps://www.globalreporting.org/standards/ (2024).
United Nations. SDG Indicator 6.4.2 — level of water stress: freshwater withdrawal as a proportion of available freshwater resources. UNhttps://unstats.un.org/sdgs/indicators/indicators-list/ (2017).
TNFD. Recommendations of the Taskforce on Nature-Related Financial Disclosures. TNFD https://tnfd.global/publication/recommendations-of-the-taskforce-on-nature-related-financial-disclosures/ (2023).
WWF. WWF water risk filter methodology documentation. WWFhttps://riskfilter.org/water (2023).
Abell, R. et al. Concordance of freshwater and terrestrial biodiversity: freshwater biodiversity concordance. Conserv. Lett. 4, 127–136 (2011).
Tickner, D. et al. Bending the curve of global freshwater biodiversity loss: an emergency recovery plan. BioScience 70, 330–342 (2020).
Darwall, W. R. T. et al. Implications of bias in conservation research and investment for freshwater species: conservation and freshwater species. Conserv. Lett. 4, 474–482 (2011).
Rodrigues, A. S. L. & Brooks, T. M. Shortcuts for biodiversity conservation planning: the effectiveness of surrogates. Annu. Rev. Ecol. Evol. Syst. 38, 713–737 (2007).
Martens, K., Fontaneto, D., Thomaz, S. M. & Naselli-Flores, L. Two celebrations and the Sustainable Development Goals. Hydrobiologia 850, 1–3 (2023).
Cooke, S. J. et al. Is it a new day for freshwater biodiversity? Reflections on outcomes of the Kunming-Montreal Global Biodiversity Framework. PLoS Sustain. Transform. 2, e0000065 (2023).
Albert, J. S. et al. Scientists’ warning to humanity on the freshwater biodiversity crisis. Ambio 50, 85–94 (2021).
Gardner, R. C. & Finlayson, C. Global Wetland Outlook: State of the World’s Wetlands and their Services to People (Ramsar Convention Secretariat, 2018).
Vorosmarty, C. J. et al. Global threats to human water security and river biodiversity. Nature 467, 555–561 (2010).
Grill, G. et al. Mapping the world’s free-flowing rivers. Nature 569, 215–221 (2019).
Moog, O., Schmutz, S. & Schwarzinger, I. in Riverine Ecosystem Management (eds. Schmutz, S. & Sendzimir, J.) 371–390 (Springer International Publishing, 2018).
Butchart, S. H. M. et al. Improvements to the Red List Index. PLoS ONE 2, e140 (2007).
Rodrigues, A., Pilgrim, J., Lamoreux, J., Hoffmann, M. & Brooks, T. The value of the IUCN Red List for conservation. Trends Ecol. Evol. 21, 71–76 (2006).
Collar, N. J. & Andrew, P. Birds to Watch: The ICBP World Checklist of Threatened Birds(International Council for Bird Preservation, 1988).
Stuart, S. N. et al. Status and trends of amphibian declines and extinctions worldwide. Science 306, 1783–1786 (2004).
Baillie, J. & Groombridge, B. 1996 IUCN Red List of Threatened Animals (IUCN, 1996).
Luedtke, J. A. et al. Ongoing declines for the world’s amphibians in the face of emerging threats. Nature 622, 308–314 (2023).
Schipper, J. et al. The status of the world’s land and marine mammals: diversity, threat, and knowledge. Science 322, 225–230 (2008).
BirdLife International. State of the World’s Birds 2022: Insights and Solutions for the Biodiversity Crisis (BirdLife International, 2022).
Cox, N. et al. A global reptile assessment highlights shared conservation needs of tetrapods. Nature 605, 285–290 (2022).
Birnie‐Gauvin, K. et al. The RACE for freshwater biodiversity: essential actions to create the social context for meaningful conservation. Conserv. Sci. Pract. 5, e12911 (2023).
Lynch, A. J. et al. Inland fish and fisheries integral to achieving the Sustainable Development Goals. Nat. Sustain. 3, 579–587 (2020).
Convention on Biological Diversity. Kunming-Montreal Global Biodiversity Framework, 18 Dec. 2022, CBD/COP/15/L.25 (Convention on Biological Diversity, 2022).
IUCN. IUCN Red List Categories and Criteria: Version 3.1 2nd edn (IUCN, 2012).
Cumberlidge, N. et al. Freshwater crabs and the biodiversity crisis: importance, threats, status, and conservation challenges. Biol. Conserv. 142, 1665–1673 (2009).
Richman, N. I. et al. Multiple drivers of decline in the global status of freshwater crayfish (Decapoda: Astacidea). Phil. Trans. R. Soc. B 370, 20140060 (2015).
De Grave, S. et al. Dead shrimp blues: a global assessment of extinction risk in freshwater shrimps (Crustacea: Decapoda: Caridea). PLoS ONE 10, e0120198 (2015).
Baillie, J. E. M. et al. Toward monitoring global biodiversity. Conserv. Lett. 1, 18–26 (2008).
Miranda, R. et al. Monitoring extinction risk and threats of the world’s fishes based on the Sampled Red List Index. Rev. Fish Biol. Fish. 32, 975–991 (2022).
Clausnitzer, V. et al. Odonata enter the biodiversity crisis debate: the first global assessment of an insect group. Biol. Conserv. 142, 1864–1869 (2009).
Böhm, M. et al. The conservation status of the world’s freshwater molluscs. Hydrobiologia 848, 3231–3254 (2021).
Reid, A. J. et al. Emerging threats and persistent conservation challenges for freshwater biodiversity. Biol. Rev. 94, 849–873 (2019).
Barbarossa, V. et al. Impacts of current and future large dams on the geographic range connectivity of freshwater fish worldwide. Proc. Natl Acad. Sci. USA 117, 3648–3655 (2020).
Barbarossa, V. et al. Threats of global warming to the world’s freshwater fishes. Nat. Commun. 12, 1701 (2021).
Mancini, G. et al. A standard approach for including climate change responses in IUCN Red List assessments. Conserv. Biol. 38, e14227 (2024).
Tydecks, L., Ibelings, B. W. & Tockner, K. A global survey of freshwater biological field stations. River Res. Appl. https://doi.org/10.1002/rra.3476 (2019).
Sutherland, W. J., Pullin, A. S., Dolman, P. M. & Knight, T. M. The need for evidence-based conservation. Trends Ecol. Evol. 19, 305–308 (2004).
Grace, M. K. et al. Testing a global standard for quantifying species recovery and assessing conservation impact. Conserv. Biol. 35, 1833–1849 (2021).
Cooke, S. J. et al. Knowledge co‐production: a pathway to effective fisheries management, conservation, and governance. Fisheries 46, 89–97 (2021).
Metcalfe, A. N., Kennedy, T. A., Mendez, G. A. & Muehlbauer, J. D. Applied citizen science in freshwater research. WIREs Water 9, e1578 (2022).
Schenekar, T. The current state of eDNA research in freshwater ecosystems: are we shifting from the developmental phase to standard application in biomonitoring? Hydrobiologia 850, 1263–1282 (2023).
Hermoso, V., Abell, R., Linke, S. & Boon, P. The role of protected areas for freshwater biodiversity conservation: challenges and opportunities in a rapidly changing world: freshwater protected areas. Aquat. Conserv. Mar. Freshw. Ecosyst. 26, 3–11 (2016).
Leal, C. G. et al. Integrated terrestrial-freshwater planning doubles conservation of tropical aquatic species. Science 370, 117–121 (2020).
Reid, A. J. et al. “Two‐eyed seeing”: an Indigenous framework to transform fisheries research and management. Fish Fish. 22, 243–261 (2021).
IUCN. A Global Standard for the Identification of Key Biodiversity Areas, Version 1.0. (IUCN, 2016).
Sayer, C. A., Palmer-Newton, A. F. & Darwall, W. R. T. Conservation Priorities for Freshwater Biodiversity in the Lake Malawi/Nyasa/Niassa Catchment (IUCN, 2019).
IUCN. An Introduction to the IUCN Red List of Ecosystems (IUCN, 2016).
IUCN Standards and Petitions Committee. Guidelines for using the IUCN Red List categories and criteria. Version 15.1. IUCNhttps://www.iucnredlist.org/documents/RedListGuidelines.pdf (2022).
IUCN. The IUCN Red List of Threatened Species. Version 2022-2. IUCNhttps://www.iucnredlist.org (2023).
Fricke, R., Eschmeyer, W. N. & Van der Laan, R. Eschmeyer’s Catalog of Fishes: genera, species, references. Institute for Biodiversity Science and Sustainabilityhttp://researcharchive.calacademy.org/research/ichthyology/catalog/fishcatmain.asp(2022).
Paulson, D. et al. World Odonata List. OdonataCentralhttps://www.odonatacentral.org/app/#/wol/ (2022).
De Grave, S. et al. Benchmarking global biodiversity of decapod crustaceans (Crustacea: Decapoda). J. Crustac. Biol. https://doi.org/10.1093/jcbiol/ruad042 (2023).
IUCN. IUCN–Toyota Partnership. IUCN Red List https://www.iucnredlist.org/about/iucn-toyota (2023).
IUCN. Documentation standards and consistency checks for IUCN Red List assessments and species accounts. Version 2. Adopted by the IUCN Red List Committee and IUCN SSC Steering Committee. IUCN Red Listhttps://nc.iucnredlist.org/redlist/content/attachment_files/RL_Standards_Consistency.pdf (2013).
IUCN SSC Red List Technical Working Group. Mapping standards and data quality for the IUCN Red List spatial data. Version 1.19. IUCN Red Listhttps://nc.iucnredlist.org/redlist/content/attachment_files/Mapping_Standards_Version_1.19_2021.pdf (2021).
Collares-Pereira, M. J. & Cowx, I. G. The role of catchment scale environmental management in freshwater fish conservation. Fish. Manag. Ecol. 11, 303–312 (2004).
Lehner, B. & Grill, G. Global river hydrography and network routing: baseline data and new approaches to study the world’s large river systems. Hydrol. Process. 27, 2171–2186 (2013).
Darwall, W. R. T. et al. The Diversity of Life in African Freshwaters: Underwater, Under Threat (IUCN, 2011).
Sayer, C. A., Maiz-Tome, L. & Darwall, W. R. T. Freshwater Biodiversity in the Lake Victoria Basin: Guidance for Species Conservation, Site Protection, Climate Resilience and Sustainable Livelihoods (IUCN, 2018).
Starnes, T. & Darwall, W. R. T. Identification and validation of western African freshwater key biodiversity areas. IUCN https://doi.org/10.2305/IUCN.CH.2021.RA.1.en (2021).
Edmondstone, M. R. J. New Species 2022: The Freshwater Fish Species Described in 2022 (Report 2) (Shoal, 2023).
Edmondstone, M. R. J., Patricio, H. C. & Baltzer, M. New Species 2021: The Freshwater Fish Species Described in 2021 (Report 1) (Shoal, 2022).
Liu, J., Slik, F., Zheng, S. & Lindenmayer, D. B. Undescribed species have higher extinction risk than known species. Conserv. Lett. 15, e12876 (2022).
IUCN. Summary statistics. IUCN Red Listhttps://www.iucnredlist.org/resources/summary-statistics (2023).
IUCN. Guidelines for appropriate uses of IUCN Red List data (version 4.0). Incorporating as annexes, the (1) guidelines for reporting on proportion threatened (version 1.2), (2) guidelines on scientific collecting of threatened species (version 1.1), (3) guidelines for the appropriate use of the IUCN Red List by business (version 1.1) and (4) guidelines for the appropriate use of IUCN Red List data in harvesting of threatened species (version 1.0). Approved by the IUCN Red List Committee. IUCN Red Listwww.iucnredlist.org/resources/guidelines-for-appropriate-uses-of-red-list-data (2022).
IUCN. Rules of procedure for IUCN Red List assessments 2017–2020. Version 3.0. Approved by the IUCN SSC Steering Committee in September 2016. IUCN Red Listhttps://nc.iucnredlist.org/redlist/content/attachment_files/Rules_of_Procedure_for_IUCN_Red_List_Assessments_2017-2020.pdf (2016).
Moilanen, A., Montesino Pouzols, F., Meller, L. & Veach, V. Conservation Planning Methods and Software Zonation. User Manual v.4 (C-BIG Conservation Biology Informatics Group, Department of Biosciences, University of Helsinki, Finland, 2014).
R Core Team. R: A Language and Environment for Statistical Computing http://www.R-project.org (R Foundation for Statistical Computing, 2023).
Lehtomaki, J. zonator. R package v.0.6.0, https://github.com/cbig/zonator (2020).
Ferrier, S. Mapping spatial pattern in biodiversity for regional conservation planning: where to from here? Syst. Biol. 51, 331–363 (2002).
Kuzma, S. et al. Aqueduct 4.0: Updated Decision-Relevant Global Water Risk Indicators; Technical Note (World Resources Institute, 2023).
Gassert, F., Luck, M., Landis, M., Reig, P. & Shiao, T. Aqueduct Global Maps 2.1: Constructing Decision-Relevant Global Water Risk Indicators. Working Paper (World Resources Institute, 2014).
Damania, R., Desbureaux, S., Rodella, A.-S., Russ, J. & Zaveri, E. D. Quality Unknown: The Invisible Water Crisis (World Bank Group, 2019).
Randall, J. Randall-HYLA/FW-surrogacy: FW_surrogacy. Zenodohttps://doi.org/10.5281/zenodo.13178145 (2024).
Strayer, D. L. & Dudgeon, D. Freshwater biodiversity conservation: recent progress and future challenges. J. North Am. Benthol. Soc. 29, 344–358 (2010).
Lynch, A. J. et al. People need freshwater biodiversity. WIREs Water 10, e1633 (2023).
Dudgeon, D. Multiple threats imperil freshwater biodiversity in the Anthropocene. Curr. Biol. 29, R960–R967 (2019).
Mair, L. et al. A metric for spatially explicit contributions to science-based species targets. Nat. Ecol. Evol. 5, 836–844 (2021).
Hoffmann, M. et al. The impact of conservation on the status of the world’s vertebrates. Science 330, 1503–1509 (2010).
IPBES. Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Serviceshttps://doi.org/10.5281/ZENODO.3831673 (IPBES, 2019)
GEF. Policy & guidelines on system for transparent allocation of resources (STAR). STAR Policy (GA/PL/01) & Guidelines (GA/GN/01). GEFhttps://www.thegef.org/sites/default/files/documents/STAR_Policy_Guidelines.pdf(2018).
Science Based Targets Network. Technical guidance: step 3 freshwater: measure, set & disclose. Science Based Targets Network https://sciencebasedtargetsnetwork.org/wp-content/uploads/2023/05/Technical-Guidance-2023-Step3-Freshwater-v1.pdf (2023).
Global Compact. CEO Water Mandate: corporate water disclosure guidelines — toward a common approach to reporting water issues. CEO Water Mandatehttps://ceowatermandate.org/disclosure/download/ (2014).
GRI. The global standards for sustainability impacts. Global Reportinghttps://www.globalreporting.org/standards/ (2024).
United Nations. SDG Indicator 6.4.2 — level of water stress: freshwater withdrawal as a proportion of available freshwater resources. UNhttps://unstats.un.org/sdgs/indicators/indicators-list/ (2017).
TNFD. Recommendations of the Taskforce on Nature-Related Financial Disclosures. TNFD https://tnfd.global/publication/recommendations-of-the-taskforce-on-nature-related-financial-disclosures/ (2023).
WWF. WWF water risk filter methodology documentation. WWFhttps://riskfilter.org/water (2023).
Abell, R. et al. Concordance of freshwater and terrestrial biodiversity: freshwater biodiversity concordance. Conserv. Lett. 4, 127–136 (2011).
Tickner, D. et al. Bending the curve of global freshwater biodiversity loss: an emergency recovery plan. BioScience 70, 330–342 (2020).
Darwall, W. R. T. et al. Implications of bias in conservation research and investment for freshwater species: conservation and freshwater species. Conserv. Lett. 4, 474–482 (2011).
Rodrigues, A. S. L. & Brooks, T. M. Shortcuts for biodiversity conservation planning: the effectiveness of surrogates. Annu. Rev. Ecol. Evol. Syst. 38, 713–737 (2007).
Martens, K., Fontaneto, D., Thomaz, S. M. & Naselli-Flores, L. Two celebrations and the Sustainable Development Goals. Hydrobiologia 850, 1–3 (2023).
Cooke, S. J. et al. Is it a new day for freshwater biodiversity? Reflections on outcomes of the Kunming-Montreal Global Biodiversity Framework. PLoS Sustain. Transform. 2, e0000065 (2023).
Albert, J. S. et al. Scientists’ warning to humanity on the freshwater biodiversity crisis. Ambio 50, 85–94 (2021).
Gardner, R. C. & Finlayson, C. Global Wetland Outlook: State of the World’s Wetlands and their Services to People (Ramsar Convention Secretariat, 2018).
Vorosmarty, C. J. et al. Global threats to human water security and river biodiversity. Nature 467, 555–561 (2010).
Grill, G. et al. Mapping the world’s free-flowing rivers. Nature 569, 215–221 (2019).
Moog, O., Schmutz, S. & Schwarzinger, I. in Riverine Ecosystem Management (eds. Schmutz, S. & Sendzimir, J.) 371–390 (Springer International Publishing, 2018).
Butchart, S. H. M. et al. Improvements to the Red List Index. PLoS ONE 2, e140 (2007).
Rodrigues, A., Pilgrim, J., Lamoreux, J., Hoffmann, M. & Brooks, T. The value of the IUCN Red List for conservation. Trends Ecol. Evol. 21, 71–76 (2006).
Collar, N. J. & Andrew, P. Birds to Watch: The ICBP World Checklist of Threatened Birds(International Council for Bird Preservation, 1988).
Stuart, S. N. et al. Status and trends of amphibian declines and extinctions worldwide. Science 306, 1783–1786 (2004).
Baillie, J. & Groombridge, B. 1996 IUCN Red List of Threatened Animals (IUCN, 1996).
Luedtke, J. A. et al. Ongoing declines for the world’s amphibians in the face of emerging threats. Nature 622, 308–314 (2023).
Schipper, J. et al. The status of the world’s land and marine mammals: diversity, threat, and knowledge. Science 322, 225–230 (2008).
BirdLife International. State of the World’s Birds 2022: Insights and Solutions for the Biodiversity Crisis (BirdLife International, 2022).
Cox, N. et al. A global reptile assessment highlights shared conservation needs of tetrapods. Nature 605, 285–290 (2022).
Birnie‐Gauvin, K. et al. The RACE for freshwater biodiversity: essential actions to create the social context for meaningful conservation. Conserv. Sci. Pract. 5, e12911 (2023).
Lynch, A. J. et al. Inland fish and fisheries integral to achieving the Sustainable Development Goals. Nat. Sustain. 3, 579–587 (2020).
Convention on Biological Diversity. Kunming-Montreal Global Biodiversity Framework, 18 Dec. 2022, CBD/COP/15/L.25 (Convention on Biological Diversity, 2022).
IUCN. IUCN Red List Categories and Criteria: Version 3.1 2nd edn (IUCN, 2012).
Cumberlidge, N. et al. Freshwater crabs and the biodiversity crisis: importance, threats, status, and conservation challenges. Biol. Conserv. 142, 1665–1673 (2009).
Richman, N. I. et al. Multiple drivers of decline in the global status of freshwater crayfish (Decapoda: Astacidea). Phil. Trans. R. Soc. B 370, 20140060 (2015).
De Grave, S. et al. Dead shrimp blues: a global assessment of extinction risk in freshwater shrimps (Crustacea: Decapoda: Caridea). PLoS ONE 10, e0120198 (2015).
Baillie, J. E. M. et al. Toward monitoring global biodiversity. Conserv. Lett. 1, 18–26 (2008).
Miranda, R. et al. Monitoring extinction risk and threats of the world’s fishes based on the Sampled Red List Index. Rev. Fish Biol. Fish. 32, 975–991 (2022).
Clausnitzer, V. et al. Odonata enter the biodiversity crisis debate: the first global assessment of an insect group. Biol. Conserv. 142, 1864–1869 (2009).
Böhm, M. et al. The conservation status of the world’s freshwater molluscs. Hydrobiologia 848, 3231–3254 (2021).
Reid, A. J. et al. Emerging threats and persistent conservation challenges for freshwater biodiversity. Biol. Rev. 94, 849–873 (2019).
Barbarossa, V. et al. Impacts of current and future large dams on the geographic range connectivity of freshwater fish worldwide. Proc. Natl Acad. Sci. USA 117, 3648–3655 (2020).
Barbarossa, V. et al. Threats of global warming to the world’s freshwater fishes. Nat. Commun. 12, 1701 (2021).
Mancini, G. et al. A standard approach for including climate change responses in IUCN Red List assessments. Conserv. Biol. 38, e14227 (2024).
Tydecks, L., Ibelings, B. W. & Tockner, K. A global survey of freshwater biological field stations. River Res. Appl. https://doi.org/10.1002/rra.3476 (2019).
Sutherland, W. J., Pullin, A. S., Dolman, P. M. & Knight, T. M. The need for evidence-based conservation. Trends Ecol. Evol. 19, 305–308 (2004).
Grace, M. K. et al. Testing a global standard for quantifying species recovery and assessing conservation impact. Conserv. Biol. 35, 1833–1849 (2021).
Cooke, S. J. et al. Knowledge co‐production: a pathway to effective fisheries management, conservation, and governance. Fisheries 46, 89–97 (2021).
Metcalfe, A. N., Kennedy, T. A., Mendez, G. A. & Muehlbauer, J. D. Applied citizen science in freshwater research. WIREs Water 9, e1578 (2022).
Schenekar, T. The current state of eDNA research in freshwater ecosystems: are we shifting from the developmental phase to standard application in biomonitoring? Hydrobiologia 850, 1263–1282 (2023).
Hermoso, V., Abell, R., Linke, S. & Boon, P. The role of protected areas for freshwater biodiversity conservation: challenges and opportunities in a rapidly changing world: freshwater protected areas. Aquat. Conserv. Mar. Freshw. Ecosyst. 26, 3–11 (2016).
Leal, C. G. et al. Integrated terrestrial-freshwater planning doubles conservation of tropical aquatic species. Science 370, 117–121 (2020).
Reid, A. J. et al. “Two‐eyed seeing”: an Indigenous framework to transform fisheries research and management. Fish Fish. 22, 243–261 (2021).
IUCN. A Global Standard for the Identification of Key Biodiversity Areas, Version 1.0. (IUCN, 2016).
Sayer, C. A., Palmer-Newton, A. F. & Darwall, W. R. T. Conservation Priorities for Freshwater Biodiversity in the Lake Malawi/Nyasa/Niassa Catchment (IUCN, 2019).
IUCN. An Introduction to the IUCN Red List of Ecosystems (IUCN, 2016).
IUCN Standards and Petitions Committee. Guidelines for using the IUCN Red List categories and criteria. Version 15.1. IUCNhttps://www.iucnredlist.org/documents/RedListGuidelines.pdf (2022).
IUCN. The IUCN Red List of Threatened Species. Version 2022-2. IUCNhttps://www.iucnredlist.org (2023).
Fricke, R., Eschmeyer, W. N. & Van der Laan, R. Eschmeyer’s Catalog of Fishes: genera, species, references. Institute for Biodiversity Science and Sustainabilityhttp://researcharchive.calacademy.org/research/ichthyology/catalog/fishcatmain.asp(2022).
Paulson, D. et al. World Odonata List. OdonataCentralhttps://www.odonatacentral.org/app/#/wol/ (2022).
De Grave, S. et al. Benchmarking global biodiversity of decapod crustaceans (Crustacea: Decapoda). J. Crustac. Biol. https://doi.org/10.1093/jcbiol/ruad042 (2023).
IUCN. IUCN–Toyota Partnership. IUCN Red List https://www.iucnredlist.org/about/iucn-toyota (2023).
IUCN. Documentation standards and consistency checks for IUCN Red List assessments and species accounts. Version 2. Adopted by the IUCN Red List Committee and IUCN SSC Steering Committee. IUCN Red Listhttps://nc.iucnredlist.org/redlist/content/attachment_files/RL_Standards_Consistency.pdf (2013).
IUCN SSC Red List Technical Working Group. Mapping standards and data quality for the IUCN Red List spatial data. Version 1.19. IUCN Red Listhttps://nc.iucnredlist.org/redlist/content/attachment_files/Mapping_Standards_Version_1.19_2021.pdf (2021).
Collares-Pereira, M. J. & Cowx, I. G. The role of catchment scale environmental management in freshwater fish conservation. Fish. Manag. Ecol. 11, 303–312 (2004).
Lehner, B. & Grill, G. Global river hydrography and network routing: baseline data and new approaches to study the world’s large river systems. Hydrol. Process. 27, 2171–2186 (2013).
Darwall, W. R. T. et al. The Diversity of Life in African Freshwaters: Underwater, Under Threat (IUCN, 2011).
Sayer, C. A., Maiz-Tome, L. & Darwall, W. R. T. Freshwater Biodiversity in the Lake Victoria Basin: Guidance for Species Conservation, Site Protection, Climate Resilience and Sustainable Livelihoods (IUCN, 2018).
Starnes, T. & Darwall, W. R. T. Identification and validation of western African freshwater key biodiversity areas. IUCN https://doi.org/10.2305/IUCN.CH.2021.RA.1.en (2021).
Edmondstone, M. R. J. New Species 2022: The Freshwater Fish Species Described in 2022 (Report 2) (Shoal, 2023).
Edmondstone, M. R. J., Patricio, H. C. & Baltzer, M. New Species 2021: The Freshwater Fish Species Described in 2021 (Report 1) (Shoal, 2022).
Liu, J., Slik, F., Zheng, S. & Lindenmayer, D. B. Undescribed species have higher extinction risk than known species. Conserv. Lett. 15, e12876 (2022).
IUCN. Summary statistics. IUCN Red Listhttps://www.iucnredlist.org/resources/summary-statistics (2023).
IUCN. Guidelines for appropriate uses of IUCN Red List data (version 4.0). Incorporating as annexes, the (1) guidelines for reporting on proportion threatened (version 1.2), (2) guidelines on scientific collecting of threatened species (version 1.1), (3) guidelines for the appropriate use of the IUCN Red List by business (version 1.1) and (4) guidelines for the appropriate use of IUCN Red List data in harvesting of threatened species (version 1.0). Approved by the IUCN Red List Committee. IUCN Red Listwww.iucnredlist.org/resources/guidelines-for-appropriate-uses-of-red-list-data (2022).
IUCN. Rules of procedure for IUCN Red List assessments 2017–2020. Version 3.0. Approved by the IUCN SSC Steering Committee in September 2016. IUCN Red Listhttps://nc.iucnredlist.org/redlist/content/attachment_files/Rules_of_Procedure_for_IUCN_Red_List_Assessments_2017-2020.pdf (2016).
Moilanen, A., Montesino Pouzols, F., Meller, L. & Veach, V. Conservation Planning Methods and Software Zonation. User Manual v.4 (C-BIG Conservation Biology Informatics Group, Department of Biosciences, University of Helsinki, Finland, 2014).
R Core Team. R: A Language and Environment for Statistical Computing http://www.R-project.org (R Foundation for Statistical Computing, 2023).
Lehtomaki, J. zonator. R package v.0.6.0, https://github.com/cbig/zonator (2020).
Ferrier, S. Mapping spatial pattern in biodiversity for regional conservation planning: where to from here? Syst. Biol. 51, 331–363 (2002).
Kuzma, S. et al. Aqueduct 4.0: Updated Decision-Relevant Global Water Risk Indicators; Technical Note (World Resources Institute, 2023).
Gassert, F., Luck, M., Landis, M., Reig, P. & Shiao, T. Aqueduct Global Maps 2.1: Constructing Decision-Relevant Global Water Risk Indicators. Working Paper (World Resources Institute, 2014).
Damania, R., Desbureaux, S., Rodella, A.-S., Russ, J. & Zaveri, E. D. Quality Unknown: The Invisible Water Crisis (World Bank Group, 2019).
Randall, J. Randall-HYLA/FW-surrogacy: FW_surrogacy. Zenodohttps://doi.org/10.5281/zenodo.13178145 (2024).
