Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Perspective
  • Published:

Utilizing aquatic environmental DNA to address global biodiversity targets

Nature Reviews Biodiversity volume 1, pages 332–346 (2025)Cite this article

Subjects

Abstract

Achieving global biodiversity goals requires assessing, attributing and reversing the ongoing, unprecedented biodiversity decline in aquatic ecosystems, and relies on adequate data to inform policy and action. Analysis of environmental DNA (eDNA) has become established as a novel and powerful approach to assess the state and functioning of aquatic ecosystems, and although increasingly implemented by stakeholders its potential is not yet fully tapped. In this Perspective, we review the current state of aquatic eDNA research, focusing in particular on the policy relevance of eDNA and its utility in contributing towards the Kunming–Montreal Global Biodiversity Framework. We summarize key technological developments in eDNA science to measure organismal diversity, its potential for spatial and temporal upscaling to become a key reference for local to global biodiversity action, and the next steps needed to effectively implement eDNA for decision-making and reaching biodiversity targets. Using eDNA to support biodiversity assessment will particularly benefit the understanding of understudied ecosystems and allow the direct calculation of ecological indices and implementation of FAIR (findable, accessible, interoperable and reusable) and inclusive data curation. Important next steps for eDNA require proper method standardization and commonly agreed quality standards, populating reference databases, and overcoming methodological constraints in retrofitting novel eDNA-based approaches to existing biodiversity monitoring approaches.

Key points

  • Aquatic biodiversity is declining from local to global scales, yet in most regions, no or only minimal data on state and change of biodiversity are available.

  • Representative, scalable and replicable monitoring of aquatic biodiversity is needed to achieve the Kunming–Montreal Global Biodiversity Framework targets.

  • Environmental DNA (eDNA) analysis is a key technology to achieve a global measurement network of state and trends in biodiversity, and many of its technical aspects are ready to be implemented.

  • eDNA analysis allows whole-community assessments, broad taxonomic coverage, high spatiotemporal resolution and calculation of environmental indices.

  • Particularly for undersampled regions, large rivers, lakes and marine systems, eDNA metabarcoding might be an effective technology to rapidly gain biodiversity data.

  • To make eDNA-based monitoring policy frameworks successful and trusted, inclusive development and uptake of international method standards are needed.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Contribution of eDNA to the Kunming–Montreal Global Biodiversity Framework.
Fig. 2: Use and application of eDNA sampled in lakes, rivers, groundwater and marine waters, and across different taxonomic groups.
Fig. 3: Development of eDNA-based technologies and availability of datasets.
Fig. 4: Machine learning enables taxonomy-free assessments of indices, such that simple, easily communicable predictions can be derived for decision-making.

Similar content being viewed by others

References

  1. Keck, F. et al. The global human impact on biodiversity. Nature https://doi.org/10.1038/s41586-025-08752-2 (2025).

  2. Pereira, H. M. et al. Global trends and scenarios for terrestrial biodiversity and ecosystem services from 1900 to 2050. Science 384, 458–465 (2024).

    Article  CAS  Google Scholar 

  3. Loreau, M. et al. Do not downplay biodiversity loss. Nature 601, E27–E28 (2022).

    Article  CAS  Google Scholar 

  4. Rockström, J. et al. A safe operating space for humanity. Nature 461, 472–475 (2009).

    Article  Google Scholar 

  5. World Economic Forum. Global Risks Report 2022, 17th edn (WEF, 2022).

  6. United Nations Convention on Biological Diversity. Kunming–Montreal Global Biodiversity Framework. CBD https://www.cbd.int/doc/decisions/cop-15/cop-15-dec-04-en.pdf (UN, 2022).

  7. Gonzalez, A. et al. A global biodiversity observing system to unite monitoring and guide action. Nat. Ecol. Evol. 7, 1947–1952 (2023).

    Article  Google Scholar 

  8. Almond, R. E., Grooten, M. & Peterson, T. World Wildlife Fund. Living Planet Report 2020 — Bending the Curve of Biodiversity Loss (WWF, 2020).

  9. Intergovernmental Science–Policy Platform on Biodiversity and Ecosystem Services. Global assessment report on biodiversity and ecosystem services (IPBES, 2019).

  10. Schwarzenbach, R. P. et al. The challenge of micropollutants in aquatic systems. Science 313, 1072–1077 (2006).

    Article  CAS  Google Scholar 

  11. Vörösmarty, C. J. et al. Global threats to human water security and river biodiversity. Nature 467, 555–561 (2010).

    Article  Google Scholar 

  12. Leclère, D. et al. Bending the curve of terrestrial biodiversity needs an integrated strategy. Nature 585, 551–556 (2020).

    Article  Google Scholar 

  13. Taberlet, P., Coissac, E., Hajibabaei, M. & Rieseberg, L. H. Environmental DNA. Mol. Ecol. 21, 1789–1793 (2012).

    Article  CAS  Google Scholar 

  14. Taberlet, P., Coissac, E., Pompanon, F., Brochmann, C. & Willerslev, E. Towards next-generation biodiversity assessment using DNA metabarcoding. Mol. Ecol. 21, 2045–2050 (2012).

    Article  CAS  Google Scholar 

  15. Pawlowski, J., Apothéloz-Perret-Gentil, L. & Altermatt, F. Environmental DNA: what’s behind the term? Clarifying the terminology and recommendations for its future use in biomonitoring. Mol. Ecol. 29, 4258–4264 (2020).

    Article  Google Scholar 

  16. Deiner, K., Fronhofer, E. A., Mächler, E., Walser, J.-C. & Altermatt, F. Environmental DNA reveals that rivers are conveyer belts of biodiversity information. Nat. Commun. 7, 12544 (2016).

    Article  CAS  Google Scholar 

  17. Zhang, H. et al. A spatial fingerprint of land–water linkage of biodiversity uncovered by remote sensing and environmental DNA. Sci. Total Environ. 867, 161365 (2023).

    Article  CAS  Google Scholar 

  18. Yates, M. C., Derry, A. M. & Cristescu, M. E. Environmental RNA: a revolution in ecological resolution? Trends Ecol. Evol. 36, 601–609 (2021).

    Article  CAS  Google Scholar 

  19. Visco, J. A. et al. Environmental monitoring: inferring the diatom index from next-generation sequencing data. Environ. Sci. Technol. 49, 7597–7605 (2015).

    Article  CAS  Google Scholar 

  20. Kagzi, K., Hechler, R. M., Fussmann, G. F. & Cristescu, M. E. Environmental RNA degrades more rapidly than environmental DNA across a broad range of pH conditions. Mol. Ecol. Resour. 22, 2640–2650 (2022).

    Article  CAS  Google Scholar 

  21. Pochon, X., Zaiko, A., Fletcher, L. M., Laroche, O. & Wood, S. A. Wanted dead or alive? Using metabarcoding of environmental DNA and RNA to distinguish living assemblages for biosecurity applications. PLoS ONE 12, e0187636 (2017).

    Article  Google Scholar 

  22. Sepulveda, A. et al. Using structured decision making to evaluate potential management responses to detection of dreissenid mussel (Dreissena spp.) environmental DNA. Manag. Biol. Invasion 13, 344–368 (2022).

    Article  Google Scholar 

  23. US Fish and Wildlife Service. Great Lakes eDNA monitoring program. Asian carp Canada https://www.asiancarp.ca/surveillance-prevention-and-response/great-lakes-edna-monitoring-program/ (US FWS, 2020).

  24. Romero, F., Acuña, V. & Sabater, S. Multiple stressors determine community structure and estimated function of river biofilm bacteria. Appl. Environ. Microbiol. 86, e00291–e00320 (2020).

    Article  CAS  Google Scholar 

  25. Beermann, A. J., Zizka, V. M. A., Elbrecht, V., Baranov, V. & Leese, F. DNA metabarcoding reveals the complex and hidden responses of chironomids to multiple stressors. Environ. Sci. Eur. 30, 26 (2018).

    Article  Google Scholar 

  26. Fediajevaite, J., Priestley, V., Arnold, R. & Savolainen, V. Meta-analysis shows that environmental DNA outperforms traditional surveys, but warrants better reporting standards. Ecol. Evol. 11, 4803–4815 (2021).

    Article  Google Scholar 

  27. Buchner, D., Macher, T.-H., Beermann, A. J., Werner, M.-T. & Leese, F. Standardized high-throughput biomonitoring using DNA metabarcoding: strategies for the adoption of automated liquid handlers. Environ. Sci. Ecotechnol. 8, 100122 (2021).

    Article  CAS  Google Scholar 

  28. Ruppert, K. M., Kline, R. J. & Rahman, M. S. Past, present, and future perspectives of environmental DNA (eDNA) metabarcoding: a systematic review in methods, monitoring, and applications of global eDNA. Glob. Ecol. Conserv. 17, e00547 (2019).

    Google Scholar 

  29. Biggs, J. et al. Using eDNA to develop a national citizen science-based monitoring programme for the great crested newt (Triturus cristatus). Biol. Conserv. 183, 19–28 (2015).

    Article  Google Scholar 

  30. Larson, E. R. et al. From eDNA to citizen science: emerging tools for the early detection of invasive species. Front. Ecol. Environ. 18, 194–202 (2020).

    Article  Google Scholar 

  31. Couton, M. et al. Integrating citizen science and environmental DNA metabarcoding to study biodiversity of groundwater amphipods in Switzerland. Sci. Rep. 13, 18097 (2023).

    Article  CAS  Google Scholar 

  32. Deiner, K. et al. Environmental DNA metabarcoding: transforming how we survey animal and plant communities. Mol. Ecol. 26, 5872–5895 (2017).

    Article  Google Scholar 

  33. Sanger, F., Nicklen, S. & Coulson, A. R. DNA sequencing with chain-terminating inhibitors. Proc. Natl Acad. Sci. USA 74, 5463–5467 (1977).

    Article  CAS  Google Scholar 

  34. Mullis, K. et al. Specific enzymatic amplification of DNA in vitro: the polymerase chain reaction. Cold Spring Harb. Symp. Quant. Biol. 51, 263–273 (1986).

    Article  CAS  Google Scholar 

  35. Higuchi, R., Fockler, C., Dollinger, G. & Watson, R. Kinetic PCR analysis: real-time monitoring of DNA amplification reactions. Biotechnology 11, 1026–1030 (1993).

    CAS  Google Scholar 

  36. Margulies, M. et al. Genome sequencing in microfabricated high-density picolitre reactors. Nature 437, 376–380 (2005).

    Article  CAS  Google Scholar 

  37. Bruce, K. et al. A Practical Guide to DNA-Based Methods for Biodiversity Assessment (Pensoft, 2021).

  38. Sigsgaard, E. E. et al. Population-level inferences from environmental DNA — current status and future perspectives. Evol. Appl. 13, 245–262 (2020).

    Article  Google Scholar 

  39. Abad-Recio, I. L., Alonso-Sáez, L. & Lanzén, A. Toward functional profiling for eDNA‐based monitoring in coastal environments: a comparison of three approaches. Environ. DNA 6, e504 (2024).

    Article  Google Scholar 

  40. MacKenzie, M. & Argyropoulos, C. An introduction to nanopore sequencing: past, present, and future considerations. Micromachines 14, 459 (2023).

    Article  Google Scholar 

  41. Bovo, S. et al. Shotgun metagenomics of honey DNA: evaluation of a methodological approach to describe a multi-kingdom honey bee derived environmental DNA signature. PLoS ONE 13, e0205575 (2018).

    Article  Google Scholar 

  42. Thomsen, P. F., Jensen, M. R. & Sigsgaard, E. E. A vision for global eDNA-based monitoring in a changing world. Cell 187, 4444–4448 (2024).

    Article  CAS  Google Scholar 

  43. Ogram, A., Sayler, G. S. & Barkay, T. The extraction and purification of microbial DNA from sediments. J. Microbiol. Methods 7, 57–66 (1987).

    Article  CAS  Google Scholar 

  44. Steffan, R. J., Goksøyr, J., Bej, A. K. & Atlas, R. M. Recovery of DNA from soils and sediments. Appl. Environ. Microbiol. 54, 2908–2915 (1988).

    Article  CAS  Google Scholar 

  45. Hebert, P. D. N., Cywinska, A., Ball, S. L. & deWaard, J. R. Biological identifications through DNA barcodes. Proc. Biol. Sci. 270, 313–321 (2003).

    Article  CAS  Google Scholar 

  46. Ficetola, G. F., Miaud, C., Pompanon, F. & Taberlet, P. Species detection using environmental DNA from water samples. Biol. Lett. 4, 423–425 (2008).

    Article  Google Scholar 

  47. Blackman, R. et al. Environmental DNA: the next chapter. Mol. Ecol. 33, e17355 (2024).

    Article  Google Scholar 

  48. Satam, H. et al. Next-generation sequencing technology: current trends and advancements. Biology 2023, 997 (2023).

    Article  Google Scholar 

  49. Foote, A. D. et al. Investigating the potential use of environmental DNA (eDNA) for genetic monitoring of marine mammals. PLoS ONE 7, e41781 (2012).

    Article  CAS  Google Scholar 

  50. Leese, F. et al. DNAqua-Net: developing new genetic tools for bioassessment and monitoring of aquatic ecosystems in Europe. Res. Ideas Outcomes 2, e11321 (2016).

    Article  Google Scholar 

  51. Takahashi, M. et al. Aquatic environmental DNA: a review of the macro-organismal biomonitoring revolution. Sci. Total Environ. 873, 162322 (2023).

    Article  CAS  Google Scholar 

  52. De Brauwer, M. et al. Best practice guidelines for environmental DNA biomonitoring in Australia and New Zealand. Environ. DNA 5, 417–423 (2023).

    Article  Google Scholar 

  53. Ferrante, J. et al. Gaining decision-maker confidence through community consensus: developing environmental DNA standards for data display on the USGS Nonindigenous Aquatic Species database. Manag. Biol. Invasion 13, 809–832 (2022).

    Article  Google Scholar 

  54. Minamoto, T. et al. An illustrated manual for environmental DNA research: water sampling guidelines and experimental protocols. Environ. DNA 3, 8–13 (2021).

    Article  CAS  Google Scholar 

  55. Andruszkiewicz Allan, E., Zhang, W. G., C. Lavery, A. & Govindarajan, F. A. Environmental DNA shedding and decay rates from diverse animal forms and thermal regimes. Environ. DNA 3, 492–514 (2021).

    Article  Google Scholar 

  56. Deiner, K. & Altermatt, F. Transport distance of invertebrate environmental DNA in a natural river. PLoS ONE 9, e88786 (2014).

    Article  Google Scholar 

  57. Burian, A. et al. Improving the reliability of eDNA data interpretation. Mol. Ecol. Resour. 21, 1422–1433 (2021).

    Article  CAS  Google Scholar 

  58. Keck, F., Couton, M. & Altermatt, F. Navigating the seven challenges of taxonomic reference databases in metabarcoding analyses. Mol. Ecol. Resour. https://doi.org/10.1111/1755-0998.13746 (2022).

    Article  Google Scholar 

  59. Goldberg, C. S., Strickler, K. M. & Pilliod, D. S. Moving environmental DNA methods from concept to practice for monitoring aquatic macroorganisms. Biol. Conserv. 183, 1–3 (2015).

    Article  Google Scholar 

  60. Gonzalez, A. & Londoño, M. C. Monitor biodiversity for action. Science 378, 1147 (2022).

    Article  Google Scholar 

  61. Norros, V. et al. Roadmap for implementing environmental DNA (eDNA) and other molecular monitoring methods in Finland — vision and action plan for 2022–2025 (Finnish Environment Institute, 2022).

  62. Blancher, P. et al. A strategy for successful integration of DNA-based methods in aquatic monitoring. MBMG 6, e85652 (2022).

    Article  Google Scholar 

  63. Kelly, R. P. et al. Toward a national eDNA strategy for the United States. Environ. DNA https://doi.org/10.1002/edn3.432 (2024).

  64. Mason, D. H. et al. Certain detection of uncertain taxa: eDNA detection of a cryptic mountain sucker (Pantosteus jordani) in the Upper Missouri River, USA. Environ. DNA 3, 449–457 (2021).

    Article  Google Scholar 

  65. Couton, M., Hürlemann, S., Studer, A., Alther, R. & Altermatt, F. Groundwater environmental DNA metabarcoding reveals hidden diversity and reflects land-use and geology. Mol. Ecol. 32, 3497–3512 (2023).

    Article  CAS  Google Scholar 

  66. Laroche, O., Kersten, O., Smith, C. R. & Goetze, E. From sea surface to seafloor: a benthic allochthonous eDNA survey for the abyssal ocean. Front. Mar. Sci. https://doi.org/10.3389/fmars.2020.00682 (2020).

  67. Lee, K. N., Kelly, R. P., Demir-Hilton, E., Laschever, E. & Allan, E. A. Adoption of environmental DNA in public agency practice. Environ. DNA https://doi.org/10.1002/edn3.470 (2024).

  68. Sander, M. et al. Environmental DNA time series analysis of a temperate stream reveals distinct seasonal community and functional shifts. River Res. Appl. 40, 850–862 (2024).

    Article  Google Scholar 

  69. Tillotson, M. D. et al. Concentrations of environmental DNA (eDNA) reflect spawning salmon abundance at fine spatial and temporal scales. Biol. Conserv. 220, 1–11 (2018).

    Article  Google Scholar 

  70. Formel, N., Enochs, I. C., Sinigalliano, C., Anderson, S. R. & Thompson, L. R. Subsurface automated samplers for eDNA (SASe) for biological monitoring and research. HardwareX 10, e00239 (2021).

    Article  Google Scholar 

  71. Hendricks, A. et al. A miniaturized and automated eDNA sampler: application to a marine environment. In OCEANS 2022, Hampton Roads https://doi.org/10.1109/oceans47191.2022.9977218 (IEEE, 2022).

  72. Hendricks, A. et al. Compact and automated eDNA sampler for in situ monitoring of marine environments. Sci. Rep. 13, 5210 (2023).

    Article  CAS  Google Scholar 

  73. George, S. D. et al. Field trials of an autonomous eDNA sampler in lotic waters. Environ. Sci. Technol. 58, 20942–20953 (2024).

    Article  CAS  Google Scholar 

  74. Preston, C. M. et al. Underwater application of quantitative PCR on an ocean mooring. PLoS ONE 6, e22522 (2011).

    Article  CAS  Google Scholar 

  75. Hansen, B. K. et al. Remote, autonomous real-time monitoring of environmental DNA from commercial fish. Sci. Rep. 10, 13272 (2020).

    Article  CAS  Google Scholar 

  76. Sepulveda, A. J. et al. Robotic environmental DNA bio-surveillance of freshwater health. Sci. Rep. 10, 14389 (2020).

    Article  CAS  Google Scholar 

  77. Maiello, G. et al. Little samplers, big fleet: eDNA metabarcoding from commercial trawlers enhances ocean monitoring. Fish. Res. 249, 106259 (2022).

    Article  Google Scholar 

  78. Chen, X. et al. Comparative evaluation of common materials as passive samplers of environmental DNA. Environ. Sci. Technol. 56, 10798–10807 (2022).

    Article  CAS  Google Scholar 

  79. Pont, D. Predicting downstream transport distance of fish eDNA in lotic environments. Mol. Ecol. Resour. 24, e13934 (2024).

    Article  CAS  Google Scholar 

  80. Van Driessche, C., Everts, T., Neyrinck, S. & Brys, R. Experimental assessment of downstream environmental DNA patterns under variable fish biomass and river discharge rates. Environ. DNA 5, 102–116 (2023).

    Article  Google Scholar 

  81. Brantschen, J. et al. Habitat suitability models reveal the spatial signal of environmental DNA in riverine networks. Ecography https://doi.org/10.1111/ecog.07267 (2024).

  82. Cantera, I. et al. Low level of anthropization linked to harsh vertebrate biodiversity declines in Amazonia. Nat. Commun. 13, 3290 (2022).

    Article  CAS  Google Scholar 

  83. Zong, S. et al. Combining environmental DNA with remote sensing variables to map fish species distributions along a large river. Remote Sens. Ecol. Conserv. 10, 220–235 (2024).

    Article  Google Scholar 

  84. Jeunen, G.-J. et al. Water stratification in the marine biome restricts vertical environmental DNA (eDNA) signal dispersal. Environ. DNA 2, 99–111 (2020).

    Article  Google Scholar 

  85. Jeunen, G.-J. et al. Environmental DNA (eDNA) metabarcoding reveals strong discrimination among diverse marine habitats connected by water movement. Mol. Ecol. Resour. 19, 426–438 (2019).

    Article  CAS  Google Scholar 

  86. Laporte, M. et al. Caged fish experiment and hydrodynamic bidimensional modeling highlight the importance to consider 2D dispersion in fluvial environmental DNA studies. Environ. DNA 2, 362–372 (2020).

    Article  Google Scholar 

  87. Sansom, B. J. & Sassoubre, L. M. Environmental DNA (eDNA) shedding and decay rates to model freshwater mussel eDNA transport in a river. Environ. Sci. Technol. 51, 14244–14253 (2017).

    Article  CAS  Google Scholar 

  88. Andruszkiewicz, E. A. et al. Modeling environmental DNA transport in the coastal ocean using Lagrangian particle tracking. Front. Mar. Sci. https://doi.org/10.3389/fmars.2019.00477 (2019).

  89. Fukaya, K. et al. Estimating fish population abundance by integrating quantitative data on environmental DNA and hydrodynamic modelling. Mol. Ecol. 30, 3057–3067 (2021).

    Article  CAS  Google Scholar 

  90. Carraro, L. & Altermatt, F. eDITH: an R‐package to spatially project eDNA‐based biodiversity across river networks with minimal prior information. Methods Ecol. Evol. 15, 806–815 (2024).

    Article  Google Scholar 

  91. Carraro, L., Mächler, E., Wüthrich, R. & Altermatt, F. Environmental DNA allows upscaling spatial patterns of biodiversity in freshwater ecosystems. Nat. Commun. 11, 3585 (2020).

    Article  Google Scholar 

  92. Blackman, R. C., Carraro, L., Keck, F. & Altermatt, F. Measuring the state of aquatic environments using eDNA-upscaling spatial resolution of biotic indices. Philos. Trans. R. Soc. Lond. B 379, 20230121 (2024).

    Article  CAS  Google Scholar 

  93. Jerde, C. L. et al. Detection of Asian carp DNA as part of a Great Lakes basin-wide surveillance program. Can. J. Fish. Aquat. Sci. 70, 522–526 (2013).

    Article  CAS  Google Scholar 

  94. Rees, H. C. et al. The application of eDNA for monitoring of the great crested newt in the UK. Ecol. Evol. 4, 4023–4032 (2014).

    Article  Google Scholar 

  95. Jahn, K. et al. Early detection and surveillance of SARS-CoV-2 genomic variants in wastewater using COJAC. Nat. Microbiol. 7, 1151–1160 (2022).

    Article  CAS  Google Scholar 

  96. Feist, S. M. & Lance, R. F. Genetic detection of freshwater harmful algal blooms: a review focused on the use of environmental DNA (eDNA) in Microcystis aeruginosa and Prymnesium parvum. Harmful Algae 110, 102124 (2021).

    Article  CAS  Google Scholar 

  97. Abdul Manaff, A. H. N. et al. Mapping harmful microalgal species by eDNA monitoring: a large-scale survey across the southwestern South China Sea. Harmful Algae 129, 102515 (2023).

    Article  CAS  Google Scholar 

  98. Blackman, R. C. et al. Targeted and passive environmental DNA approaches outperform established methods for detection of quagga mussels, Dreissena rostriformis bugensis in flowing water. Ecol. Evol. 10, 13248–13259 (2020).

    Article  Google Scholar 

  99. Danziger, A. M. & Frederich, M. Challenges in eDNA detection of the invasive European green crab, Carcinus maenas. Biol. Invasions 24, 1881–1894 (2022).

    Article  Google Scholar 

  100. Mansfeldt, C. et al. Microbial community shifts in streams receiving treated wastewater effluent. Sci. Total Environ. 709, 135727 (2020).

    Article  CAS  Google Scholar 

  101. Yamamoto, S. et al. Environmental DNA metabarcoding reveals local fish communities in a species-rich coastal sea. Sci. Rep. 7, 40368 (2017).

    Article  CAS  Google Scholar 

  102. Inoue, Y., Miyata, K., Yamane, M. & Honda, H. Environmental nucleic acid pollution: characterization of wastewater generating false positives in molecular ecological surveys. ACS ES&T Water 3, 756–764 (2023).

    Article  CAS  Google Scholar 

  103. Darling, J. A., Jerde, C. L. & Sepulveda, A. J. What do you mean by false positive. Environ. DNA 3, 879–883 (2020).

    Article  Google Scholar 

  104. Ficetola, G. F., Taberlet, P. & Coissac, E. How to limit false positives in environmental DNA and metabarcoding? Mol. Ecol. Resour. 16, 604–607 (2016).

    Article  CAS  Google Scholar 

  105. McCauley, M., Koda, S. A., Loesgen, S. & Duffy, D. J. Multicellular species environmental DNA (eDNA) research constrained by overfocus on mitochondrial DNA. Sci. Total Environ. 912, 169550 (2024).

    Article  CAS  Google Scholar 

  106. Pilliod, D. S., Goldberg, C. S., Arkle, R. S. & Waits, L. P. Estimating occupancy and abundance of stream amphibians using environmental DNA from filtered water samples. Can. J. Fish. Aquat. Sci. 70, 1123–1130 (2013).

    Article  CAS  Google Scholar 

  107. Doi, H. et al. Environmental DNA analysis for estimating the abundance and biomass of stream fish. Freshw. Biol. 62, 30–39 (2017).

    Article  CAS  Google Scholar 

  108. Di Muri, C. et al. Read counts from environmental DNA (eDNA) metabarcoding reflect fish abundance and biomass in drained ponds. MBMG 4, e56959 (2020).

    Article  Google Scholar 

  109. Pont, D. et al. Quantitative monitoring of diverse fish communities on a large scale combining eDNA metabarcoding and qPCR. Mol. Ecol. Resour. 23, 396–409 (2023).

    Article  CAS  Google Scholar 

  110. Nakagawa, H., Fukushima, K., Sakai, M., Wu, L. & Minamoto, T. Relationships between the eDNA concentration obtained from metabarcoding and stream fish abundance estimated by the removal method under field conditions. Environ. DNA 4, 1369–1380 (2022).

    Article  CAS  Google Scholar 

  111. Fonseca, V. G. Pitfalls in relative abundance estimation using eDNA metabarcoding. Mol. Ecol. Resour. 18, 923–926 (2018).

    Article  CAS  Google Scholar 

  112. Yates, M. C., Fraser, D. J. & Derry, A. M. Meta‐analysis supports further refinement of eDNA for monitoring aquatic species‐specific abundance in nature. Environ. DNA 1, 5–13 (2019).

    Article  Google Scholar 

  113. Sepulveda, A. J. et al. It’s complicated environmental DNA as a predictor of trout and char abundance in streams. Can. J. Fish. Aquat. Sci. 78, 422–432 (2021).

    Article  CAS  Google Scholar 

  114. Sigsgaard, E. E. et al. Population characteristics of a large whale shark aggregation inferred from seawater environmental DNA. Nat. Ecol. Evol. 1, 0004 (2016).

    Article  Google Scholar 

  115. Weitemier, K. et al. Estimating the genetic diversity of Pacific salmon and trout using multigene eDNA metabarcoding. Mol. Ecol. 30, 4970–4990 (2021).

    Article  CAS  Google Scholar 

  116. Parsons, K. M., Everett, M., Dahlheim, M. & Park, L. Water, water everywhere: environmental DNA can unlock population structure in elusive marine species. R. Soc. Open Sci. 5, 180537 (2018).

    Article  Google Scholar 

  117. Elbrecht, V., Vamos, E. E., Steinke, D. & Leese, F. Estimating intraspecific genetic diversity from community DNA metabarcoding data. PeerJ 6, e4644 (2018).

    Article  Google Scholar 

  118. Turon, X., Antich, A., Palacín, C., Praebel, K. & Wangensteen, O. S. From metabarcoding to metaphylogeography: separating the wheat from the chaff. Ecol. Appl. 30, e02036 (2020).

    Article  Google Scholar 

  119. Couton, M., Viard, F. & Altermatt, F. Opportunities and inherent limits of using environmental DNA for population genetics. Environ. DNA 5, 1048–1064 (2023).

    Article  CAS  Google Scholar 

  120. Andres, K. J., Sethi, S. A., Lodge, D. M. & Andrés, J. Nuclear eDNA estimates population allele frequencies and abundance in experimental mesocosms and field samples. Mol. Ecol. 30, 685–697 (2021).

    Article  CAS  Google Scholar 

  121. Wolf, K. K. E. et al. Revealing environmentally driven population dynamics of an Arctic diatom using a novel microsatellite PoolSeq barcoding approach. Environ. Microbiol. 23, 3809–3824 (2021).

    Article  CAS  Google Scholar 

  122. Barbour, M. T., Gerritsen, J., Snyder, B. D. & Stribling, J. B. US Environmental Protection Agency. Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers (US EPA, 1999).

  123. Cordier, T. et al. Ecosystems monitoring powered by environmental genomics: a review of current strategies with an implementation roadmap. Mol. Ecol. 30, 2937–2958 (2021).

    Article  Google Scholar 

  124. Yang, J. et al. Ecogenomics of zooplankton community reveals ecological threshold of ammonia nitrogen. Environ. Sci. Technol. 51, 3057–3064 (2017).

    Article  CAS  Google Scholar 

  125. Nuy, J. K. et al. Responses of stream microbes to multiple anthropogenic stressors in a mesocosm study. Sci. Total Environ. 633, 1287–1301 (2018).

    Article  CAS  Google Scholar 

  126. Li, F. et al. Application of environmental DNA metabarcoding for predicting anthropogenic pollution in rivers. Environ. Sci. Technol. 52, 11708–11719 (2018).

    CAS  Google Scholar 

  127. Blackman, R. C., Ho, H.-C., Walser, J.-C. & Altermatt, F. Spatio-temporal patterns of multi-trophic biodiversity and food-web characteristics uncovered across a river catchment using environmental DNA. Commun. Biol. 5, 259 (2022).

    Article  Google Scholar 

  128. Stevens, J. D. & Parsley, M. B. Environmental RNA applications and their associated gene targets for management and conservation. Environ. DNA 5, 227–239 (2023).

    Article  CAS  Google Scholar 

  129. Bergsveinson, J. et al. Metatranscriptomic insights into the response of river biofilm communities to ionic and nano-zinc oxide exposures. Front. Microbiol. 11, 267 (2020).

    Article  Google Scholar 

  130. Hechler, R. M., Yates, M. C., Chain, F. J. J. & Cristescu, M. E. Environmental transcriptomics under heat stress: can environmental RNA reveal changes in gene expression of aquatic organisms? Mol. Ecol. https://doi.org/10.1111/mec.17152 (2023).

  131. Cordier, T. et al. Predicting the ecological quality status of marine environments from eDNA metabarcoding data using supervised machine learning. Environ. Sci. Technol. 51, 9118–9126 (2017).

    Article  CAS  Google Scholar 

  132. Keck, F., Brantschen, J. & Altermatt, F. A combination of machine-learning and eDNA reveals the genetic signature of environmental change at the landscape levels. Mol. Ecol. 32, 4791–4800 (2023).

    Article  CAS  Google Scholar 

  133. Salis, R. K., Bruder, A., Piggott, J. J., Summerfield, T. C. & Matthaei, C. D. High-throughput amplicon sequencing and stream benthic bacteria: identifying the best taxonomic level for multiple-stressor research. Sci. Rep. 7, 44657 (2017).

    Article  CAS  Google Scholar 

  134. Sagova-Mareckova, M. et al. Expanding ecological assessment by integrating microorganisms into routine freshwater biomonitoring. Water Res. 191, 116767 (2021).

    Article  CAS  Google Scholar 

  135. Cordier, T., Lanzén, A., Apothéloz-Perret-Gentil, L., Stoeck, T. & Pawlowski, J. Embracing environmental genomics and machine learning for routine biomonitoring. Trends Microbiol. 27, 387–397 (2019).

    Article  CAS  Google Scholar 

  136. Martínez-Santos, M. et al. Treated and untreated wastewater effluents alter river sediment bacterial communities involved in nitrogen and sulphur cycling. Sci. Total Environ. 633, 1051–1061 (2018).

    Article  Google Scholar 

  137. Andújar, C. et al. Metabarcoding of freshwater invertebrates to detect the effects of a pesticide spill. Mol. Ecol. 27, 146–166 (2018).

    Article  Google Scholar 

  138. Vasselon, V., Rimet, F., Tapolczai, K. & Bouchez, A. Assessing ecological status with diatoms DNA metabarcoding: scaling-up on a WFD monitoring network (Mayotte island, France). Ecol. Indic. 82, 1–12 (2017).

    Article  CAS  Google Scholar 

  139. Apothéloz-Perret-Gentil, L. et al. Taxonomy-free molecular diatom index for high-throughput eDNA biomonitoring. Mol. Ecol. Resour. 17, 1231–1242 (2017).

    Article  Google Scholar 

  140. Feio, M. J. et al. A taxonomy-free approach based on machine learning to assess the quality of rivers with diatoms. Sci. Total Environ. 722, 137900 (2020).

    Article  CAS  Google Scholar 

  141. Frühe, L. et al. Supervised machine learning is superior to indicator value inference in monitoring the environmental impacts of salmon aquaculture using eDNA metabarcodes. Mol. Ecol. 30, 2988–3006 (2021).

    Article  Google Scholar 

  142. Cordier, T. et al. Supervised machine learning outperforms taxonomy-based environmental DNA metabarcoding applied to biomonitoring. Mol. Ecol. Resour. 18, 1381–1391 (2018).

    Article  CAS  Google Scholar 

  143. Wilkinson, S. P. et al. TICI: a taxon-independent community index for eDNA-based ecological health assessment. PeerJ 12, e16963 (2024).

    Article  Google Scholar 

  144. Zhang, Y., Zhang, X., Li, F. & Altermatt, F. Fishing eDNA in one of the world’s largest rivers: a case study of cross-sectional and depth profile sampling in the Yangtze. Environ. Sci. Technol. 57, 21691–21703 (2023).

    Article  CAS  Google Scholar 

  145. Gold, Z., Sprague, J., Kushner, D. J., Zerecero Marin, E. & Barber, P. H. eDNA metabarcoding as a biomonitoring tool for marine protected areas. PLoS ONE 16, e0238557 (2021).

    Article  CAS  Google Scholar 

  146. Stewart, K., Ma, H., Zheng, J. & Zhao, J. Using environmental DNA to assess population-wide spatiotemporal reserve use. Conserv. Biol. 31, 1173–1182 (2017).

    Article  Google Scholar 

  147. McClenaghan, B. et al. Harnessing the power of eDNA metabarcoding for the detection of deep-sea fishes. PLoS ONE 15, e0236540 (2020).

    Article  CAS  Google Scholar 

  148. Fujiwara, Y. et al. Detection of the largest deep-sea-endemic teleost fish at depths of over 2,000 m through a combination of eDNA metabarcoding and baited camera observations. Front. Mar. Sci. https://doi.org/10.3389/fmars.2022.945758 (2022).

  149. van der Heyde, M. et al. Taking eDNA underground: factors affecting eDNA detection of subterranean fauna in groundwater. Mol. Ecol. Resour. 23, 1257–1274 (2023).

    Article  Google Scholar 

  150. Savio, D. et al. Bacterial diversity along a 2600 km river continuum. Environ. Microbiol. 17, 4994–5007 (2015).

    Article  CAS  Google Scholar 

  151. De Ventura, L., Kopp, K., Seppälä, K. & Jokela, J. Tracing the quagga mussel invasion along the Rhine River system using eDNA markers: early detection and surveillance of invasive zebra and quagga mussels. MBio 8, 101–112 (2017).

    Article  Google Scholar 

  152. Adams, A. J. et al. From eDNA to decisions using a multi-method approach to restoration planning in streams. Sci. Rep. 14, 14335 (2024).

    Article  CAS  Google Scholar 

  153. Mahon, A. R. et al. Validation of eDNA surveillance sensitivity for detection of Asian carps in controlled and field experiments. PLoS ONE 8, e58316 (2013).

    Article  CAS  Google Scholar 

  154. US Fish and Wildlife Service. Quality assurance project plan eDNA monitoring of bighead and silver carps. USFWS Great Lakes region 3 (US FWS, 2022).

  155. Ellis, M. R. et al. Detecting marine pests using environmental DNA and biophysical models. Sci. Total Environ. 816, 151666 (2022).

    Article  CAS  Google Scholar 

  156. Matejusova, I. et al. Environmental DNA based surveillance for the highly invasive carpet sea squirt Didemnum vexillum: a targeted single-species approach. Front. Mar. Sci. https://doi.org/10.3389/fmars.2021.728456 (2021).

  157. Sepulveda, A. J. et al. When are environmental DNA early detections of invasive species actionable? J. Environ. Manage. 343, 118216 (2023).

    Article  CAS  Google Scholar 

  158. Li, F. et al. Human activities’ fingerprint on multitrophic biodiversity and ecosystem functions across a major river catchment in China. Glob. Chang. Biol. 26, 6867–6879 (2020).

    Article  Google Scholar 

  159. Lanzén, A., Dahlgren, T. G., Bagi, A. & Hestetun, J. T. Benthic eDNA metabarcoding provides accurate assessments of impact from oil extraction, and ecological insights. Ecol. Indic. 130, 108064 (2021).

    Article  Google Scholar 

  160. Suzzi, A. L. et al. eDNA metabarcoding reveals shifts in sediment eukaryote communities in a metal contaminated estuary. Mar. Pollut. Bull. 191, 114896 (2023).

    Article  CAS  Google Scholar 

  161. Stoeck, T. et al. Environmental DNA metabarcoding of benthic bacterial communities indicates the benthic footprint of salmon aquaculture. Mar. Pollut. Bull. 127, 139–149 (2018).

    Article  CAS  Google Scholar 

  162. Keck, F. et al. Meta-analysis shows both congruence and complementarity of DNA and eDNA metabarcoding to traditional methods for biological community assessment. Mol. Ecol. 31, 1820–1835 (2022).

    Article  CAS  Google Scholar 

  163. Leese, F. et al. Why we need sustainable networks bridging countries, disciplines, cultures and generations for aquatic biomonitoring 2.0: a perspective derived from the DNAqua-net COST action. Adv. Ecol. Res. 58, 63–99 (2018).

    Article  Google Scholar 

  164. Pont, D. et al. The future of fish-based ecological assessment of European rivers: from traditional EU Water Framework Directive compliant methods to eDNA metabarcoding-based approaches. J. Fish Biol. 98, 354–366 (2021).

    Article  Google Scholar 

  165. Pawlowski, J., Bonin, A., Boyer, F., Cordier, T. & Taberlet, P. Environmental DNA for biomonitoring. Mol. Ecol. 30, 2931–2936 (2021).

    Article  Google Scholar 

  166. Meyer, A. et al. Morphological vs. DNA metabarcoding approaches for the evaluation of stream ecological status with benthic invertebrates: testing different combinations of markers and strategies of data filtering. Mol. Ecol. 30, 3203–3220 (2021).

    Article  CAS  Google Scholar 

  167. Blackman, R. C. et al. Advancing the use of molecular methods for routine freshwater macroinvertebrate biomonitoring — the need for calibration experiments. MBMG 3, e34735 (2019).

    Article  Google Scholar 

  168. Pawlowski, J., Apothéloz-Perret-Gentil, L., Mächler, E. & Altermatt, F. Environmental DNA Applications for Biomonitoring and Bioassessment in Aquatic Ecosystems. Guidelines. Environmental Studies no. 2010: 71 (Federal Office for the Environment, 2020).

  169. Laamanen, T. et al. Technology readiness level of biodiversity monitoring with molecular methods – where are we on the road to routine implementation? Metabarcoding Metagenom. 9, e130834 (2025).

    Article  Google Scholar 

  170. Yang, J., Zhang, L., Mu, Y. & Zhang, X. Small changes make big progress: a more efficient eDNA monitoring method for freshwater fish. Environ. DNA 5, 363–374 (2023).

    Article  CAS  Google Scholar 

  171. Shea, M. M. et al. Systematic review of marine environmental DNA metabarcoding studies: toward best practices for data usability and accessibility. PeerJ 11, e14993 (2023).

    Article  Google Scholar 

  172. Li, J., Lawson Handley, L.-J., Read, D. S. & Hänfling, B. The effect of filtration method on the efficiency of environmental DNA capture and quantification via metabarcoding. Mol. Ecol. Resour. 18, 1102–1114 (2018).

    Article  CAS  Google Scholar 

  173. Deiner, K. et al. Optimising the detection of marine taxonomic richness using environmental DNA metabarcoding: the effects of filter material, pore size and extraction method. MBMG 2, e28963 (2018).

    Article  Google Scholar 

  174. Loeza-Quintana, T., Abbott, C. L., Heath, D. D., Bernatchez, L. & Hanner, R. H. Pathway to increase standards and competency of eDNA surveys (PISCeS)— advancing collaboration and standardization efforts in the field of eDNA. Environ. DNA 2, 255–260 (2020).

    Article  Google Scholar 

  175. Altermatt, F. et al. Quantifying biodiversity using eDNA from water bodies: general principles and recommendations for sampling designs. Environ. DNA 5, 671–682 (2023).

    Article  CAS  Google Scholar 

  176. ISO/DIS 17805:2023. Water Quality — Sampling, Capture and Preservation of Environmental DNA from Water (ISO, 2023).

  177. Weigand, H. et al. DNA barcode reference libraries for the monitoring of aquatic biota in Europe: gap-analysis and recommendations for future work. Sci. Total Environ. 678, 499–524 (2019).

    Article  CAS  Google Scholar 

  178. Li, F. et al. Gap analysis for DNA-based biomonitoring of aquatic ecosystems in China. Ecol. Indic. 137, 108732 (2022).

    Article  CAS  Google Scholar 

  179. Briski, E., Ghabooli, S., Bailey, S. A. & MacIsaac, H. J. Are genetic databases sufficiently populated to detect non-indigenous species? Biol. Invasions 18, 1911–1922 (2016).

    Article  Google Scholar 

  180. Mc Cartney, A. M. et al. The European Reference Genome Atlas: piloting a decentralised approach to equitable biodiversity genomics. npj Biodivers. 3, 28 (2024).

    Article  Google Scholar 

  181. Hebert, P. D. N., Floyd, R., Jafarpour, S. & Prosser, S. W. J. Barcode 100K specimens: in a single nanopore run. Mol. Ecol. Resour. 25, e14028 (2025).

    Article  Google Scholar 

  182. Pomerantz, A. et al. Real-time DNA barcoding in a rainforest using nanopore sequencing: opportunities for rapid biodiversity assessments and local capacity building. Gigascience 7, giy033 (2018).

    Article  Google Scholar 

  183. Lin, D. et al. The TRUST principles for digital repositories. Sci. Data 7, 144 (2020).

    Article  Google Scholar 

  184. Leigh, D. M. et al. Best practices for genetic and genomic data archiving. Nat. Ecol. Evol. 8, 1224–1232 (2024).

    Article  Google Scholar 

  185. Nilsson, R. H. et al. Introducing guidelines for publishing DNA-derived occurrence data through biodiversity data platforms. MBMG 6, e84960 (2022).

    Article  Google Scholar 

  186. Berry, O. et al. Making environmental DNA (eDNA) biodiversity records globally accessible. Environ. DNA 3, 699–705 (2021).

    Article  Google Scholar 

  187. Yilmaz, P. et al. Minimum information about a marker gene sequence (MIMARKS) and minimum information about any (x) sequence (MIxS) specifications. Nat. Biotechnol. 29, 415–420 (2011).

    Article  CAS  Google Scholar 

  188. Meyer, R. et al. Aligning standards communities for omics biodiversity data: sustainable Darwin Core–MIxS interoperability. Biodivers. Data J. 11, e112420 (2023).

    Article  Google Scholar 

  189. Abarenkov, K. et al. Publishing DNA-derived data through biodiversity data platforms v1.3 (GBIF Secretariat, 2023).

  190. Klymus, K. E. et al. The MIEM guidelines: minimum information for reporting of environmental metabarcoding data. MBMG https://doi.org/10.3897/mbmg.8.128689 (2024).

  191. Takahashi, M. et al. Best practice for publishing environmental DNA (eDNA) data according to FAIR principles. Biodivers. Inf. Sci. Stand. https://doi.org/10.3897/biss.8.137742 (2024).

  192. Wilkinson, M. D. et al. The FAIR guiding principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016).

    Article  Google Scholar 

  193. Goldberg, C. S. et al. Critical considerations for the application of environmental DNA methods to detect aquatic species. Methods Ecol. Evol. 7, 1299–1307 (2016).

    Article  Google Scholar 

  194. Shen, E. W., Vandenberg, J. M. & Moore, A. Sensing inequity: technological solutionism, biodiversity conservation, and environmental DNA. Biosocieties 19, 501–525 (2024).

    Article  Google Scholar 

  195. Carroll, S. R. et al. The CARE principles for indigenous data governance. Data Sci. J. 19, 43 (2020).

    Article  Google Scholar 

  196. Secretariat of the Convention on Biological Diversity. Nagoya Protocol on Access to Genetic Resources and the Fair and Equitable Sharing of Benefits Arising from their Utilization to the Convention on Biological Diversity (Convention on Biological Diversity, United Nations, 2011).

  197. Stammnitz, M. R., Hartman Scholz, A. & Duffy, D. J. Environmental DNA without borders: let’s embrace decentralised genomics to meet the UN’s biodiversity targets. EMBO Rep. 25, 4095–4099 (2024).

    Article  Google Scholar 

  198. Handsley-Davis, M., Kowal, E., Russell, L. & Weyrich, L. S. Researchers using environmental DNA must engage ethically with Indigenous communities. Nat. Ecol. Evol. 5, 146–148 (2021).

    Article  Google Scholar 

  199. Wauchope, H. S. et al. What is a unit of nature? Measurement challenges in the emerging biodiversity credit market. Proc. Biol. Sci. 291, 20242353 (2024).

    Google Scholar 

  200. Bhutta, U. S., Tariq, A., Farrukh, M., Raza, A. & Iqbal, M. K. Green bonds for sustainable development: review of literature on development and impact of green bonds. Technol. Forecast. Soc. Change 175, 121378 (2022).

    Article  Google Scholar 

  201. Watt, R. The fantasy of carbon offsetting. Environ. Politics 30, 1069–1088 (2021).

    Article  Google Scholar 

  202. Ford, H. V. et al. A technological biodiversity monitoring toolkit for biocredits. J. Appl. Ecol. 61, 2007–2019 (2024).

    Article  Google Scholar 

  203. Jarman, S. N., Berry, O. & Bunce, M. The value of environmental DNA biobanking for long-term biomonitoring. Nat. Ecol. Evol. 2, 1192–1193 (2018).

    Article  Google Scholar 

Download references

Acknowledgements

Funding is from the Swiss National Science Foundation (grant nos. 31003A_173074 and 310030_197410 to F.A., and PZ00P2_202010 to L.C.). X.Z. and Y.Z. are supported by the National Key Research and Development Program of China (2022YFC32021001 and 2021YFC3201003).

Author information

Author notes

  1. These authors contributed equally: Florian Altermatt, Marjorie Couton, Rosetta C. Blackman.

Authors and Affiliations

  1. Department of Aquatic Ecology, Eawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland

    Florian Altermatt, Marjorie Couton, Luca Carraro, François Keck & Rosetta C. Blackman

  2. Department of Evolutionary Biology and Environmental Studies, Universität Zürich, Zürich, Switzerland

    Florian Altermatt, Marjorie Couton, Luca Carraro, François Keck & Rosetta C. Blackman

  3. Lake Ecosystems Group, UK Centre for Ecology and Hydrology, Lancaster Environment Centre, Bailrigg, UK

    Lori Lawson-Handley

  4. Aquatic Ecosystem Research, University of Duisburg-Essen, Essen, Germany

    Florian Leese

  5. Centre for Water and Environmental Research, University of Duisburg-Essen, Essen, Germany

    Florian Leese

  6. School of Ecology and Environmental Science, Yunnan University, Kunming, China

    Xiaowei Zhang

  7. State Key Laboratory of Water Pollution Control and Green Resource Recycling, School of the Environment, Nanjing University, Nanjing, China

    Yan Zhang

Authors
  1. Florian Altermatt
    View author publications

    Search author on:PubMed Google Scholar

  2. Marjorie Couton
    View author publications

    Search author on:PubMed Google Scholar

  3. Luca Carraro
    View author publications

    Search author on:PubMed Google Scholar

  4. François Keck
    View author publications

    Search author on:PubMed Google Scholar

  5. Lori Lawson-Handley
    View author publications

    Search author on:PubMed Google Scholar

  6. Florian Leese
    View author publications

    Search author on:PubMed Google Scholar

  7. Xiaowei Zhang
    View author publications

    Search author on:PubMed Google Scholar

  8. Yan Zhang
    View author publications

    Search author on:PubMed Google Scholar

  9. Rosetta C. Blackman
    View author publications

    Search author on:PubMed Google Scholar

Contributions

Overall project lead and coordination by F.A. Overall conceptualization by F.A., M.C. and R.C.B. Discussion of content by all authors. Lead writing of article by F.A., M.C. and R.C.B. Writing and lead for specific sections by F.A., M.C., L.C., F.K., L.L.-H., F.L., X.Z., Y.Z. and R.C.B. Conceptualization of figures by F.A., M.C., R.C.B., L.C. and Y.Z. Reviewing and editing of manuscript before submission by all authors.

Corresponding author

Correspondence to Florian Altermatt.

Ethics declarations

Competing interests

X.Z. directs a translation project at Nanjing University that develops apparatus for routine eDNA biomonitoring. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Reviews Biodiversity thanks David Duffy, Adam Sepulveda and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

DDBJ Sequence Read Archive: https://www.ddbj.nig.ac.jp/dra/index-e.html

DEFRA DNA Center of Excellence: https://jncc.gov.uk/our-work/dna-coe/

European Nucleotide Archive: https://www.ebi.ac.uk/ena/browser/home

GBIF: https://www.gbif.org/

GEO BON: https://geobon.org/

Global Genome Biodiversity Network: https://www.tdwg.org/standards/ggbn/

International Nucleotide Sequence Database Collaboration: https://www.insdc.org/

Joint Research Center: https://joint-research-centre.ec.europa.eu/

Nagoya Protocol: https://www.cbd.int/abs/default.shtml

National eDNA Reference Centre: https://www.ecodna.org.au/nrc

OBIS: https://obis.org/

Sequence Read Archive: https://www.ncbi.nlm.nih.gov/sra

Glossary

Amplicon sequence variant

Inferred unique sequence(s) derived from high-throughput sequencing after removal of erroneous sequences.

Biological indicator

Taxonomic group, such as fish, macroinvertebrates or diatoms, specifically used to assess environmental conditions in relation to legislative frameworks.

DNA barcoding

Identification of a specimen using a short DNA fragment called a (genetic) marker.

High-throughput sequencing

Approaches used to sequence millions of DNA sequences in a rapid and cost-effective manner (also known as next-generation sequencing).

Marker (or genetic marker)

A DNA sequence of a gene or part of a gene with a known location in the genome used to identify specific species.

Metabarcoding

Identification of the multiple organisms represented in a sample by sequencing a common DNA marker using high-throughput sequencing.

Operational taxonomic unit

An operational definition of clustered sequences based on their sequence similarity (for example, >97% similarity) to reflect approximated taxonomic units.

Polymerase chain reaction (PCR)

The process used to multiply target DNA sequences in a sample to facilitate their identification.

Primer

A short, single-stranded DNA sequence (~18–25 bp) used to target a region of the gene to be amplified during the polymerase chain reaction.

Quantitative PCR (qPCR)

Dye-based or probe-based PCR method that allows the quantification of target DNA at each PCR amplification cycle.

Read (amplicon read)

Individual sequence of base pairs (here, amplified through PCR) that corresponds to a single DNA fragment.

Species-specific assay

An approach in which a single species is targeted, typically using standard, quantitative or digital PCR (as opposed to metabarcoding).

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Altermatt, F., Couton, M., Carraro, L. et al. Utilizing aquatic environmental DNA to address global biodiversity targets. Nat. Rev. Biodivers. 1, 332–346 (2025). https://doi.org/10.1038/s44358-025-00044-x

Download citation

  • Accepted:

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s44358-025-00044-x

This article is cited by

Search

Advanced search

Quick links

Nature Briefing Anthropocene

Sign up for the Nature Briefing: Anthropocene newsletter — what matters in anthropocene research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: Anthropocene