Keck, F. et al. The global human impact on biodiversity. Nature https://doi.org/10.1038/s41586-025-08752-2 (2025).
Pereira, H. M. et al. Global trends and scenarios for terrestrial biodiversity and ecosystem services from 1900 to 2050. Science 384, 458–465 (2024).
Loreau, M. et al. Do not downplay biodiversity loss. Nature 601, E27–E28 (2022).
Rockström, J. et al. A safe operating space for humanity. Nature 461, 472–475 (2009).
World Economic Forum. Global Risks Report 2022, 17th edn (WEF, 2022).
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).
Gonzalez, A. et al. A global biodiversity observing system to unite monitoring and guide action. Nat. Ecol. Evol. 7, 1947–1952 (2023).
Almond, R. E., Grooten, M. & Peterson, T. World Wildlife Fund. Living Planet Report 2020 — Bending the Curve of Biodiversity Loss (WWF, 2020).
Intergovernmental Science–Policy Platform on Biodiversity and Ecosystem Services. Global assessment report on biodiversity and ecosystem services (IPBES, 2019).
Schwarzenbach, R. P. et al. The challenge of micropollutants in aquatic systems. Science 313, 1072–1077 (2006).
Vörösmarty, C. J. et al. Global threats to human water security and river biodiversity. Nature 467, 555–561 (2010).
Leclère, D. et al. Bending the curve of terrestrial biodiversity needs an integrated strategy. Nature 585, 551–556 (2020).
Taberlet, P., Coissac, E., Hajibabaei, M. & Rieseberg, L. H. Environmental DNA. Mol. Ecol. 21, 1789–1793 (2012).
Taberlet, P., Coissac, E., Pompanon, F., Brochmann, C. & Willerslev, E. Towards next-generation biodiversity assessment using DNA metabarcoding. Mol. Ecol. 21, 2045–2050 (2012).
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).
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).
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).
Yates, M. C., Derry, A. M. & Cristescu, M. E. Environmental RNA: a revolution in ecological resolution? Trends Ecol. Evol. 36, 601–609 (2021).
Visco, J. A. et al. Environmental monitoring: inferring the diatom index from next-generation sequencing data. Environ. Sci. Technol. 49, 7597–7605 (2015).
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).
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).
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).
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).
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).
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).
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).
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).
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).
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).
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).
Couton, M. et al. Integrating citizen science and environmental DNA metabarcoding to study biodiversity of groundwater amphipods in Switzerland. Sci. Rep. 13, 18097 (2023).
Deiner, K. et al. Environmental DNA metabarcoding: transforming how we survey animal and plant communities. Mol. Ecol. 26, 5872–5895 (2017).
Sanger, F., Nicklen, S. & Coulson, A. R. DNA sequencing with chain-terminating inhibitors. Proc. Natl Acad. Sci. USA 74, 5463–5467 (1977).
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).
Higuchi, R., Fockler, C., Dollinger, G. & Watson, R. Kinetic PCR analysis: real-time monitoring of DNA amplification reactions. Biotechnology 11, 1026–1030 (1993).
Margulies, M. et al. Genome sequencing in microfabricated high-density picolitre reactors. Nature 437, 376–380 (2005).
Bruce, K. et al. A Practical Guide to DNA-Based Methods for Biodiversity Assessment (Pensoft, 2021).
Sigsgaard, E. E. et al. Population-level inferences from environmental DNA — current status and future perspectives. Evol. Appl. 13, 245–262 (2020).
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).
MacKenzie, M. & Argyropoulos, C. An introduction to nanopore sequencing: past, present, and future considerations. Micromachines 14, 459 (2023).
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).
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).
Ogram, A., Sayler, G. S. & Barkay, T. The extraction and purification of microbial DNA from sediments. J. Microbiol. Methods 7, 57–66 (1987).
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).
Hebert, P. D. N., Cywinska, A., Ball, S. L. & deWaard, J. R. Biological identifications through DNA barcodes. Proc. Biol. Sci. 270, 313–321 (2003).
Ficetola, G. F., Miaud, C., Pompanon, F. & Taberlet, P. Species detection using environmental DNA from water samples. Biol. Lett. 4, 423–425 (2008).
Blackman, R. et al. Environmental DNA: the next chapter. Mol. Ecol. 33, e17355 (2024).
Satam, H. et al. Next-generation sequencing technology: current trends and advancements. Biology 2023, 997 (2023).
Foote, A. D. et al. Investigating the potential use of environmental DNA (eDNA) for genetic monitoring of marine mammals. PLoS ONE 7, e41781 (2012).
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).
Takahashi, M. et al. Aquatic environmental DNA: a review of the macro-organismal biomonitoring revolution. Sci. Total Environ. 873, 162322 (2023).
De Brauwer, M. et al. Best practice guidelines for environmental DNA biomonitoring in Australia and New Zealand. Environ. DNA 5, 417–423 (2023).
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).
Minamoto, T. et al. An illustrated manual for environmental DNA research: water sampling guidelines and experimental protocols. Environ. DNA 3, 8–13 (2021).
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).
Deiner, K. & Altermatt, F. Transport distance of invertebrate environmental DNA in a natural river. PLoS ONE 9, e88786 (2014).
Burian, A. et al. Improving the reliability of eDNA data interpretation. Mol. Ecol. Resour. 21, 1422–1433 (2021).
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).
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).
Gonzalez, A. & Londoño, M. C. Monitor biodiversity for action. Science 378, 1147 (2022).
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).
Blancher, P. et al. A strategy for successful integration of DNA-based methods in aquatic monitoring. MBMG 6, e85652 (2022).
Kelly, R. P. et al. Toward a national eDNA strategy for the United States. Environ. DNA https://doi.org/10.1002/edn3.432 (2024).
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).
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).
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).
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).
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).
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).
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).
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).
Hendricks, A. et al. Compact and automated eDNA sampler for in situ monitoring of marine environments. Sci. Rep. 13, 5210 (2023).
George, S. D. et al. Field trials of an autonomous eDNA sampler in lotic waters. Environ. Sci. Technol. 58, 20942–20953 (2024).
Preston, C. M. et al. Underwater application of quantitative PCR on an ocean mooring. PLoS ONE 6, e22522 (2011).
Hansen, B. K. et al. Remote, autonomous real-time monitoring of environmental DNA from commercial fish. Sci. Rep. 10, 13272 (2020).
Sepulveda, A. J. et al. Robotic environmental DNA bio-surveillance of freshwater health. Sci. Rep. 10, 14389 (2020).
Maiello, G. et al. Little samplers, big fleet: eDNA metabarcoding from commercial trawlers enhances ocean monitoring. Fish. Res. 249, 106259 (2022).
Chen, X. et al. Comparative evaluation of common materials as passive samplers of environmental DNA. Environ. Sci. Technol. 56, 10798–10807 (2022).
Pont, D. Predicting downstream transport distance of fish eDNA in lotic environments. Mol. Ecol. Resour. 24, e13934 (2024).
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).
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).
Cantera, I. et al. Low level of anthropization linked to harsh vertebrate biodiversity declines in Amazonia. Nat. Commun. 13, 3290 (2022).
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).
Jeunen, G.-J. et al. Water stratification in the marine biome restricts vertical environmental DNA (eDNA) signal dispersal. Environ. DNA 2, 99–111 (2020).
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).
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).
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).
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).
Fukaya, K. et al. Estimating fish population abundance by integrating quantitative data on environmental DNA and hydrodynamic modelling. Mol. Ecol. 30, 3057–3067 (2021).
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).
Carraro, L., Mächler, E., Wüthrich, R. & Altermatt, F. Environmental DNA allows upscaling spatial patterns of biodiversity in freshwater ecosystems. Nat. Commun. 11, 3585 (2020).
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).
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).
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).
Jahn, K. et al. Early detection and surveillance of SARS-CoV-2 genomic variants in wastewater using COJAC. Nat. Microbiol. 7, 1151–1160 (2022).
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).
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).
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).
Danziger, A. M. & Frederich, M. Challenges in eDNA detection of the invasive European green crab, Carcinus maenas. Biol. Invasions 24, 1881–1894 (2022).
Mansfeldt, C. et al. Microbial community shifts in streams receiving treated wastewater effluent. Sci. Total Environ. 709, 135727 (2020).
Yamamoto, S. et al. Environmental DNA metabarcoding reveals local fish communities in a species-rich coastal sea. Sci. Rep. 7, 40368 (2017).
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).
Darling, J. A., Jerde, C. L. & Sepulveda, A. J. What do you mean by false positive. Environ. DNA 3, 879–883 (2020).
Ficetola, G. F., Taberlet, P. & Coissac, E. How to limit false positives in environmental DNA and metabarcoding? Mol. Ecol. Resour. 16, 604–607 (2016).
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).
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).
Doi, H. et al. Environmental DNA analysis for estimating the abundance and biomass of stream fish. Freshw. Biol. 62, 30–39 (2017).
Di Muri, C. et al. Read counts from environmental DNA (eDNA) metabarcoding reflect fish abundance and biomass in drained ponds. MBMG 4, e56959 (2020).
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).
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).
Fonseca, V. G. Pitfalls in relative abundance estimation using eDNA metabarcoding. Mol. Ecol. Resour. 18, 923–926 (2018).
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).
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).
Sigsgaard, E. E. et al. Population characteristics of a large whale shark aggregation inferred from seawater environmental DNA. Nat. Ecol. Evol. 1, 0004 (2016).
Weitemier, K. et al. Estimating the genetic diversity of Pacific salmon and trout using multigene eDNA metabarcoding. Mol. Ecol. 30, 4970–4990 (2021).
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).
Elbrecht, V., Vamos, E. E., Steinke, D. & Leese, F. Estimating intraspecific genetic diversity from community DNA metabarcoding data. PeerJ 6, e4644 (2018).
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).
Couton, M., Viard, F. & Altermatt, F. Opportunities and inherent limits of using environmental DNA for population genetics. Environ. DNA 5, 1048–1064 (2023).
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).
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).
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).
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).
Yang, J. et al. Ecogenomics of zooplankton community reveals ecological threshold of ammonia nitrogen. Environ. Sci. Technol. 51, 3057–3064 (2017).
Nuy, J. K. et al. Responses of stream microbes to multiple anthropogenic stressors in a mesocosm study. Sci. Total Environ. 633, 1287–1301 (2018).
Li, F. et al. Application of environmental DNA metabarcoding for predicting anthropogenic pollution in rivers. Environ. Sci. Technol. 52, 11708–11719 (2018).
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).
Stevens, J. D. & Parsley, M. B. Environmental RNA applications and their associated gene targets for management and conservation. Environ. DNA 5, 227–239 (2023).
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).
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).
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).
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).
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).
Sagova-Mareckova, M. et al. Expanding ecological assessment by integrating microorganisms into routine freshwater biomonitoring. Water Res. 191, 116767 (2021).
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).
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).
Andújar, C. et al. Metabarcoding of freshwater invertebrates to detect the effects of a pesticide spill. Mol. Ecol. 27, 146–166 (2018).
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).
Apothéloz-Perret-Gentil, L. et al. Taxonomy-free molecular diatom index for high-throughput eDNA biomonitoring. Mol. Ecol. Resour. 17, 1231–1242 (2017).
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).
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).
Cordier, T. et al. Supervised machine learning outperforms taxonomy-based environmental DNA metabarcoding applied to biomonitoring. Mol. Ecol. Resour. 18, 1381–1391 (2018).
Wilkinson, S. P. et al. TICI: a taxon-independent community index for eDNA-based ecological health assessment. PeerJ 12, e16963 (2024).
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).
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).
Stewart, K., Ma, H., Zheng, J. & Zhao, J. Using environmental DNA to assess population-wide spatiotemporal reserve use. Conserv. Biol. 31, 1173–1182 (2017).
McClenaghan, B. et al. Harnessing the power of eDNA metabarcoding for the detection of deep-sea fishes. PLoS ONE 15, e0236540 (2020).
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).
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).
Savio, D. et al. Bacterial diversity along a 2600 km river continuum. Environ. Microbiol. 17, 4994–5007 (2015).
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).
Adams, A. J. et al. From eDNA to decisions using a multi-method approach to restoration planning in streams. Sci. Rep. 14, 14335 (2024).
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).
US Fish and Wildlife Service. Quality assurance project plan eDNA monitoring of bighead and silver carps. USFWS Great Lakes region 3 (US FWS, 2022).
Ellis, M. R. et al. Detecting marine pests using environmental DNA and biophysical models. Sci. Total Environ. 816, 151666 (2022).
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).
Sepulveda, A. J. et al. When are environmental DNA early detections of invasive species actionable? J. Environ. Manage. 343, 118216 (2023).
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).
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).
Suzzi, A. L. et al. eDNA metabarcoding reveals shifts in sediment eukaryote communities in a metal contaminated estuary. Mar. Pollut. Bull. 191, 114896 (2023).
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).
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).
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).
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).
Pawlowski, J., Bonin, A., Boyer, F., Cordier, T. & Taberlet, P. Environmental DNA for biomonitoring. Mol. Ecol. 30, 2931–2936 (2021).
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).
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).
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).
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).
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).
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).
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).
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).
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).
Altermatt, F. et al. Quantifying biodiversity using eDNA from water bodies: general principles and recommendations for sampling designs. Environ. DNA 5, 671–682 (2023).
ISO/DIS 17805:2023. Water Quality — Sampling, Capture and Preservation of Environmental DNA from Water (ISO, 2023).
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).
Li, F. et al. Gap analysis for DNA-based biomonitoring of aquatic ecosystems in China. Ecol. Indic. 137, 108732 (2022).
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).
Mc Cartney, A. M. et al. The European Reference Genome Atlas: piloting a decentralised approach to equitable biodiversity genomics. npj Biodivers. 3, 28 (2024).
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).
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).
Lin, D. et al. The TRUST principles for digital repositories. Sci. Data 7, 144 (2020).
Leigh, D. M. et al. Best practices for genetic and genomic data archiving. Nat. Ecol. Evol. 8, 1224–1232 (2024).
Nilsson, R. H. et al. Introducing guidelines for publishing DNA-derived occurrence data through biodiversity data platforms. MBMG 6, e84960 (2022).
Berry, O. et al. Making environmental DNA (eDNA) biodiversity records globally accessible. Environ. DNA 3, 699–705 (2021).
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).
Meyer, R. et al. Aligning standards communities for omics biodiversity data: sustainable Darwin Core–MIxS interoperability. Biodivers. Data J. 11, e112420 (2023).
Abarenkov, K. et al. Publishing DNA-derived data through biodiversity data platforms v1.3 (GBIF Secretariat, 2023).
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).
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).
Wilkinson, M. D. et al. The FAIR guiding principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016).
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).
Shen, E. W., Vandenberg, J. M. & Moore, A. Sensing inequity: technological solutionism, biodiversity conservation, and environmental DNA. Biosocieties 19, 501–525 (2024).
Carroll, S. R. et al. The CARE principles for indigenous data governance. Data Sci. J. 19, 43 (2020).
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).
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).
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).
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).
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).
Watt, R. The fantasy of carbon offsetting. Environ. Politics 30, 1069–1088 (2021).
Ford, H. V. et al. A technological biodiversity monitoring toolkit for biocredits. J. Appl. Ecol. 61, 2007–2019 (2024).
Jarman, S. N., Berry, O. & Bunce, M. The value of environmental DNA biobanking for long-term biomonitoring. Nat. Ecol. Evol. 2, 1192–1193 (2018).