Environmental DNA (or eDNA) refers to genetic material deposited into the environment in a form of:

• Free-floating DNA molecules.
• Cellular debris such as shed skin, hair or degrading tissue.
• Biological secretions such as blood or mucus.
• Reproductive propagules such as larvae or spores.
• Intact organisms such as bacteria or micro-eukaryotes¹.

Environmental DNA is released by various organisms and can be obtained from a range of samples, such as soil, water, biofouling, and faeces^2-5. This means even a small sample of water or sediments contains a footprint of recent biodiversity. The inherent information embedded within eDNA can tell us about organisms that were or are present in the ambient environment, from microbes to whales. This overcomes the need to physically observe, isolate, identify or cultivate individual specimens and circumvents many of the difficulties associated with conventional morphological identification including morphological complexities, cryptic life stages, and globally declining taxonomic expertise^6-8.

Depending on the question and the taxa of interest, a number of different molecular methodologies can be used for the analysis of DNA sampled from the environment⁹. The common step is an extraction of bulk DNA from the sample, with the appropriate method selected based on sample type, sample preservation method and anticipated downstream analyses. The most common approaches for deciphering biodiversity are described below and can be roughly categorised by the type of biodiversity information they can deliver¹⁰:

• General species lists
• Presence/absence or (semi)quantitative information on target taxa.

PCR-based amplification methods

Despite being somewhat superseded by more sensitive methods (discussed below), traditional end-point Polymerase Chain Reaction (PCR) is still regularly used for deriving biodiversity information from eDNA samples. Target-specific primers are used, followed by visualization of amplicons on an agarose gel, and (if needed) Sanger sequencing of the PCR amplicons. The sequences derived from these amplicons are then compared to global databases such as the Barcode of Life Data system (BOLD) or the National Center for Biotechnology Information (NCBI) to verify target organism identity, or undertake follow-up phylogenetic analyses.

Quantitative Polymerase Chain Reaction (qPCR, also known as real-time PCR) is an advancement on end-point PCR and is one of the most promising molecular tools for highly specific and sensitive detection of one or a few targets. It enables rapid turnaround and simultaneous analysis of multiple samples. Quantitative PCR assays rely on primers, or primers and a probe that have been designed to be specific for the target species. The amplification of this target can then be measured in real-time either through the use of intercalating dyes¹¹ or probe-based detection systems¹². In recent years, assays have been designed for different species that are of particular managerial interest, e.g. human or animal pathogens, endangered species or marine pests^13-15.

A recent advancement in PCR methods is digital droplet PCR (ddPCR), where target DNA is randomly allocated into discrete droplets via microfluidics and each droplet is then thermally cycled and individually screened via fluorescence measurement for the presence of target DNA^{16, 17}. This negates the need to use standard curves and enables extremely low-level detection. A recent comparative application of qPCR and ddPCR for detecting invasive aquatic species in the Laurentian Great Lakes¹⁸, suggested similar sensitivities between the two methods, with higher cost-efficiency demonstrated for ddPCR (when capital expenditure was not considered).

High-throughput sequencing metabarcoding and non-PCR dependent methods

The advent of high-throughput sequencing (HTS) has made it possible to produce enormous volumes of sequence data rapidly. Metabarcoding has become a well-established method for characterizing the biodiversity of biological communities in different types of environmental samples^{1, 19-23}. It relies on simultaneous PCR amplification of a standardised short part of a genome (~100-600 base pairs, called a barcode) and enables the identification of many species by matching these barcodes obtained from HTS of PCR amplicons to reference sequences. Metabarcoding has proven to be very effective for characterizing biological communities in different fields of environmental science^{10, 24-26}, however, there are some prerequisites required when applying metabarcoding for characterizing biotic assemblages and identifying particular species:

• Sufficient taxonomic resolution provided by the target gene.
• ‘Universality’ of the primers (i.e., capacity to amplify the target gene from a wide variety of taxa).
• Availability of robust reference databases for reliable taxonomic assignments of obtained sequences²⁷.

There are several emerging and rapidly advancing non-PCR dependent methods for downstream eDNA analyses, such as shotgun sequencing²⁸, mitochondrial enrichment²⁹, and gene enrichment^{30, 31}. These have limited application for routine biodiversity surveys to date, as they require considerable sequencing and computing effort or additional laboratory processing which increases associated cost. These methods have an important advantage though, as they overcome PCR-inherent biases that result in the preferential amplification of certain DNA templates causing overrepresentation of some taxa and non-detection of others.

The emerging and rapidly developing field of eDNA analyses holds great potential for investigating biodiversity (including that hidden in microbial world, deep oceans and the most remote places on Earth). It will allow us to better understand how ecosystems function, how healthy they are, and elucidate threats posed by human activities. While there are a number of outstanding questions surrounding how best to implement and standardise eDNA workflows, at this point in time it is fair to say that it consistently outperforms current (morphology-based) methods for broad-scale taxonomic resolution. However, morphology and eDNA can, and in some cases should, be used synergistically.

Pressing research questions to maximise the application of eDNA in environmental management include exploring:

• Quantitativeness of eDNA metabarcoding data.
• Optimisation of assay sensitivity, specificity and transferability.
• Standardisation of the workflows and implementing quality assurance standards for improving repeatability and consistency of results.
• Differentiating viable and dead organisms.
• Enhancing speed and cost while at the same time exploring the portability of systems.
• Constant improvement of the reference sequence database.

Similar to classical taxonomy, eDNA-based methods are and will continue to be, a work in progress.

About The Author

Anastasija Zaiko is a Marine Scientist at Cawthron Institute, New Zealand. Her major areas of expertise are in aquatic ecology and biosecurity. She has had leading roles in many national and international research programmes, projects and field expeditions, conducting experimental and observational studies in aquatic ecosystems employing a range of different surveillance techniques (traditional and molecular).

References

1. Taberlet, P., E. Coissac, M. Hajibabaei, and L.H. Rieseberg (2012) Environmental DNA: a special issue on DNA metabarcoding. Molecular Ecology. 21: p. 1789-1793.
2. Ficetola, G.F., C. Miaud, F. Pompanon, and P. Taberlet (2008) Species detection using environmental DNA from water samples. Biology Letters. 4: p. 423-425.
3. Epp, L., S. Boessenkool, E.P. Bellemain, J. Haile, A. Esposito, T. Riaz, C. Erseus, V.I. Gusarov, M.E. Edwards, A. Johnsen, H.K. Stenoien, K. Hassel, H. Kauserud, N.G. Yoccoz, K.A. Brathen, E. Willerslev, P. Taberlet, E. Coissac, and C. Brochmann (2012) New environmental metabarcodes for analysing soil DNA: potential for studying past and present ecosystems. Molecular Ecology. 21(8): p. 1821-1833.
4. Andersen, K., K.L. Bird, M. Rasmussen, J. Haile, H. Breuning-Mdsen, K.H. Kjaer, L. Orlando, M.T.P. Gilbert, and E. Willerslev (2012) Meta-barcoding of 'dirt' DNA from soil reflects vertebrate biodiversity. Molecular Ecology. 21(8): p. 1966-1979.
5. Ibañez de Aldecoa, A.L., O. Zafra, and J.E. Gonzalez-Pastor (2017) Mechanisms and Regulation of Extracellular DNA Release and Its Biological Roles in Microbial Communities. Front Microbiol. 8: p. 1390.
6. Wheeler, Q.D., P.H. Raven, and E.O. Wilson (2004) Taxonomy: impediment or expedient? Science. 303: p. 285.
7. Costello, M.J., R.M. May, and N.E. Stork (2013) Can we name Earth's species before they go extinct? Science. 339(6118): p. 413-416.
8. Kelly, R.P., J.A. Port, K.M. Yamahara, R.G. Martone, N. Lowell, P.F. Thomsen, M.E. Mach, M. Bennet, E. Prahler, M.R. Caldwell, and L.B. Crowder (2014) Harnessing DNA to improve environmental management. Science. 344(6191): p. 1455-1456.
9. Bourlat, S.J., A. Borja, J. Gilbert, M.I. Taylor, N. Davies, S.B. Weisberg, J.F. Griffith, T. Lettieri, J. Benzie, F.O. Glöckner, N. Rodriguez-Expeleta, D.P. Faith, T.P. Bean, and M. Obst (2013) Genomics in marine monitoring: new opportunities for assessing marine health status. Marine Pollution Bulletin. 74(1): p. 19-31.
10. Zaiko, A., X. Pochon, E. Garcia-Vazquez, S. Olenin, and S.A. Wood (2018) Advantages and limitations of environmental DNA/RNA tools for marine biosecurity: management and surveillance of non-indigenous species. Frontiers in Marine Science. 5(322).
11. Becker, A., A. Reith, J. Napiwotzki, and B. Kadenbach (1996) A quantitative method of determining initial amounts of DNA by polymerase chain reaction cycle titration using digital imaging and a novel DNA stain. Analytical Biochemistry. 237: p. 204-207.
12. Heid, C.A., J. Stevens, K.J. Livak, and P.M. Williams (1996) Real time quantitative PCR. Genome Research. 6: p. 986-994.
13. Rodríguez-Lázaro, D., M. Hernández, M. Scortti, T. Esteve, J.A. Vázquez-Boland, and M. Pla (2004) Quantitative Detection of Listeria monocytogenes and Listeria innocua by Real-Time PCR: Assessment of hly, iap, and lin02483 Targets and AmpliFluor Technology. American Society for Microbiology. 70(3): p. 1366-1377.
14. Cardeñosa, D., J. Quinlan, K. Ho Shea, and D.D. Chapman (2018) Multiplex real-time PCR assay to detect illegal trade of CITES-listed shark species. Scientific Reports. 8: p. 16313.
15. Wood, S.A., A. Zaiko, I. Richter, G.J. Inglis, and X. Pochon (2017) Development of a real-time polymerase chain reaction assay for the detection of the invasive Mediterranean fanworm, Sabella spallanzanii, in environmental samples. Environmental Science and Pollution Research. 24(21): p. 17373-17382.
16. Pinheiro, L.B., V.A. Coleman, C.M. Hindson, J. Herrmann, B.J. Hindson, S. Bhat, and K.R. Emslie (2012) Evaluation of a droplet digital polymerase chain reaction format for DNA copy number quantification. Anal Chem. 84(2): p. 1003-11.
17. Hindson, B.J., K.D. Ness, D.A. Masquelier, P. Belgrader, N.J. Heredia, A.J. Makarewicz, I.J. Bright, M.Y. Lucero, A.L. Hiddessen, T.C. Legler, T.K. Kitano, M.R. Hodel, J.F. Petersen, P.W. Wyatt, E.R. Steenblock, P.H. Shah, L.J. Bousse, C.B. Troup, J.C. Mellen, D.K. Wittmann, N.G. Erndt, T.H. Cauley, R.T. Koehler, A.P. So, S. Dube, K.A. Rose, L. Montesclaros, S. Wang, D.P. Stumbo, S.P. Hodges, S. Romine, F.P. Milanovich, H.E. White, J.F. Regan, G.A. Karlin-Neumann, C.M. Hindson, S. Saxonov, and B.W. Colston (2011) High-throughput droplet digital PCR system for absolute quantitation of DNA copy number. Anal Chem. 83(22): p. 8604-10.
18. Nathan, L.M., M. Simmons, B.J. Wegleitner, C.L. Jerde, and A.R. Mahon (2014) Quantifying environmental DNA signals for aquatic invasive species across multiple detection platforms. Environmental Science & Technology. 48(12800-1286).
19. Chariton, A.A., L.N. Court, D.M. Hartley, M.J. Colloff, and C.M. Hardy (2010) Ecological assessment of estuarine sediments by pyrosequencing eukaryotic ribosomal DNA. Frontiers in Ecology and the Environment. 8(5): p. 233-238.
20. Shokralla, S., J.L. Spall, J.F. Gibson, and M. Hajibabaei (2012) Next-generation sequencing technologies for environmental DNA research. Molecular Ecology. 21(8): p. 1794-1805.
21. de Vargas, C., S. Audic, N. Henry, J. Decelle, F. Mahe, R. Logares, E. Lara, C. Berney, N. Le Bescot, I. Probert, M. Carmichael, J. Poulain, S. Romac, S. Colin, J.-M. Aury, L. Bittner, S. Chaffron, M. Dunthorn, S. Engelen, O. Flegontova, L. Guidi, A. Horak, O. Jaillon, G. Lima-Mendez, J. Lukes, S. Malviya, R. Morard, M. Mulot, E. Scalco, R. Siano, F. Vincent, A. Zingone, C. Dimier, M. Picheral, S. Searson, S. Kandels-Lewis, T.O. Coordinators, S.G. Acinas, P. Bork, C. Bowler, G. Gorsky, N. Grimsley, P. Hingamp, D. Iudicone, F. Not, H. Ogata, S. Pesant, J. Raes, M.E. Sieracki, S. Speich, L. Stemmann, S. Sunagawa, J. Weissenbach, P. Wincker, and E. Karsenti (2015) Eukaryotic plankton diversity in the sunlit ocean. Science. 348(6237): p. 1261605.
22. Aylagas, E., A. Borja, and N. Rodrigues-Ezpeleta (2014) Environmental status assessment using DNA metabarcoding: towards a genetic based marine biotic index (gAMBI). PLoS One. 9(3): p. e90529. doi:10.1371/journal.pone.0090529.
23. Domaizon, I., A. Winegardner, E. Capo, J. Gauthier, and I. Gregory-Eaves (2017) DNA-based methods in paleolimnology: new opportunities for investigating long-term dynamics of lacustrine biodiversity. Journal of Paleolimnology. 58(1): p. 1-21.
24. Aylagas, E., Á. Borja, X. Irigoien, and N. Rodríguez-Ezpeleta (2016) Benchmarking DNA Metabarcoding for Biodiversity-Based Monitoring and Assessment. Frontiers in Marine Science. 3: p. Article 96.
25. Pochon, X., S.A. Wood, N.B. Keeley, F. Lejzerowicz, P. Esling, J. Drew, and J. Pawlowski (2015) Accurate assessment of the impact of salmon farming on benthic sediment enrichment using foraminiferal metabarcoding. Marine Pollution Bulletin. 100(1): p. 370-382.
26. von Ammon, U., S.A. Wood, O. Laroche, A. Zaiko, L. Tait, S. Lavery, G. Inglis, and X. Pochon (2018) The impact of artificial surfaces on marine bacterial and eukaryotic biofouling assemblages: A high-throughput sequencing analysis. Marine Environmental Research. 133: p. 57-66.
27. Geller, J., C. Meyer, M. Parker, and H. Hawk (2013) Redesign of PCR primers for mitochondrial cytochrome c oxidase subunit I for marine invertebrates and application in all-taxa biotic surveys. Molecular Ecology. 13(5): p. 851-861.
28. Wang, J., P. McLenachan, P. Biggs, L. Winder, B. Schoenfeld, V. Narayan, B. Phiri, and P. Lockhart (2013) Environmental bio-monitoring with high-throughput sequencing. Brief Bioinform. 14(5): p. 575-588.
29. Zhou, X., Y. Li, S. Liu, Q. Yang, X. Su, L. Zhou, M. Tang, R. Fu, J. Li, and Q. Huang (2013) Ultra-deep sequencing enables high-fidelity recovery of biodiversity for bulk arthropod samples without PCR amplification. Gigascience. 2(1): p. 4.
30. Mertes, F., A. Elsharawy, S. Sauer, J. van Helvoort, P. van der Zaag, A. Franke, M. Nilsson, H. Lehrach, and A. Brookes (2011) Targeted enrichment of genomic DNA regions for next-generation sequencing. Brief Funct Genomics. 10(6): p. 374-386.
31. Dowle, E., X. Pochon, J. Banks, K. Shearer, and S.A. Wood (2016) Targeted gene enrichment and high throughput sequencing for environmental biomonitoring: a case study using freshwater macroinvertebrates. Molecular Ecology Resources 16(5): p. 1240-1254.