Introduction

Invasive marine species (IMS) are non-native organisms that are transported from their native range and establish in a new region. The cumulative global costs of all invasive aquatic species is estimated at US$345 billion, with IMS forming a substantial, but currently unquantified proportion of this (Cuthbert et al. 2021). The primary ecosystem effects of IMS establishment are reductions in native species diversity and biomass and loss of ecosystem services (Bax et al. 2003; Havel et al. 2015; Pyšek et al. 2020). Major sources of IMS include shipping that transports organisms attached to the hulls and other vessel surface areas, as well as in ballast water; and the surfaces of mobile structures used in mineral and petrochemical extraction and aquaculture (Bax et al. 2003). Detection of IMS on their transport vectors or upon incursion is often a challenging task, as dispersive stages of many IMS such as larvae, cysts, eggs, and fragments are difficult to identify. This material often lacks distinct morphological features that enable unequivocal identification (Carvalho et al. 2023). Improved identification and control measures for IMS have become available in the past two decades (Wan et al. 2021; Castro et al. 2022). However, the volume of international shipping, and marine resource extraction activities have also increased markedly (Yan et al. 2020; Castro et al. 2022; Grzelakowski et al. 2022). As a result of these activities, the global rate of IMS establishment is steadily increasing (Pyšek et al. 2020; Cuthbert et al. 2021).

IMS establishments are a major threat to biodiversity, ecosystem stability, economies and human well-being (Giakoumi et al. 2019). IMS management costs government agencies billions of dollars in remediation and eradication programs (Barbour et al. 2011; Cuthbert et al. 2021). It is nearly impossible to eradicate an IMS once a population is established in a new area (Green and Grosholz 2020). Early detection of incipient populations is therefore critical if control actions are to be both effective and affordable (Larson et al. 2020; Castro et al. 2021). IMS detection consequently remains an imperative for management of marine ecosystems (Kvamsdal et al. 2023). Examples of successful local eradications exist, for example of the invasive bivalve Perna perna (Hopkins et al. 2011). Counter-examples of established IMS that have persisted despite substantial efforts to eliminate them include lionfish (Pterois volitans) in the western Atlantic (Barbour et al. 2011); North Pacific seastars (Asterias amurensis) in south-eastern Australian waters (Ellis et al. 2022); ascidians (Styela sp.) in Patagonia (Castro et al. 2021); and fanworms (Sabella sp.) around New Zealand (Read et al. 2011).

The most effective new technologies for IMS detection come from applied environmental genomics (Bailey et al. 2020; Andrés et al. 2023; Takahashi et al. 2023). The use of environmental genomics methods for IMS detection is summarised in Fig. 1. The genetic material may be either environmental DNA (eDNA) or environmental RNA (eRNA) or both (environmental nucleic acids, eNA) (Bowers et al. 2021; Littlefair et al. 2022). IMS detection by analysis of eNA has been demonstrated to be highly effective (e.g. Gargan et al. 2022; Dubreuil et al. 2022). eNA analyses comprise a set of technologies, each having different strengths and weaknesses for IMS detection (Zaiko et al. 2018; Taberlet et al. 2018; De Brauwer et al. 2023). However, there is inevitably publication bias towards successful studies, especially as this new technology group has rapidly been adopted for implementation by policymakers (Morisette et al. 2021; Gold et al. 2022; De Brauwer et al. 2023). The successful cases may obscure the known and unknown limitations of these eNA approaches for IMS detection (Gold et al. 2021).

Fig. 1
figure 1

A generalised overview of IMS detection by different example eNA analysis technologies with a workflow indicated by blue arrows. A Collection of eNA is the first step in any IMS detection procedure. Sampling can be by filtering water (e.g. Stat et al. 2017), passive methods (e.g. Bessey et al. 2021; Cai et al. 2022), natural passive sampling systems like sponges (Mariani et al. 2019; Jeunen et al. 2023), and direct sampling of biofilms and other material from stationary substrates (e.g. Koziol et al. 2019). The mixed biological and mineral material that these methods collect contains large amounts of eNA from live microbial communities as well as small quantities of DNA and RNA shed from macro organisms, as tissue chunks, single, or degraded cells (Mauvisseau et al. 2022). B Targeted species detection technologies allow IMS to be detected in the purified eDNA (see Berry et al. 2021). Detection of pre-defined IMS by a specialised testing procedure is commonly done by qPCR (quantitative Polymerase Chain Reaction) (e.g. Gargan et al. 2022) or ddPCR methods (Doi et al. 2015). These approaches have advantages of being high throughput and sensitive, but will only detect one IMS that is pre-defined for each test (Manfrin et al. 2022). Field-deployable tests are possible, but not widely applied currently (e.g. Baerwald et al. 2020). C DNA metabarcoding is the method of PCR amplifying a small DNA region that varies in sequence among species. The unique DNA barcode sequences can be determined for a range of species in parallel using high throughput DNA sequencing technology. The range of species that DNA metabarcoding can detect is determined by how many test groups are analysed. For example, in the IMS context, invasive fish might be the only group of interest, and a single eDNA metabarcoding test might be used to identify many fish (e.g. Jo et al. 2022). The “Tree of life’ eDNA metabarcoding approach is to run a wide range of eDNA tests on each eDNA sample to attempt to identify as much of the biodiversity represented in the eDNA as possible (Stat et al. 2017; Adams et al. 2023)

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The goal of this study was to examine the current capacity for eNA analyses to contribute to IMS management. In particular, we hoped to identify overlaps between emerging eNA technologies and the priorities of policymakers, commercial groups and regulators of IMS. This was motivated by the knowledge that many institutes, agencies and companies cannot currently use eNA for IMS detection with complete certainty. The emerging eNA technologies as commonly practised do not yet provide levels of confidence in true and false positive and negative detection rates that would be required for management adoption, although this is technically possible (e.g. Song et al. 2020). There is therefore significant value in understanding the priorities of managers and researchers for developing eNA technologies, and in coordinating research efforts in these areas to reduce effort duplication.

We analysed the discussions of focus groups of IMS researchers and managers in Australia and New Zealand to identify current opinion on what eNA analysis can reliably contribute to IMS management now, what questions it will never be able to address, and what aspects of IMS management eNA analyses are likely to be able to solve in the future. Environmental genomics is a rapidly developing field (Jo et al. 2022) and we surveyed research literature published in the last three years to provide an international context for the results of these discussions. Our evaluation of IMS detection by eNA analyses will help to direct both environmental genomics technology development, and IMS policy directions of the future.

Methods

To provide a solid foundation to the research, the initial steps comprised a literature review examining recent knowledge of the application of eNA to IMS detection globally. The aim was to reveal the breadth of literature and the issues and opportunities identified to date. Research publications on eNA detection of IMS since 2021 were prioritised as representing current practices. Searches were conducted with Google Scholar and papers selected by relevance to the themes of IMS detection by eNA. The search terms used were “[eDNA,eRNA,eNA], invasive, marine” with a time range of 2018–2023.

This was followed by two stakeholder workshops seeking to identify knowledge gaps and associated research priorities. By actively involving stakeholders in the research, each was able to be party to the conversations, gain a more holistic understanding and based on this prioritise the research direction. This had the benefit of ensuring both a comprehensive consideration of research priorities and ownership of the outcomes.

Elicitation of stakeholder views

Stakeholders in IMS detection include policy makers, regulators who implement the policies, commercial companies with marine operations, researchers on IMS and their detection by eNA, and commercial companies providing IMS eNA detection services. in the second step of the analysis we captured the views of a range of stakeholders from these groups in a structured manner. Given that the project sought to elicit the views from both Australia and New Zealand, care was taken to ensure a representative sample – using purposive sampling processes that capitalised on the existing relationships and networks held by the research team. The purpose of the focus groups was to identify the research gaps associated with the use of eDNA methods for detecting IMS, so the full range of stakeholders were engaged. The stakeholder workshops were designed to allow for participants to contribute well-formed concepts following from the discussions, with time to develop them in a group setting.

To ensure involvement from across the focus groups, workshops were conducted using a Group Support System (GSS) designed for effective stakeholder engagement (Ackermann and Eden 2020). The particular GSS (Strategyfinder) employs a modelling technique, causal mapping. Each focus group was comprised a particular stakeholder group. Group 1 focused on managers and policymakers from industrial and regulatory organisations. Group 2 focused on researchers and practitioners of eNA IMS detection from research and business organisations. This decision to keep the two groups separate was partly an interest to see if there were any differences between those in a research environment compared with those faced with dealing with eDNA in their business. The two workshops were conducted between May and June 2023 and included 21 participants from New Zealand and Australia. The organisations represented included seven government departments involved in environmental management, two commercial eNA analysis laboratories, eNA researchers from three universities, and eNA researchers from two research organisations.

We used an established modelling process (causal mapping) that enables contributions to be captured, structured and explored (Eden and Ackermann 2010; Ackermann and Eden 2020). Each workshop followed the same design and lasted for three hours, beginning with an introduction to the research objectives, and a review of the agenda. After a brief explanation of the mechanics associated with the Group Support System being used, both workshops commenced with participants being asked to enter into the system their responses to the question “What are the issues and opportunities associated with using eDNA/eRNA methods for detecting invasive marine species?” Participants were asked to append each statement with a notation of which mode they applied to (for example, BF = biofoul, BW = ballast water, MMS = manmade marine structures, or ALL) as this would allow a more nuanced understanding.

To enable as wide a range of views to be captured, and in an authentic manner, individuals were able to enter their issues and opportunities anonymously and directly via their own devices (key features of GSS). This process ensured that the contributions were captured in the language of the participants rather than risk being changed through a facilitator paraphrasing them. It also ensured all the views were captured and not lost. Allowing participants to anonymously contribute their issues and opportunities directly helps reduce conformity pressures allowing for more wide-ranging views to be captured. It also enables each participant to speak ‘simultaneously’ enabling an increase in productivity (Ackermann 2020). In addition, the process enables a breadth of material (attending to the requirement for a comprehensive consideration) to be captured, avoiding the constraints imposed by surveys which frequently present a list of options from which to choose.

Each participant was able to see their own material and that of others as it was generated. This both enables the prompting of further material where participants can piggyback off one another’s contributions; and digestion of others’ contributions. Participants were able to immerse themselves in the wide range of views in each workshop and gain a deeper appreciation of the topics as viewed by their peers.

During the generation phase, contributions were clustered according to content by the facilitator. This aided the navigation of the material as typically over 50 statements were captured in a very short time and, by clustering the material, it is possible to manage the unfolding complexity. Once participants had generated all the issues/opportunities that came to mind, a review of the clusters was undertaken by the group and led by the facilitator. As each participant could see the clusters on their screen, they were able to actively engage with the review and offer suggestions. The review was to enable:

  • A check to ensure contributions were in the most appropriate cluster. Contributions often ‘fit’ more than one cluster, so determining the most appropriate cluster helps to direct further conversation.

  • Check each contribution to ensure it was clear to all—sometimes resulting in the wording being elaborated to enable a shared understanding.

  • The generation of new material arising from each cluster is explored. New meanings arising in discussion are defined and added to the cluster.

  • The clusters identified by the facilitator constituted ‘themes’.

Connecting issues with opportunities in eNA IMS detection

The next step was to explore the systemic impacts between the issues and opportunities. This constitutes identifying connections between contributions in the form of causal links (chains of argument) reflecting that issues impact other issues and opportunities (see Fig. 2). This linking process enables the creation of a network further assisting with the development of shared understanding, revealing systemic properties, and facilitating analysis. Recognition of the interactivity of issues and opportunities emerges early in the cluster review process as participants provide explanations as to why a statement should be in one cluster or another. The process of linking also reveals that issues and/or opportunities can impact more than one valued outcome, illustrating multiple ramifications and potentially uncovering potent opportunities (supporting more than one value) or risky issues (having multiple negative consequences). The process of exploring the impact of contributions on one another:

  • Facilitates the prompting of further material as participants are exposed to the thinking of one another and how they perceive the world and as such participants respond to differences in opinion by teasing out alternative chains of argument.

  • Enables the building of a deeper understanding of the topic as issues and opportunities are seen in context.

  • Assists the group to move from divergence to convergence attending to the objective of increasing awareness.

Fig. 2
figure 2

A section of the notes taken from discussions organised to link similar themes arising in discussion. The top linking theme of this discussion was education. Discussion points on sub-themes are indicated by a shared colour

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Each emergent theme was then examined in depth, capturing further detail, and exploring the implicit research questions. Each workshop generated around 20 research questions.

Prioritisation of key research questions

The final session of the focus group workshop centred on prioritising the research questions. Participants were asked to rate the importance of each question by giving a score from 0 (least important) to 10 (most important).

Results

Views of managers and researchers

Each workshop generated 127 statements that were clustered into seventeen themes, as presented below in Table 1. Workshop 1 produced 11 themes, and workshop 2 produced 6, prompting 26 and 23 research questions, respectively (Tables 1, 2, 3).

Table 1 Themes and associated research questions arising from statements of two workshop focus groups on the use of eNA in IMS detection. Where a theme had sub-themes associated with it, it has been noted. In some instances, a research question could address more than one of the themes
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Table 2 Knowledge gaps identified in workshop 1 ranked by aggregate scores for importance
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Table 3 Knowledge gaps and research questions identified in workshop 2
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There was some clear overlap between the workshops in the top three identified areas, which were:

  • DNA reference libraries and standards (relating to workshop 1 validation & interpretation of results, workshop 2 reference DNA from validated samples and standardise protocols),

  • Community involvement. (Workshop 1 – citizen science, workshop 2 non-experts collecting samples).

  • Tools and methods (workshop 1 sampling design and methodology, workshop 2 – tools for managing eDNA detections)

The only differences in process between the two workshops lay in the rating procedure. In the first workshop, there was more consideration of the three different areas (biofoul, ballast water and manmade marine structures) giving rise to three rating activities. In the second workshop, participants viewed most of the material relating to all three and so a single rating exercise was conducted.

Themes arising from synthesis of focus group discussions

The results of the two focus group questions and ranking by priority are noted in Table 2 for workshop 1 and Table 3 for workshop 2.

Workshop 1 revealed some interesting differences among discussed themes. Whilst research questions relating to reference libraries emerged as important to all 3 areas, there were research questions that were particular to an area. For example, the greatest score for discussions on BW was for how eNA can meet the needs of the Ballast Water Convention, while negligible scores were received for this in the BF and MMS discussions.

Workshop 2 identified a different range of priority knowledge gaps to workshop 1. The top-ranked priority was that IMS detection is currently the main use for eNA in IMS research, and that going beyond that should be pursued. Several different and disparate themes were also highly ranked, perhaps reflecting the diversity of researchers’ interests. There was a surprising lack of overlap in priorities between the two workshops. For example, workshop 2 did not highlight DNA barcode library improvement as a priority, whereas this was a priority for several applications identified in workshop 1.

Discussion

Management of IMS can clearly benefit from the best available methods for their detection. The uses of eNA analysis for this purpose was explored through discussion with researchers working in eNA detection of IMS, and managers of marine areas that are the end users of these research approaches. A synthesis of the theme areas arising from the focus groups with the current state of knowledge assessed by a literature analysis allows us to identify strengths and weaknesses of current eNA analyses for IMS detection. These weaknesses often represent gaps in the current state of eNA technology that are being actively researched.

Policies for IMS management

IMS management policies inevitably reflect the technologies current when they were formulated. All focus group members identified that this was a particular problem for IMS detection by eNA (van Rees et al. 2022). There was consensus on the need to educate policy makers on the value of eNA technologies. Policy makers would need to receive a balanced assessment of the strengths and weaknesses of eNA methods for IMS detection. A need for champions to improve knowledge transfer between eNA practitioners and policy makers was identified. This has happened to some extent, with recent efforts by groups of eDNA researchers to promote standardised practices (De Brauwer et al. 2023), the formation of eDNA research societies (https://sednasociety.com/), and policy papers focusing on the use of eNA for specific purposes such as fisheries management (Gilbey et al. 2021).

The highest-ranked research gap that our focus groups identified (Table 3) was the management-related biological insights into IMS that eNA can produce that include information beyond simple IMS detections. One example of this would be the deeper analyses of eRNA that are possible. eRNA has attracted significant research attention recently because species detected by eRNA are more likely to be recently alive than those detected by eDNA (Bowers et al. 2021; Giroux et al. 2022). eRNA can be purified from the same samples as eDNA, and can be co-purified from the same sub-sample by some methods (Picard et al. 2023). Total RNA is generally thought to degrade faster than DNA and this property has been suggested as a means of detecting only living and metabolically active communities (Yates et al. 2021), whereas DNA may persist for longer after organism death. All of the commonly used eDNA barcodes have an RNA equivalent. Ratios of the eRNA and eDNA versions of the same marker genes in eNA metabarcoding analyses have been used as an indicator of how long ago eDNA was deposited in an environment (Wood et al. 2020; Marshall et al. 2021; Jo 2023). This measure can be considered a proxy for the proportion of viable individuals per species represented in an eNA sample (Yates et al. 2021).

eRNA can also be used to measure levels of gene function (Cordier et al. 2021; Laroche et al. 2021). This allows for much more specialised analysis of IMS physiology, developmental stage, or population characteristics. Population features like age structure determined by ratios among developmental stages, or sex ratios can be estimated via eRNA analyses (Stevens and Parsley 2023). This complements some eDNA studies, where eDNA detection rate is affected by life cycle stage (Crane et al. 2021). eRNA could be used to measure physiological processes involved in the establishment of IMS, such as reproduction, growth, and transition between developmental stages (Yates et al. 2021; Stevens and Parsley 2023). However, eRNA analyses are more challenging than eDNA in the field, where preserving it for return to a laboratory is challenging, and in laboratory analysis, where RNA analysis methods require more time and expenditure than DNA.

Communication

Communication of the capacities of eNA analyses to the non-research community followed similar themes to those identified for policies on IMS management in “Policies for IMS management” section. Groups other than policy makers such as community groups and non-eNA scientists require an accurate picture of what can be done with eNA analysis. Advocacy for the use of eNA analyses will maintain funding that will improve the field, so it is important that some eNA researchers also promote the value of this approach in IMS detection in an accessible and balanced way. It is important to communicate both the value of eNA approaches and their limitations, or the eNA technology group will fail to deliver on unrealistic, inflated expectations. This has happened with similar research areas in the past, where ancient DNA research provides a cautionary example (Gilbert et al. 2005). Communicating to policy makers a realistic and accurate assessment of the use for eNA analysis in IMS monitoring is essential if the policies being developed are to have real value in managing IMS (Darling et al. 2020; Aylagas et al. 2020).

A specific value of eNA methods is that they can be used by citizen scientists. This creates opportunities for community engagement and empowerment in IMS detection. The engagement follows from effective communication of eNA applications by practitioners and researchers, who need to make eNA collection materials available, and provide training in collection methods and data analyses. Citizen science programs leverage the limited amount of public funding available for environmental monitoring and develop deeper public engagement with natural values (Larson et al. 2020; Miya et al. 2022; Bunce and Freeth 2022; Suzuki-Ohno et al. 2023). Invasive fish species were successfully identified in citizen science programmes sampling eDNA from Danish waters. This national-scale programme engaged a broad section of the community and delivered results not otherwise possible without their efforts (Agersnap et al. 2022).

Sampling eNA for IMS detection

Environmental sampling in difficult to access areas or remote locations to track introduction or spread of IMS was identified as a management need by all focus group participants. Semi-automated sampling to increase sampling efficiency on human-operated missions with devices such as remotely operated vehicles (ROVs) has been demonstrated, with a system that can take multiple samples on a single ROV mission now available (Hendricks et al. 2023). Automated samplers of aquatic eNA that can take samples at pre-defined time points can be tethered or towed to take multiple samples from an area (Formel et al. 2021). Fully automated eDNA samplers that can complete a process of analysis and report IMS detection results have only been demonstrated for a few test cases (e.g. Jothinarayanan et al. 2023).

The need for an increase in accuracy and ease of in-field detections of IMS was identified as a management priority for enabling rapid screening of structures for IMS presence. Identification of IMS where they occur, in a short timeframe (< 4 h), and by untrained staff, is now possible with specialised eDNA technologies. Components of standard eDNA analysis procedures may require modification to transfer them from a laboratory to a field setting. DNA purification procedures require simplification (e.g. Jeunen et al. 2022). DNA barcode amplification procedures can be done with field-deployable thermal cyclers (e.g. Thomas et al. 2020; Manfrin et al. 2022). Another set of approaches use isothermal amplification of DNA barcodes with methods such as Loop Mediated isothermal Amplification (LAMP), Recombinase Polymerase Amplification (RPA) or Rolling Circle Amplification (RCA) that can be carried out at a single temperature and require simple, lightweight equipment (Fu et al. 2020; van Dongen et al. 2020). Amplicons generated by PCR, LAMP or RCA can be detected by lateral flow assays for in-field identification (Doyle and Uthicke 2021). More recent detection approaches for specific DNA sequences that utilise modifications of CRISPR-Cas9 gene editing technology enable convenient in-field-detection of IMS without any amplification equipment (Williams et al. 2019; Baerwald et al. 2020). These technologies allow detection of pre-defined marine species without eDNA purification so that the substrate is examined directly. They use a single-temperature enzymatic amplification reaction that can be conducted with a hand-held device and produce a final positive or negative result in the form of a lateral flow test similar to the sort of tests that are now widely available for COVID-19 screening in humans (Baerwald et al. 2020).

Point of need DNA metabarcoding for IMS detection has so far been restricted to Oxford Nanopore DNA sequencers. These portable devices allow DNA metabarcoding analysis after standard PCR and library preparation methods, which still require laboratory work, but it can be carried out in a field office, ship cabin, or similar non-specialised facility (Egeter et al. 2022; Fonseca et al. 2023). This approach has some of the strengths of laboratory-based DNA metabarcoding in that it can detect many species in one assay. However, the error rate inherent in current nanopore DNA sequencing can lead to false positive or negative detections at a rate greater than found with more accurate DNA sequencing platforms (De Brauwer et al. 2023). IMS detections by this approach have been demonstrated in bivalves (Egeter et al. 2022).

Reference libraries and biobanks

The DNA metabarcoding process characterises DNA sequences from environmental samples that can be matched to databases of known IMS species to provide identifications. A current limitation of this approach for IMS detection is a lack of reference sequences in databases for both known IMS and native near relatives of IMS (Berry et al. 2021). This limitation was identified as a key research priority in workshop 1. This is essential for ensuring that native species are not producing false positive signals for their IMS relatives as well as allowing precise identification of invasive taxa to ensure an appropriate and tailored response. DNA reference sequence data is currently distributed among multiple online repositories, each with their own standards for taxonomy, and curation of both biological reference material and reference DNA sequences (Gostel and Kress 2022). Current DNA barcode databases are striving to reach the Findable, Accessible, Interoperable and Reusable (FAIR) standards (Wilkinson et al. 2016). Biodiversity databases are beginning to keep records of eNA detections of species (e.g. Atlas of Living Australia, Nonindigenous Aquatc Species Database), but consensus on record formats has not been reached, and the databases are not interoperable (Thompson and Thielen 2023).

DNA barcode database creation is a seemingly simple challenge. High-throughput genome skimming methods are available to make the process affordable and fast for processing thousands of samples (Gostel et al. 2020). National schemes are developing to barcode as much of the biodiversity present within their jurisdiction as possible (National Biodiversity DNA Library). The challenges are found in the rights of groups of humans to samples and the data derived from them. For example, the Nagoya Protocol is an international legal framework designed to promote the fair sharing of the benefits of biodiversity among nations, and indigenous groups specifically, and some suggest that DNA sequence data should be included in this protocol (Ambler et al. 2021). However, if monetary values are put on DNA barcode data, it immediately contradicts FAIR principles, as the data become unavailable to many. This is a contentious area, and one where policy will need ongoing development if the benefits of eNA for IMS detection are to be realised (Shea et al. 2023).

Biobanks are archives of any biological material with systems for curation, maintenance, and sample retrieval. Biological material for IMS detection could be biobanked, either as environmental samples, filters or other DNA collection devices, or as purified eNA. Biobanks for eNA would be valuable for establishing baselines of biodiversity at given time points, and also enable re-analysis of samples when eNA technologies improve (Jarman et al. 2018). An example of an eNA biobank is the National Biodiversity Cryobank of Canada. This is accessible to researchers and follows governance and technical standards along the lines of medical biobanks (Gille et al. 2020).

eNA data analysis and interpretation

Most eDNA detection methods for IMS do not yet conform to a rigorous standard of “confusion matrix” diagnostic testing such as is used in medical tests, even when they focus on single-species detections, although there are notable exceptions (Wilcox et al. 2020). Cross-reactivity of eNA tests with native relatives of IMS species was the highest ranked issue in Workshop 1, which affects the potential to achieve this significantly. The limits of detection (LOD) and quantification (LOQ) of single-species PCR tests can be determined and there are good frameworks for these analyses (Klymus et al. 2020). Single-species eNA tests for IMS have an inherent strength in that the testing is not significantly influenced by other DNA types in the sample. This is a factor in DNA metabarcoding studies, where the LOD of a specific IMS may be altered by the presence of DNA from unrelated organisms in the sample.

For metabarcoding studies, acquiring the data to estimate LOD, LOQ, false positive, and false negative rates for the thousands of species that might be analysed is even more challenging than for single-species tests. Analyses of eDNA metabarcoding data tend to follow standards that have evolved, rather than those designed as a rigorous framework for developing levels of statistical confidence in IMS detection (Zinger et al. 2019; Tedersoo et al. 2022). eDNA-specific data analysis packages are available that help to filter data effectively before analysing its biological significance (Kandlikar et al. 2018; Zinger et al. 2021). Common approaches to interpreting the biological signals in metabarcoding datasets include multi-dimensional scaling analyses for data exploration and visualisation (e.g. Koziol et al. 2019; Oka et al. 2021; Adams et al. 2023). Testing of significant differences among sample groups from different conditions can be done by multivariate statistical approaches such as PERMANOVA (e.g. Nguyen et al. 2020).

Challenges for using eNA in IMS management

There are a range of challenges for developing eNA analyses into an ideal system for IMS management (Aylagas et al. 2020). Both workshops identified research on better interpretation frameworks for positive IMS eDNA results as a priority. Single-species testing for IMS is currently the most verifiable way to identify IMS, but there is great appeal in metabarcoding approaches for their affordable power in screening for many IMS at one time (Stat et al. 2017; Adams et al. 2023). However, different species have different detection probabilities in metabarcoding analysis because they shed eDNA at different rates, have different biomass, have DNA barcodes that are differentially amplified by PCR, different DNA metabarcode copy numbers per cell, and a host of other factors (Skelton et al. 2022). Furthermore, eNA is often physically separate from the organisms that deposited it. Interpretations of results should consider this if trying to associate an IMS detection with a specific location or substrate like a particular vessel. Results can be interpreted with water or substrate movement alongside detection time limits caused by eNA degradation as a consideration to assist in appropriate interpretation (Larson et al. 2022).

Quantification of IMS from eNA samples would increase the value of eNA for management significantly (Ramón-Laca et al. 2021). The current systems that provide presence or absence records of any given IMS per eNA sample can generate limited quantitative information if sampling regimes are extensive enough to model occupancy of IMS from frequency of occurrence data. This approach can even generate rank abundance of the higher abundance species within a defined group (Skelton et al. 2022). However, true population size estimates of specific IMS may be possible if genotypes can be reliably generated from eNA samples. Genetic mark-recapture approaches are a proven method for estimating the size of any animal population (e.g. Beatty et al. 2022). Close-kin mark recapture methods are an alternative, and because they focus on single sampling events may be particularly appealing for IMS studies (Ruzzante et al. 2019). Progress in genotype extraction from eNA alone has come from mitochondrial haplotype sequencing (Dugal et al. 2022). However, the most likely way to identify complete genotypes is by single cell environmental cell genotyping (Bump and Lubeck 2023). If single cell genotyping from environmental samples becomes more reliable and affordable, it will allow genetic mark-recapture, or close-kin mark-recapture population size analysis of particular species from seawater samples.

Conclusion

Distillation of the ranked research priorities that our analyses identified leads to several research priorities that would improve management efficacy when applying eNA for IMS detection. These include:

  1. 1.

    Developing management-relevant biological insights from the datasets developed for IMS detection. This would involve iterative interaction between managers and researchers to develop a shared understanding of the information that eNA metabarcoding can deliver.

  2. 2.

    Production of frameworks for robust sampling design for eDNA-based IMS detections. The current sampling and statistical frameworks for IMS detection need to be improved to provide probabilities of true detection and distribution estimates.

  3. 3.

    Improving DNA barcode libraries. DNA barcode libraries need to represent the full range of IMS, as well as their near relatives to avoid misidentifications, especially for species that have native congenerics.

  4. 4.

    Better sampling methods. eNA sampling and preservation methods should be faster, cheaper, allow sampling in remote locations, by non-experts or automated samplers.

Management priorities for IMS detection synthesised from highly-ranked themes of the workshops were:

  1. 1.

    Community empowerment and knowledge feedback with eNA tools. Generating a community appreciation for marine biodiversity and IMS management could be enhanced.

  2. 2.

    Bridging the gap between science and policy. Policies for IMS detection with eNA don’t keep up with the changing science, and are frequently under-informed on the capacity for eNA analyses to deliver different data types. A clear example is identifying how the needs of the Ballast Water Convention can be met with eNA methods.

  3. 3.

    Educating policy makers, practitioners and the community on the strengths, weaknesses, details, and changing nature of eNA analysis. The field of eNA analysis is evolving rapidly. We need policy makers who are educated in current methods and have opportunities to keep up to date as the field evolves.

The Utopia of a perfect eNA-based IMS detection system would be a cost-effective, field-deployable method that can identify any IMS with perfect accuracy from any substrate sampled by autonomous systems or untrained humans. Utopias of any sort are never reached, but they provide a useful paradigm to work towards. Like any IMS detection system, there are inherent limitations of using eNA as an IMS detection method, but it remains the most powerful method for this application that we have.

There are clear paths to improvement of current eNA technologies that should yield IMS management value far beyond the simple detections that are currently being generated. The potential for this field to contribute to real world marine ecosystem health is vast, and we hope that policy makers and experts on eNA detection of IMS will continue to work together to develop the most effective management systems for the ongoing global problem of IMS.