Sensitivity and accuracy of high-throughput metabarcoding methods for early detection of invasive fish species

Rapid detection of the invasive tomato leaf miner, Phthorimaea absoluta using simple template LAMP assay

The tomato leaf miner (TLM), Phthorimaea absoluta Meyrick, 1917 (Lepidoptera: Gelechiidae) is a destructive invasive insect that has expanded its global distribution. Rapid and accurate identification of invasive pests is essential to support subsequent management and devise control measures. To accurately diagnose P. absoluta, a Loop Mediated Isothermal Amplification (LAMP) assay (TLM-LAMP) was developed to amplify the target region of mitochondrial cytochrome oxidase subunit I (COI) gene. The TLM-LAMP assay can identify the P. absoluta within 60 min at 65 °C after sample extraction. Cross-reactivity analysis against three closely related non-target species, Phthorimaea operculella (Zeller, 1873), Pectinophora gossypiella (Saunders, 1844), and Aproaerema modicella (Deventer, 1904) confirmed species specificity. The TLM-LAMP assay showed high sensitivity to P. absoluta DNA up to 1 × 10- 8 ng/µL and in plasmid DNA template up to 1 × 10-14 ng/µL. In addition, the TLM-LAMP assay was successful in laboratory detection of larvae, pupa, and adult stages of P. absoluta. We have tested the TLM-LAMP assay for field application with quick and simple crude insect extraction procedures and found double distilled water (ddH2O) as an effective extraction solution. The new TLM-LAMP assay was validated in the field and polyhouse using moths collected from pheromone traps followed by ddH2O crude insect extract preparation and incubation. The assay could successfully detect the P. absoluta within 45 min at 65 °C. Sensitivity, specificity, repeatability, and field compatibility of the TLM-LAMP highlights the novelty of the developed method. TLM-LAMP assay is a novel molecular tool for detection of P. absoluta in the laboratory and field which will help in monitoring and aiding biosecurity responses.

CRISPR-based diagnostics detects invasive insect pests

Rapid identification of organisms is essential for many biological and medical disciplines, from understanding basic ecosystem processes, disease diagnosis, to the detection of invasive pests. CRISPR-based diagnostics offers a novel and rapid alternative to other identification methods and can revolutionize our ability to detect organisms with high accuracy. Here we describe a CRISPR-based diagnostic developed with the universal cytochrome-oxidase 1 gene (CO1). The CO1 gene is the most sequenced gene among Animalia, and therefore our approach can be adopted to detect nearly any animal. We tested the approach on three difficult-to-identify moth species (Keiferia lycopersicella, Phthorimaea absoluta and Scrobipalpa atriplicella) that are major invasive pests globally. We designed an assay that combines recombinase polymerase amplification (RPA) with CRISPR for signal generation. Our approach has a much higher sensitivity than real-time PCR assays and achieved 100% accuracy for identification of all three species, with a detection limit of up to 120 fM for P. absoluta and 400 fM for the other two species. Our approach does not require a sophisticated laboratory, reduces the risk of cross-contamination, and can be completed in less than 1 h. This work serves as a proof of concept that has the potential to revolutionize animal detection and monitoring.

CRISPR-based diagnostics detects invasive insect pests

Rapid identification of organisms is essential across many biological and medical disciplines, from understanding basic ecosystem processes and how organisms respond to environmental change, to disease diagnosis and detection of invasive pests. CRISPR-based diagnostics offers a novel and rapid alternative to other identification methods and can revolutionize our ability to detect organisms with high accuracy. Here we describe a CRISPR-based diagnostic developed with the universal cytochrome-oxidase 1 gene (CO1). The CO1 gene is the most sequenced gene among Animalia, and therefore our approach can be adopted to detect nearly any animal. We tested the approach on three difficult-to-identify moth species (Keiferia lycopersicella, Phthorimaea absoluta, and Scrobipalpa atriplicella) that are major invasive pests globally. We designed an assay that combines recombinase polymerase amplification (RPA) with CRISPR for signal generation. Our approach has a much higher sensitivity than other real time-PCR assays and achieved 100% accuracy for identification of all three species, with a detection limit of up to 120 fM for P. absoluta and 400 fM for the other two species. Our approach does not require a lab setting, reduces the risk of cross-contamination, and can be completed in less than one hour. This work serves as a proof of concept that has the potential to revolutionize animal detection and monitoring.

Signal and noise in metabarcoding data

Metabarcoding is a powerful molecular tool for simultaneously surveying hundreds to thousands of species from a single sample, underpinning microbiome and environmental DNA (eDNA) methods. Deriving quantitative estimates of underlying biological communities from metabarcoding is critical for enhancing the utility of such approaches for health and conservation. Recent work has demonstrated that correcting for amplification biases in genetic metabarcoding data can yield quantitative estimates of template DNA concentrations. However, a major source of uncertainty in metabarcoding data stems from non-detections across technical PCR replicates where one replicate fails to detect a species observed in other replicates. Such non-detections are a special case of variability among technical replicates in metabarcoding data. While many sampling and amplification processes underlie observed variation in metabarcoding data, understanding the causes of non-detections is an important step in distinguishing signal from noise in metabarcoding studies. Here, we use both simulated and empirical data to 1) suggest how non-detections may arise in metabarcoding data, 2) outline steps to recognize uninformative data in practice, and 3) identify the conditions under which amplicon sequence data can reliably detect underlying biological signals. We show with both simulations and empirical data that, for a given species, the rate of non-detections among technical replicates is a function of both the template DNA concentration and species-specific amplification efficiency. Consequently, we conclude metabarcoding datasets are strongly affected by (1) deterministic amplification biases during PCR and (2) stochastic sampling of amplicons during sequencing-both of which we can model-but also by (3) stochastic sampling of rare molecules prior to PCR, which remains a frontier for quantitative metabarcoding. Our results highlight the importance of estimating species-specific amplification efficiencies and critically evaluating patterns of non-detection in metabarcoding datasets to better distinguish environmental signal from the noise inherent in molecular detections of rare targets.

Evaluation of DNA metabarcoding for identifying fish eggs: a case study on the West Florida Shelf

A critical factor in fisheries management is the protection of spawning sites for ecologically and economically important fish species. DNA barcoding (i.e., amplification and sequencing of the mitochondrial cytochrome c oxidase I (COI) gene) of fish eggs has emerged as a powerful technique for identifying spawning sites. However, DNA barcoding of individual fish eggs is time-consuming and expensive. In an attempt to reduce costs and effort for long-term fisheries monitoring programs, here we used DNA metabarcoding, in which DNA is extracted and amplified from a composited sample containing all the fish eggs collected at a given site, to identify fish eggs from 49 stations on the West Florida Shelf. A total of 37 taxa were recovered from 4,719 fish eggs. Egg distributions on the West Florida Shelf corresponded with the known habitat types occupied by these taxa, which included burrower, coastal pelagic, epipelagic, mesopelagic, demersal, deep demersal, commensal, and reef-associated taxa. Metabarcoding of fish eggs was faster and far less expensive than barcoding individual eggs; however, this method cannot provide absolute taxon proportions due to variable copy numbers of mitochondrial DNA in different taxa, different numbers of cells within eggs depending on developmental stage, and PCR amplification biases. In addition, some samples yielded sequences from more taxa than the number of eggs present, demonstrating the presence of contaminating DNA and requiring the application of a threshold proportion of sequences required for counting a taxon as present. Finally, we review the advantages and disadvantages of using metabarcoding vs. individual fish egg barcoding for long-term monitoring programs.

Discovering marine biodiversity in the 21st century

We review the current knowledge of the biodiversity of the ocean as well as the levels of decline and threat for species and habitats. The lack of understanding of the distribution of life in the ocean is identified as a significant barrier to restoring its biodiversity and health. We explore why the science of taxonomy has failed to deliver knowledge of what species are present in the ocean, how they are distributed and how they are responding to global and regional to local anthropogenic pressures. This failure prevents nations from meeting their international commitments to conserve marine biodiversity with the results that investment in taxonomy has declined in many countries. We explore a range of new technologies and approaches for discovery of marine species and their detection and monitoring. These include: imaging methods, molecular approaches, active and passive acoustics, the use of interconnected databases and citizen science. Whilst no one method is suitable for discovering or detecting all groups of organisms many are complementary and have been combined to give a more complete picture of biodiversity in marine ecosystems. We conclude that integrated approaches represent the best way forwards for accelerating species discovery, description and biodiversity assessment. Examples of integrated taxonomic approaches are identified from terrestrial ecosystems. Such integrated taxonomic approaches require the adoption of cybertaxonomy approaches and will be boosted by new autonomous sampling platforms and development of machine-speed exchange of digital information between databases.

A DNA Extraction Method for Insects From Sticky Traps: Targeting a Low Abundance Pest, Phthorimaea absoluta (Lepidoptera: Gelechiidae), in Mixed Species Communities

Invasive insects can cause catastrophic damage to ecosystems and cost billions of dollars each year due to management expenses and lost revenue. Rapid detection is an important step to prevent invasive insects from spreading, but improvements in detection capabilities are needed for bulk collections like those from sticky traps. Here we present a bulk DNA extraction method designed for the detection of Phthorimaea absoluta Meyrick (Lepidoptera: Gelechiidae), an invasive moth that can decimate tomato crops. We test the extraction method for insect specimens on sticky traps, subjected to different temperature and humidity conditions, and among mock insect communities left in the field for up to 21 d. We find that the extraction method yielded high success (>92%) in recovering target DNA across field and lab trials, without a decline in recovery after three weeks, across all treatments. These results may have a large impact on tomato growing regions where P. absoluta is in the early stages of invasion or not yet present. The extraction method can also be used to improve detection capabilities for other bulk insect collections, especially those using sticky traps, to the benefit of pest surveys and biodiversity studies.

Speeding up the detection of invasive bivalve species using environmental DNA: a Nanopore and Illumina sequencing comparison

Traditional detection of aquatic invasive species via morphological identification is often time-consuming and can require a high level of taxonomic expertise, leading to delayed mitigation responses. Environmental DNA (eDNA) detection approaches of multiple species using Illumina-based sequencing technology have been used to overcome these hindrances, but sample processing is often lengthy. More recently, portable nanopore sequencing technology has become available, which has the potential to make molecular detection of invasive species more widely accessible and substantially decrease sample turnaround times. However, nanopore-sequenced reads have a much higher error rate than those produced by Illumina platforms, which has so far hindered the adoption of this technology. We provide a detailed laboratory protocol and bioinformatic tools (msi package) to increase the reliability of nanopore sequencing to detect invasive species, and we test its application using invasive bivalves while comparing it with Illumina-based sequencing. We sampled water from sites with pre-existing bivalve occurrence and abundance data, and contrasting bivalve communities, in Italy and Portugal. Samples were extracted, amplified, and sequenced by the two platforms. The mean agreement between sequencing methods was 69% and the difference between methods was non-significant. The lack of detections of some species at some sites could be explained by their known low abundances. This is the first reported use of MinION to detect aquatic invasive species from eDNA samples.

Early detection monitoring for non-indigenous fishes; comparison of survey approaches during two species introductions in a Great Lakes port

Assessing relative performance of different sampling methods used for early detection monitoring (EDM) is a critical step in understanding the likelihood of detecting new non-indigenous species (NIS) in an environment of interest. EDM performance metrics are typically based on the probability of detecting established NIS or rare indigenous species; however, detection probability estimates for these proxies may not accurately reflect survey effectiveness for newly introduced NIS. We used data from three different EDM survey approaches that varied by targeted life-stage (adult-juvenile versus ichthyoplankton), media (physical fish versus environmental DNA), and taxonomic method (morphology-based versus DNA-based taxonomy) to explore relative detection sensitivity for recently introduced white bass (Morone chrysops) and gizzard shad (Dorosoma cepedianum) in the Port of Duluth-Superior, a NIS introduction hot spot within the Laurentian Great Lakes. Detection efficiency, measured by the effort (number of samples) required to achieve 95% probability of detection, differed by EDM approach and species. Also, the relative sensitivity (detection rate) of each survey approach differed by species. For both species, detection in surveys using DNA-based taxonomy was generally as good or better than the adult-juvenile survey using morphology-based taxonomy. While both species appear to have been detected at early stages of invasion, white bass were likely present up to 5 years prior to initial detection, whereas gizzard shad may have been detected in the first year of introduction. We conclude that using complimentary sampling methods can help to balance the strengths and weaknesses of each approach and provide more reliable early detection of new invaders.

Issues of under-representation in quantitative DNA metabarcoding weaken the inference about diet of the tundra vole Microtus oeconomus

During the last decade, methods based on high-throughput sequencing such as DNA metabarcoding have opened up for a range of new questions in animal dietary studies. One of the major advantages of dietary metabarcoding resides in the potential to infer a quantitative relationship between sequence read proportions and biomass of ingested food. However, this relationship's robustness is highly dependent on the system under study, calling for case-specific assessments. Herbivorous small rodents often play important roles in the ecosystem, and the use of DNA metabarcoding for analyses of rodent diets is increasing. However, there has been no direct validation of the quantitative reliability of DNA metabarcoding for small rodents. Therefore, we used an experimental approach to assess the relationship between input plant biomass and sequence reads proportions from DNA metabarcoding in the tundra vole Microtus oeconomus. We found a weakly positive relationship between the number of high-throughput DNA sequences and the expected biomass proportions of food plants. The weak relationship was possibly caused by a systematic under-amplification of one of the three plant taxa fed. Generally, our results add to the growing evidence that case-specific validation studies are required to reliably make use of sequence read abundance as a proxy of relative food proportions in the diet.

Comparison of Larval Fish Detections Using Morphology-Based Taxonomy versus High-Throughput Sequencing for Invasive Species Early Detection

When first introduced, invasive species typically evade detection; DNA barcoding coupled with high-throughput sequencing (HTS) may be more sensitive and accurate than morphology-based taxonomy, and thereby improve invasive (or rare) species detection. We quantified the relative error of species detection between morphology-based and HTS-based taxonomic identification of ichthyoplankton collections from the Port of Duluth, Minnesota, an aquatic non-native species introduction 'hot-spot' in the Laurentian Great Lakes. We found HTS-based taxonomy identified 28 species and morphology-based taxonomy 30 species, of which 27 were common to both. Among samples, 76% of family-level taxonomic assignments agreed; however, only 42% of species assignments agreed. Most errors were attributed to morphology-based taxonomy, whereas HTS-based taxonomy error was low. For this study system, for most non-native fishes, the detection probability by randomized survey for larvae was similar to that by a survey that is optimized for non-native species early detection of juveniles and adults. We conclude that classifying taxonomic errors by comparing HTS results against morphology-based taxonomy is an important step toward incorporating HTS-based taxonomy into biodiversity surveys.

Status and prospects of marine NIS detection and monitoring through (e)DNA metabarcoding

In coastal ecosystems, non-indigenous species (NIS) are recognized as a major threat to biodiversity, ecosystem functioning and socio-economic activities. Here we present a systematic review on the use of metabarcoding for NIS surveillance in marine and coastal ecosystems, through the analysis of 42 publications. Metabarcoding has been mainly applied to environmental DNA (eDNA) from water samples, but also to DNA extracted from bulk organismal samples. DNA extraction kits have been widely used and the 18S rRNA and the COI genes the most employed markers, but less than half of the studies targeted more than one marker loci. The Illumina MiSeq platform has been used in >50% of the publications. Current weaknesses include potential occurrence of false negatives due to the primer-biased or faulty DNA amplification and the incompleteness of reference libraries. This is particularly concerning in the case of NIS surveillance, where proficiency in species level detection is critical. Until these weaknesses are resolved, ideally NIS metabarcoding should be supported by complementary approaches, such as morphological analysis or more targeted molecular approaches (e.g. qPCR, ddPCR). Even so, metabarcoding has already proved to be a highly sensitive tool to detect small organisms or undifferentiated life stages across a wide taxonomic range. In addition, it also seems to be very effective in ballast water management and to improve the spatial and temporal sampling frequency of NIS surveillance in marine and coastal ecosystems. Although specific protocols may be required for species-specific NIS detection, for general monitoring it would be vital to settle on a standard protocol able to generate comparable results among surveillance campaigns and regions of the globe, seeking the best approach for detecting the broadest range of species, while minimizing the chances of a false positive or negative detection.

Efficacy of metabarcoding for identification of fish eggs evaluated with mock communities

There is urgent need for effective and efficient monitoring of marine fish populations. Monitoring eggs and larval fish may be more informative than that traditional fish surveys since ichthyoplankton surveys reveal the reproductive activities of fish populations, which directly impact their population trajectories. Ichthyoplankton surveys have turned to molecular methods (DNA barcoding & metabarcoding) for identification of eggs and larval fish due to challenges of morphological identification. In this study, we examine the effectiveness of using metabarcoding methods on mock communities of known fish egg DNA. We constructed six mock communities with known ratios of species. In addition, we analyzed two samples from a large field collection of fish eggs and compared metabarcoding results with traditional DNA barcoding results. We examine the ability of our metabarcoding methods to detect species and relative proportion of species identified in each mock community. We found that our metabarcoding methods were able to detect species at very low input proportions; however, levels of successful detection depended on the markers used in amplification, suggesting that the use of multiple markers is desirable. Variability in our quantitative results may result from amplification bias as well as interspecific variation in mitochondrial DNA copy number. Our results demonstrate that there remain significant challenges to using metabarcoding for estimating proportional species composition; however, the results provide important insights into understanding how to interpret metabarcoding data. This study will aid in the continuing development of efficient molecular methods of biological monitoring for fisheries management.

Prospects and challenges of implementing DNA metabarcoding for high-throughput insect surveillance

Trap-based surveillance strategies are widely used for monitoring of invasive insect species, aiming to detect newly arrived exotic taxa as well as track the population levels of established or endemic pests. Where these surveillance traps have low specificity and capture non-target endemic species in excess of the target pests, the need for extensive specimen sorting and identification creates a major diagnostic bottleneck. While the recent development of standardized molecular diagnostics has partly alleviated this requirement, the single specimen per reaction nature of these methods does not readily scale to the sheer number of insects trapped in surveillance programmes. Consequently, target lists are often restricted to a few high-priority pests, allowing unanticipated species to avoid detection and potentially establish populations. DNA metabarcoding has recently emerged as a method for conducting simultaneous, multi-species identification of complex mixed communities and may lend itself ideally to rapid diagnostics of bulk insect trap samples. Moreover, the high-throughput nature of recent sequencing platforms could enable the multiplexing of hundreds of diverse trap samples on a single flow cell, thereby providing the means to dramatically scale up insect surveillance in terms of both the quantity of traps that can be processed concurrently and number of pest species that can be targeted. In this review of the metabarcoding literature, we explore how DNA metabarcoding could be tailored to the detection of invasive insects in a surveillance context and highlight the unique technical and regulatory challenges that must be considered when implementing high-throughput sequencing technologies into sensitive diagnostic applications.

How quantitative is metabarcoding: A meta-analytical approach

Metabarcoding has been used in a range of ecological applications such as taxonomic assignment, dietary analysis and the analysis of environmental DNA. However, after a decade of use in these applications there is little consensus on the extent to which proportions of reads generated corresponds to the original proportions of species in a community. To quantify our current understanding, we conducted a structured review and meta‐analysis. The analysis suggests that a weak quantitative relationship may exist between the biomass and sequences produced (slope = 0.52 ± 0.34, p < 0.01), albeit with a large degree of uncertainty. None of the tested moderators, sequencing platform type, the number of species used in a trial or the source of DNA, were able to explain the variance. Our current understanding of the factors affecting the quantitative performance of metabarcoding is still limited: additional research is required before metabarcoding can be confidently utilized for quantitative applications. Until then, we advocate the inclusion of mock communities when metabarcoding as this facilitates direct assessment of the quantitative ability of any given study.

Combining morpho-taxonomy and metabarcoding enhances the detection of non-indigenous marine pests in biofouling communities

Marine infrastructure can favor the spread of non-indigenous marine biofouling species by providing a suitable habitat for them to proliferate. Cryptic organisms or those in early life stages can be difficult to distinguish by conventional morphological taxonomy. Molecular tools, such as metabarcoding, may improve their detection. In this study, the ability of morpho-taxonomy and metabarcoding (18S rRNA and COI) using three reference databases (PR2, BOLD and NCBI) to characterize biodiversity and detect non-indigenous species (NIS) in biofouling was compared on 60 passive samplers deployed over summer and winter in a New Zealand marina. Highest resolution of metazoan taxa was identified using 18S rRNA assigned to PR2. There were higher assignment rates to NCBI reference sequences, but poorer taxonomic identification. Using all methods, 48 potential NIS were identified. Metabarcoding detected the largest proportion of those NIS: 77% via 18S rRNA/PR2 and NCBI and 35% via COI/BOLD and NCBI. Morpho-taxonomy detected an additional 14% of all identified NIS comprising mainly of bryozoan taxa. The data highlight several on-going challenges, including: differential marker resolution, primer biases, incomplete sequence reference databases, and variations in bioinformatic pipelines. Combining morpho-taxonomy and molecular analysis methods will likely enhance the detection of NIS from complex biofouling.

Multi-marker metabarcoding approach to study mesozooplankton at basin scale

Zooplankton plays a pivotal role in marine ecosystems and the characterisation of its biodiversity still represents a challenge for marine ecologists. In this study, mesozooplankton composition from 46 samples collected in summer along the western Adriatic Sea, was retrieved by DNA metabarcoding analysis. For the first time, the highly variable fragments of the mtDNA COI and the V9 region of 18S rRNA genes were used in a combined matrix to compile an inventory of mesozooplankton at basin scale. The number of sequences retrieved after quality filtering were 824,148 and 223,273 for COI and 18S (V9), respectively. The taxonomical assignment against reference sequences, using 95% (for COI) and 97% (for 18S) similarity thresholds, recovered 234 taxa. NMDS plots and cluster analysis divided coastal from offshore samples and the most representative species of these clusters were distributed according to the dominant surface current pattern of the Adriatic for the summer period. For selected sampling sites, mesozooplankton species were also identified under a stereo microscope providing insights on the strength and weakness of the two approaches. In addition, DNA metabarcoding was shown to be helpful for the monitoring of non-indigenous marine metazoans and spawning areas of commercial fish species. We defined pros and cons of applying this approach at basin scale and the benefits of combining the datasets from two genetic markers.

Optimization and performance testing of a sequence processing pipeline applied to detection of nonindigenous species

Genetic taxonomic assignment can be more sensitive than morphological taxonomic assignment, particularly for small, cryptic or rare species. Sequence processing is essential to taxonomic assignment, but can also produce errors because optimal parameters are not known a priori. Here, we explored how sequence processing parameters influence taxonomic assignment of 18S sequences from bulk zooplankton samples produced by 454 pyrosequencing. We optimized a sequence processing pipeline for two common research goals, estimation of species richness and early detection of aquatic invasive species (AIS), and then tested most optimal models' performances through simulations. We tested 1,050 parameter sets on 18S sequences from 20 AIS to determine optimal parameters for each research goal. We tested optimized pipelines' performances (detectability and sensitivity) by computationally inoculating sequences of 20 AIS into ten bulk zooplankton samples from ports across Canada. We found that optimal parameter selection generally depends on the research goal. However, regardless of research goal, we found that metazoan 18S sequences produced by 454 pyrosequencing should be trimmed to 375-400 bp and sequence quality filtering should be relaxed (1.5 ≤ maximum expected error ≤ 3.0, Phred score = 10). Clustering and denoising were only viable for estimating species richness, because these processing steps made some species undetectable at low sequence abundances which would not be useful for early detection of AIS. With parameter sets optimized for early detection of AIS, 90% of AIS were detected with fewer than 11 target sequences, regardless of whether clustering or denoising was used. Despite developments in next-generation sequencing, sequence processing remains an important issue owing to difficulties in balancing false-positive and false-negative errors in metabarcoding data.

Early detection monitoring for aquatic non-indigenous species: Optimizing surveillance, incorporating advanced technologies, and identifying research needs

Following decades of ecologic and economic impacts from a growing list of nonindigenous and invasive species, government and management entities are committing to systematic early- detection monitoring (EDM). This has reinvigorated investment in the science underpinning such monitoring, as well as the need to convey that science in practical terms to those tasked with EDM implementation. Using the context of nonindigenous species in the North American Great Lakes, this article summarizes the current scientific tools and knowledge - including limitations, research needs, and likely future developments - relevant to various aspects of planning and conducting comprehensive EDM. We begin with the scope of the effort, contrasting target-species with broad-spectrum monitoring, reviewing information to support prioritization based on species and locations, and exploring the challenge of moving beyond individual surveys towards a coordinated monitoring network. Next, we discuss survey design, including effort to expend and its allocation over space and time. A section on sample collection and analysis overviews the merits of collecting actual organisms versus shed DNA, reviews the capabilities and limitations of identification by morphology, DNA target markers, or DNA barcoding, and examines best practices for sample handling and data verification. We end with a section addressing the analysis of monitoring data, including methods to evaluate survey performance and characterize and communicate uncertainty. Although the body of science supporting EDM implementation is already substantial, research and information needs (many already actively being addressed) include: better data to support risk assessments that guide choice of taxa and locations to monitor; improved understanding of spatiotemporal scales for sample collection; further development of DNA target markers, reference barcodes, genomic workflows, and synergies between DNA-based and morphology-based taxonomy; and tools and information management systems for better evaluating and communicating survey outcomes and uncertainty.