Ryan P. Kelly

Molecular data from environmental samples can reflect the abundance of species’ DNA, an index immediately relevant to natural-resources management at a broad geographic scale. These data commonly derive from assays designed and targeted for individual species ( e.g ., using quantitative polymerase chain reaction (qPCR) or digital droplet polymerase chain reaction (ddPCR)), or from metabarcoding that use more general PCR primers to amplify many species simultaneously. Multispecies analyses make efficient use of field samples and laboratory time and speak to inherently multispecies questions of management and ecological interest. However, unlike single-species techniques, metabarcoding alone generally reflects only the environmental DNA (eDNA) proportions of target-species DNA present, not their absolute quantities. Here, we combine qPCR and metabarcoding data derived from the same samples to estimate quantities of eDNA from many fish species along the US West Coast in three dimensions, demonstrating a technique of practical relevance for both management and ecology. We derive spatially explicit maps of eDNA abundances for 12 common species of ecological and management importance and point the way to quantitative surveys of wild species using molecules alone. We find that species distribution maps derived from eDNA largely mirror known species niches and known spatial distributions. Notably, our analyses identify biodiversity hotspots that align with previously documented regions of ecological significance, such as the Columbia River plume and the Heceta Bank.

Datasets derived from mixed‐template DNA amplification and sequencing (‘metabarcoding’) are common in environmental DNA (eDNA), microbiome or microbial ecology analyses. Such metabarcoding data have been broadly used to inform estimates of biodiversity, distribution and abundance across a wide range of species and habitats. Generally, data from metabarcoding consist of a table of read‐counts assigned to each of a set of taxa or sequence variants. As such, they appear to contain quantitative information about the environment under study, immediately eligible for traditional community ecology analyses. In fact, metabarcoding data are compositional, can be biased by variation in PCR amplification rates among taxa, and have other statistical attributes that are distinct from many other common ecological observations. The difficulty in understanding and accounting for the complexity of metabarcoding observations has hindered their ecological interpretation and limited their integration with other ecological data types. Here, we use both simulations and empirical observations to show that the variability among replicate metabarcoding observations is closely tied to the absolute quantities of DNA present in a sample. We then develop a general statistical model for estimating DNA abundance from metabarcoding observations using the relationship between mean and variance of metabarcoding reads. We demonstrate that the model can effectively estimate DNA abundances using simulations and from experimentally created DNA samples. The model can be applied to a single marker or to multiple markers simultaneously, allowing the direct integration of information from multiple genetic markers derived from a common biological sample. Estimates of absolute DNA abundance are the logical unit to connect DNA observations to organismal abundance or biomass. Our methods can be used to link together multiple types of DNA observations or to connect DNA observations with non‐genetic ecological methods for surveying ecosystems. However, important aspects of the ‘ecology of DNA’ remain unresolved and may limit our ability to link observed concentrations to organismal abundances in a general way.

Environmental molecular monitoring has transformed biodiversity detection, yet most methods capture only species presence through amplified DNA fragments, missing the biological information encoded in native nucleic acids. Here we show that a single shotgun Oxford Nanopore sequencing library from water can be read through several independent biological lenses, and that each class of molecule (environmental RNA, CpG methylation, and bulk DNA) differs in its environmental persistence, which sets the window over which signal can be trusted. Sampling Pacific salmon spawning runs, we simultaneously recovered environmental RNA indicating fresh biological input, CpG methylation profiles reflecting age demographics, and pathogen signatures reflecting health. Environmental RNA tracked spawning phenology, qPCR corroborated the timing of fresh biological input over the weeks sampled, and methylation-based age inference held only while fresh DNA dominated, a condition we term the’Freshness Gate.’ Because environmental water is not a tissue sample, we detect functional transcripts as evidence of biological input but do not infer differential gene expression, and we read methylation state but do not infer biological age outside the Freshness Gate. Variance partitioning is consistent with biological freshness, rather than technical factors such as fragment length or sequencing depth, structuring environmental methylation, though these comparisons did not reach significance at nine time points. Comparable dynamics were observed in Chinook salmon, with timing offsets following their distinct spawning season. By defining when environmental molecules faithfully encode living biology and when degradation decouples signal from current state, our framework sets practical and theoretical boundaries for multi-omic environmental monitoring.

Changes in natural and artificial light presumably have dramatic influences on ecological communities, yet measuring these effects can be challenging due to the difficulty in distinguishing ecological signals from background variability. Here, we used environmental DNA (eDNA) to test for light‐associated changes in a nearshore marine community. Over 8 days at Friday Harbor Laboratories, Washington, USA, we sampled seawater at midday and midnight with and without an artificial light treatment, analyzing a total of 84 samples (3 L triplicate samples) using COI and 12S markers on an Oxford Nanopore MinION platform. We detected 229 eukaryotic (COI) and 77 vertebrate (12S) taxa across 11 trophic groups, dominated by primary producers (89 species), benthic invertebrates, and fishes. Metabarcoding captured community restructuring within 30 min, such that we could observe meaningful effects of artificial light treatments at night (particularly among spawning polychaetes), as well as more fundamental community shifts in day vs. night (e.g., with primary producers, gelatinous zooplankton, and filter feeders more common during the day). These techniques offer a means of near‐real‐time assessment of wholescale changes in ecological communities, and our results here illustrate species‐ and group‐specific effects of light levels that are likely important in structuring nearshore marine communities worldwide.

Environmental DNA (eDNA) samples capture mixtures of DNA from a wide range of organisms and often yield metabarcoding datasets that include taxa beyond the intended scope of the primer set used. The presence of this non-target DNA (termed “molecular bycatch”) can make it difficult to interpret trends in the primary targets. Here, we present an example in which including non-target detections alters the ecological interpretation of a metabarcoding time series and highlight a quantitative approach that can account for molecular bycatch in a biologically meaningful way. We collected high-resolution temporal samples from an estuary (Hood Canal, Washington, USA), using a surface-stationed autonomous sampler over a 48-hour period. We metabarcoded samples with a primer set that targets fish, but also amplifies (non-target) marine mammals with lower efficiency. We then transformed sequence read proportions into absolute DNA concentrations using a mock community–based bias correction and single-species concentrations via droplet digital PCR (ddPCR). We found that non-target mammals were substantially under-represented in the raw metabarcoding data relative to their biased-corrected proportions. Cross-validation of this quantitative metabarcoding approach showed that predicted DNA concentrations closely matched independent ddPCR measurements for total 12S DNA and two single-species concentrations (Atlantic bottlenose dolphin and Pacific herring) in both mock communities and environmental samples. After estimating absolute species concentrations for all species, we revealed species-specific patterns that were not apparent in the observed read counts alone. Our results underscore the importance of correcting for species-specific amplification bias when evaluating community composition from metabarcoding data, particularly in the context of non-target detections.

Rare species are difficult to observe in the wild, particularly in the ocean where large spatial scales and accessibility hinder effective sampling. Environmental DNA (eDNA) is a non-destructive, scalable sampling method with the potential to inform the distribution of rare species in marine ecosystems. We sample eDNA within the California Current ecosystem to estimate the distribution of eulachon (Thaleichthys pacificus), a threatened anadromous smelt ranging along the coastal Northeast Pacific. We amplify eulachon DNA from thousands of water samples collected at night across two years and more than 200,000 square kilometers along the U.S. west coast. We then use spatiotemporal models to derive quantitative estimates of eulachon DNA across space, depth, and time relative to environmental covariates. We find that eulachon DNA has a distribution weighted towards the ocean surface, spatially associated with major river mouths and productive offshore banks. Temperature and prey density are key covariates, with eulachon more likely to be found in warmer waters with higher prey concentrations. We discuss how our results can augment the information currently used in eulachon recovery planning, and describe the wide applicability of our statistical models for estimating distribution and abundance for other species of conservation concern.

Environmental DNA (eDNA) provides powerful insights into species presence and community composition but remains limited in its capacity to infer species abundance or population structure. Here, we show that the deviation between within‐sample haplotype frequencies and the overall population‐level haplotype frequencies can be used to estimate the number of individual contributors to a given sample. We first establish the theoretical framework for approximating population haplotype frequencies directly from eDNA data, enabling application even in the absence of tissue‐derived references. Building on this foundation, we introduce a maximum likelihood estimator to infer the number of contributors and assess its performance through simulations spanning a range of haplotype frequency distributions and noise scenarios. These approaches assume that all samples are drawn from a single, panmictic population. We find that accurate estimates are attainable when haplotypes are sufficiently variable, population frequencies are well‐characterised, and samples are large enough to capture frequency deviations. By bridging population genetic theory and eDNA, our method complements existing molecular approaches and offers a novel path towards quantifying abundance from eDNA metabarcoding data.

The use of environmental DNA (eDNA) in routine biomonitoring is transforming our ability to detect and quantify aquatic species. However, translating these indirect molecular detections into ecological insight requires understanding how long eDNA persists and how it moves through the environment. Here, we present a systematic mapping review of the controls on eDNA fate and transport and provide the most comprehensive synthesis to date of mechanistic evidence across aquatic systems. Drawing on over 200 manuscripts, we introduce the concept of the eDNA profile, defined by state, molecular properties, and molecular origin, as a unifying framework for interpreting why eDNA signals differ across environments and taxa. Where eDNA profile has been quantified, source, state, particle size, fragment length, and molecular origin measurably alter decay and transport, confirming that different eDNA forms experience distinct fates and movement distances within aquatic systems. Using this lens, we then synthesize the evidence for the effects of abiotic factors, biotic factors, and transport on eDNA fate. For abiotic factors, we find that temperature, pH, nutrients, suspended particles, substrates, dissolved organic matter, and dissolved oxygen measurably modulate eDNA decay, whereas light exposure (UV and visible light) and salinity show weak or no direct effects across studies. For biotic factors, we conclude that microbial activity, extracellular enzymes, and biofilms are likely major drivers of eDNA degradation, although the identities, functions, and dynamics of responsible communities are poorly resolved. Finally, we show that transport patterns differ strongly among aquatic systems, with depth-structured and short-range signals common in lakes, highly variable downstream distances in rivers that depend on discharge and eDNA particle size, and longer potential transport in coastal and open-ocean systems that often still yield predominantly local signals. In these advective environments, robust interpretation commonly requires coupling eDNA observations with hydrological and oceanographic circulation models. Finally, we highlight three priorities for a predictive framework: (1) explicitly resolve eDNA profiles, (2) isolate the causal mechanisms that govern decay and removal, and (3) identify which microbes and microbial processes drive degradation across environments. Our goal is to integrate the field, highlight critical gaps, and ultimately advance the interpretation of eDNA-based detections for biodiversity monitoring.

Environmental DNA (eDNA) metabarcoding provides powerful insights into species presence and community composition, but remains limited in its ability to quantify species abundance or structure. Here, we show that deviation between observed haplotype frequencies within a given sample and the population haplotype frequencies can be used to infer the number of individual contributors to an eDNA sample. We also lay out the theory for how population haplotype frequencies can be approximated from eDNA data alone, enabling broad applicability even in the absence of tissue-based references. We then present an estimator to derive the number of individual contributors to a given eDNA sample and validate its performance using simulations with variable allele frequencies and noise. Our framework demonstrates that differences between expected and observed frequencies carry meaningful biological information in eDNA data. Our results show that the number of contributors can be recovered under a range of conditions, particularly with hypervariable markers and sufficient sampling. This approach complements existing molecular methods and opens a new avenue for inferring abundance from eDNA metabarcoding datasets.

Although aquatic, aerial, and terrestrial habitats are often treated as separate ecological systems, these environments exist along a continuum of connectivity: flows of biomass and energy routinely create linkages across water and air, blurring traditional boundaries. Here, we use eDNA sequencing to illustrate and quantify the movement of trace genetic information between water and air. We collected 27 paired air-and-water samples from two urban–wildland interface sites using passive air sampling and active water filtering. Metabarcoding with the MiFish-U 12S marker recovered 35 vertebrate taxa, 40% of which were detected in both water and air, ranging from the strictly aquatic salmon to wholly terrestrial cottontail rabbit. This reciprocal relationship suggests that eDNA pools form within each partition (water or air), with the probability of transfer governed by DNA concentration. In this view, detecting aquatic eDNA in the air is not contamination or stochastic noise but an expected, repeatable phenomenon. Logistic models confirm that higher abundance in one medium predicts spillover into the other. For instance, peaks in Coho and Chinook salmon eDNA align within 24 hours, demonstrating that passive air sampling reflects the temporal abundance trends of the most common aquatic species. In contrast, low-read-abundance taxa appear only sporadically, implying that rare detections are the first to drop out of cross-medium transfer and therefore demand intensified sampling in their primary habitat. Together, our findings bridge the conventional separation of aquatic and airborne eDNA and establish a unified, non-invasive framework for holistic vertebrate monitoring at the land–water interface. This approach offers transformative potential for conservation, invasive-species early warning, and One Health surveillance.