Environmental DNA最新文献

Environmental DNA (eDNA) is a cost-efficient, noninvasive method to monitor fish populations, but the quantitative aspect of this technique (e.g., estimating biomass or densities) remains underexplored. Few studies have established relationships between fish DNA concentration and biomass/density. Here, we investigate the relationship between eDNA concentration (copies per liter) and trout biomass and densities estimated by electrofishing in mountain streams of Picos de Europa National Park (Spain). We assessed eDNA effectiveness in inferring biomass/density using 18S rRNA (18S) and cytochrome c oxidase I (COI) metabarcoding, and quantitative PCR with a COI-specific Salmo trutta primer, each performed with different datasets from the same sampling points. Salmonidae eDNA concentration positively correlates with trout biomass and density. Both 18S and specific-COI markers showed a significant increase in DNA concentration as trout biomass and density rose in electrofishing surveys. However, general COI did not exhibit significant trout DNA concentration and biomass/density relationships, despite providing greater taxonomic resolution at the species level. Further analysis exploring eDNA concentration and biomass/densities across different trout size classes (fry, juvenile, and adult) revealed that juvenile trout biomass contributed the most to the observed eDNA concentration–biomass/density relationship. Our results suggest that DNA concentration estimated from metabarcoding, when using an appropriately selected primer, can reliably indicate trout biomass and density in these mountain streams where trout is the dominant species. Although quantitative PCR showed similar trends, it had lower explanatory power. This study highlights the importance of integrating a quantitative framework in metabarcoding for ecological monitoring and biodiversity assessments. Factors such as amplicon length, genetic region, marker specificity, or fish size class can influence the relationship between sequencing reads and electrofishing data. This methodology could aid the conservation and management of fish populations and other communities, though further research is needed to extend these results and assess eDNA detection reliability.

Environmental pollution is regarded as one of the most substantial threats facing freshwater ecosystems. Two contaminant types in particular, metals and pesticides, represent both long-term and widespread hazards to aquatic environments. Contamination negatively affects the health of aquatic flora and fauna. Constructed wetlands are an important mitigation measure for managing stormwater in urban landscapes. Given that constructed wetlands are designed to capture and retain contaminants from stormwater in sediments, the resulting chemical profiles are largely representative of contamination in the surrounding local catchment and aquatic environments. Environmental DNA is expanding the field of aquatic monitoring, introducing the ability to assess biological profiles quickly and reliably, and expanding the knowledge of how contamination affects ecosystems down to a microbial level. Here, we used environmental DNA (eDNA) metabarcoding to characterize the sedimentary microbial profiles of nine urban stormwater wetlands and one rural wetland with low levels of contamination and assess the variation in community structures between sites as well as between the inlet and outlet within each site. We found significant variation in microbial communities between the rural (reference) wetland and stormwater wetlands, variation between some stormwater wetlands, and between over half of the inlets to outlets. The bacterial phyla Cyanobacteria and Proteobacteria were mostly driving this variation, along with Planctomycetota and Bacteroidota. We also found correlations between microbial community structure and chemical occurrences, particularly zinc and barium, with the latter not previously reported to be associated with microbial dynamics in freshwater environments. Our study validates the ability of eDNA metabarcoding to reliably evaluate sedimentary microbial profiles in stormwater wetlands and highlights its value in the assessment and prediction of contamination in these environments.

Quantifications of phytoplankton biomass and species composition are crucial for monitoring biodiversity and population dynamics in aquatic environments, and both direct microscopic counts and fluorescence-based methods have been widely used for monitoring. Recent advancements in DNA metabarcoding offer an alternative way of easily assessing diversity and species composition. However, a comprehensive comparison of the relative merits and limitations of DNA- and fluorescence-based methods is currently lacking. Here we compare phytoplankton community composition measured via fluorescence and DNA metabarcoding in an outdoor, replicated mesocosm experiment. We show that there is a positive correlation between fluorescence-measured biomass and DNA read and amplicon sequence variants (ASV) numbers for cyanobacteria, but either weak or no correlation for the other phytoplankton groups assessed (cryptophytes, chromophytes, and green algae). In addition, DNA metabarcoding was systematically better at detecting cryptophytes, which were rarely detected via fluorescence. Hence, while DNA metabarcoding may not provide reliable biomass estimates for the majority of phytoplankton groups, metabarcoding analysis offers higher taxonomic resolution and the capability to detect rare phytoplankton groups. Overall, our findings provide new insights into the strengths and limitations of each method and highlight the considerable potential and importance of including DNA barcoding in freshwater ecosystem assessment and biomonitoring programmes with a focus on biodiversity assessments.

This study investigates the benthic invertebrate community in the Venice Lagoon using environmental DNA (eDNA) metabarcoding based on superficial water samples. Our objective is to understand if, in a shallow lagoon system, sampling at the surface can provide information on benthic biodiversity, allowing us to establish a baseline for future assessments and to monitor the community's seasonal and spatial variation. eDNA was collected from surface water samples at two sites during nine sampling campaigns from November 2018 to October 2019, and metabarcoding was performed using an available primer pair targeting 16S mitochondrial rDNA of echinoderms, never tested empirically before. Analyses revealed 80 taxonomic units, five not assigned at the species level, belonging not only to the phylum Echinodermata but predominantly assigned to Mollusca, with the majority of the identified species (60 out of 75) representing benthic invertebrates. Several species known to be invasive were detected, some previously recorded in the Venice Lagoon environment and others detected for the first time. Significant spatial differences in species composition were observed between the northern and the southern site of the lagoon. Temporal variation of the benthic community was also observed, mainly due to the distinctiveness of autumn samples, highlighting the dynamic nature of the Venice Lagoon ecosystem. Our results confirm the utility of eDNA for ongoing biodiversity monitoring and management and show that eDNA isolated from superficial water can provide information on the benthic invertebrate community, which might be particularly useful for biodiversity assessment in lagoons, ports, and areas characterized by limited or interdicted access.

An important evolutionary hypothesis posits that much of the biodiversity we see today arose during episodes of natural habitat fragmentation through the interplay of colonization, extinction, adaptation, and speciation. To interrogate the generality of this hypothesis, we leverage the natural experiment provided by arthropod communities in kīpuka—patches of Hawaiian wet forest isolated by lava flows. With DNA metabarcoding, we provide the first simultaneous exploration of ecological and evolutionary characteristics in the kīpuka system. At both species-equivalent (3% radius OTUs) and haplotype-equivalent (zOTUs) scales, we find that richness increases with kīpuka area, and that kīpuka exhibit faster distance decay of similarity compared to continuous forest. Kīpuka also differ in OTU and zOTU composition from continuous forest, notably hosting higher proportions of non-native OTUs for an arthropod order in which we can comprehensively classify native/non-native OTUs (Araneae). These findings reveal that natural habitat fragmentation drives parallel changes at species and haplotype scales in the kīpuka system. By integrating ecological and evolutionary perspectives, our study underscores the importance of studying both processes simultaneously if we are to understand, better predict, and more intelligently manage the responses of biological communities to environmental change.

The ability to accurately detect harmful aquatic invasive species in a species-specific manner is crucial to monitoring and management efforts. The Canadian province of Alberta currently harbors North America's only invasive Prussian Carp populations, in addition to invasive Goldfish populations. The ability to quickly and accurately distinguish between these phenotypically similar fish, while also determining their presence in various waterbodies, is important in tracking invasions. In this work, we develop a cytochrome B–based assay, as well as an ND2-based assay to distinguish between these two fish. The 84-bp-long CytB assay featured a limit of detection of 5.8 and 4.8 copies/sample for Prussian Carp and Goldfish, respectively, while the 95-bp-long ND2 assay featured LODs of 6.3 and 1.6 copies/sample, respectively. We demonstrate that each of these assays fails to amplify these markers in closely related fish species common to Alberta. They also fail to amplify key invasive carp species, apart from the Goldfish ND2 assay, which cross-reacts with Common Carp. We then implement these assays and find 13 Goldfish and 47 Prussian Carp environmental DNA detection events throughout the Canadian province of Alberta. Finally, we show that assays broadly agree with visual observation data gathered from various reporting mechanisms, highlighting their validity in a monitoring program.

Global efforts aimed at safeguarding and restoring biodiversity require methods to monitor progress towards conservation objectives. Such methods should provide a systematic and robust assessment of biodiversity for the lowest cost. River environmental DNA (eDNA) metabarcoding has been successfully applied to measure biodiversity in dendritic riverine habitats and is increasingly used to describe communities of terrestrial vertebrates in ecosystems that are challenging to survey using traditional methods. However, interpreting eDNA surveys in riverine habitats requires an understanding of the influence of eDNA transport, decay, and production on the distribution of eDNA. To this end, the hydrology-based eDITH (eDNA Integrating Transport and Hydrology) model incorporates such factors and can recover reliable spatial biodiversity patterns for aquatic taxa, but its potential to successfully model terrestrial taxa is so far unexplored. Here, we applied eDITH to eDNA metabarcoding data for terrestrial mammals collected over two mountainous catchments (575 and 745 km²) in British Columbia, Canada. We assessed prediction transferability between neighboring catchments and compared model predictions with observations from camera trapping. We found that for 9 out of 15 taxa detected by both eDNA and camera traps, predicted distributions predominantly matched observations from camera trap surveys, illustrating that eDITH can uncover patterns of mammal distribution in mountainous catchments. While lacking knowledge of actual taxon density prevents us from determining whether discrepancies stem from data limitations or complex eDNA production-density relationships, good transferability of predictions to the neighboring catchments suggests that eDNA distribution of some terrestrial and semi-aquatic mammals is partly determined by habitat preference and hydrology. Downstream sampling can recover most biodiversity across the catchment, but the inclusion of upstream samples can aid in detecting elusive species. This study underscores the broader applications of river eDNA beyond aquatic species and illustrates its potential use in addressing terrestrial mammal biodiversity monitoring objectives with tailored sampling approaches.

Most bats are insectivorous, but some species have evolved the ability to prey on fish. Although piscivory has been confirmed in the Rickett's big-footed myotis (Myotis pilosus), the extent of piscivory of other cohabiting Myotis species is uncertain. This study aims to explore the dietary niches and fish consumption of three Myotis species in a subtropical East Asian region, and specifically the fish diet of M. pilosus. Our findings reveal, for the first time, that M. pilosus consumes marine fishes, in contrast to previous research conducted in inland regions that suggested year-round consumption of cyprinids in freshwater habitats. We also observed seasonal variation in the diets of M. pilosus. It predominately hunted wide-banded hardyhead silverside [31% relative read abundance (RRA) of all 12S reads], sailfin flying fish, and shorthead anchovy during the wet season, while mainly preying upon mullets (31%) during the dry months. In more inland areas, M. pilosus was found to primarily feed on invasive freshwater poeciliids (13%). Furthermore, M. pilosus consumed more fish during the dry season, while there was a greater consumption of insects during the wet months. Most notably among our findings is the consumption of fish by two individuals of Horsfield's myotis (M. horsfieldii), indicating that the species is potentially piscivorous. We revealed that both M. horsfieldii and M. pilosus consumed water striders, suggesting that foraging of aquatic insects could be driving the evolution of fishing behavior. Our findings have also shed light on the flexibility of foraging behavior in piscivorous bats.

Effective monitoring strategies are key for aquatic species conservation, but traditional methods often require significant resources, especially in large aquatic systems. The emergence of quantitative environmental DNA (eDNA) is a promising alternative. Yet, few studies have evaluated the possibility of quantifying Atlantic salmon abundance with eDNA when individuals are at low abundance and occur in large river systems. In this study, we tested the efficacy of eDNA to monitor and quantify daily variation in smolt counts during their downstream migration in a large river system with low abundance of smolts. During the 2021 and 2022 downstream migrations, trap nets were used to conduct a daily census of smolts in the Romaine and Puyjalon rivers (Québec, Canada) while eDNA samples were collected daily over a transect perpendicular to the riverbank. Using quantitative real-time qPCR, we showed that discharge-corrected eDNA concentrations were positively correlated with daily smolt counts for both years. In addition, we found that controlling for temperature and precipitation improved model transferability between years, showing the importance of considering environmental correlates when using eDNA for abundance quantification. Finally, smolt counts were correlated with eDNA concentrations on the same day, but not with eDNA concentrations 1 or 2 days prior, highlighting the capacity of the model to track daily fluctuations in smolt abundance. Our results underscore the potential of using eDNA to monitor Atlantic salmon in large river systems with low smolt abundance when the river hydrology and environmental conditions are documented.

With increasing availability of ancient and modern environmental DNA technology, whole-community species occurrence and abundance data over time and space is becoming more available. Sedimentary ancient DNA data can be used to infer associations between species, which can generate hypotheses about biotic interactions, a key part of ecosystem function and biodiversity science. Here, we have developed a realistic simulation to evaluate five common methods from different fields for this type of inference. We find that across all methods tested, false discovery rates of interspecies associations are high under simulation conditions where the assumptions of the methods are violated in a variety of ecologically realistic ways. Additionally, we find that for more realistic simulation scenarios, with sample sizes that are currently realistic for this type of data, models are typically unable to detect interactions better than random assignment of associations. Different methods perform differentially well depending on the number of taxa in the dataset. Some methods (SPIEC-EASI, SparCC) assume that there are large numbers of taxa in the dataset, and we find that SPIEC-EASI is highly sensitive to this assumption while SparCC is not. Additionally, we find that for many methods, default calibration can result in high false discovery rates. We find that for small numbers of species, no method consistently outperforms logistic and linear regression, indicating a need for further testing and methods development.