Ecosystems monitoring powered by environmental genomics: A review of current strategies with an implementation roadmap

Taxonomic accuracy and complementarity between bulk and eDNA metabarcoding provides an alternative to morphology for biological assessment of freshwater macroinvertebrates

Determining biological status of freshwater ecosystems is critical for ensuring ecosystem health and maintaining associated services to such ecosystems. Freshwater macroinvertebrates respond predictably to environmental disturbances and are widely used in biomonitoring programs. However, many freshwater species are difficult to capture and sort from debris or substrate and morphological identification is challenging, especially larval stages, damaged specimens, or hyperdiverse groups such as Diptera. The advent of high throughput sequencing technologies has enhanced DNA barcoding tools to automatise species identification for whole communities, as metabarcoding is increasingly used to monitor biodiversity. However, recent comparisons have revealed little congruence between morphological and molecular-based identifications. Using broad range universal primers for DNA barcode marker cox1, we compare community composition captured between morphological and molecular-based approaches from different sources - tissue-based (bulk benthic and bulk drift samples) and environmental DNA (eDNA, filtered water) metabarcoding - for samples collected along a gradient of anthropogenic disturbances. For comparability, metabarcoding taxonomic assignments were filtered by taxa included in the standardised national biological metric IBMWP. At the family level, bulk benthic metabarcoding showed the highest congruence with morphology, and the most abundant taxa were captured by all techniques. Richness captured by morphology and bulk benthic metabarcoding decreased along the gradient, whereas richness recorded by eDNA remained constant and increased downstream when sequencing bulk drift. Estimates of biological metrics were higher using molecular than morphological identification. At species level, diversity captured by bulk benthic samples were higher than the other techniques. Importantly, bulk benthic and eDNA metabarcoding captured different and complementary portions of the community - benthic versus water column, respectively - and their combined use is recommended. While bulk benthic metabarcoding can likely replace morphology using similar benthic biological indices, water eDNA will require new metrics because this technique sequences a different portion of the community.

Assessing the potential of nematode metabarcoding for benthic monitoring of offshore oil platforms

Environmental DNA metabarcoding is gaining momentum as a time and cost-effective tool for biomonitoring and environmental impact assessment. Yet, its use as a replacement for the conventional marine benthic monitoring based on morphological analysis of macrofauna is still challenging. Here we propose to study the meiofauna, which is much better represented in sediment DNA samples. We focus on nematodes, which are the most numerous and diverse group of meiofauna. Our aim is to assess the potential of nematode metabarcoding to monitor impacts associated with offshore oil platform activities. To achieve this goal, we used nematode-optimized marker (18S V1V2-Nema) and universal eukaryotic marker (18S V9) region to analyse 252 sediment DNA samples collected near three offshore oil platforms in the North Sea. For both markers, we analysed changes in alpha and beta diversity in relation to distance from the platforms and environmental variables. We also defined three impact classes based on selected environmental variables that are associated with oil extraction activities and used random forest classifiers to compare the predictive performance of both datasets. Our results show that alpha- and beta-diversity of nematodes varies with the increasing distance from the platforms. The variables directly related to platform activity, such as Ba and THC, strongly influence the nematode community. The nematode metabarcoding data provide more robust predictive models than eukaryotic data. Furthermore, the nematode community appears more stable in time and space, as illustrated by the overlap of nematode datasets obtained from the same platform three years apart. A significative negative correlation between distance and Shannon diversity also advocates for higher performance of the V1V2-Nema over the V9. Overall, these results suggest that the sensitivity of nematodes is higher compared to the eukaryotic community. Hence, nematode metabarcoding has the potential to become an effective tool for benthic monitoring in marine environment.

Revolutionizing aquatic eco-environmental monitoring: Utilizing the RPA-Cas-FQ detection platform for zooplankton

The integration of recombinase polymerase amplification (RPA) with CRISPR/Cas technology has revolutionized molecular diagnostics and pathogen detection due to its unparalleled sensitivity and trans-cleavage ability. However, its potential in the ecological and environmental monitoring scenarios for aquatic ecosystems remains largely unexplored, particularly in accurate qualitative/quantitative detection, and its actual performance in handling complex real environmental samples. Using zooplankton as a model, we have successfully optimized the RPA-CRISPR/Cas12a fluorescence detection platform (RPA-Cas-FQ), providing several crucial "technical tips". Our findings indicate the sensitivity of CRISPR/Cas12a alone is 5 × 109 copies/reaction, which can be dramatically increased to 5 copies/reaction when combined with RPA. The optimized RPA-Cas-FQ enables reliable qualitative and semi-quantitative detection within 50 min, and exhibits a good linear relationship between fluorescence intensity and DNA concentration (R2 = 0.956-0.974***). Additionally, we developed a rapid and straightforward identification procedure for single zooplankton by incorporating heat-lysis and DNA-barcode techniques. We evaluated the platform's effectiveness using real environmental DNA (eDNA) samples from the Three Gorges Reservoir, confirming its practicality. The eDNA-RPA-Cas-FQ demonstrated strong consistency (Kappa = 0.43***) with eDNA-Metabarcoding in detecting species presence/absence in the reservoir. Furthermore, the two semi-quantitative eDNA technologies showed a strong positive correlation (R2 = 0.58-0.87***). This platform also has the potential to monitor environmental pollutants by selecting appropriate indicator species. The novel insights and methodologies presented in this study represent a significant advancement in meeting the complex needs of aquatic ecosystem protection and monitoring.

An integrated spatio-temporal view of riverine biodiversity using environmental DNA metabarcoding

Anthropogenically forced changes in global freshwater biodiversity demand more efficient monitoring approaches. Consequently, environmental DNA (eDNA) analysis is enabling ecosystem-scale biodiversity assessment, yet the appropriate spatio-temporal resolution of robust biodiversity assessment remains ambiguous. Here, using intensive, spatio-temporal eDNA sampling across space (five rivers in Europe and North America, with an upper range of 20-35 km between samples), time (19 timepoints between 2017 and 2018) and environmental conditions (river flow, pH, conductivity, temperature and rainfall), we characterise the resolution at which information on diversity across the animal kingdom can be gathered from rivers using eDNA. In space, beta diversity was mainly dictated by turnover, on a scale of tens of kilometres, highlighting that diversity measures are not confounded by eDNA from upstream. Fish communities showed nested assemblages along some rivers, coinciding with habitat use. Across time, seasonal life history events, including salmon and eel migration, were detected. Finally, effects of environmental conditions were taxon-specific, reflecting habitat filtering of communities rather than effects on DNA molecules. We conclude that riverine eDNA metabarcoding can measure biodiversity at spatio-temporal scales relevant to species and community ecology, demonstrating its utility in delivering insights into river community ecology during a time of environmental change.

Successive accumulation of biotic assemblages at a fine spatial scale along glacier-fed waters

Glacier-fed waters create strong environmental filtering for biota, whereby different organisms may assume distinct distribution patterns. By using environmental DNA-based metabarcoding, we investigated the multi-group biodiversity distribution patterns of the Parlung No. 4 Glacier, on the Tibetan Plateau. Altogether, 642 taxa were identified from the meltwater stream and the downstream Ranwu Lake, including 125 cyanobacteria, 316 diatom, 183 invertebrate, and 18 vertebrate taxa. As the distance increased from the glacier terminus, community complexity increased via sequential occurrences of cyanobacteria, diatoms, invertebrates, and vertebrates, as well as increasing taxa numbers. The stream and lake showed different community compositions and distinct taxa. Furthermore, the correlations with environmental factors and community assembly mechanisms showed group- and habitat-specific patterns. Our results reveal the rapid spatial succession and increasing community complexity along glacial flowpaths and highlight the varying adaptivity of different organisms, while also providing insight into the ecosystem responses to global change.

Digitalizing river aquatic ecosystems

Traditional river health assessment relies on limited water quality indices and representative organism activity, but does not comprehensively obtain biotic and abiotic information of the ecosystem. Here, we propose a new approach to evaluate the ecological and health risks of river aquatic ecosystems. First, detailed physicochemical and biological characterization of a river ecosystem can be obtained through pollutant determination (especially emerging pollutants) and DNA/RNA sequencing. Second, supervised machine learning can be applied to perform classification analysis of characterization data and ascertain river ecosystem ecology and health. Our proposed methodology transforms river ecosystem health assessment and can be applied in river management.

TICI: a taxon-independent community index for eDNA-based ecological health assessment

Global biodiversity is declining at an ever-increasing rate. Yet effective policies to mitigate or reverse these declines require ecosystem condition data that are rarely available. Morphology-based bioassessment methods are difficult to scale, limited in scope, suffer prohibitive costs, require skilled taxonomists, and can be applied inconsistently between practitioners. Environmental DNA (eDNA) metabarcoding offers a powerful, reproducible and scalable solution that can survey across the tree-of-life with relatively low cost and minimal expertise for sample collection. However, there remains a need to condense the complex, multidimensional community information into simple, interpretable metrics of ecological health for environmental management purposes. We developed a riverine taxon-independent community index (TICI) that objectively assigns indicator values to amplicon sequence variants (ASVs), and significantly improves the statistical power and utility of eDNA-based bioassessments. The TICI model training step uses the Chessman iterative learning algorithm to assign health indicator scores to a large number of ASVs that are commonly encountered across a wide geographic range. New sites can then be evaluated for ecological health by averaging the indicator value of the ASVs present at the site. We trained a TICI model on an eDNA dataset from 53 well-studied riverine monitoring sites across New Zealand, each sampled with a high level of biological replication (n = 16). Eight short-amplicon metabarcoding assays were used to generate data from a broad taxonomic range, including bacteria, microeukaryotes, fungi, plants, and animals. Site-specific TICI scores were strongly correlated with historical stream condition scores from macroinvertebrate assessments (macroinvertebrate community index or MCI; R2 = 0.82), and TICI variation between sample replicates was minimal (CV = 0.013). Taken together, this demonstrates the potential for taxon-independent eDNA analysis to provide a reliable, robust and low-cost assessment of ecological health that is accessible to environmental managers, decision makers, and the wider community.

Insights into freshwater ciliate diversity through high throughput DNA metabarcoding

The freshwater bodies of India are highly biodiverse but still understudied, especially concerning ciliates. Ciliates constitute a significant portion of eukaryotic diversity and play crucial roles in microbial loops, nutrient recycling, and ecosystem maintenance. The present study aimed to elucidate ciliate diversity in three freshwater sites in the Delhi region of India: Okhla Bird Sanctuary (OBS), Sanjay Lake (SL), and Raj Ghat pond (RJ). This study represents the first investigation into the taxonomic diversity and richness of freshwater ciliates in India using a high-throughput DNA metabarcoding approach. For the analysis, total environmental DNA was extracted from the three freshwater samples, followed by sequencing of the 18S V4 barcode region and subsequent phylogenetic analyses. Operational taxonomic units (OTU) analyses revealed maximum species diversity in OBS (106), followed by SL (104) and RJ (99) sites. Ciliates from the classes Oligohymenophorea, Prostomatea, and Spirotrichea were dominant in the three sites. The study discusses the ability of the metabarcoding approach to uncover unknown and rare species. The study highlights the need for refined reference databases and cautious interpretation of the high-throughput sequencing-generated data while emphasizing the complementary nature of molecular and morphological approaches in studying ciliate diversity.

Cutting edge tools in the field of soil microbiology

The study of the whole of the genetic material contained within the microbial populations found in a certain environment is made possible by metagenomics. This technique enables a thorough knowledge of the variety, function, and interactions of microbial communities that are notoriously difficult to research. Due to the limitations of conventional techniques such as culturing and PCR-based methodologies, soil microbiology is a particularly challenging field. Metagenomics has emerged as an effective technique for overcoming these obstacles and shedding light on the dynamic nature of the microbial communities in soil. This review focuses on the principle of metagenomics techniques, their potential applications and limitations in soil microbial diversity analysis. The effectiveness of target-based metagenomics in determining the function of individual genes and microorganisms in soil ecosystems is also highlighted. Targeted metagenomics, including high-throughput sequencing and stable-isotope probing, is essential for studying microbial taxa and genes in complex ecosystems. Shotgun metagenomics may reveal the diversity of soil bacteria, composition, and function impacted by land use and soil management. Sanger, Next Generation Sequencing, Illumina, and Ion Torrent sequencing revolutionise soil microbiome research. Oxford Nanopore Technology (ONT) and Pacific Biosciences (PacBio)'s third and fourth generation sequencing systems revolutionise long-read technology. GeoChip, clone libraries, metagenomics, and metabarcoding help comprehend soil microbial communities. The article indicates that metagenomics may improve environmental management and agriculture despite existing limitations.Metagenomics has revolutionised soil microbiology research by revealing the complete diversity, function, and interactions of microorganisms in soil. Metagenomics is anticipated to continue defining the future of soil microbiology research despite some limitations, such as the difficulty of locating the appropriate sequencing method for specific genes.

From biomarkers to community composition: Negative effects of UV/chlorine-treated reclaimed urban wastewater on freshwater biota

The use of urban wastewater reclaimed water has recently increased across the globe to restore stream environmental flows and mitigate the effects of water scarcity. Reclaimed water is disinfected using different treatments, but their effects into the receiving rivers are little studied. Physiological bioassays and biomarkers can detect sub-lethal effects on target species, but do not provide information on changes in community structure. In contrast, official monitoring programs use community structure information but often at coarse taxonomic resolution level that may fail to detect species level impacts. Here, we combined commonly used biomonitoring approaches from organism physiology to community species composition to scan a broad range of effects of disinfection of reclaimed water by UV-light only and both UV/chlorine on the biota. We (1) performed bioassays in one laboratory species (water flea Daphnia magna) and measured biomarkers in two wild species (caddisfly Hydropsyche exocellata and the barbel Luciobarbus graellsii), (2) calculated standard indices of biotic quality (IBQ) for diatoms, benthic macroinvertebrates, and fishes, and (3) analysed community species composition of eukaryotes determined by Cytochrome Oxidase C subunit I (cox1) metabarcoding. Only the UV/chlorine treatment caused significant changes in feeding rates of D. magna and reduced antioxidant defenses, increased anaerobic metabolism and altered the levels of lipid peroxidiation in H. exocellata. However, inputs of reclaimed water were significantly associated with a greater proportion of circulating neutrophils and LG-PAS cells in L. graellsii. Despite IBQ did not discriminate between the two water treatments, metabarcoding data detected community composition changes upon exposure to UV/chlorine reclaimed water. Overall, despite the effects of UV/chlorine-treated water were transient, our study suggests that UV-light treated is less harmful for freshwater biota than UV/chlorine-treated reclaimed water, but those effects depend of the organizational level.

Plant pangenomes for crop improvement, biodiversity and evolution

Plant genome sequences catalogue genes and the genetic elements that regulate their expression. Such inventories further research aims as diverse as mapping the molecular basis of trait diversity in domesticated plants or inquiries into the origin of evolutionary innovations in flowering plants millions of years ago. The transformative technological progress of DNA sequencing in the past two decades has enabled researchers to sequence ever more genomes with greater ease. Pangenomes - complete sequences of multiple individuals of a species or higher taxonomic unit - have now entered the geneticists' toolkit. The genomes of crop plants and their wild relatives are being studied with translational applications in breeding in mind. But pangenomes are applicable also in ecological and evolutionary studies, as they help classify and monitor biodiversity across the tree of life, deepen our understanding of how plant species diverged and show how plants adapt to changing environments or new selection pressures exerted by human beings.

Environmental DNA metabarcoding reveals the effect of environmental selection on phytoplankton community structure along a subtropical river

The Dongjiang River, a major tributary of the Pearl River system that supplies water to more than 40 million people in Guangdong Province and neighboring regions of China, harbors rich biodiversity, including many endemic and endangered species. However, human activities such as urbanization, agriculture, and industrialization have posed serious threats to its water quality and biodiversity. To assess the status and drivers of phytoplankton diversity, which is a key indicator of aquatic ecosystem health, this study used Environmental DNA (eDNA) metabarcoding combined with machine learning methods to explore spatial variations in the composition and structure of phytoplankton communities along the Dongjiang River, including its estuary. The results showed that phytoplankton diversity exhibited spatial distribution patterns, with higher community structure similarity and lower network complexity in the upstream than in the downstream regions. Environmental selection was the main mechanism shaping phytoplankton community composition, with natural factors driving the dominance of Pyrrophyta, Ochrophyta, and Cryptophyta in the upstream regions and estuaries. In contrast, the downstream regions was influenced by high concentrations of pollutants, resulting in increased abundance of Cryptophyta. The random forest model identified temperature, dissolved oxygen, chlorophyll a, NO2-, and NH4+ as the main factors influencing the primary phytoplankton communities and could be used to predict changes during wet periods. This study provides valuable insights into the factors influencing phytoplankton diversity and community composition in the Dongjiang River, and demonstrates the application value of eDNA metabarcoding technique in large-scale, long-distance river biodiversity monitoring.

Coupling remote sensing and eDNA to monitor environmental impact: A pilot to quantify the environmental benefits of sustainable agriculture in the Brazilian Amazon

Monitoring is essential to ensure that environmental goals are being achieved, including those of sustainable agriculture. Growing interest in environmental monitoring provides an opportunity to improve monitoring practices. Approaches that directly monitor land cover change and biodiversity annually by coupling the wall-to-wall coverage from remote sensing and the site-specific community composition from environmental DNA (eDNA) can provide timely, relevant results for parties interested in the success of sustainable agricultural practices. To ensure that the measured impacts are due to the environmental projects and not exogenous factors, sites where projects have been implemented should be benchmarked against counterfactuals (no project) and control (natural habitat) sites. Results can then be used to calculate diverse sets of indicators customized to monitor different projects. Here, we report on our experience developing and applying one such approach to assess the impact of shaded cocoa projects implemented by the Instituto de Manejo e Certificação Florestal e Agrícola (IMAFLORA) near São Félix do Xingu, in Pará, Brazil. We used the Continuous Degradation Detection (CODED) and LandTrendr algorithms to create a remote sensing-based assessment of forest disturbance and regeneration, estimate carbon sequestration, and changes in essential habitats. We coupled these remote sensing methods with eDNA analyses using arthropod-targeted primers by collecting soil samples from intervention and counterfactual pasture field sites and a control secondary forest. We used a custom set of indicators from the pilot application of a coupled monitoring framework called TerraBio. Our results suggest that, due to IMAFLORA's shaded cocoa projects, over 400 acres were restored in the intervention area and the community composition of arthropods in shaded cocoa is closer to second-growth forests than that of pastures. In reviewing the coupled approach, we found multiple aspects worked well, and we conclude by presenting multiple lessons learned.

The road forward to incorporate seawater microbes in predictive reef monitoring

Marine bacterioplankton underpin the health and function of coral reefs and respond in a rapid and sensitive manner to environmental changes that affect reef ecosystem stability. Numerous meta-omics surveys over recent years have documented persistent associations of opportunistic seawater microbial taxa, and their associated functions, with metrics of environmental stress and poor reef health (e.g. elevated temperature, nutrient loads and macroalgae cover). Through positive feedback mechanisms, disturbance-triggered heterotrophic activity of seawater microbes is hypothesised to drive keystone benthic organisms towards the limit of their resilience and translate into shifts in biogeochemical cycles which influence marine food webs, ultimately affecting entire reef ecosystems. However, despite nearly two decades of work in this space, a major limitation to using seawater microbes in reef monitoring is a lack of a unified and focused approach that would move beyond the indicator discovery phase and towards the development of rapid microbial indicator assays for (near) real-time reef management and decision-making. By reviewing the current state of knowledge, we provide a comprehensive framework (defined as five phases of research and innovation) to catalyse a shift from fundamental to applied research, allowing us to move from descriptive to predictive reef monitoring, and from reactive to proactive reef management.

Inferring the ecological and evolutionary determinants of community genetic diversity

Understanding the relative contributions of ecological and evolutionary processes to the structuring of ecological communities is needed to improve our ability to predict how communities may respond to future changes in an increasingly human-modified world. Metabarcoding methods make it possible to gather population genetic data for all species within a community, unlocking a new axis of data to potentially unveil the origins and maintenance of biodiversity at local scales. Here, we present a new eco-evolutionary simulation model for investigating community assembly dynamics using metabarcoding data. The model makes joint predictions of species abundance, genetic variation, trait distributions and phylogenetic relationships under a wide range of parameter settings (e.g. high speciation/low dispersal or vice versa) and across a range of community states, from pristine and unmodified to heavily disturbed. We first demonstrate that parameters governing metacommunity and local community processes leave detectable signatures in simulated biodiversity data axes. Next, using a simulation-based machine learning approach we show that neutral and non-neutral models are distinguishable and that reasonable estimates of several model parameters within the local community can be obtained using only community-scale genetic data, while phylogenetic information is required to estimate those describing metacommunity dynamics. Finally, we apply the model to soil microarthropod metabarcoding data from the Troodos mountains of Cyprus, where we find that communities in widespread forest habitats are structured by neutral processes, while high-elevation and isolated habitats act as an abiotic filter generating non-neutral community structure. We implement our model within the ibiogen R package, a package dedicated to the investigation of island, and more generally community-scale, biodiversity using community-scale genetic data.

Simultaneous analysis of seven 16S rRNA hypervariable gene regions increases efficiency in marine bacterial diversity detection

Environmental DNA sequencing is the gold standard to reveal microbial community structures. In most applications, a one-fragment PCR approach is applied to amplify a taxonomic marker gene, usually a hypervariable region of the 16S rRNA gene. We used a new reverse complement (RC)-PCR-based assay that amplifies seven out of the nine hypervariable regions of the 16S rRNA gene, to interrogate bacterial communities in sediment samples collected from different coastal marine sites with an impact gradient. In parallel, we employed a traditional one-fragment analysis of the hypervariable V3-V4 region to investigate whether the RC-PCR reveals more of the 'unseen' diversity obtained by the one-fragment approach. As a benchmark for the full deck of diversity, we subjected the samples to PCR-free metagenomic sequencing. None of the two PCR-based approaches recorded the full taxonomic repertoire obtained from the metagenomics datasets. However, the RC-PCR approach detected 2.8 times more bacterial genera compared to the near-saturation sequenced V3-V4 samples. RC-PCR is an ideal compromise between the standard one-fragment approach and metagenomics sequencing and may guide future environmental sequencing studies, in which bacterial diversity is a central subject.

Environmental DNA-based biomonitoring of Cuban Crocodylus and their accompanying vertebrate fauna from Zapata Swamp, Cuba

Crocodylians globally face considerable challenges, including population decline and extensive habitat modification. Close monitoring of crocodylian populations and their habitats is imperative for the timely detection of population trends, especially in response to management interventions. Here we use eDNA metabarcoding to identify the Critically Endangered Crocodylus rhombifer and the Vulnerable C. acutus, as well as vertebrate community diversity, in Cuba's Zapata Swamp. We tested four different primer sets, including those used previously in Crocodylus population genetic and phylogenetic research, for their efficiency at detecting crocodylian eDNA. We detected C. rhombifer eDNA in 11 out of 15 sampled locations within its historical geographic distribution. We found that data analyses using the VertCOI primers and the mBRAVE bioinformatics pipeline were the most effective molecular marker and pipeline combination for identifying this species from environmental samples. We also identified 55 vertebrate species in environmental samples across the four bioinformatics pipelines- ~ 85% known to be present in the Zapata ecosystem. Among them were eight species previously undetected in the area and eight alien species, including known predators of hatchling crocodiles (e.g., Clarias sp.) and egg predators (e.g., Mus musculus). This study highlights eDNA metabarcoding as a powerful tool for crocodylian biomonitoring within fragile and diverse ecosystems, particularly where fast, non-invasive methods permit detection in economically important areas and will lead to a better understanding of complex human-crocodile interactions and evaluate habitat suitability for potential reintroductions or recovery programs for threatened crocodylian species.

New deep learning-based methods for visualizing ecosystem properties using environmental DNA metabarcoding data

Environmental DNA (eDNA) metabarcoding provides an efficient approach for documenting biodiversity patterns in marine and terrestrial ecosystems. The complexity of these data prevents current methods from extracting and analyzing all the relevant ecological information they contain, and new methods may provide better dimensionality reduction and clustering. Here we present two new deep learning-based methods that combine different types of neural networks (NNs) to ordinate eDNA samples and visualize ecosystem properties in a two-dimensional space: the first is based on variational autoencoders and the second on deep metric learning. The strength of our new methods lies in the combination of two inputs: the number of sequences found for each molecular operational taxonomic unit (MOTU) detected and their corresponding nucleotide sequence. Using three different datasets, we show that our methods accurately represent several biodiversity indicators in a two-dimensional latent space: MOTU richness per sample, sequence α-diversity per sample, Jaccard's and sequence β-diversity between samples. We show that our nonlinear methods are better at extracting features from eDNA datasets while avoiding the major biases associated with eDNA. Our methods outperform traditional dimension reduction methods such as Principal Component Analysis, t-distributed Stochastic Neighbour Embedding, Nonmetric Multidimensional Scaling and Uniform Manifold Approximation and Projection for dimension reduction. Our results suggest that NNs provide a more efficient way of extracting structure from eDNA metabarcoding data, thereby improving their ecological interpretation and thus biodiversity monitoring.

Creating, curating and evaluating a mitogenomic reference database to improve regional species identification using environmental DNA

Species detection using eDNA is revolutionizing global capacity to monitor biodiversity. However, the lack of regional, vouchered, genomic sequence information-especially sequence information that includes intraspecific variation-creates a bottleneck for management agencies wanting to harness the complete power of eDNA to monitor taxa and implement eDNA analyses. eDNA studies depend upon regional databases of mitogenomic sequence information to evaluate the effectiveness of such data to detect and identify taxa. We created the Oregon Biodiversity Genome Project to create a database of complete, nearly error-free mitogenomic sequences for all of Oregon's fishes. We have successfully assembled the complete mitogenomes of 313 specimens of freshwater, anadromous and estuarine fishes representing 24 families, 55 genera and 129 species and lineages. Comparative analyses of these sequences illustrate that many regions of the mitogenome are taxonomically informative, that the short (~150 bp) mitochondrial 'barcode' regions typically used for eDNA assays do not consistently diagnose for species and that complete single or multiple genes of the mitogenome are preferable for identifying Oregon's fishes. This project provides a blueprint for other researchers to follow as they build regional databases, illustrates the taxonomic value and limits of complete mitogenomic sequences and offers clues as to how current eDNA assays and environmental genomics methods of the future can best leverage this information.

Integrated Biogeography and Assembly Mechanisms of Microeukaryotic Communities in Coastal Waters Near Shellfish Cultivation

The Lianjiang coast in the East China Sea is a typical subtropical marine ecosystem, and shellfish cultivation occupies almost all of the available tidal flats. Many studies have investigated the effects of shellfish cultivation on benthic organisms and sediments, while the impact of shellfish cultivation on plankton ecosystems is still poorly understood. This study investigated the biogeographical patterns of microeukaryotic communities from Lianjiang coastal waters in four seasons using 18S ribosomal RNA gene amplicon sequencing. Microeukaryotes were mainly comprised of Dinoflagellata, Diatomea, Arthropoda, Ciliophora, Chlorophyta, Protalveolata, Cryptophyceae, and Ochrophyta, and presented significant differences in three habitats (the aquaculture area, confluent area, and offshore area) and four seasons. Similarity percentage analysis revealed that Paracalanus parvus, Heterocapsa rotundata, Bestiolina similis, and five additional key taxa contributed to spatio-temporal differences. Seasonal environmental and spatial factors explained 27.47% of microeukaryotic community variation on average, with 11.11% of the variation shared. Environmental variables, particularly depth, pH, and nitrite concentration, were strongly associated with the microeukaryotic community compositions. The neutral community model further demonstrated that stochastic processes were sufficient in shaping substantial variation in microeukaryotic communities across four seasons, which may reveal the remaining unexplained microeukaryotic community variation. We further divided four seasons into the aquaculture stages and non-aquaculture stages, and speculated that aquaculture activities may increase the dispersal limitation of microeukaryotes in coastal waters, especially for the big bodied-microbes like Arthropoda. The results provide a better understanding of the biogeographical patterns, processes, and mechanisms of microeukaryotic communities near shellfish cultivation.

Microbial biomarkers of tree water status for next-generation biomonitoring of forest ecosystems

Next-generation biomonitoring proposes to combine machine-learning algorithms with environmental DNA data to automate the monitoring of the Earth's major ecosystems. In the present study, we searched for molecular biomarkers of tree water status to develop next-generation biomonitoring of forest ecosystems. Because phyllosphere microbial communities respond to both tree physiology and climate change, we investigated whether environmental DNA data from tree phyllosphere could be used as molecular biomarkers of tree water status in forest ecosystems. Using an amplicon sequencing approach, we analysed phyllosphere microbial communities of four tree species (Quercus ilex, Quercus robur, Pinus pinaster and Betula pendula) in a forest experiment composed of irrigated and non-irrigated plots. We used these microbial community data to train a machine-learning algorithm (Random Forest) to classify irrigated and non-irrigated trees. The Random Forest algorithm detected tree water status from phyllosphere microbial community composition with more than 90% accuracy for oak species, and more than 75% for pine and birch. Phyllosphere fungal communities were more informative than phyllosphere bacterial communities in all tree species. Seven fungal amplicon sequence variants were identified as candidates for the development of molecular biomarkers of water status in oak trees. Altogether, our results show that microbial community data from tree phyllosphere provides information on tree water status in forest ecosystems and could be included in next-generation biomonitoring programmes that would use in situ, real-time sequencing of environmental DNA to help monitor the health of European temperate forest ecosystems.

Monitoring Endangered and Rare Wildlife in the Field: A Foundation Deep Learning Model Integrating Human Knowledge for Incremental Recognition with Few Data and Low Cost

Intelligent monitoring of endangered and rare wildlife is important for biodiversity conservation. In practical monitoring, few animal data are available to train recognition algorithms. The system must, therefore, achieve high accuracy with limited resources. Simultaneously, zoologists expect the system to be able to discover unknown species to make significant discoveries. To date, none of the current algorithms have these abilities. Therefore, this paper proposed a KI-CLIP method. Firstly, by first introducing CLIP, a foundation deep learning model that has not yet been applied in animal fields, the powerful recognition capability with few training resources is exploited with an additional shallow network. Secondly, inspired by the single-image recognition abilities of zoologists, we incorporate easily accessible expert description texts to improve performance with few samples. Finally, a simple incremental learning module is designed to detect unknown species. We conducted extensive comparative experiments, ablation experiments, and case studies on 12 datasets containing real data. The results validate the effectiveness of KI-CLIP, which can be trained on multiple real scenarios in seconds, achieving in our study over 90% recognition accuracy with only 8 training samples, and over 97% with 16 training samples. In conclusion, KI-CLIP is suitable for practical animal monitoring.

Fuzzy logic, geostatistics, and multiple linear models to evaluate irrigation metrics and their influencing factors in a drought-prone agricultural region

The quality of water used for irrigation is one of the major threats to maintaining the long-term sustainability of agricultural practices. Although some studies have addressed the suitability of irrigation water in different parts of Bangladesh, the irrigation water quality in the drought-prone region has yet to be thoroughly studied using integrated novel approaches. This study aims to assess the suitability of irrigation water in the drought-prone agricultural region of Bangladesh using traditional irrigation metrics such as sodium percentage (NA%), magnesium adsorption ratio (MAR), Kelley's ratio (KR), sodium adsorption ratio (SAR), total hardness (TH), permeability index (PI), and soluble sodium percentage (SSP), along with novel irrigation indices such as irrigation water quality index (IWQI) and fuzzy irrigation water quality index (FIWQI). Thirty-eight water samples were taken from tube wells, river systems, streamlets, and canals in agricultural areas, then analyzed for cations and anions. The multiple linear regression model predicted that SAR (0.66), KR (0.74), and PI (0.84) were the primary important elements influencing electrical conductivity (EC). Based on the IWQI, all water samples fall into the "suitable" category for irrigation. The FIWQI suggests that 75% of the groundwater and 100% of the surface water samples are excellent for irrigation. The semivariogram model indicates that most irrigation metrics have moderate to low spatial dependence, suggesting strong agricultural and rural influence. Redundancy analysis shows that Na+, Ca2+, Cl-, K+, and HCO3- in water increase with decreasing temperature. Surface water and some groundwater in the southwestern and southeastern parts are suitable for irrigation. The northern and central parts are less suitable for agriculture because of elevated K+ and Mg2+ levels. This study determines irrigation metrics for regional water management and pinpoints suitable areas in the drought-prone region, which provides a comprehensive understanding of sustainable water management and actionable steps for stakeholders and decision-makers.

Flushing away the future: The effects of wastewater treatment plants on aquatic invertebrates

Wastewater treatment plants (WWTP) are essential infrastructure in our developing world. However, with the development and release of novel entities and without modern upgrades, they are ineffective at fully removing micropollutants before treated effluents are released back into aquatic environments. Thus, WWTPs may represent additional point source impacts to freshwater environments, further pressuring aquatic fauna and already vulnerable insect communities. Previous studies - mostly focusing on single WWTPs - have shown general trends of freshwater invertebrate communities becoming dominated by pollution tolerant taxa. To expand on these findings, the current study is the first to comprehensively investigate data on the effects of 170 WWTPs on invertebrate taxonomic composition. We compared data for several diversity and pollution indices, as well as the taxonomic composition both upstream and downstream of the WWTPs (366 sampling sites). In terms of abundance, the three most frequent and negatively impacted orders were the Plecoptera, Trichoptera and Gastropoda, while the Turbellaria, Hirudinea and Crustacea increased in abundance. Although strong changes in community composition were observed between upstream and downstream sites (mean species turnover of 61%), commonly used diversity indices were not sensitive to these changes, highlighting their potential inadequacy in accurately assessing ecological health. Our results indicate that WWTPs change downstream conditions in favour of pollution tolerant taxa to the detriment of sensitive taxa. Order-level taxonomic responses can be informative but should be interpreted with caution, since they can be driven by a few taxa, or opposing responses of species in the same group can result in an overall low order-level response. Upgrading WWTPs via additional treatment steps or merging may be beneficial, provided upstream sections are unimpacted and/or are in a good chemical and structural condition.

A combination of machine-learning and eDNA reveals the genetic signature of environmental change at the landscape levels

The current advances of environmental DNA (eDNA) bring profound changes to ecological monitoring and provide unique insights on the biological diversity of ecosystems. The very nature of eDNA data is challenging yet also revolutionizing how biological monitoring information is analysed. In particular, new metrics and approaches should take full advantage of the extent and detail of molecular data produced by genetic methods. In this perspective, machine learning algorithms are particularly promising as they can capture complex relationships between the multiple environmental pressures and the diversity of biological communities. We investigated the potential of a new generation of biomonitoring tools that implement machine-learning techniques to fully exploit eDNA datasets. We trained a machine learning model to discriminate between reference and impacted communities of freshwater macroinvertebrates and assessed its performances using a large eDNA dataset collected at 64 standard federal monitoring sites across Switzerland. We show that a model trained on eDNA is significantly better than a naive model and performs similarly to a model trained on traditional data. Our proof-of-concept shows that such a combination of eDNA and machine learning approaches has the potential to complement or even replace traditional environmental monitoring, and could be scaled along temporal or spatial dimensions.

Climate Change Causes Salinity To Become Determinant in Shaping the Microeukaryotic Spatial Distribution among the Lakes of the Inner Mongolia-Xinjiang Plateau

Climate change greatly affects lake microorganisms in arid and semiarid zones, which alters ecosystem functions and the ecological security of lakes. However, the responses of lake microorganisms, especially microeukaryotes, to climate change are poorly understood. Here, using 18S ribosomal RNA (rRNA) high-throughput sequencing, we investigated the distribution patterns of microeukaryotic communities and whether and how climate change directly or indirectly affected the microeukaryotic communities on the Inner Mongolia-Xinjiang Plateau. Our results showed that climate change, as the main driving force of lake change, drives salinity to become a determinant of the microeukaryotic community among the lakes of the Inner Mongolia-Xinjiang Plateau. Salinity shapes the diversity and trophic level of the microeukaryotic community and further affects lake carbon cycling. Co-occurrence network analysis further revealed that increasing salinity reduced the complexity but improved the stability of microeukaryotic communities and changed ecological relationships. Meanwhile, increasing salinity enhanced the importance of deterministic processes in microeukaryotic community assembly, and the dominance of stochastic processes in freshwater lakes transformed into deterministic processes in salt lakes. Furthermore, we established lake biomonitoring and climate sentinel models by integrating microeukaryotic information, which would provide substantial improvements to our predictive ability of lake responses to climate change. IMPORTANCE Our findings have important implications for understanding the distribution patterns and the driving mechanisms of microeukaryotic communities among the lakes of the Inner Mongolia-Xinjiang Plateau and whether and how climate change directly or indirectly affects microeukaryotic communities. Our study also establishes the groundwork to use the lake microbiome for the assessment of aquatic ecological health and climate change, which is critical for ecosystem management and for projecting the ecological consequences of future climate warming.

It Is Not Only Data-Freshwater Invertebrates Misused in Biological Monitoring

The article presents and discusses the issues of the use of free-living invertebrates to assess the ecological status of freshwater environments with different methods of biological monitoring. Invertebrates are excluded from ethical consideration in the procedures of environmental protection, which results in the killing of many more individuals during sampling than necessary. Biomonitoring is used as a routine method for environmental protection that results in the cruel death of even millions of aquatic animals annually. In many cases, the mortality of animals used in such types of activities has been shown as excessive, e.g., because the vast majority die due to unnecessary subsampling procedures. Improperly planned and conducted procedures which result in excessive mortality have or may have a negative impact on the environment and biodiversity. Their existence as sensitive beings is reduced to an information function; they become only data useful for biomonitoring purposes. The main problem when trying to determine the mortality of invertebrates due to biomonitoring activities and its impact on natural populations seems to be the lack of access to raw data presenting how many animals were killed during sampling.

Amplicon sequencing allows differential quantification of closely related parasite species: an example from rodent Coccidia (Eimeria)

BACKGROUND

Quantifying infection intensity is a common goal in parasitological studies. We have previously shown that the amount of parasite DNA in faecal samples can be a biologically meaningful measure of infection intensity, even if it does not agree well with complementary counts of transmission stages (oocysts in the case of Coccidia). Parasite DNA can be quantified at relatively high throughput using quantitative polymerase chain reaction (qPCR), but amplification needs a high specificity and does not simultaneously distinguish between parasite species. Counting of amplified sequence variants (ASVs) from high-throughput marker gene sequencing using a relatively universal primer pair has the potential to distinguish between closely related co-infecting taxa and to uncover the community diversity, thus being both more specific and more open-ended.

METHODS

We here compare qPCR to the sequencing-based amplification using standard PCR and a microfluidics-based PCR to quantify the unicellular parasite Eimeria in experimentally infected mice. We use multiple amplicons to differentially quantify Eimeria spp. in a natural house mouse population.

RESULTS

We show that sequencing-based quantification has high accuracy. Using a combination of phylogenetic analysis and the co-occurrence network, we distinguish three Eimeria species in naturally infected mice based on multiple marker regions and genes. We investigate geographical and host-related effects on Eimeria spp. community composition and find, as expected, prevalence to be largely explained by sampling locality (farm). Controlling for this effect, the novel approach allowed us to find body condition of mice to be negatively associated with Eimeria spp. abundance.

CONCLUSIONS

We conclude that amplicon sequencing provides the underused potential for species distinction and simultaneous quantification of parasites in faecal material. The method allowed us to detect a negative effect of Eimeria infection on the body condition of mice in the natural environment.

Comparing quantile regression spline analyses and supervised machine learning for environmental quality assessment at coastal marine aquaculture installations

Organic enrichment associated with marine finfish aquaculture is a local stressor of marine coastal ecosystems. To maintain ecosystem services, the implementation of biomonitoring programs focusing on benthic diversity is required. Traditionally, impact-indices are determined by extracting and identifying benthic macroinvertebrates from samples. However, this is a time-consuming and expensive method with low upscaling potential. A more rapid, inexpensive, and robust method to infer the environmental quality of marine environments is eDNA metabarcoding of bacterial communities. To infer the environmental quality of coastal habitats from metabarcoding data, two taxonomy-free approaches have been successfully applied for different geographical regions and monitoring goals, namely quantile regression splines (QRS) and supervised machine learning (SML). However, their comparative performance remains untested for monitoring the impact of organic enrichment introduced by aquaculture on marine coastal environments. We compared the performance of QRS and SML using bacterial metabarcoding data to infer the environmental quality of 230 aquaculture samples collected from seven farms in Norway and seven farms in Scotland along an organic enrichment gradient. As a measure of environmental quality, we used the Infaunal Quality Index (IQI) calculated from benthic macrofauna data (reference index). The QRS analysis plotted the abundance of amplicon sequence variants (ASVs) as a function to the IQI from which the ASVs with a defined abundance peak were assigned to eco-groups and a molecular IQI was subsequently calculated. In contrast, the SML approach built a random forest model to directly predict the macrofauna-based IQI. Our results show that both QRS and SML perform well in inferring the environmental quality with 89% and 90% accuracy, respectively. For both geographic regions, there was high correspondence between the reference IQI and both the inferred molecular IQIs (p < 0.001), with the SML model showing a higher coefficient of determination compared to QRS. Among the 20 most important ASVs identified by the SML approach, 15 were congruent with the good quality spline ASV indicators identified via QRS for both Norwegian and Scottish salmon farms. More research on the response of the ASVs to organic enrichment and the co-influence of other environmental parameters is necessary to eventually select the most powerful stressor-specific indicators. Even though both approaches are promising to infer environmental quality based on metabarcoding data, SML showed to be more powerful in handling the natural variability. For the improvement of the SML model, addition of new samples is still required, as background noise introduced by high spatio-temporal variability can be reduced. Overall, we recommend the development of a powerful SML approach that will be onwards applied for monitoring the impact of aquaculture on marine ecosystems based on eDNA metabarcoding data.

A Decade of Death and Other Dynamics: Deepening Perspectives on the Diversity and Distribution of Sea Stars and Wasting

AbstractMass mortality events provide valuable insight into biological extremes and also ecological interactions more generally. The sea star wasting epidemic that began in 2013 catalyzed study of the microbiome, genetics, population dynamics, and community ecology of several high-profile species inhabiting the northeastern Pacific but exposed a dearth of information on the diversity, distributions, and impacts of sea star wasting for many lesser-known sea stars and a need for integration across scales. Here, we combine datasets from single-site to coast-wide studies, across time lines from weeks to decades, for 65 species. We evaluated the impacts of abiotic characteristics hypothetically associated with sea star wasting (sea surface temperature, pelagic primary productivity, upwelling wind forcing, wave exposure, freshwater runoff) and species characteristics (depth distribution, developmental mode, diet, habitat, reproductive period). We find that the 2010s sea star wasting outbreak clearly affected a little over a dozen species, primarily intertidal and shallow subtidal taxa, causing instantaneous wasting prevalence rates of 5%-80%. Despite the collapse of some populations within weeks, environmental and species variation protracted the outbreak, which lasted 2-3 years from onset until declining to chronic background rates of ∼2% sea star wasting prevalence. Recruitment began immediately in many species, and in general, sea star assemblages trended toward recovery; however, recovery was heterogeneous, and a marine heatwave in 2019 raised concerns of a second decline. The abiotic stressors most associated with the 2010s sea star wasting outbreak were elevated sea surface temperature and low wave exposure, as well as freshwater discharge in the north. However, detailed data speaking directly to the biological, ecological, and environmental cause(s) and consequences of the sea star wasting outbreak remain limited in scope, unavoidably retrospective, and perhaps always indeterminate. Redressing this shortfall for the future will require a broad spectrum of monitoring studies not less than the taxonomically broad cross-scale framework we have modeled in this synthesis.

Environmental DNA metabarcoding reveals the influence of human activities on microeukaryotic plankton along the Chinese coastline

Microeukaryotic plankton, with its extremely diverse taxa, is a key component in both the marine food web and biogeochemical cycling. Coastal seas, which are home to the numerous microeukaryotic plankton that underpin the functions of these aquatic ecosystems, are often impacted by human activities. However, understanding the biogeographical patterns of diversity and community structure of microeukaryotic plankton and the role that major shaping factors play at the continent scale is still a challenge in coastal ecology. Here, the biogeographic patterns of biodiversity, community structure, and co-occurrence patterns were investigated by environmental DNA (eDNA) based approaches. Unlike most eDNA studies, we combined several methods (in silico PCR, mock and environmental communities) to systematically evaluate the specificity and coverage of primers to overcome the limitation of marker selection on biodiversity recovery. The 1380F/1510R primer set showed the best performance for the amplification of coastal plankton with the highest coverage, sensitivity, and resolution. We showed a unimodal pattern for planktonic alpha diversity with latitude (P < 0.001), and nutrient-related factors (NO₃N, NO₂N, and NH₄N) were the leading predictors for spatial patterning. Significant regional biogeographic patterns and potential drivers for planktonic communities were found across coastal regions. All communities generally fitted the regional distance-decay relationship (DDR) model with the strongest spatial turnover rate was found in the Yalujiang (YLJ) estuary (P < 0.001). The environmental factors, especially inorganic nitrogen and heavy metals (HMs), had the greatest impact on planktonic community similarity in the Beibu Bay (BB) and East China Sea (ECS). Furthermore, we observed spatial plankton co-occurrence patterns, and the networked topology and structure were strongly driven by potential anthropogenic activity factors (nutrients and HMs). Overall, our study provided a systematic approach for metabarcode primer selection in eDNA-based biodiversity monitoring and revealed that the spatial pattern of the microeukaryotic plankton community was mainly controlled by regional human activity-related factors.

Application of fuzzy C-means clustering and fuzzy EDAS to assess groundwater irrigation suitability and prioritization for agricultural development in a complex hydrogeological basin

The current research is focused on detecting a river basin suitable for agriculture and priority for management using a new clustering tool of groundwater quality with fuzzy logic technique in R and Geographical Information System. A new fuzzy clustering-soft computing technique has been executed to determine the different hydrochemical zones considering 13 essential parameters such as electrical conductivity, hardness, chloride, sodium adsorption ratio, residual sodium carbonate, soluble sodium percent, magnesium hazard, permeability index, potential salinity, residual sodium bicarbonate, Kelly's ratio, synthetic harmful coefficient, and exchangeable sodium percentage. The derived fuzzy C-mean clustering (FCM) outperformed other available hard computing techniques like hierarchical clustering, K-means clustering, and agglomerative clustering. It divided the sampling sites into 2 clustering groups (FCM I and FCM II) which has been validated using fuzzy silhouette index (0.85), the partition coefficient (0.76), the partial entropy (0.68), and the modified partition coefficient (0.52). The hydrogeochemical analysis confirmed that the rock-water interaction, chemical weathering, and ion exchange process are predominant in the aquifer system of the study area. According to the correlation plots, the studied groundwater samples largely evolved from [Formula: see text], mixed [Formula: see text] types, and [Formula: see text] types. The spatial distribution map and the hydrochemical analysis also gives a clear depiction of the fluoride (> 1.0 mg/l) and high iron (> 0.3 mg/l) contamination in groundwater quality, making it unsuitable for both drinking and irrigation. A fuzzy EDAS priority map has been prepared based on all the irrigation suitability parameters which concludes that the groundwater at the upstream and downstream section of the basin requires the most attention. Based on the highest priority for management, five zones have been delineated: very high (5.98%), high (22.31%), medium (16.39%), low (32.30%), and very low (23.02). The findings of this study will be beneficial to planners and policymakers as they can develop schemes to solve similar problems across the country.

Using Machine Learning to make nanomaterials sustainable

Sustainable development is a key challenge for contemporary human societies; failure to achieve sustainability could threaten human survival. In this review article, we illustrate how Machine Learning (ML) could support more sustainable development, covering the basics of data gathering through each step of the Environmental Risk Assessment (ERA). The literature provides several examples showing how ML can be employed in most steps of a typical ERA.A key observation is that there are currently no clear guidance for using such autonomous technologies in ERAs or which standards/checks are required. Steering thus seems to be the most important task for supporting the use of ML in the ERA of nano- and smart-materials. Resources should be devoted to developing a strategy for implementing ML in ERA with a strong emphasis on data foundations, methodologies, and the related sensitivities/uncertainties. We should recognise historical errors and biases (e.g., in data) to avoid embedding them during ML programming.

Encapsulated in sediments: eDNA deciphers the ecosystem history of one of the most polluted European marine sites

The Anthropocene is characterized by dramatic ecosystem changes driven by human activities. The impact of these activities can be assessed by different geochemical and paleontological proxies. However, each of these proxies provides only a fragmentary insight into the effects of anthropogenic impacts. It is highly challenging to reconstruct, with a holistic view, the state of the ecosystems from the preindustrial period to the present day, covering all biological components, from prokaryotes to multicellular eukaryotes. Here, we used sedimentary ancient DNA (sedaDNA) archives encompassing all trophic levels of biodiversity to reconstruct the two century-natural history in Bagnoli-Coroglio (Gulf of Pozzuoli, Tyrrhenian Sea), one of the most polluted marine-coastal sites in Europe. The site was characterized by seagrass meadows and high eukaryotic diversity until the beginning of the 20th century. Then, the ecosystem completely changed, with seagrasses and associated fauna as well as diverse groups of planktonic and benthic protists being replaced by low diversity biota dominated by dinophyceans and infaunal metazoan species. The sedaDNA analysis revealed a five-phase evolution of the area, where changes appear as the result of a multi-level cascade effect of impacts associated with industrial activities, urbanization, water circulation and land-use changes. The sedaDNA allowed to infer reference conditions that must be considered when restoration actions are to be implemented.

Are genomic indices effective alternatives to morphology based benthic indices in biomonitoring studies? Perspectives from a major harbour and marine protected area

Ecological assessments are currently being conducted by traditional morpho-taxonomical identification techniques that are time-consuming and often inaccurate. Biomonitoring programs are increasingly being complemented by the more rapid and efficient DNA barcoding approach. We compared the congruency of morpho-taxonomic (AMBI - AZTI's Marine Biotic Index) and genomic (gAMBI) benthic indices in ecological quality status (EcoQS) assignation in Mumbai harbour and Malvan Marine Protected area (MPA). The study, first of its kind to adopt the gAMBI tool in the selected milieu, contributed substantial number of macrobenthic cytochrome c oxidase subunit I gene (COI) sequences that were previously unavailable in the reference library, adding sufficient genetic resources for establishing ecostatus. AMBI and gAMBI values based on presence/absence data related significantly with those derived from abundance data matrices. Taxonomic and genomic indices derived ecostatus corresponded sufficiently well despite minor discrepancies, underscoring the viability of gAMBI as a superior alternative to AMBI in monitoring studies.

Net overboard: Comparing marine eDNA sampling methodologies at sea to unravel marine biodiversity

Environmental DNA (eDNA) analyses are powerful for describing marine biodiversity but must be optimized for their effective use in routine monitoring. To maximize eDNA detection probabilities of sparsely distributed populations, water samples are usually concentrated from larger volumes and filtered using fine-pore membranes, often a significant cost-time bottleneck in the workflow. This study aimed to streamline eDNA sampling by investigating plankton net versus bucket sampling, direct versus sequential filtration including self-preserving filters. Biodiversity was assessed using metabarcoding of the small ribosomal subunit (18S rRNA) and mitochondrial cytochrome c oxidase I (COI) genes. Multispecies detection probabilities were estimated for each workflow using a probabilistic occupancy modelling approach. Significant workflow-related differences in biodiversity metrics were reported. Highest amplicon sequence variant (ASV) richness was attained by the bucket sampling combined with self-preserving filters, comprising a large portion of microplankton. Less diversity but more metazoan taxa were captured in the net samples combined with 5 μm pore size filters. Prefiltered 1.2 μm samples yielded few or no unique ASVs. The highest average (~32%) metazoan detection probabilities in the 5 μm pore size net samples confirmed the effectiveness of preconcentration plankton for biodiversity screening. These results contribute to streamlining eDNA sampling protocols for uptake and implementation in marine biodiversity research and surveillance.

VTAM: A robust pipeline for validating metabarcoding data using controls

To obtain accurate estimates for biodiversity and ecological studies, metabarcoding studies should be carefully designed to minimize both false positive (FP) and false negative (FN) occurrences. Internal controls (mock samples and negative controls), replicates, and overlapping markers allow controlling metabarcoding errors but current metabarcoding software packages do not explicitly integrate these additional experimental data to optimize filtering. We have developed the metabarcoding analysis software VTAM, which uses explicitly these elements of the experimental design to find optimal parameter settings that minimize FP and FN occurrences. VTAM showed similar sensitivity, but a higher precision compared to two other pipelines using three datasets and two different markers (COI, 16S). The stringent filtering procedure implemented in VTAM aims to produce robust metabarcoding data to obtain accurate ecological estimates and represents an important step towards a non-arbitrary and standardized validation of metabarcoding data for conducting ecological studies. VTAM is implemented in Python and available from: https://github.com/aitgon/vtam. The VTAM benchmark code is available from: https://github.com/aitgon/vtam_benchmark.

Functional responses of key marine bacteria to environmental change – toward genetic counselling for coastal waters

Coastal ecosystems deteriorate globally due to human-induced stress factors, like nutrient loading and pollution. Bacteria are critical to marine ecosystems, e.g., by regulating nutrient cycles, synthesizing vitamins, or degrading pollutants, thereby providing essential ecosystem services ultimately affecting economic activities. Yet, until now bacteria are overlooked both as mediators and indicators of ecosystem health, mainly due to methodological limitations in assessing bacterial ecosystem functions. However, these limitations are largely overcome by the advances in molecular biology and bioinformatics methods for characterizing the genetics that underlie functional traits of key bacterial populations – “key” in providing important ecosystem services, being abundant, or by possessing high metabolic rates. It is therefore timely to analyze and define the functional responses of bacteria to human-induced effects on coastal ecosystem health. We posit that categorizing the responses of key marine bacterial populations to changes in environmental conditions through modern microbial oceanography methods will allow establishing the nascent field of genetic counselling for our coastal waters. This requires systematic field studies of linkages between functional traits of key bacterial populations and their ecosystem functions in coastal seas, complemented with systematic experimental analyses of the responses to different stressors. Research and training in environmental management along with dissemination of results and dialogue with societal actors are equally important to ensure the role of bacteria is understood as fundamentally important for coastal ecosystems. Using the responses of microorganisms as a tool to develop genetic counselling for coastal ecosystems can ultimately allow for integrating bacteria as indicators of environmental change.

The biospheric emergency calls for scientists to change tactics

Our current economic and political structures have an increasingly devastating impact on the Earth's climate and ecosystems: we are facing a biospheric emergency, with catastrophic consequences for both humans and the natural world on which we depend. Life scientists - including biologists, medical scientists, psychologists and public health experts - have had a crucial role in documenting the impacts of this emergency, but they have failed to drive governments to take action in order to prevent the situation from getting worse. Here we, as members of the movement Scientist Rebellion, call on life scientists to re-embrace advocacy and activism - which were once hallmarks of academia - in order to highlight the urgency and necessity of systemic change across our societies. We particularly emphasise the need for scientists to engage in nonviolent civil resistance, a form of public engagement which has proven to be highly effective in social struggles throughout history.

Environmental DNA-based profiling of benthic bacterial and eukaryote communities along a crude oil spill gradient in a coral reef in the Persian Gulf

Coral reef ecosystems in the Persian Gulf are frequently exposed to crude oil spills. We investigated benthic bacterial and eukaryote community structures at such coral reef sites subjected to different degrees of polycyclic aromatic hydrocarbon (PAH) pollution using environmental DNA (eDNA) metabarcoding. Both bacterial and eukaryote communities responded with pronounced shifts to crude oil pollution and distinguished control sites, moderately and heavily impacted sites with significant confidentiality. The observed community patterns were predominantly driven by Alphaproteobacteria and metazoans. Among these, we identified individual genera that were previously linked to oil spill stress, but also taxa, for which a link to hydrocarbon still remains to be established. Considering the lack of an early-warning system for the environmental status of coral reef ecosystems exposed to frequent crude-oil spills, our results encourage further research towards the development of an eDNA-based biomonitoring tool that exploits benthic bacterial and eukaryote communities as bioindicators.

Fishing for fish environmental DNA: Ecological applications, methodological considerations, surveying designs, and ways forward

Vast global declines of freshwater and marine fish diversity and population abundance pose serious threats to both ecosystem sustainability and human livelihoods. Environmental DNA (eDNA)-based biomonitoring provides robust, efficient, and cost-effective assessment of species occurrences and population trends in diverse aquatic environments. Thus, it holds great potential for improving conventional surveillance frameworks to facilitate fish conservation and fisheries management. However, the many technical considerations and rapid developments underway in the eDNA arena can overwhelm researchers and practitioners new to the field. Here, we systematically analysed 416 fish eDNA studies to summarize research trends in terms of investigated targets, research aims, and study systems, and reviewed the applications, rationales, methodological considerations, and limitations of eDNA methods with an emphasis on fish and fisheries research. We highlighted how eDNA technology may advance our knowledge of fish behaviour, species distributions, population genetics, community structures, and ecological interactions. We also synthesized the current knowledge of several important methodological concerns, including the qualitative and quantitative power eDNA has to recover fish biodiversity and abundance, and the spatial and temporal representations of eDNA with respect to its sources. To facilitate ecological applications implementing fish eDNA techniques, recent literature was summarized to generate guidelines for effective sampling in lentic, lotic, and marine habitats. Finally, we identified current gaps and limitations, and pointed out newly emerging research avenues for fish eDNA. As methodological optimization and standardization improve, eDNA technology should revolutionize fish monitoring and promote biodiversity conservation and fisheries management that transcends geographic and temporal boundaries.

Assessing sediment organic pollution via machine learning models and resource performance

Due to the potential ecological risks of organic pollution in sediments, aquatic ecosystems are currently facing substantial environmental threats. Assessing and controlling sediment pollution has become a huge challenge. Therefore, this study proposes a novel strategy for predicting organic pollution indicators for sediment, as well as an effective resource-utilization method. Contaminated sediments were converted into catalysts for sulfate radical advanced oxidation technologies by a one-step calcination method. The results revealed that the catalyst excelled in activating peroxymonosulfate to degrade tetracycline via a non-radical pathway. Most importantly, a predictive model of organic pollution indicators was established by machine learning. This study provides a novel approach for resource utilization and a strategy for assessing organic pollution in sediments.

Fine-scale differences in eukaryotic communities inside and outside salmon aquaculture cages revealed by eDNA metabarcoding

Aquaculture impacts on marine benthic ecosystems are widely recognized and monitored. However, little is known about the community changes occurring in the water masses surrounding aquaculture sites. In the present study, we studied the eukaryotic communities inside and outside salmonid aquaculture cages through time to assess the community changes in the neighbouring waters of the farm. Water samples were taken biweekly over five months during the production phase from inside the cages and from nearby points located North and South of the salmon farm. Eukaryotic communities were analyzed by eDNA metabarcoding of the partial COI Leray-XT fragment. The results showed that eukaryotic communities inside the cages were significantly different from those in the outside environment, with communities inside the cages having higher diversity values and more indicator species associated with them. This is likely explained by the appearance of fouling species that colonize the artificial structures, but also by other species that are attracted to the cages by other means. Moreover, these effects were highly localized inside the cages, as the communities identified outside the cages, both North and South, had very similar eukaryotic composition at each point in time. Overall, the eukaryotic communities, both inside and outside the cages, showed similar temporal fluctuations through the summer months, with diversity peaks occurring at the end of July, beginning of September, and in the beginning of November, with the latter showing the highest Shannon diversity and richness values. Hence, our study suggests that seasonality, together with salmonid aquaculture, are the main drivers of eukaryotic community structure in surface waters surrounding the farm.

Metagenomics versus total RNA sequencing: most accurate data-processing tools, microbial identification accuracy and perspectives for ecological assessments

Metagenomics and total RNA sequencing (total RNA-Seq) have the potential to improve the taxonomic identification of diverse microbial communities, which could allow for the incorporation of microbes into routine ecological assessments. However, these target-PCR-free techniques require more testing and optimization. In this study, we processed metagenomics and total RNA-Seq data from a commercially available microbial mock community using 672 data-processing workflows, identified the most accurate data-processing tools, and compared their microbial identification accuracy at equal and increasing sequencing depths. The accuracy of data-processing tools substantially varied among replicates. Total RNA-Seq was more accurate than metagenomics at equal sequencing depths and even at sequencing depths almost one order of magnitude lower than those of metagenomics. We show that while data-processing tools require further exploration, total RNA-Seq might be a favorable alternative to metagenomics for target-PCR-free taxonomic identifications of microbial communities and might enable a substantial reduction in sequencing costs while maintaining accuracy. This could be particularly an advantage for routine ecological assessments, which require cost-effective yet accurate methods, and might allow for the incorporation of microbes into ecological assessments.

DNA sequence and taxonomic gap analyses to quantify the coverage of aquatic cyanobacteria and eukaryotic microalgae in reference databases: Results of a survey in the Alpine region

The taxonomic identification of organisms based on the amplification of specific genetic markers (metabarcoding) implicitly requires adequate discriminatory information and taxonomic coverage of environmental DNA sequences in taxonomic databases. These requirements were quantitatively examined by comparing the determination of cyanobacteria and microalgae obtained by metabarcoding and light microscopy. We used planktic and biofilm samples collected in 37 lakes and 22 rivers across the Alpine region. We focused on two of the most used and best represented genetic markers in the reference databases, namely the 16S rRNA and 18S rRNA genes. A sequence gap analysis using blastn showed that, in the identity range of 99-100%, approximately 30% (plankton) and 60% (biofilm) of the sequences did not find any close counterpart in the reference databases (NCBI GenBank). Similarly, a taxonomic gap analysis showed that approximately 50% of the cyanobacterial and eukaryotic microalgal species identified by light microscopy were not represented in the reference databases. In both cases, the magnitude of the gaps differed between the major taxonomic groups. Even considering the species determined under the microscope and represented in the reference databases, 22% and 26% were still not included in the results obtained by the blastn at percentage levels of identity ≥95% and ≥97%, respectively. The main causes were the absence of matching sequences due to amplification and/or sequencing failure and potential misidentification in the microscopy step. Our results quantitatively demonstrated that in metabarcoding the main obstacles in the classification of 16S rRNA and 18S rRNA sequences and interpretation of high-throughput sequencing biomonitoring data were due to the existence of important gaps in the taxonomic completeness of the reference databases and the short length of reads. The study focused on the Alpine region, but the extent of the gaps could be much greater in other less investigated geographic areas.

A Critical Assessment of the Congruency between Environmental DNA and Palaeoecology for the Biodiversity Monitoring and Palaeoenvironmental Reconstruction

The present study suggests that standardized methodology, careful site selection, and stratigraphy are essential for investigating ancient ecosystems in order to evaluate biodiversity and DNA-based time series. Based on specific keywords, this investigation reviewed 146 publications using the SCOPUS, Web of Science (WoS), PUBMED, and Google Scholar databases. Results indicate that environmental deoxyribose nucleic acid (eDNA) can be pivotal for assessing and conserving ecosystems. Our review revealed that in the last 12 years (January 2008–July 2021), 63% of the studies based on eDNA have been reported from aquatic ecosystems, 25% from marine habitats, and 12% from terrestrial environments. Out of studies conducted in aquatic systems using the environmental DNA (eDNA) technique, 63% of the investigations have been reported from freshwater ecosystems, with an utmost focus on fish diversity (40%). Further analysis of the literature reveals that during the same period, 24% of the investigations using the environmental DNA technique were carried out on invertebrates, 8% on mammals, 7% on plants, 6% on reptiles, and 5% on birds. The results obtained clearly indicate that the environmental DNA technique has a clear-cut edge over other biodiversity monitoring methods. Furthermore, we also found that eDNA, in conjunction with different dating techniques, can provide better insight into deciphering eco-evolutionary feedback. Therefore, an attempt has been made to offer extensive information on the application of dating methods for different taxa present in diverse ecosystems. Last, we provide suggestions and elucidations on how to overcome the caveats and delineate some of the research avenues that will likely shape this field in the near future. This paper aims to identify the gaps in environmental DNA (eDNA) investigations to help researchers, ecologists, and decision-makers to develop a holistic understanding of environmental DNA (eDNA) and its utility as a palaeoenvironmental contrivance.

A strategy for successful integration of DNA-based methods in aquatic monitoring

Recent advances in molecular biomonitoring open new horizons for aquatic ecosystem assessment. Rapid and cost-effective methods based on organismal DNA or environmental DNA (eDNA) now offer the opportunity to produce inventories of indicator taxa that can subsequently be used to assess biodiversity and ecological quality. However, the integration of these new DNA-based methods into current monitoring practices is not straightforward, and will require coordinated actions in the coming years at national and international levels.

To plan and stimulate such an integration, the European network DNAqua-Net (COST Action CA15219) brought together international experts from academia, as well as key environmental biomonitoring stakeholders from different European countries. Together, this transdisciplinary consortium developed a roadmap for implementing DNA-based methods with a focus on inland waters assessed by the EU Water Framework Directive (2000/60/EC). This was done through a series of online workshops held in April 2020, which included fifty participants, followed by extensive synthesis work.

The roadmap is organised around six objectives: 1) to highlight the effectiveness and benefits of DNA-based methods, 2) develop an adaptive approach for the implementation of new methods, 3) provide guidelines and standards for best practice, 4) engage stakeholders and ensure effective knowledge transfer, 5) support the environmental biomonitoring sector to achieve the required changes, 6) steer the process and harmonise efforts at the European level.

This paper provides an overview of the forum discussions and the common European views that have emerged from them, while reflecting the diversity of situations in different countries. It highlights important actions required for a successful implementation of DNA-based biomonitoring of aquatic ecosystems by 2030.

Emerging technologies revolutionise insect ecology and monitoring

Insects are the most diverse group of animals on Earth, but their small size and high diversity have always made them challenging to study. Recent technological advances have the potential to revolutionise insect ecology and monitoring. We describe the state of the art of four technologies (computer vision, acoustic monitoring, radar, and molecular methods), and assess their advantages, current limitations, and future potential. We discuss how these technologies can adhere to modern standards of data curation and transparency, their implications for citizen science, and their potential for integration among different monitoring programmes and technologies. We argue that they provide unprecedented possibilities for insect ecology and monitoring, but it will be important to foster international standards via collaboration.

Applying convolutional neural networks to speed up environmental DNA annotation in a highly diverse ecosystem

High-throughput DNA sequencing is becoming an increasingly important tool to monitor and better understand biodiversity responses to environmental changes in a standardized and reproducible way. Environmental DNA (eDNA) from organisms can be captured in ecosystem samples and sequenced using metabarcoding, but processing large volumes of eDNA data and annotating sequences to recognized taxa remains computationally expensive. Speed and accuracy are two major bottlenecks in this critical step. Here, we evaluated the ability of convolutional neural networks (CNNs) to process short eDNA sequences and associate them with taxonomic labels. Using a unique eDNA data set collected in highly diverse Tropical South America, we compared the speed and accuracy of CNNs with that of a well-known bioinformatic pipeline (OBITools) in processing a small region (60 bp) of the 12S ribosomal DNA targeting freshwater fishes. We found that the taxonomic labels from the CNNs were comparable to those from OBITools, with high correlation levels for the composition of the regional fish fauna. The CNNs enabled the processing of raw fastq files at a rate of approximately 1 million sequences per minute, which was about 150 times faster than with OBITools. Given the good performance of CNNs in the highly diverse ecosystem considered here, the development of more elaborate CNNs promises fast deployment for future biodiversity inventories using eDNA.