Next-Generation Global Biomonitoring: Large-scale, Automated Reconstruction of Ecological Networks

Revisiting the current and emerging concepts of postharvest fresh fruit and vegetable pathology for next-generation antifungal technologies

Fungal infections of fresh fruits and vegetables (FFVs) can lead to safety problems, including consumer poisoning by mycotoxins. Various strategies exist to control fungal infections of FFVs, but their effectiveness and sustainability are limited. Recently, new concepts based on the microbiome and pathobiome have emerged and offer a more holistic perspective for advancing postharvest pathogen control techniques. Understanding the role of the microbiome in FFV infections is essential for developing sustainable control strategies. This review examines current and emerging approaches to postharvest pathology. It reviews what is known about the initiation and development of infections in FFVs. As a promising concept, the pathobiome offers new insights into the basic mechanisms of microbial infections in FFVs. The underlying mechanisms uncovered by the pathobiome are being used to develop more relevant global antifungal strategies. This review will also focus on new technologies developed to target the microbiome and members of the pathobiome to control infections in FFVs and improve safety by limiting mycotoxin contamination. Specifically, this review stresses emerging technologies related to FFVs that are relevant for modifying the interaction between FFVs and the microbiome and include the use of microbial consortia, the use of genomic technology to manipulate host and microbial community genes, and the use of databases, deep learning, and artificial intelligence to identify pathobiome markers. Other approaches include programming the behavior of FFVs using synthetic biology, modifying the microbiome using sRNA technology, phages, quorum sensing, and quorum quenching strategies. Rapid adoption and commercialization of these technologies are recommended to further improve the overall safety of FFVs.

Distinct multitrophic biodiversity composition and community organization in a freshwater lake and a hypersaline lake on the Tibetan Plateau

Alpine lakes play pivotal roles in plateau hydrological processes but are highly sensitive to climate change, yet we lack comprehensive knowledge of their multitrophic biodiversity patterns. Here, we compared the biodiversity characteristics of diverse taxonomic groups across water depths and in surface sediments from a freshwater lake and a hypersaline lake on the northwestern Tibetan Plateau. Using multi-marker environmental DNA metabarcoding, we detected 134 cyanobacteria, 443 diatom, 1,519 invertebrate, and 28 vertebrate taxa. Each group had a substantially different community composition in the two lakes, and differences were also found between water and sediments within each lake. Cooccurrence network analysis revealed higher network complexity, lower modularity, and fewer negative cohesions in the hypersaline lake, suggesting that high salinity may destabilize ecological networks. Our results provide the first holistic view of Tibetan lake biodiversity under contrasting salinity levels and reveal structural differences in the ecological networks that may impact ecosystem resilience.

Combining environmental DNA and visual surveys can inform conservation planning for coral reefs

Environmental DNA (eDNA) metabarcoding has the potential to revolutionize conservation planning by providing spatially and taxonomically comprehensive data on biodiversity and ecosystem conditions, but its utility to inform the design of protected areas remains untested. Here, we quantify whether and how identifying conservation priority areas within coral reef ecosystems differs when biodiversity information is collected via eDNA analyses or traditional visual census records. We focus on 147 coral reefs in Indonesia's hyper-diverse Wallacea region and show large discrepancies in the allocation and spatial design of conservation priority areas when coral reef species were surveyed with underwater visual techniques (fishes, corals, and algae) or eDNA metabarcoding (eukaryotes and metazoans). Specifically, incidental protection occurred for 55% of eDNA species when targets were set for species detected by visual surveys and 71% vice versa. This finding is supported by generally low overlap in detection between visual census and eDNA methods at species level, with more overlap at higher taxonomic ranks. Incomplete taxonomic reference databases for the highly diverse Wallacea reefs, and the complementary detection of species by the two methods, underscore the current need to combine different biodiversity data sources to maximize species representation in conservation planning.

Species distribution modeling combined with environmental DNA analysis to explore distribution of invasive alien mosquitofish (Gambusia affinis) in China

China has become one of the most serious countries suffering from biological invasions in the world. In the context of global climate change, invasive alien species (IAS) are likely to invade a wider area, posing greater ecological and economic threats in China. Western mosquitofish (Gambusia affinis), which is known as one of the 100 most invasive alien species, has distributed widely in southern China and is gradually spreading to the north, causing serious ecological damage and economic losses. However, its distribution in China is still unclear. Hence, there is an urgent need for a more convenient way to detect and monitor the distribution of G. affinis to put forward specific management. Therefore, we detected the distribution of G. affinis in China under current and future climate change by combing Maxent modeling prediction and eDNA verification, which is a more time-saving and reliable method to estimate the distribution of species. The Maxent modeling showed that G. affinis has a broad habitat suitability in China (especially in southern China) and would continue to spread in the future with ongoing climate change. However, eDNA monitoring showed that occurrences can already be detected in regions that Maxent still categorized as unsuitable. Besides temperature, precipitation and human influence were the most important environmental factors affecting the distribution of G. affinis in China. In addition, by environmental DNA analysis, we verified the presence of G. affinis predicted by Maxent in the Qinling Mountains where the presence of G. affinis had not been previously recorded.

Novel community data in ecology-properties and prospects

New technologies for monitoring biodiversity such as environmental (e)DNA, passive acoustic monitoring, and optical sensors promise to generate automated spatiotemporal community observations at unprecedented scales and resolutions. Here, we introduce 'novel community data' as an umbrella term for these data. We review the emerging field around novel community data, focusing on new ecological questions that could be addressed; the analytical tools available or needed to make best use of these data; and the potential implications of these developments for policy and conservation. We conclude that novel community data offer many opportunities to advance our understanding of fundamental ecological processes, including community assembly, biotic interactions, micro- and macroevolution, and overall ecosystem functioning.

Nature 4.0: A networked sensor system for integrated biodiversity monitoring

Ecosystem functions and services are severely threatened by unprecedented global loss in biodiversity. To counteract these trends, it is essential to develop systems to monitor changes in biodiversity for planning, evaluating, and implementing conservation and mitigation actions. However, the implementation of monitoring systems suffers from a trade-off between grain (i.e., the level of detail), extent (i.e., the number of study sites), and temporal repetition. Here, we present an applied and realized networked sensor system for integrated biodiversity monitoring in the Nature 4.0 project as a solution to these challenges, which considers plants and animals not only as targets of investigation, but also as parts of the modular sensor network by carrying sensors. Our networked sensor system consists of three main closely interlinked components with a modular structure: sensors, data transmission, and data storage, which are integrated into pipelines for automated biodiversity monitoring. We present our own real-world examples of applications, share our experiences in operating them, and provide our collected open data. Our flexible, low-cost, and open-source solutions can be applied for monitoring individual and multiple terrestrial plants and animals as well as their interactions. Ultimately, our system can also be applied to area-wide ecosystem mapping tasks, thereby providing an exemplary cost-efficient and powerful solution for biodiversity monitoring. Building upon our experiences in the Nature 4.0 project, we identified ten key challenges that need to be addressed to better understand and counteract the ongoing loss of biodiversity using networked sensor systems. To tackle these challenges, interdisciplinary collaboration, additional research, and practical solutions are necessary to enhance the capability and applicability of networked sensor systems for researchers and practitioners, ultimately further helping to ensure the sustainable management of ecosystems and the provision of ecosystem services.

On a roll: a direct comparison of extraction methods for the recovery of eDNA from roller swabbing of surfaces

OBJECTIVE

Roller swabbing of surfaces is an effective way to obtain environmental DNA, but the current DNA extraction method for these samples is equipment heavy, time consuming, and increases potential contamination through multiple handling. Here, we used rollers to swab a dog kennel and compared three DNA extraction approaches (water filtration, roller trimming and direct buffer) using two different platforms (Qiacube, Kingfisher). DNA extraction methods were evaluated based on cost, effort, DNA concentration and PCR result.

RESULTS

The roller trim method emerged as the optimal method with the best PCR results, DNA concentration and cost efficiency, while the buffer-based methods were the least labour intensive but produced mediocre PCR results and DNA concentrations. Additionally, the Kingfisher magnetic bead extractions generally ranked higher in all categories over the Qiacube column-based DNA extractions. Ultimately, the ideal DNA extraction method for a particular study is influenced by logistical constraints in the field such as the size of the roller, the availability of cold storage, and time constraints on the project. Our results demonstrate the strengths and weaknesses of each approach, allowing for informed decision making by researchers.

Collective and harmonized high throughput barcoding of insular arthropod biodiversity: Toward a Genomic Observatories Network for islands

Current understanding of ecological and evolutionary processes underlying island biodiversity is heavily shaped by empirical data from plants and birds, although arthropods comprise the overwhelming majority of known animal species, and as such can provide key insights into processes governing biodiversity. Novel high throughput sequencing (HTS) approaches are now emerging as powerful tools to overcome limitations in the availability of arthropod biodiversity data, and hence provide insights into these processes. Here, we explored how these tools might be most effectively exploited for comprehensive and comparable inventory and monitoring of insular arthropod biodiversity. We first reviewed the strengths, limitations and potential synergies among existing approaches of high throughput barcode sequencing. We considered how this could be complemented with deep learning approaches applied to image analysis to study arthropod biodiversity. We then explored how these approaches could be implemented within the framework of an island Genomic Observatories Network (iGON) for the advancement of fundamental and applied understanding of island biodiversity. To this end, we identified seven island biology themes at the interface of ecology, evolution and conservation biology, within which collective and harmonized efforts in HTS arthropod inventory could yield significant advances in island biodiversity research.

Soil Micro-eukaryotic Diversity Patterns Along Elevation Gradient Are Best Estimated by Increasing the Number of Elevation Steps Rather than Within Elevation Band Replication

The development of high-throughput sequencing (HTS) of environmental DNA (eDNA) has stimulated the study of soil microbial diversity patterns and drivers at all scales. However, given the heterogeneity of soils, a challenge is to define effective and efficient sampling protocols that allow sound comparison with other records, especially vegetation. In studies of elevational diversity pattern, a trade-off is choosing between replication within elevation bands vs. sampling more elevation bands. We addressed this question for soil protists along an elevation gradient on Mt. Asahi, Hokkaido, Japan. We compared two sampling approaches: (1) the replicate strategy (five replicates at six elevational bands, total = 30) and (2) the transect strategy (one sample in each of 16 different elevational bands). Despite a nearly twofold lower sampling effort, the transect strategy yielded congruent results compared to the replicate strategy for the estimation of elevational alpha diversity pattern: the regression coefficients between diversity indices and elevation did not differ between the two options. Furthermore, for a given total number of samples, gamma diversity estimated across the entire transect was higher when sampling more elevational bands as compared to replication from fewer elevational bands. Beta diversity (community composition turnover) was lower within a given elevational band than between adjacent bands and increased with elevation distance. In redundancy analyses, soil organic matter-related variable (the first principal component of soil organic matter, water content, total organic carbon, and nitrogen by whom were highly correlated) and elevation best explained elevational beta diversity pattern for both sampling approaches. Taken together, our results suggest that sampling a single plot per elevation band will be sufficient to obtain a good estimate of soil micro-eukaryotic diversity patterns along elevation gradients. This study demonstrated the effectiveness of the transect strategy in estimating diversity patterns along elevation gradients which is instructive for future environmental or even experimental studies. While not advocating for completely replacing replication-based sampling practices, it is important to note that both replicate and transect strategies have their merits and can be employed based on specific research goals and resource limitations.

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.

Estimating co-extinction threats in terrestrial ecosystems

The biosphere is changing rapidly due to human endeavour. Because ecological communities underlie networks of interacting species, changes that directly affect some species can have indirect effects on others. Accurate tools to predict these direct and indirect effects are therefore required to guide conservation strategies. However, most extinction-risk studies only consider the direct effects of global change-such as predicting which species will breach their thermal limits under different warming scenarios-with predictions of trophic cascades and co-extinction risks remaining mostly speculative. To predict the potential indirect effects of primary extinctions, data describing community interactions and network modelling can estimate how extinctions cascade through communities. While theoretical studies have demonstrated the usefulness of models in predicting how communities react to threats like climate change, few have applied such methods to real-world communities. This gap partly reflects challenges in constructing trophic network models of real-world food webs, highlighting the need to develop approaches for quantifying co-extinction risk more accurately. We propose a framework for constructing ecological network models representing real-world food webs in terrestrial ecosystems and subjecting these models to co-extinction scenarios triggered by probable future environmental perturbations. Adopting our framework will improve estimates of how environmental perturbations affect whole ecological communities. Identifying species at risk of co-extinction (or those that might trigger co-extinctions) will also guide conservation interventions aiming to reduce the probability of co-extinction cascades and additional species losses.

Facing the infinity: tackling large samples of challenging Chironomidae (Diptera) with an integrative approach

Background

Integrative taxonomy is becoming ever more significant in biodiversity research as scientists are tackling increasingly taxonomically challenging groups. Implementing a combined approach not only guarantees more accurate species identification, but also helps overcome limitations that each method presents when applied on its own. In this study, we present one application of integrative taxonomy for the highly abundant and particularly diverse fly taxon Chironomidae (Diptera). Although non-biting midges are key organisms in merolimnic systems, they are often cast aside in ecological surveys because they are very challenging to identify and extremely abundant.

Methods

Here, we demonstrate one way of applying integrative methods to tackle this highly diverse taxon. We present a three-level subsampling method to drastically reduce the workload of bulk sample processing, then apply morphological and molecular identification methods in parallel to evaluate species diversity and to examine inconsistencies across methods.

Results

Our results suggest that using our subsampling approach, identifying less than 10% of a sample's contents can reliably detect >90% of its diversity. However, despite reducing the processing workload drastically, the performance of our taxonomist was affected by mistakes, caused by large amounts of material. We conducted misidentifications for 9% of vouchers, which may not have been recovered had we not applied a second identification method. On the other hand, we were able to provide species information in cases where molecular methods could not, which was the case for 14% of vouchers. Therefore, we conclude that when wanting to implement non-biting midges into ecological frameworks, it is imperative to use an integrative approach.

LazyFox: fast and parallelized overlapping community detection in large graphs

The detection of communities in graph datasets provides insight about a graph's underlying structure and is an important tool for various domains such as social sciences, marketing, traffic forecast, and drug discovery. While most existing algorithms provide fast approaches for community detection, their results usually contain strictly separated communities. However, most datasets would semantically allow for or even require overlapping communities that can only be determined at much higher computational cost. We build on an efficient algorithm, Fox, that detects such overlapping communities. Fox measures the closeness of a node to a community by approximating the count of triangles which that node forms with that community. We propose LazyFox, a multi-threaded adaptation of the Fox algorithm, which provides even faster detection without an impact on community quality. This allows for the analyses of significantly larger and more complex datasets. LazyFox enables overlapping community detection on complex graph datasets with millions of nodes and billions of edges in days instead of weeks. As part of this work, LazyFox's implementation was published and is available as a tool under an MIT licence at https://github.com/TimGarrels/LazyFox.

A spatial fingerprint of land-water linkage of biodiversity uncovered by remote sensing and environmental DNA

Aquatic and terrestrial ecosystems are tightly connected via spatial flows of organisms and resources. Such land-water linkages integrate biodiversity across ecosystems and suggest a spatial association of aquatic and terrestrial biodiversity. However, knowledge about the extent of this spatial association is limited. By combining satellite remote sensing (RS) and environmental DNA (eDNA) extraction from river water across a 740-km² mountainous catchment, we identify a characteristic spatial land-water fingerprint. Specifically, we find a spatial association of riverine eDNA diversity with RS spectral diversity of terrestrial ecosystems upstream, peaking at a 400 m distance yet still detectable up to a 2.0 km radius. Our findings show that biodiversity patterns in rivers can be linked to the functional diversity of surrounding terrestrial ecosystems and provide a dominant scale at which these linkages are strongest. Such spatially explicit information is necessary for a functional understanding of land-water linkages.

Tracking contemporary microbial evolution in a changing ocean

Ocean microbes are fundamental for the functioning of the Earth system. Yet, our understanding of how they are reacting to global change in terms of evolution is limited. Microbes typically grow in large populations and reproduce quickly, which may allow them to rapidly adapt to environmental stressors compared to larger organisms. However, genetic evidence of contemporary evolution in wild microbes is scarce. We must begin coordinated efforts to establish new microbial time-series and explore novel tools, experiments, and data to fill this knowledge gap. The development of coordinated microbial 'genomic' observatories will provide the unprecedented opportunity to track contemporary microbial evolution in the ocean and explore the role of evolution in enabling wild microbes to respond to global change.

Drone-assisted collection of environmental DNA from tree branches for biodiversity monitoring

The protection and restoration of the biosphere is crucial for human resilience and well-being, but the scarcity of data on the status and distribution of biodiversity puts these efforts at risk. DNA released into the environment by organisms, i.e., environmental DNA (eDNA), can be used to monitor biodiversity in a scalable manner if equipped with the appropriate tool. However, the collection of eDNA in terrestrial environments remains a challenge because of the many potential surfaces and sources that need to be surveyed and their limited accessibility. Here, we propose to survey biodiversity by sampling eDNA on the outer branches of tree canopies with an aerial robot. The drone combines a force-sensing cage with a haptic-based control strategy to establish and maintain contact with the upper surface of the branches. Surface eDNA is then collected using an adhesive surface integrated in the cage of the drone. We show that the drone can autonomously land on a variety of branches with stiffnesses between 1 and 10³ newton/meter without prior knowledge of their structural stiffness and with robustness to linear and angular misalignments. Validation in the natural environment demonstrates that our method is successful in detecting animal species, including arthropods and vertebrates. Combining robotics with eDNA sampling from a variety of unreachable aboveground substrates can offer a solution for broad-scale monitoring of biodiversity.

Microbial-Based Products to Control Soil-Borne Pathogens: Methods to Improve Efficacy and to Assess Impacts on Microbiome

Microbial-based products (either as biopesticide or biofertilizers) have a long history of application, though their use is still limited, mainly due to a perceived low and inconsistent efficacy under field conditions. However, their efficacy has always been compared to chemical products, which have a completely different mechanism of action and production process, following the chemical paradigm of agricultural production. This paradigm has also been applied to regulatory processes, particularly for biopesticides, making the marketing of microbial-based formulations difficult. Increased knowledge about bioinocula behavior after application to the soil and their impact on soil microbiome should foster better exploitation of microbial-based products in a complex environment such as the soil. Moreover, the multifunctional capacity of microbial strains with regard to plant growth promotion and protection should also be considered in this respect. Therefore, the methods utilized for these studies are key to improving the knowledge and understanding of microbial-based product activity and improving their efficacy, which, from farmers' point of view, is the parameter to assess the usefulness of a treatment. In this review, we are thus addressing aspects related to the production and formulation process, highlighting the methods that can be used to evaluate the functioning and impact of microbial-based products on soil microbiome, as tools supporting their use and marketing.

Decomposing predictability to identify dominant causal drivers in complex ecosystems

Ecosystems are complex systems of various physical, biological, and chemical processes. Since ecosystem dynamics are composed of a mixture of different levels of stochasticity and nonlinearity, handling these data is a challenge for existing methods of time series-based causal inferences. Here, we show that, by harnessing contemporary machine learning approaches, the concept of Granger causality can be effectively extended to the analysis of complex ecosystem time series and bridge the gap between dynamical and statistical approaches. The central idea is to use an ensemble of fast and highly predictive artificial neural networks to select a minimal set of variables that maximizes the prediction of a given variable. It enables decomposition of the relationship among variables through quantifying the contribution of an individual variable to the overall predictive performance. We show how our approach, EcohNet, can improve interaction network inference for a mesocosm experiment and simulated ecosystems. The application of the method to a long-term lake monitoring dataset yielded interpretable results on the drivers causing cyanobacteria blooms, which is a serious threat to ecological integrity and ecosystem services. Since performance of EcohNet is enhanced by its predictive capabilities, it also provides an optimized forecasting of overall components in ecosystems. EcohNet could be used to analyze complex and hybrid multivariate time series in many scientific areas not limited to ecosystems.

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.

Machine learning ecological networks

Deep-learning tools can help to construct historical, modern-day, and future food webs.

Predicting catchment suitability for biodiversity at national scales

Biomonitoring of water quality and catchment management are often disconnected, due to mismatching scales. Considerable effort and money are spent each year on routine reach-scale surveying across many sites, particularly in countries like the UK, where nationwide sampling has been conducted using standardised techniques for many decades. Most of these traditional freshwater biomonitoring schemes focus on pre-defined indicators of organic pollution to compare observed vs expected subsets of common macroinvertebrate indicator species. Other taxa, including many threatened species, are often ignored due to their rarity, as are many invasive species, which are seen as undesirable despite becoming increasingly common and widespread in freshwaters, especially in urban ecosystems. Both these types of taxa are often monitored separately for reasons related to biodiversity concerns rather than for gauging water quality. Repurposing such data could therefore provide important new biomonitoring tools that can help catchment managers to directly link the water quality they aim to control with the biodiversity they are trying to protect. Here we used extensive data held in the England Non-Native and Rare/Protected species records that track these two groups of species as a proof-of-concept for linking catchment scale management of freshwater ecosystems and biodiversity to a range of potential drivers across England. We used national land use (Centre for Ecology and Hydrology land cover map) and water quality indicator (Environment Agency water quality data archive) datasets to predict, at the catchment scale, the presence or absence of 48 focal threatened or invasive species of concern routinely sampled by the English Environment Agency, with a median accuracy of 0.81 area under the receiver operating characteristic curve. A variety of water quality indicators and land-use types were useful in predictions, highlighting that future biomonitoring schemes could use such complementary measures to capture a wider spectrum of drivers and responses. In particular, the percentage of a catchment covered by freshwater was the single most important metric, reinforcing the need for space/habitat to support biodiversity, but we were also able to resolve a range of key environmental drivers for particular focal species. We show how our method could inform new catchment management approaches, by highlighting how key relationships can be identified and how to understand, visualise and prioritise catchments that are most suitable for restoration or water quality interventions. The scale of this work, in terms of number of species, drivers and locations, represents a significant step towards forging a new approach to catchment management that enables managers to link drivers they can control (water quality and land use) to the biota they are trying to protect (biodiversity).

Toward global integration of biodiversity big data: a harmonized metabarcode data generation module for terrestrial arthropods

Metazoan metabarcoding is emerging as an essential strategy for inventorying biodiversity, with diverse projects currently generating massive quantities of community-level data. The potential for integrating across such data sets offers new opportunities to better understand biodiversity and how it might respond to global change. However, large-scale syntheses may be compromised if metabarcoding workflows differ from each other. There are ongoing efforts to improve standardization for the reporting of inventory data. However, harmonization at the stage of generating metabarcode data has yet to be addressed. A modular framework for harmonized data generation offers a pathway to navigate the complex structure of terrestrial metazoan biodiversity. Here, through our collective expertise as practitioners, method developers, and researchers leading metabarcoding initiatives to inventory terrestrial biodiversity, we seek to initiate a harmonized framework for metabarcode data generation, with a terrestrial arthropod module. We develop an initial set of submodules covering the 5 main steps of metabarcode data generation: (i) sample acquisition; (ii) sample processing; (iii) DNA extraction; (iv) polymerase chain reaction amplification, library preparation, and sequencing; and (v) DNA sequence and metadata deposition, providing a backbone for a terrestrial arthropod module. To achieve this, we (i) identified key points for harmonization, (ii) reviewed the current state of the art, and (iii) distilled existing knowledge within submodules, thus promoting best practice by providing guidelines and recommendations to reduce the universe of methodological options. We advocate the adoption and further development of the terrestrial arthropod module. We further encourage the development of modules for other biodiversity fractions as an essential step toward large-scale biodiversity synthesis through harmonization.

Strategies for sample labelling and library preparation in DNA metabarcoding studies

Metabarcoding of DNA extracted from environmental or bulk specimen samples is increasingly used to profile biota in basic and applied biodiversity research because of its targeted nature that allows sequencing of genetic markers from many samples in parallel. To achieve this, PCR amplification is carried out with primers designed to target a taxonomically informative marker within a taxonomic group, and sample-specific nucleotide identifiers are added to the amplicons prior to sequencing. The latter enables assignment of the sequences back to the samples they originated from. Nucleotide identifiers can be added during the metabarcoding PCR and during "library preparation", that is, when amplicons are prepared for sequencing. Different strategies to achieve this labelling exist. All have advantages, challenges and limitations, some of which can lead to misleading results, and in the worst case compromise the fidelity of the metabarcoding data. Given the range of questions addressed using metabarcoding, ensuring that data generation is robust and fit for the chosen purpose is critically important for practitioners seeking to employ metabarcoding for biodiversity assessments. Here, we present an overview of the three main workflows for sample-specific labelling and library preparation in metabarcoding studies on Illumina sequencing platforms; one-step PCR, two-step PCR, and tagged PCR. Further, we distill the key considerations for researchers seeking to select an appropriate metabarcoding strategy for their specific study. Ultimately, by gaining insights into the consequences of different metabarcoding workflows, we hope to further consolidate the power of metabarcoding as a tool to assess biodiversity across a range of applications.

Airborne environmental DNA for terrestrial vertebrate community monitoring

Biodiversity monitoring at the community scale is a critical element of assessing and studying species distributions, ecology, diversity, and movements, and it is key to understanding and tracking environmental and anthropogenic effects on natural ecosystems.1, 2, 3, 4 Vertebrates in terrestrial ecosystems are experiencing extinctions and declines in both population numbers and sizes due to increasing threats from human activities and environmental change.5, 6, 7, 8 Terrestrial vertebrate monitoring using existing methods is generally costly and laborious, and although environmental DNA (eDNA) is becoming the tool of choice to assess biodiversity, few sample types effectively capture terrestrial vertebrate diversity. We hypothesized that eDNA captured from air could allow straightforward collection and characterization of terrestrial vertebrate communities. We filtered air at three localities in the Copenhagen Zoo: a stable, outside between the outdoor enclosures, and in the Rainforest House. Through metabarcoding of airborne eDNA, we detected 49 vertebrate species spanning 26 orders and 37 families: 30 mammal, 13 bird, 4 fish, 1 amphibian, and 1 reptile species. These spanned animals kept at the zoo, species occurring in the zoo surroundings, and species used as feed in the zoo. The detected species comprise a range of taxonomic orders and families, sizes, behaviors, and abundances. We found shorter distance to the air sampling device and higher animal biomass to increase the probability of detection. We hereby show that airborne eDNA can offer a fundamentally new way of studying and monitoring terrestrial communities.

•

49 vertebrate species detected through metabarcoding of airborne eDNA from the zoo

•

Detections included 30 mammal, 13 bird, 4 fish, 1 amphibian, and 1 reptile species

•

6 to 21 vertebrate species were detected per air filtering sample

•

Shorter geographical distance and higher biomass increased probability of detection

From insect-plant interactions to ecological networks

Abstract Since its inception, biodiversity has largely been understood as species diversity and assessed as such. Interactions among species or functional groups are gradually becoming part of an expanded concept of biodiversity. As a case study of the development of a research program in biodiversity, we summarize our multi-decade studies on interactions of Asteraceae and flowerhead-feeding insects in Brazil. Initially, host species were treated as independent replicates in order to assess the local and turnover components of their herbivore diversity. Research then expanded into sampling entire interactive communities of host plants and their associated herbivores in different localities and regions, enabling new research lines to be pursued. Interaction diversity could be assessed and factored into spatial and among-host components, suggesting a new field of interaction geography. Second, host specialization, a key component of interaction diversity, was reframed considering simultaneously relatedness and local availability of plant hosts. Third, with the influence of complex network theory, community-wide species interactions were probed for topological patterns. Having identified the modular structure of these plant-herbivore systems, later we demonstrated that they fit a compound hierarchical topology, in which interactions are nested within large-scale modules. In a brief survey of research funded by Fapesp, especially within the Biota-Fapesp program, we highlight several lines of internationally recognized research on interaction diversity, notably on plant-frugivore and plant-pollinator interactions, together with new theoretical models. The interplay of field studies with new theoretical and analytical approaches has established interaction diversity as an essential component for monitoring, conserving and restoring biodiversity in its broader sense.

Combining DNA metabarcoding and ecological networks to inform conservation biocontrol by small vertebrate predators

In multifunctional landscapes, diverse communities of flying vertebrate predators provide vital services of insect pest control. In such landscapes, conservation biocontrol should benefit service-providing species to enhance the flow, stability and resilience of pest control services supporting the production of food and fiber. However, this would require identifying key service providers, which may be challenging when multiple predators interact with multiple pests. Here we provide a framework to identify the functional role of individual species to pest control in multifunctional landscapes. First, we used DNA metabarcoding to provide detailed data on pest species predation by diverse predator communities. Then, these data were fed into an extensive network analysis, in which information relevant for conservation biocontrol is gained from parameters describing network structure (e.g., modularity) and species roles in such network (e.g., centrality, specialization). We applied our framework to a Mediterranean landscape, where 19 bat species were found to feed on 132 insect pest species. Metabarcoding data revealed potentially important bats that consumed insect pest species in high frequency and/or diversity. Network analysis showed a modular structure, indicating sets of bat species that are required to regulate specific sets of insect pests. A few generalist bats had particularly important roles, either at network or module levels. Extinction simulations highlighted six bats, including species of conservation concern, which were sufficient to ensure that over three-quarters of the pest species had at least one bat predator. Combining DNA metabarcoding and ecological network analysis provides a valuable framework to identify individual species within diverse predator communities that might have a disproportionate contribution to pest control services in multifunctional landscapes. These species can be regarded as candidate targets for conservation biocontrol, although additional information is needed to evaluate their actual effectiveness in pest regulation.

Inductive logic programming at 30

Inductive logic programming (ILP) is a form of logic-based machine learning. The goal is to induce a hypothesis (a logic program) that generalises given training examples and background knowledge. As ILP turns 30, we review the last decade of research. We focus on (i) new meta-level search methods, (ii) techniques for learning recursive programs, (iii) new approaches for predicate invention, and (iv) the use of different technologies. We conclude by discussing current limitations of ILP and directions for future research.

A roadmap towards predicting species interaction networks (across space and time)

Networks of species interactions underpin numerous ecosystem processes, but comprehensively sampling these interactions is difficult. Interactions intrinsically vary across space and time, and given the number of species that compose ecological communities, it can be tough to distinguish between a true negative (where two species never interact) from a false negative (where two species have not been observed interacting even though they actually do). Assessing the likelihood of interactions between species is an imperative for several fields of ecology. This means that to predict interactions between species-and to describe the structure, variation, and change of the ecological networks they form-we need to rely on modelling tools. Here, we provide a proof-of-concept, where we show how a simple neural network model makes accurate predictions about species interactions given limited data. We then assess the challenges and opportunities associated with improving interaction predictions, and provide a conceptual roadmap forward towards predictive models of ecological networks that is explicitly spatial and temporal. We conclude with a brief primer on the relevant methods and tools needed to start building these models, which we hope will guide this research programme forward. This article is part of the theme issue 'Infectious disease macroecology: parasite diversity and dynamics across the globe'.

Nitrogen cycling processes and the role of multi-trophic microbiota in dam-induced river-reservoir systems

The nitrogen (N) cycle is one of the most important nutrient cycles in river systems, and it plays an important role in maintaining biogeochemical balance and global climate stability. One of the main ways that humans have altered riverine ecosystems is through the construction of hydropower dams, which have major effects on biogeochemical cycles. Most previous studies examining the effects of damming on N cycling have focused on the whole budget or flux along rivers, and the role of river as N sources or sinks at the global or catchment scale. However, so far there is still lack of comprehensive and systematic summarize on N cycling and the controlling mechanisms in reservoirs affected by dam impoundment. In this review, we firstly summarize N cycling processes along the longitudinal riverine-transition-lacustrine gradient and the vertically stratified epilimnion-thermocline-hypolimnion gradient. Specifically, we highlight the direct and indirect roles of multi-trophic microbiota and their interactions in N cycling and discuss the main factors controlling these biotic processes. In addition, future research directions and challenges in incorporating multi-trophic levels in bioassessment, environmental flow design, as well as reservoir regulation and restoration are summarized. This review will aid future studies of N fluxes along dammed rivers and provide an essential reference for reservoir management to meet ecological needs.

A unified model of species abundance, genetic diversity, and functional diversity reveals the mechanisms structuring ecological communities

Biodiversity accumulates hierarchically by means of ecological and evolutionary processes and feedbacks. Within ecological communities drift, dispersal, speciation, and selection operate simultaneously to shape patterns of biodiversity. Reconciling the relative importance of these is hindered by current models and inference methods, which tend to focus on a subset of processes and their resulting predictions. Here we introduce massive ecoevolutionary synthesis simulations (MESS), a unified mechanistic model of community assembly, rooted in classic island biogeography theory, which makes temporally explicit joint predictions across three biodiversity data axes: (i) species richness and abundances, (ii) population genetic diversities, and (iii) trait variation in a phylogenetic context. Using simulations we demonstrate that each data axis captures information at different timescales, and that integrating these axes enables discriminating among previously unidentifiable community assembly models. MESS is unique in generating predictions of community‐scale genetic diversity, and in characterizing joint patterns of genetic diversity, abundance, and trait values. MESS unlocks the full potential for investigation of biodiversity processes using multidimensional community data including a genetic component, such as might be produced by contemporary eDNA or metabarcoding studies. We combine MESS with supervised machine learning to fit the parameters of the model to real data and infer processes underlying how biodiversity accumulates, using communities of tropical trees, arthropods, and gastropods as case studies that span a range of data availability scenarios, and spatial and taxonomic scales.

Environmental DNA gives comparable results to morphology-based indices of macroinvertebrates in a large-scale ecological assessment

Anthropogenic activities are changing the state of ecosystems worldwide, affecting community composition and often resulting in loss of biodiversity. Rivers are among the most impacted ecosystems. Recording their current state with regular biomonitoring is important to assess the future trajectory of biodiversity. Traditional monitoring methods for ecological assessments are costly and time-intensive. Here, we compared monitoring of macroinvertebrates based on environmental DNA (eDNA) sampling with monitoring based on traditional kick-net sampling to assess biodiversity patterns at 92 river sites covering all major Swiss river catchments. From the kick-net community data, a biotic index (IBCH) based on 145 indicator taxa had been established. The index was matched by the taxonomically annotated eDNA data by using a machine learning approach. Our comparison of diversity patterns only uses the zero-radius Operational Taxonomic Units assigned to the indicator taxa. Overall, we found a strong congruence between both methods for the assessment of the total indicator community composition (gamma diversity). However, when assessing biodiversity at the site level (alpha diversity), the methods were less consistent and gave complementary data on composition. Specifically, environmental DNA retrieved significantly fewer indicator taxa per site than the kick-net approach. Importantly, however, the subsequent ecological classification of rivers based on the detected indicators resulted in similar biotic index scores for the kick-net and the eDNA data that was classified using a random forest approach. The majority of the predictions (72%) from the random forest classification resulted in the same river status categories as the kick-net approach. Thus, environmental DNA validly detected indicator communities and, combined with machine learning, provided reliable classifications of the ecological state of rivers. Overall, while environmental DNA gives complementary data on the macroinvertebrate community composition compared to the kick-net approach, the subsequently calculated indices for the ecological classification of river sites are nevertheless directly comparable and consistent.

Capture by hybridization for full-length barcode-based eukaryotic and prokaryotic biodiversity inventories of deep sea ecosystems

Biodiversity inventory of marine systems remains limited due to unbalanced access to the three ocean dimensions. The use of environmental DNA (eDNA) for metabarcoding allows fast and effective biodiversity inventory and is forecast as a future biodiversity research and biomonitoring tool. However, in poorly understood ecosystems, eDNA results remain difficult to interpret due to large gaps in reference databases and PCR bias limiting the detection of some major phyla. Here, we aimed to circumvent these limitations by avoiding PCR and recollecting larger DNA fragments to improve assignment of detected taxa through phylogenetic reconstruction. We applied capture by hybridization (CBH) to enrich DNA from deep-sea sediment samples and compared the results with those obtained through an up-to-date metabarcoding PCR-based approach (MTB). Originally developed for bacterial communities and targeting 16S rDNA, the CBH approach was applied to 18S rDNA to improve the detection of species forming benthic communities of eukaryotes, with a particular focus on metazoans. The results confirmed the possibility of extending CBH to metazoans with two major advantages: (i) CBH revealed a broader spectrum of prokaryotic, eukaryotic, and particularly metazoan diversity, and (ii) CBH allowed much more robust phylogenetic reconstructions of full-length barcodes with up to 1900 base pairs. This is particularly important for taxa whose assignment is hampered by gaps in reference databases. This study provides a database and probes to apply 18S CBH to diverse marine systems, confirming this promising new tool to improve biodiversity assessments in data-poor ecosystems such as those in the deep sea.

Inferring species interactions from ecological survey data: A mechanistic approach to predict quantitative food webs of seed feeding by carabid beetles

Ecological networks are valuable for ecosystem analysis but their use is often limited by a lack of data because many types of ecological interaction, for example, predation, are short-lived and difficult to observe or detect. While there are different methods for inferring the presence of interactions, they have rarely been used to predict the interaction strengths that are required to construct weighted, or quantitative, ecological networks.Here, we develop a trait-based approach suitable for inferring weighted networks, that is, with varying interaction strengths. We developed the method for seed-feeding carabid ground beetles (Coleoptera: Carabidae) although the principles can be applied to other species and types of interaction.Using existing literature data from experimental seed-feeding trials, we predicted a per-individual interaction cost index based on carabid and seed size. This was scaled up to the population level to create inferred weighted networks using the abundance of carabids and seeds from empirical samples and energetic intake rates of carabids from the literature. From these weighted networks, we also derived a novel measure of expected predation pressure per seed type per network.This method was applied to existing ecological survey data from 255 arable fields with carabid data from pitfall traps and plant seeds from seed rain traps. Analysis of these inferred networks led to testable hypotheses about how network structure and predation pressure varied among fields.Inferred networks are valuable because (a) they provide null models for the structuring of food webs to test against empirical species interaction data, for example, DNA analysis of carabid gut regurgitates and (b) they allow weighted networks to be constructed whenever we can estimate interactions between species and have ecological census data available. This permits ecological network analysis even at times and in places when interactions were not directly assessed.

Cascading effects of moth outbreaks on subarctic soil food webs

The increasing severity and frequency of natural disturbances requires a better understanding of their effects on all compartments of biodiversity. In Northern Fennoscandia, recent large-scale moth outbreaks have led to an abrupt change in plant communities from birch forests dominated by dwarf shrubs to grass-dominated systems. However, the indirect effects on the belowground compartment remained unclear. Here, we combined eDNA surveys of multiple trophic groups with network analyses to demonstrate that moth defoliation has far-reaching consequences on soil food webs. Following this disturbance, diversity and relative abundance of certain trophic groups declined (e.g., ectomycorrhizal fungi), while many others expanded (e.g., bacterivores and omnivores) making soil food webs more diverse and structurally different. Overall, the direct and indirect consequences of moth outbreaks increased belowground diversity at different trophic levels. Our results highlight that a holistic view of ecosystems improves our understanding of cascading effects of major disturbances on soil food webs.

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

A decade after environmental scientists integrated high-throughput sequencing technologies in their toolbox, the genomics-based monitoring of anthropogenic impacts on the biodiversity and functioning of ecosystems is yet to be implemented by regulatory frameworks. Despite the broadly acknowledged potential of environmental genomics to this end, technical limitations and conceptual issues still stand in the way of its broad application by end-users. In addition, the multiplicity of potential implementation strategies may contribute to a perception that the routine application of this methodology is premature or "in development", hence restraining regulators from binding these tools into legal frameworks. Here, we review recent implementations of environmental genomics-based methods, applied to the biomonitoring of ecosystems. By taking a general overview, without narrowing our perspective to particular habitats or groups of organisms, this paper aims to compare, review and discuss the strengths and limitations of four general implementation strategies of environmental genomics for monitoring: (a) Taxonomy-based analyses focused on identification of known bioindicators or described taxa; (b) De novo bioindicator analyses; (c) Structural community metrics including inferred ecological networks; and (d) Functional community metrics (metagenomics or metatranscriptomics). We emphasise the utility of the three latter strategies to integrate meiofauna and microorganisms that are not traditionally utilised in biomonitoring because of difficult taxonomic identification. Finally, we propose a roadmap for the implementation of environmental genomics into routine monitoring programmes that leverage recent analytical advancements, while pointing out current limitations and future research needs.

Intrinsic ecological dynamics drive biodiversity turnover in model metacommunities

Turnover of species composition through time is frequently observed in ecosystems. It is often interpreted as indicating the impact of changes in the environment. Continuous turnover due solely to ecological dynamics-species interactions and dispersal-is also known to be theoretically possible; however the prevalence of such autonomous turnover in natural communities remains unclear. Here we demonstrate that observed patterns of compositional turnover and other important macroecological phenomena can be reproduced in large spatially explicit model ecosystems, without external forcing such as environmental change or the invasion of new species into the model. We find that autonomous turnover is triggered by the onset of ecological structural instability-the mechanism that also limits local biodiversity. These results imply that the potential role of autonomous turnover as a widespread and important natural process is underappreciated, challenging assumptions implicit in many observation and management tools. Quantifying the baseline level of compositional change would greatly improve ecological status assessments.

Measurements of the swimming speeds of motile microorganisms using object tracking and their correlation with water pollution and rheology levels

Self-propelled microscopic organisms are ubiquitous in water. Such organisms' motility depends on hydrodynamic and physical factors related to the rheology of the surrounding media and biological factors depending on the organisms' state and well-being. Here we demonstrate that the swimming speed of Paramecium aurelia, a unicellular protozoan, globally found in fresh, brackish, and salt waters, can be used as a measurable frugal indicator of the presence of pollutants in water. This study establishes a significant and consistent relationship between Paramecia's swimming speed and the presence of five different organic and inorganic contaminants at varying concentrations centered around drinking water thresholds. The large size and ubiquity of the targeted microorganism, the avoidance of reagents or specialized tools for the measurement, and the simple data collection based on an object tracking algorithm enable the automatization of the assessment and real-time results using globally available technology.

Integrating Environmental DNA Results With Diverse Data Sets to Improve Biosurveillance of River Health

Autonomous, robotic environmental (e)DNA samplers now make it possible for biological observations to match the scale and quality of abiotic measurements collected by automated sensor networks. Merging these automated data streams may allow for improved insight into biotic responses to environmental change and stressors. Here, we merged eDNA data collected by robotic samplers installed at three U.S. Geological Survey (USGS) streamgages with gridded daily weather data, and daily water quality and quantity data into a cloud-hosted database. The eDNA targets were a rare fish parasite and a more common salmonid fish. We then used computationally expedient Bayesian hierarchical occupancy models to evaluate associations between abiotic conditions and eDNA detections and to simulate how uncertainty in result interpretation changes with the frequency of autonomous robotic eDNA sample collection. We developed scripts to automate data merging, cleaning and analysis steps into a chained-step, workflow. We found that inclusion of abiotic covariates only provided improved insight for the more common salmonid fish since its DNA was more frequently detected. Rare fish parasite DNA was infrequently detected, which caused occupancy parameter estimates and covariate associations to have high uncertainty. Our simulations found that collecting samples at least once per day resulted in more detections and less parameter uncertainty than less frequent sampling. Our occupancy and simulation results together demonstrate the advantages of robotic eDNA samplers and how these samples can be combined with easy to acquire, publicly available data to foster real-time biosurveillance and forecasting.

Connecting high-throughput biodiversity inventories: Opportunities for a site-based genomic framework for global integration and synthesis

High-throughput sequencing (HTS) is increasingly being used for the characterization and monitoring of biodiversity. If applied in a structured way, across broad geographical scales, it offers the potential for a much deeper understanding of global biodiversity through the integration of massive quantities of molecular inventory data generated independently at local, regional and global scales. The universality, reliability and efficiency of HTS data can potentially facilitate the seamless linking of data among species assemblages from different sites, at different hierarchical levels of diversity, for any taxonomic group and regardless of prior taxonomic knowledge. However, collective international efforts are required to optimally exploit the potential of site-based HTS data for global integration and synthesis, efforts that at present are limited to the microbial domain. To contribute to the development of an analogous strategy for the nonmicrobial terrestrial domain, an international symposium entitled "Next Generation Biodiversity Monitoring" was held in November 2019 in Nicosia (Cyprus). The symposium brought together evolutionary geneticists, ecologists and biodiversity scientists involved in diverse regional and global initiatives using HTS as a core tool for biodiversity assessment. In this review, we summarize the consensus that emerged from the 3-day symposium. We converged on the opinion that an effective terrestrial Genomic Observatories network for global biodiversity integration and synthesis should be spatially led and strategically united under the umbrella of the metabarcoding approach. Subsequently, we outline an HTS-based strategy to collectively build an integrative framework for site-based biodiversity data generation.

A Network Perspective on the Vectoring of Human Disease

Blood-sucking insects are important vectors of disease, with biting Diptera (flies) alone transmitting diseases that cause an estimated 700 000 human deaths a year. Insect vectors also bite nonhuman hosts, linking them into host-biting networks. While the major vectors of prominent diseases, such as malaria, yellow fever, dengue, and Zika, are intensively studied, there has been limited focus on the wider interactions of biting insects with nonhuman hosts. Drawing on network analysis and visualisation approaches from food-web ecology, we discuss the value of a network perspective for understanding host-insect-disease interactions, with a focus on Diptera vectors. Potential applications include highlighting pathways of disease transmission, highlighting reservoirs of infection, and identifying emerging and previously unrecognised vectors.

Microbial networks inferred from environmental DNA data for biomonitoring ecosystem change: Strengths and pitfalls

Environmental DNA contains information on the species interaction networks that support ecosystem functions and services. Next-generation biomonitoring proposes the use of this data to reconstruct ecological networks in real time and then compute network-level properties to assess ecosystem change. We investigated the relevance of this proposal by assessing: (i) the replicability of DNA-based networks in the absence of ecosystem change, and (ii) the benefits and shortcomings of community- and network-level properties for monitoring change. We selected crop-associated microbial networks as a case study because they support disease regulation services in agroecosystems and analysed their response to change in agricultural practice between organic and conventional systems. Using two statistical methods of network inference, we showed that network-level properties, especially β-properties, could detect change. Moreover, consensus networks revealed robust signals of interactions between the most abundant species, which differed between agricultural systems. These findings complemented those obtained with community-level data that showed, in particular, a greater microbial diversity in the organic system. The limitations of network-level data included (i) the very high variability of network replicates within each system; (ii) the low number of network replicates per system, due to the large number of samples needed to build each network; and (iii) the difficulty in interpreting links of inferred networks. Tools and frameworks developed over the last decade to infer and compare microbial networks are therefore relevant to biomonitoring, provided that the DNA metabarcoding data sets are large enough to build many network replicates and progress is made to increase network replicability and interpretation.

Metabarcoding From Microbes to Mammals: Comprehensive Bioassessment on a Global Scale

Global biodiversity loss is unprecedented, and threats to existing biodiversity are growing. Given pervasive global change, a major challenge facing resource managers is a lack of scalable tools to rapidly and consistently measure Earth's biodiversity. Environmental genomic tools provide some hope in the face of this crisis, and DNA metabarcoding, in particular, is a powerful approach for biodiversity assessment at large spatial scales. However, metabarcoding studies are variable in their taxonomic, temporal, or spatial scope, investigating individual species, specific taxonomic groups, or targeted communities at local or regional scales. With the advent of modern, ultra-high throughput sequencing platforms, conducting deep sequencing metabarcoding surveys with multiple DNA markers will enhance the breadth of biodiversity coverage, enabling comprehensive, rapid bioassessment of all the organisms in a sample. Here, we report on a systematic literature review of 1,563 articles published about DNA metabarcoding and summarize how this approach is rapidly revolutionizing global bioassessment efforts. Specifically, we quantify the stakeholders using DNA metabarcoding, the dominant applications of this technology, and the taxonomic groups assessed in these studies. We show that while DNA metabarcoding has reached global coverage, few studies deliver on its promise of near-comprehensive biodiversity assessment. We then outline how DNA metabarcoding can help us move toward real-time, global bioassessment, illustrating how different stakeholders could benefit from DNA metabarcoding. Next, we address barriers to widespread adoption of DNA metabarcoding, highlighting the need for standardized sampling protocols, experts and computational resources to handle the deluge of genomic data, and standardized, open-source bioinformatic pipelines. Finally, we explore how technological and scientific advances will realize the promise of total biodiversity assessment in a sample—from microbes to mammals—and unlock the rich information genomics exposes, opening new possibilities for merging whole-system DNA metabarcoding with (1) abundance and biomass quantification, (2) advanced modeling, such as species occupancy models, to improve species detection, (3) population genetics, (4) phylogenetics, and (5) food web and functional gene analysis. While many challenges need to be addressed to facilitate widespread adoption of environmental genomic approaches, concurrent scientific and technological advances will usher in methods to supplement existing bioassessment tools reliant on morphological and abiotic data. This expanded toolbox will help ensure that the best tool is used for the job and enable exciting integrative techniques that capitalize on multiple tools. Collectively, these new approaches will aid in addressing the global biodiversity crisis we now face.