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Classical biomonitoring techniques have focused primarily on measures linked to various biodiversity metrics and indicator species. Next-generation biomonitoring (NGB) describes a suite of tools and approaches that allow the examination of a broader spectrum of organizational levels—from genes to entire ecosystems. Here, we frame 10 key questions that we envisage will drive the field of NGB over the next decade. While not exhaustive, this list covers most of the key challenges facing NGB, and provides the basis of the next steps for research and implementation in this field. These questions have been grouped into current- and outlook-related categories, corresponding to the organization of this paper.

An ongoing challenge for ecological studies has been the collection of data with high precision and accuracy at a suitable scale to detect and manage critical global change processes. A major hurdle has been the time-consuming and challenging process of sorting and identification of organisms, but the rapid development of DNA metabarcoding as a biodiversity observation tool provides a potential solution. As high-throughput sequencing becomes more rapid and cost-effective, a “big data” revolution is anticipated, based on higher and more accurate taxonomic resolution, more efficient detection, and greater sample processing capacity. These advances have the potential to amplify the power of ecological studies to detect change and diagnose its cause, through a methodology termed “Biomonitoring 2.0.” Despite its promise, the unfamiliar terminology and pace of development in high-throughput sequencing technologies has contributed to a growing concern that an unproven technology is supplanting tried and tested approaches, lowering trust among potential users, and reducing uptake by ecologists and environmental management practitioners. While it is reasonable to exercise caution, we argue that any criticism of new methods must also acknowledge the shortcomings and lower capacity of current observation methods. Broader understanding of the statistical properties of metabarcoding data will help ecologists to design, test and review evidence for new hypotheses. We highlight the uncertainties and challenges underlying DNA metabarcoding and traditional methods for compositional analysis, specifically comparing the interpretation of otherwise identical bulk-community samples of freshwater benthic invertebrates. We explore how taxonomic resolution, sample similarity, taxon misidentification, and taxon abundance affect the statistical properties of these samples, but recognize these issues are relevant to applications across all ecosystem types. In conclusion, metabarcoding has the

A workshop was held in Wageningen, The Netherlands, in September 2017 to collate data and literature on three aquatic ecosystem types (agricultural drainage ditches, urban floodplains, and urban estuaries), and develop a general framework for the assessment of multiple stressors on the structure and functioning of these systems. An assessment framework considering multiple stressors is crucial for our understanding of ecosystem responses within a multiply stressed environment, and to inform appropriate environmental management strategies. The framework consists of two components: (i) problem identification and (ii) impact assessment. Both assessments together proceed through the following steps: 1) ecosystem selection; 2) identification of stressors and quantification of their intensity; 3) identification of receptors or sensitive groups for each stressor; 4) identification of stressor-response relationships and their potential interactions; 5) construction of an ecological model that includes relevant functional groups and endpoints; 6) prediction of impacts of multiple stressors, 7) confirmation of these predictions with experimental and monitoring data, and 8) potential adjustment of the ecological model. Steps 7 and 8 allow the assessment to be adaptive and can be repeated until a satisfactory match between model predictions and experimental and monitoring data has been obtained. This paper is the preface of the MAEGA (Making Aquatic Ecosystems Great Again) special section that includes three associated papers which are also published in this volume, which present applications of the framework for each of the three aquatic systems.

Ecological networks are powerful tools for visualizing biodiversity data and assessing ecosystem health and function. Constructing these networks requires considerable empirical efforts, and this remains highly challenging due to sampling limitations and the laborious and notoriously limited, error-prone process of traditional taxonomic identification. Recent advancements in high-throughput gene sequencing and high-performance computing provide new ways to address these challenges. DNA metabarcoding, a method of bulk taxonomic identification from DNA extracted from environmental samples, can generate detailed biodiversity information through a standardizable analytical pipeline for species detection. When this biodiversity information is annotated with prior knowledge on taxon interactions, body size, and trophic position, it is possible to generate trait-based networks, which we call “heuristic food webs”. Although curating trait matrices for constructing heuristic food webs is a laborious, often intractable process using manual literature surveys, it can be greatly accelerated via text mining, allowing knowledge of relevant traits to be gathered across large databases. To explore this possibility, we employed a General Architecture for Text Engineering (GATE) system to create a hybrid text-mining pipeline combining rule-based and machine-learning modules. This pipeline was then used to query online repositories of published papers for missing data on a key trait, body size, that could not be gathered from existing trophic link libraries of freshwater benthic macroinvertebrates. Combining text-mined body size information with feeding information from existing sources allowed us to generate a database of over 20,000 pairwise trophic interactions. Next, we developed a pipeline that uses taxa lists generated from DNA metabarcoding and annotates this matrix with trophic information from existing databases and text-mined body size data. In this way, we generated heurist

There has been considerable focus on the impacts of environmental change on ecosystem function arising from changes in species richness. However, environmental change may affect ecosystem function without affecting richness, most notably by affecting population densities and community composition. Using a theoretical model, we find that, despite invariant richness, (1) small environmental effects may already lead to a collapse of function; (2) competitive strength may be a less important determinant of ecosystem function change than the selectivity of the environmental change driver and (3) effects on ecosystem function increase when effects on composition are larger. We also present a complementary statistical analysis of 13 data sets of phytoplankton and periphyton communities exposed to chemical stressors and show that effects on primary production under invariant richness ranged from −75% to +10%. We conclude that environmental protection goals relying on measures of richness could underestimate ecological impacts of environmental change.