Towards harmonized standards for freshwater biodiversity monitoring and biological assessment using benthic macroinvertebrates.

Monitoring programs at sub-national and national scales lack coordination, harmonization, and systematic review and analysis at continental and global scales, and thus fail to adequately assess and evaluate drivers of biodiversity and ecosystem degradation and loss at large spatial scales. Here we review the state of the art, gaps and challenges in the freshwater assessment programs for both the biological condition (bioassessment) and biodiversity monitoring of freshwater ecosystems using the benthic macroinvertebrate community. To assess the existence of nationally- and regionally- (sub-nationally-) accepted freshwater benthic macroinvertebrate protocols that are put in practice/used in each country, we conducted a survey from November 2022 to May 2023. Responses from 110 respondents based in 67 countries were received. Although the responses varied in their consistency, the responses clearly demonstrated a lack of biodiversity monitoring being done at both national and sub-national levels for lakes, rivers and artificial waterbodies. Programs for bioassessment were more widespread, and in some cases even harmonized among several countries. We identified 20 gaps and challenges, which we classed into five major categories, these being (a) field sampling, (b) sample processing and identification, (c) metrics and indices, (d) assessment, and (e) other gaps and challenges. Above all, we identify the lack of harmonization as one of the most important gaps, hindering efficient collaboration and communication. We identify the IUCN SSC Global Freshwater Macroinvertebrate Sampling Protocols Task Force (GLOSAM) as a means to address the lack of globally-harmonized biodiversity monitoring and biological assessment protocols.

Cost-efficiency assessments of marine monitoring methods lack rigor-a systematic mapping of literature and an end-user view on optimal cost-efficiency analysis.

Global deterioration of marine ecosystems, together with increasing pressure to use them, has created a demand for new, more efficient and cost-efficient monitoring tools that enable assessing changes in the status of marine ecosystems. However, demonstrating the cost-efficiency of a monitoring method is not straightforward as there are no generally applicable guidelines. Our study provides a systematic literature mapping of methods and criteria that have been proposed or used since the year 2000 to evaluate the cost-efficiency of marine monitoring methods. We aimed to investigate these methods but discovered that examples of actual cost-efficiency assessments in literature were rare, contradicting the prevalent use of the term "cost-efficiency." We identified five different ways to compare the cost-efficiency of a marine monitoring method: (1) the cost-benefit ratio, (2) comparative studies based on an experiment, (3) comparative studies based on a literature review, (4) comparisons with other methods based on literature, and (5) subjective comparisons with other methods based on experience or intuition. Because of the observed high frequency of insufficient cost-benefit assessments, we strongly advise that more attention is paid to the coverage of both cost and efficiency parameters when evaluating the actual cost-efficiency of novel methods. Our results emphasize the need to improve the reliability and comparability of cost-efficiency assessments. We provide guidelines for future initiatives to develop a cost-efficiency assessment framework and suggestions for more unified cost-efficiency criteria.

Deep learning and computer vision will transform entomology.

Most animal species on Earth are insects, and recent reports suggest that their abundance is in drastic decline. Although these reports come from a wide range of insect taxa and regions, the evidence to assess the extent of the phenomenon is sparse. Insect populations are challenging to study, and most monitoring methods are labor intensive and inefficient. Advances in computer vision and deep learning provide potential new solutions to this global challenge. Cameras and other sensors can effectively, continuously, and noninvasively perform entomological observations throughout diurnal and seasonal cycles. The physical appearance of specimens can also be captured by automated imaging in the laboratory. When trained on these data, deep learning models can provide estimates of insect abundance, biomass, and diversity. Further, deep learning models can quantify variation in phenotypic traits, behavior, and interactions. Here, we connect recent developments in deep learning and computer vision to the urgent demand for more cost-efficient monitoring of insects and other invertebrates. We present examples of sensor-based monitoring of insects. We show how deep learning tools can be applied to exceptionally large datasets to derive ecological information and discuss the challenges that lie ahead for the implementation of such solutions in entomology. We identify four focal areas, which will facilitate this transformation: 1) validation of image-based taxonomic identification; 2) generation of sufficient training data; 3) development of public, curated reference databases; and 4) solutions to integrate deep learning and molecular tools.

Gaps in current Baltic Sea environmental monitoring - Science versus management perspectives.

Legislations and commitments regulate Baltic Sea status assessments and monitoring. These assessments suffer from monitoring gaps that need prioritization. We used three sources of information; scientific articles, project reports and a stakeholder survey to identify gaps in relation to requirements set by the HELCOM's Baltic Sea Action Plan, the Marine Strategy Framework Directive and the Water Framework Directive. The most frequently mentioned gap was that key requirements are not sufficiently monitored in space and time. Biodiversity monitoring was the category containing most gaps. However, whereas more than half of the gaps in reports related to biodiversity, scientific articles pointed out many gaps in the monitoring of pollution and water quality. An important finding was that the three sources differed notably with respect to which gaps were mentioned most often. Thus, conclusions about gap prioritization for management should be drawn after carefully considering the different viewpoints of scientists and stakeholders.

The value of perfect and imperfect information in lake monitoring and management.

Uncertainty in the information obtained through monitoring complicates decision making about aquatic ecosystems management actions. We suggest the value of information (VOI) to assess the profitability of paying for additional monitoring information, when taking into account the costs and benefits of monitoring and management actions, as well as associated uncertainty. Estimating the monetary value of the ecosystem needed for deriving VOI is challenging. Therefore, instead of considering a single value, we evaluate the sensitivity of VOI to varying monetary value. We also extend the VOI analysis to the more realistic context where additional information does not result in perfect, but rather in imperfect information on the true state of the environment. Therefore, we analytically derive the value of perfect information in the case of two alternative decisions and two states of uncertainty. Second, we describe a Monte Carlo type of approach to evaluate the value of imperfect information about a continuous classification variable. Third, we determine confidence intervals for the VOI with a percentile bootstrap method. Results for our case study on 144 Finnish lakes suggest that generally, the value of monitoring exceeds the cost. It is particularly profitable to monitor lakes that meet the quality standards a priori, to ascertain that expensive and unnecessary management can be avoided. The VOI analysis provides a novel tool for lake and other environmental managers to estimate the value of additional monitoring data for a particular, single case, e.g. a lake, when an additional benefit is attainable through remedial management actions.

Data summarizing monitoring and evaluation for three European environmental policies in 9 cases across Europe.

The data presented in this DiB article provide an overview of Monitoring and Evaluation (M&E) carried out for 3 European environmental policies (the Water Framework Directive, the Natura 2000 network of protected areas, and Agri-Environment Schemes implemented under the Common Agricultural Policy), as implemented in 9 cases (Catalonia (Spain), Estonia, Finland, Flanders (Belgium), Hungary, Romania, Slovakia, Scotland (UK), Sweden). These data are derived from reports and documents about monitoring programs that were publicly-available online in 2017. The literature on M&E to support adaptive management structured the issues that have been extracted and summarized. The data is related to the research article entitled "Policy-driven monitoring and evaluation: does it support adaptive management of socio-ecological systems?" [Stem et al., 2005]. The information provides a first overview of monitoring and evaluation that has been implemented in response to key European environmental policies. It provides a structured overview that permits a comparison of cases and policies and can assist other scholars and practitioners working on monitoring and evaluation.

Policy-driven monitoring and evaluation: Does it support adaptive management of socio-ecological systems?

Inadequate Monitoring and Evaluation (M&E) is often thought to hinder adaptive management of socio-ecological systems. A key influence on environmental management practices are environmental policies: however, their consequences for M&E practices have not been well-examined. We examine three policy areas - the Water Framework Directive, the Natura 2000 Directives, and the Agri-Environment Schemes of the Common Agricultural Policy - whose statutory requirements influence how the environment is managed and monitored across Europe. We use a comparative approach to examine what is monitored, how monitoring is carried out, and how results are used to update management, based on publicly available documentation across nine regional and national cases. The requirements and guidelines of these policies have provided significant impetus for monitoring: however, we find this policy-driven M&E usually does not match the ideals of what is needed to inform adaptive management. There is a tendency to focus on understanding state and trends rather than tracking the effect of interventions; a focus on specific biotic and abiotic indicators at the expense of understanding system functions and processes, especially social components; and limited attention to how context affects systems, though this is sometimes considered via secondary data. The resulting data are sometimes publicly-accessible, but it is rarely clear if and how these influence decisions at any level, whether this be in the original policy itself or at the level of measures such as site management plans. Adjustments to policy-driven M&E could better enable learning for adaptive management, by reconsidering what supports a balanced understanding of socio-ecological systems and decision-making. Useful strategies include making more use of secondary data, and more transparency in data-sharing and decision-making. Several countries and policy areas already offer useful examples. Such changes are essential given the influence of policy, and the urgency of enabling adaptive management to safeguard socio-ecological systems.

Protecting and restoring Europe's waters: An analysis of the future development needs of the Water Framework Directive.

The Water Framework Directive (WFD) is a pioneering piece of legislation that aims to protect and enhance aquatic ecosystems and promote sustainable water use across Europe. There is growing concern that the objective of good status, or higher, in all EU waters by 2027 is a long way from being achieved in many countries. Through questionnaire analysis of almost 100 experts, we provide recommendations to enhance WFD monitoring and assessment systems, improve programmes of measures and further integrate with other sectoral policies. Our analysis highlights that there is great potential to enhance assessment schemes through strategic design of monitoring networks and innovation, such as earth observation. New diagnostic tools that use existing WFD monitoring data, but incorporate novel statistical and trait-based approaches could be used more widely to diagnose the cause of deterioration under conditions of multiple pressures and deliver a hierarchy of solutions for more evidence-driven decisions in river basin management. There is also a growing recognition that measures undertaken in river basin management should deliver multiple benefits across sectors, such as reduced flood risk, and there needs to be robust demonstration studies that evaluate these. Continued efforts in 'mainstreaming' water policy into other policy sectors is clearly needed to deliver wider success with WFD goals, particularly with agricultural policy. Other key policy areas where a need for stronger integration with water policy was recognised included urban planning (waste water treatment), flooding, climate and energy (hydropower). Having a deadline for attaining the policy objective of good status is important, but even more essential is to have a permanent framework for river basin management that addresses the delays in implementation of measures. This requires a long-term perspective, far beyond the current deadline of 2027.

Environmental Characteristics and Anthropogenic Impact Jointly Modify Aquatic Macrophyte Species Diversity.

Species richness and spatial variation in community composition (i.e., beta diversity) are key measures of biodiversity. They are largely determined by natural factors, but also increasingly affected by anthropogenic factors. Thus, there is a need for a clear understanding of the human impact on species richness and beta diversity, the underlying mechanisms, and whether human-induced changes can override natural patterns. Here, we dissect the patterns of species richness, community composition and beta diversity in relation to different environmental factors as well as human impact in one framework: aquatic macrophytes in 66 boreal lakes in Eastern Finland. The lakes had been classified as having high, good or moderate status (according to ecological classification of surface waters in Finland) reflecting multifaceted human impact. We used generalized least square models to study the association between different environmental variables (Secchi depth, irregularity of the shoreline, total phosphorus, pH, alkalinity, conductivity) and species richness. We tested the null hypothesis that the observed community composition can be explained by random distribution of species. We used multivariate distance matrix regression to test the effect of each environmental variable on community composition, and distance-based test for homogeneity of multivariate dispersion to test whether lakes classified as high, good or moderate status have different beta diversity. We showed that environmental drivers of species richness and community composition were largely similar, although dependent on the particular life-form group studied. The most important ones were characteristics of water quality (pH, alkalinity, conductivity) and irregularity of the shoreline. Differences in community composition were related to environmental variables independently of species richness. Species richness was higher in lakes with higher levels of human impact. Lakes with different levels of human impact had different community composition. Between-lake beta diversity did not differ in high, good or moderate status groups. However, the variation in environmental variables shaping community composition was larger in lakes with moderate status compared to other lakes. Hence, beta diversity in lakes with moderate status was smaller than what could be expected on the basis of these environmental characteristics. This could be interpreted as homogenization.

Implementation options for DNA-based identification into ecological status assessment under the European Water Framework Directive.

Assessment of ecological status for the European Water Framework Directive (WFD) is based on "Biological Quality Elements" (BQEs), namely phytoplankton, benthic flora, benthic invertebrates and fish. Morphological identification of these organisms is a time-consuming and expensive procedure. Here, we assess the options for complementing and, perhaps, replacing morphological identification with procedures using eDNA, metabarcoding or similar approaches. We rate the applicability of DNA-based identification for the individual BQEs and water categories (rivers, lakes, transitional and coastal waters) against eleven criteria, summarised under the headlines representativeness (for example suitability of current sampling methods for DNA-based identification, errors from DNA-based species detection), sensitivity (for example capability to detect sensitive taxa, unassigned reads), precision of DNA-based identification (knowledge about uncertainty), comparability with conventional approaches (for example sensitivity of metrics to differences in DNA-based identification), cost effectiveness and environmental impact. Overall, suitability of DNA-based identification is particularly high for fish, as eDNA is a well-suited sampling approach which can replace expensive and potentially harmful methods such as gill-netting, trawling or electrofishing. Furthermore, there are attempts to replace absolute by relative abundance in metric calculations. For invertebrates and phytobenthos, the main challenges include the modification of indices and completing barcode libraries. For phytoplankton, the barcode libraries are even more problematic, due to the high taxonomic diversity in plankton samples. If current assessment concepts are kept, DNA-based identification is least appropriate for macrophytes (rivers, lakes) and angiosperms/macroalgae (transitional and coastal waters), which are surveyed rather than sampled. We discuss general implications of implementing DNA-based identification into standard ecological assessment, in particular considering any adaptations to the WFD that may be required to facilitate the transition to molecular data.

Classification and retrieval on macroinvertebrate image databases.

Aquatic ecosystems are continuously threatened by a growing number of human induced changes. Macroinvertebrate biomonitoring is particularly efficient in pinpointing the cause-effect structure between slow and subtle changes and their detrimental consequences in aquatic ecosystems. The greatest obstacle to implementing efficient biomonitoring is currently the cost-intensive human expert taxonomic identification of samples. While there is evidence that automated recognition techniques can match human taxa identification accuracy at greatly reduced costs, so far the development of automated identification techniques for aquatic organisms has been minimal. In this paper, we focus on advancing classification and data retrieval that are instrumental when processing large macroinvertebrate image datasets. To accomplish this for routine biomonitoring, in this paper we shall investigate the feasibility of automated river macroinvertebrate classification and retrieval with high precision. Besides the state-of-the-art classifiers such as Support Vector Machines (SVMs) and Bayesian Classifiers (BCs), the focus is particularly drawn on feed-forward artificial neural networks (ANNs), namely multilayer perceptrons (MLPs) and radial basis function networks (RBFNs). Since both ANN types have been proclaimed superior by different investigations even for the same benchmark problems, we shall first show that the main reason for this ambiguity lies in the static and rather poor comparison methodologies applied in most earlier works. Especially the most common drawback occurs due to the limited evaluation of the ANN performances over just one or few network architecture(s). Therefore, in this study, an extensive evaluation of each classifier performance over an ANN architecture space is performed. The best classifier among all, which is trained over a dataset of river macroinvertebrate specimens, is then used in the MUVIS framework for the efficient search and retrieval of particular macroinvertebrate peculiars. Classification and retrieval results present high accuracy and can match an experts' ability for taxonomic identification.

The role of trout in stream food webs: integrating evidence from field surveys and experiments.

1. We evaluated the effects of brown trout on boreal stream food webs using field surveys and enclosure/exclosure experiments. Experimental results were related to prey preference of uncaged trout in the same stream, as well as to a survey of macroinvertebrate densities in streams with vs. without trout. Finally, we assessed the generality of our findings by examining salmonid predation on three groups of macroinvertebrate prey (chironomid midges, epibenthic grazers, invertebrate predators) in a meta-analysis. 2. In a preliminary experiment, invertebrate predators showed a strong negative response to trout, whereas chironomids benefited from trout presence. In the main experiment, trout impact increased with prey size. Trout had the strongest effect on invertebrate predators and cased caddis larvae, whereas Baetis mayfly and chironomid larvae were unaffected. Trout impact on the largest prey seemed mainly consumptive, because prey emigration rates were low and independent of fish presence. Despite strong effects on macroinvertebrates, trout did not induce a trophic cascade on periphyton. Uncaged trout showed a strong preference for the largest prey items (predatory invertebrates and aerial prey), whereas Baetis mayflies and chironomids were avoided by trout. 3. Densities of invertebrate predators were significantly higher in troutless streams. Baetis mayflies also were less abundant in trout streams, whereas densities of chironomids were positively, although non-significantly, related to trout presence. Meta-analysis showed a strong negative impact of trout on invertebrate predators, a negative but variable impact on mobile grazers (mainly mayfly larvae) and a slightly positive impact on chironomid larvae. 4. Being size-selective predators, salmonid fishes have a strong impact on the largest prey types available, and this effect spans several domains of scale. Discrepancies between our experimental findings and those from the field survey and meta-analysis show, however, that for most lotic prey, small-scale experiments do not reflect fish impact reliably at stream-wide scales. 5. Our findings suggest that small-scale experiments will be useful only if the experimental results are evaluated carefully against natural history information about the experimental system and interacting species across a wide array of spatial scales.