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Freshwater species are particularly vulnerable to emerging threats linked to climate change because they are often already heavily impacted by habitat destruction, pollution, and exploitation. For many harvested populations of freshwater fish, these combined impacts have been mitigated for decades through stocking with captive-bred individuals. However, stocking may lead to loss of genetic variation, which may be crucial for adaptation under climate change. Exploration of sustainable alternatives is therefore paramount. We used a female-based integral projection model (IPM) to assess the consequences of terminating a long-term stocking programme for a population of landlocked, migratory brown trout Salmo trutta, and to evaluate relative effectiveness of alternative management strategies involving harvest regulations and river habitat improvement. The IPM classified individuals by body size, life history stage, and location relative to a hydropower dam, and was parameterised with 50 yr of individual-based data, supplemented with literature values. Model simulations indicated a strong population decline of 22-29% per year without stocking, much of which was attributed to high harvest mortality. Consequently, drastic reductions in fishing pressure were predicted to be necessary to ensure population viability without stocking. Mitigation measures reducing mortality associated with the hydropower dam or restoring spawning areas could further contribute to population viability when combined with changes in harvest regulations. Our results thus emphasise that large changes in management strategies, such as termination of long-term stocking programmes, require a thorough assessment of potential consequences and alternative mitigation strategies using data and models, or, at the very least, a precautionary approach under consideration of on-going climate change. Migratory salmonid · Salmo trutta · Integral projection model · Harvest · Fishing · Stocking · Dam · Hydropower · Trout

The EuropaBON project seeks to design a European Biodiversity Observation Network to monitor the status and trends of European biodiversity and ecosystems in the coming years. To accomplish this, the project has brought together biodiversity monitoring experts and other relevant stakeholders from various sectors (policy, NGO, academia, business, citizen science) from all over Europe. These have agreed on a list of 70 Essential Biodiversity Variables (Deliverable 4.1) that should enable tracking the progress of biodiversity-oriented policy instruments and provide information on changes in biodiversity at different levels.

The co-design of a European Observatory Observation Network requires information on the existing monitoring capacity in Europe, including the quantity and quality of the data available to generate the Essential Biodiversity Variables (EBVs) identified in Task 4.1 at the spatial- and temporal resolutions desired by users and policy. In this document, we provide a framework to identify the main monitoring gaps to produce European-wide EBVs. Specifically, we provide a detailed and spatially explicit information (country-level) on monitoring gaps for the production of 44 EBVs by analyzing the data flowing to current and past monitoring integration initiatives according to the defined criteria (country coverage; taxonomic/ecosystem coverage; standardized monitoring; time-series data; long-term monitoring; ongoing monitoring; sampling frequency; spatial coverage density; minimum sampling unit; raw data available). Results are presented in factsheets for each EBV and summarized across EBV classes and realms.

Data Availability statement: No data has been generated in the writing of this article. However, all articles which have been cited or influenced the summarization outlined are provided in supplementary material on a section-by-section basis. Supporting Information is available online at https://setac.onlinelibrary.wiley.com/doi/10.1002/etc.5578#support-information-section . Copyright © 2023 The Authors. In 2012, 20 key questions related to hazard and exposure assessment and environmental and health risks of pharmaceuticals and personal care products in the natural environment were identified. A decade later, this article examines the current level of knowledge around one of the lowest-ranking questions at that time, number 19: “Can nonanimal testing methods be developed that will provide equivalent or better hazard data compared with current in vivo methods?” The inclusion of alternative methods that replace, reduce, or refine animal testing within the regulatory context of risk and hazard assessment of chemicals generally faces many hurdles, although this varies both by organism (human-centric vs. other), sector, and geographical region or country. Focusing on the past 10 years, only works that might reasonably be considered to contribute to advancements in the field of aquatic environmental risk assessment are highlighted. Particular attention is paid to methods of contemporary interest and importance, representing progress in (1) the development of methods which provide equivalent or better data compared with current in vivo methods such as bioaccumulation, (2) weight of evidence, or (3) -omic-based applications. Evolution and convergence of these risk assessment areas offer the basis for fundamental frameshifts in how data are collated and used for the protection of taxa across the breadth of the aquatic environment. Looking to the future, we are at a tipping point, with a need for a global and inclusive approach to establish consensus. Bringing together these methods (both new and old) for regulatory assessment and decision-making will require a concerted effort and orchestration.

Pollution by agricultural pesticides is one of the most important pressures affecting Mediterranean coastal wetlands. Pesticide risks are expected to be influenced by climate change, which will result in an increase of temperatures and a decrease in annual precipitation. On the other hand, pesticide dosages are expected to change given the increase in pest resistance and the implementation of environmental policies like the European ´Farm-to-Fork` strategy, which aims for a 50 % reduction in pesticide usage by 2030. The influence of climate change and pesticide use practices on the ecological risks of pesticides needs to be evaluated making use of realistic environmental scenarios. This study investigates how different climate change and pesticide use practices affect the ecological risks of pesticides in the Albufera Natural Park (Valencia, Spain), a protected Mediterranean coastal wetland. We performed a probabilistic risk assessment for nine pesticides applied in rice production using three climatic scenarios (for the years 2008, 2050 and 2100), three pesticide dosage regimes (the recommended dose, and 50 % increase and 50 % decrease), and their combinations. The scenarios were used to simulate pesticide exposure concentrations in the water column of the rice paddies using the RICEWQ model. Pesticide effects were characterized using acute and chronic Species Sensitivity Distributions built with toxicity data for aquatic organisms. Risk quotients were calculated as probability distributions making use of Bayesian networks. Our results show that future climate projections will influence exposure concentrations for some of the studied pesticides, yielding higher dissipation and lower exposure in scenarios dominated by an increase of temperatures, and higher exposure peaks in scenarios where heavy precipitation events occur right after pesticide application. Our case study shows that pesticides such as azoxystrobin, difenoconazole and MCPA are posing unacceptable ecological risks for aquatic organisms, and that the implementation of the ´Farm-to-Fork` strategy is crucial to reduce them.

Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ) Fundacao de Apoio a Pesquisa do Distrito Federal (FAPDF), Fundacao de Amparo a Pesquisa do Estado do Amazonas (FAPEAM), BIODIVERSA/FAPEAM, Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ), Bundesministerium fuer Bildung und Forschung, Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES), FAPEAM-Program POSGRAD, Fundacao de Amparo a Pesquisa do Estado de Minas Gerais (FAPEMIG), INPA/MCTI 465540/2014-7, Leibniz Competition 0621187/2017, Leibniz-Gemeinschaft R20F0002, Instituto Nacional de Pesquisas da Amazpnia, unidade de~pesquisa Ministerio da Ciencia, Tecnologia e Inovacoes (INPA/MCTI) 403758/2021-1, Programa Peixe Vivo of the Companhia Energetica de Minas Gerais 033W034A, Royal Society of New Zealand, Tertiary Education Commission, Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP), Foundation for Science and Technology, Associate LaboratoryARNET J45/2018, CEEC

Conventional environmental risk assessment of chemicals is based on a calculated risk quotient, representing the ratio of exposure to effects of the chemical, in combination with assessment factors to account for uncertainty. Probabilistic risk assessment approaches can offer more transparency by using probability distributions for exposure and/or effects to account for variability and uncertainty. In this study, a probabilistic approach using Bayesian network modeling is explored as an alternative to traditional risk calculation. Bayesian networks can serve as meta-models that link information from several sources and offer a transparent way of incorporating the required characterization of uncertainty for environmental risk assessment. To this end, a Bayesian network has been developed and parameterized for the pesticides azoxystrobin, metribuzin, and imidacloprid. We illustrate the development from deterministic (traditional) risk calculation, via intermediate versions, to fully probabilistic risk characterization using azoxystrobin as an example. We also demonstrate the seasonal risk calculation for the three pesticides. Integr Environ Assess Manag 2022;18:1072-1087. © 2021 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental ToxicologyChemistry (SETAC).

Pollution by agricultural pesticides is one of the most important pressures affecting Mediterranean coastal wetlands. Pesticide risks are expected to be influenced by climate change, which will result in an increase of temperatures and a decrease in annual precipitation rates in this region. On the other hand, pesticide dosages are expected to change given the increase in pest resistance and the implementation of environmental policies like the European ‘Farm-to-Fork’ strategy, which aims for a 50% reduction in pesticide usage by 2030. The influence of climate change and pesticide use practices on the ecological risks of pesticides needs to be evaluated making use of realistic environmental scenarios. This study aimed to assess how different climate change and pesticide use practices affect the ecological risks of pesticides in the Albufera Natural Park (Valencia, Spain), a protected Mediterranean coastal wetland. We performed a probabilistic risk assessment for nine pesticides applied in rice production using scenarios comprised of three climatic regimes (the 2008 record, and projections for 2050 and 2100), three pesticide application regimes (the recommended dose, and 50% increase and 50% decrease), and their combinations. The scenarios were used to simulate pesticide exposure concentrations in the water column of the rice paddies using the RICEWQ model. Pesticide effects were characterized using acute and chronic Species Sensitivity Distributions built with laboratory toxicity data for aquatic organisms. Risk quotients were calculated as probability distributions making use of a Bayesian network approach, and best fitting distributions for the calculated exposure data and the SSDs. Our results show that future climate projections will influence exposure concentrations for some of the studied pesticides, yielding higher dissipation and lower exposure in scenarios dominated by an increase of temperatures, and higher exposure for scenarios in which heavy precipitation events occur after pesticide application. Our case study shows that pesticides such as azoxystrobin, difenoconazole and MCPA are posing high ecological risks for aquatic organisms, and that the implementation of the ‘Farm-to-Fork’ strategy is crucial to reduce them, although will need additional measures to eliminate them.

In recent years, Bayesian network (BN) models have become more popular as a tool to support probabilistic environmental risk assessments (ERA). They can better account for and communicate uncertainty compared to the deterministic approaches currently used in traditional ERA. In this study, we used the BN as a meta-model to predict the potential effect of various pesticides on different biological levels in the aquatic ecosystem. The meta-model links the inputs and outputs of a process-based exposure model (RICEWQ), that is run with various scenarios combination built on meteorological, hydrological, and agricultural scenarios, and a probabilistic case-based effect model (PERPEST), which bases its prediction on a database of microcosm and mesocosm experiments. The research focused on the pesticide exposure in rice fields surrounding a Spanish Natural Park, considering three selected pesticides for this case study: acetamiprid (insecticide), MCPA (herbicide), and azoxystrobin (fungicide). For each of the pesticide types, the developed BN model enabled the prediction of their effects on biological endpoints, endpoint groups, and community in an aquatic ecosystem. Also, it enables comparison between the different pesticide types, their effects on endpoint groups and community. While directly linking future scenarios of climate and agricultural practice to the exposure concentration and indirectly linking them to the effect on biological endpoints as well as community. In summary, azoxystrobin and MCPA seem to have a higher predicted risk for the community with at least one of the biological endpoint being effected compared to acetamiprid. Generally, the developed approach facilitates the communication of uncertainties associated with the predicted effect on different biological levels of the aquatic ecosystem. This transparency in all model components can aid risk management and decision making.

Freshwater management is challenging, and advance warning that poor water quality was likely, a season ahead, could allow for preventative measures to be put in place. To this end, we developed a Bayesian network (BN) for seasonal lake water quality prediction. BNs have become popular in recent years, but the vast majority are discrete. Here, we developed a Gaussian Bayesian network (GBN), a simple class of continuous BN. The aim was to forecast, in spring, mean total phosphorus (TP) and chlorophyll a (chl a) concentration, mean water colour, and maximum cyanobacteria biovolume for the upcoming growing season (May–October) in Vansjø, a shallow nutrient-rich lake in southeastern Norway. To develop the model, we first identified controls on interannual variability in seasonally aggregated water quality. These variables were then included in a GBN, and conditional probability densities were fit using observations (≤39 years). GBN predictions had R2 values of 0.37 (chl a) to 0.75 (colour) and classification errors of 32 % (TP) to 17 % (cyanobacteria). For all but lake colour, including weather variables did not improve the predictive performance (assessed through cross-validation). Overall, we found the GBN approach to be well suited to seasonal water quality forecasting. It was straightforward to produce probabilistic predictions, including the probability of exceeding management-relevant thresholds. The GBN could be sensibly parameterised using only the observed data, despite the small dataset. Developing a comparable discrete BN was much more subjective and time-consuming. Although low interannual variability and high temporal autocorrelation in the study lake meant the GBN performed only slightly better than a seasonal naïve forecast (where the forecasted value is simply the value observed the previous growing season), we believe that the forecasting approach presented here could be particularly useful in areas with higher sensitivity to catchment nutrient delivery and seasonal climate and for forecasting at shorter (daily or monthly) timescales. Despite the parametric constraints of GBNs, their simplicity, together with the relative accessibility of BN software with GBN handling, means they are a good first choice for BN development with continuous variables.

Environmental and ecological risk assessments are defined as the process for evaluating the likelihood that the environment may be impacted as a result of exposure to stressors. Although this definition implies the calculation of probabilities, risk assessments traditionally rely on nonprobabilistic methods such as calculation of a risk quotient. Bayesian network (BN) models are a tool for probabilistic and causal modeling, increasingly used in many fields of environmental science. Bayesian networks are defined as directed acyclic graphs where the causal relationships and the associated uncertainty are quantified in conditional probability tables. Bayesian networks inherently incorporate uncertainty and can integrate a variety of information types, including expert elicitation. During the last 2 decades, there has been a steady increase in reports on BN applications in environmental risk assessment and management. At recent annual meetings of the Society of Environmental Toxicology and Chemistry (SETAC) North America and SETAC Europe, a number of applications of BN models were presented along with new theoretical developments. Likewise, recent meetings of the European Geosciences Union (EGU) have dedicated sessions to Bayesian modeling in relation to water quality. This special series contains 10 articles based on presentations in these sessions, reflecting a range of BN applications to systems, ranging from cells and populations to watersheds and national scale. The articles report on recent progress in many topics, including climate and management scenarios, ecological and socioeconomic endpoints, machine learning, diagnostic inference, and model evaluation. They demonstrate that BNs can be adapted to established conceptual frameworks used to support environmental risk assessments, such as adverse outcome pathways and the relative risk model. The contributions from EGU demonstrate recent advancements in areas such as spatial (geographic information system [GIS]–based) and temporal (dynamic) BN modeling. In conclusion, this special series supports the prediction that increased use of Bayesian network models will improve environmental risk assessments., EDITOR’S NOTE: This article is part of the special series “Applications of Bayesian Networks for Environmental Risk Assessment and Management” and was generated from a session on the use of Bayesian networks (BNs) in environmental modeling and assessment in 1 of 3 recent conferences: SETAC North America 2018 (Sacramento, CA, USA), SETAC Europe 2019 (Helsinki, Finland), and European Geosciences Union 2019 (Vienna, Austria). The 3 sessions aimed at showing the state-of-the art and new directions in the use of BN models in environmental assessment, focusing on ecotoxicology and water quality modeling. This series aims at reflecting the broad applicability of BN methodology in environmental assessment across a range of ecosystem types and scales, and discusses the relevance for environmental management.

Acute fish toxicity (AFT) is a key endpoint in nearly all regulatory implementations of environmental hazard assessments of chemicals globally. Although it is an early tier assay, the AFT assay is complex and uses many juvenile fish each year for the registration and assessment of chemicals. Thus, it is imperative to seek animal alternative approaches to replace or reduce animal use for environmental hazard assessments. A Bayesian Network (BN) model has been developed that brings together a suite of lines of evidence (LoEs) to produce a probabilistic estimate of AFT without the testing of additional juvenile fish. Lines of evidence include chemical descriptors, mode of action (MoA) assignment, knowledge of algal and daphnid acute toxicity, and animal alternative assays such as fish embryo tests and in vitro fish assays (e.g., gill cytotoxicity). The effort also includes retrieval, assessment, and curation of quality acute fish toxicity data because these act as the baseline of comparison with model outputs. An ideal outcome of this effort would be to have global applicability, acceptance and uptake, relevance to predominant fish species used in chemical assessments, be expandable to allow incorporation of future knowledge, and data to be publicly available. The BN model can be conceived as having incorporated principles of tiered assessment and whose outcomes will be directed by the available evidence in combination with prior information. We demonstrate that, as additional evidence is included in the prediction of a given chemical's ecotoxicity profile, both the accuracy and the precision of the predicted AFT can increase. Ultimately an improved environmental hazard assessment will be achieved. Integr Environ Assess Manag 2022;00:1-15. © 2022 SETAC.

Estimating survival using data on marked individuals is a key component of population dynamics studies resulting management and conservation decisions. When human impacts on populations are considerable, such decisions require not only estimating survival but also quantifying how much mortality is due to anthropogenic versus natural causes. This is of particular importance when individuals vary in their vulnerability to different causes of mortality due to, for example, their body size, developmental stage, or reproductive state. In this study we used multistate mark-recapture models to estimate the effects of individual body size on harvest- and background mortality risks of large freshwater brown trout (Salmo trutta). In doing so, we accounted for additional individual differences in vulnerability to cause-specific mortality by distinguishing individuals passing a hydroelectric dam on their spawning migration from those that reproduced below the dam instead and further investigate temporal patterns of and correlations among mortality hazard rates over 50 years. We found that harvest mortality was highest for intermediate-sized trout, and outweighed background mortality for almost the entire observed size range of trout. For trout spawning above the dam, background mortality decreased for larger body sizes and at lower river discharge. Both mortality causes, as well as the probability of spawning above the dam, varied substantially over time but a trend was evident only for fishers’ reporting rate, which decreased from an average of 80% to only 10% over half a century. Our model demonstrates how continuous size effects can be integrated into analyses of cause-specific mortality by using a novel parameterisation with hazard rates. This allowed us to estimate effects of both size and environment on harvest- and background mortality with-out confounding, and provided an intuitive way to estimate temporal patterns within and correlation among the mortality sources. In combination with compu-tationally fast custom MCMC solutions this modelling framework provides unique opportunities for studying individual heterogeneity in cause-specific mortality using mark-recapture-recovery data.

Long-term data from marked animals provide a wealth of opportunities for studies with high relevance to both basic ecological understanding and successful management in a changing world. The key strength of such data is that they allow us to quantify individual variation in vital rates (e.g. survival, growth, reproduction) and then link it mechanistically to dynamics at the population level. However, maintaining the collection of individual-based data over long time periods comes with large logistic efforts and costs and studies spanning over decades are therefore rare. This is the case particularly for migratory aquatic species, many of which are in decline despite their high ecological, cultural and economical value. This paper describes two unique publicly available time series of individual-based data originating from a 51-year mark-recapture study of a land-locked population of large-sized migratory brown trout (Salmo trutta) in Norway: the Hunder trout. In the period 1966-2015, nearly 14,000 adult Hunder trout have been captured and individually marked during their spawning migration from Lake Mjøsa to the river Gubrandsdalslågen. Almost a third of those individuals were later recaptured alive during a later spawning run and/or captured by fishermen and reported dead or alive. This has resulted in the first data series: a mark-recapture-recovery dataset spanning half a century and more than 18,000 capture records. The second data series consists of additional data on juvenile and adult growth and life-history schedules from half of the marked individuals, obtained by means of scale-sample analysis. The two datasets offer a rare long-term perspective on individuals and population dynamics and provide unique opportunities to gain insights into questions surrounding management, conservation and restoration of migratory salmonid populations and freshwater ecosystems.

Cyanobacteria blooms in lakes and reservoirs currently threaten water security and affect the ecosystem services provided by these freshwater ecosystems, such as drinking water and recreational use. Climate change is expected to further exacerbate the situation in the future because of higher temperatures, extended droughts and nutrient enrichment, due to urbanisation and intensified agriculture. Nutrients are considered critical for the deterioration of water quality in lakes and reservoirs and responsible for the widespread increase in cyanobacterial blooms. We model the response of cyanobacteria abundance to variations in lake Total Phosphorus (TP) and Total Nitrogen (TN) concentrations, using a data set from 822 Northern European lakes. We divide lakes in ten groups based on their physico-chemical characteristics, following a modified lake typology defined for the Water Framework Directive (WFD). This classification is used in a Bayesian hierarchical linear model which employs a probabilistic approach, transforming uncertainty into probability thresholds. The hierarchical model is used to calculate probabilities of cyanobacterial concentrations exceeding risk levels for human health associated with the use of lakes for recreational activities, as defined by the World Health Organization (WHO). Different TN and TP concentration combinations result in variable probabilities to exceed pre-set thresholds. Our objective is to support lake managers in estimating acceptable nutrient concentrations and allow them to identify actions that would achieve compliance of cyanobacterial abundance risk levels with a given confidence level.

The adverse outcome pathway (AOP) framework has gained international recognition as a systematic approach linking mechanistic processes to toxicity endpoints. Nevertheless, successful implementation into risk assessments is still limited by the lack of quantitative AOP models (qAOPs) and assessment of uncertainties. The few published qAOP models so far are typically based on data‐demanding systems biology models. Here, we propose a less data‐demanding approach for quantification of AOPs and AOP networks, based on regression modeling and Bayesian networks (BNs). We demonstrate this approach with the proposed AOP #245, “Uncoupling of photophosphorylation leading to reduced ATP production associated growth inhibition,” using a small experimental data set from exposure of Lemna minor to the pesticide 3,5‐dichlorophenol. The AOP‐BN reflects the network structure of AOP #245 containing 2 molecular initiating events (MIEs), 3 key events (KEs), and 1 adverse outcome (AO). First, for each dose–response and response–response (KE) relationship, we quantify the causal relationship by Bayesian regression modeling. The regression models correspond to dose–response functions commonly applied in ecotoxicology. Secondly, we apply the fitted regression models with associated uncertainty to simulate 10 000 response values along the predictor gradient. Thirdly, we use the simulated values to parameterize the conditional probability tables of the BN model. The quantified AOP‐BN model can be run in several directions: 1) prognostic inference, run forward from the stressor node to predict the AO level; 2) diagnostic inference, run backward from the AO node; and 3) omnidirectionally, run from the intermediate MIEs and/or KEs. Internal validation shows that the AOP‐BN can obtain a high accuracy rate, when run is from intermediate nodes and when a low resolution is acceptable for the AO. Although the performance of this AOP‐BN is limited by the small data set, our study demonstrates a proof‐of‐concept: the combined use of Bayesian regression modeling and Bayesian network modeling for quantifying AOPs. Integr Environ Assess Manag 2021;17:147–164. © 2020 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC), KEY POINTS We propose a probabilistic modeling approach for quantification of adverse outcome pathways (AOPs) and AOP networks, based on regression modeling and Bayesian networks.We tested this approach with the proposed AOP #245, “Uncoupling of photophosphorylation leading to reduced ATP production associated growth inhibition,” using a small experimental data set from exposure of the plant Lemna minor to the pesticide 3,5‐dichlorophenol.Each dose–response and response–response (key event) relationship was quantified by Bayesian regression modeling, based on dose–response functions commonly applied in ecotoxicology.This proof‐of‐concept study demonstrates that the combined use of regression modeling and Bayesian network modeling is a promising approach for quantification of AOPs, even for data‐poor cases.

Cyanobacterial blooms are considered a major threat to global water security with documented impacts on lake ecosystems and public health. Given that cyanobacteria possess highly adaptive traits that favor them to prevail under different and often complicated stressor regimes, predicting their abundance is challenging. A dataset from 822 Northern European lakes is used to determine which variables better explain the variation of cyanobacteria biomass (CBB) by means of stepwise multiple linear regression. Chlorophyll-a (Chl-a) and total nitrogen (TN) provided the best modelling structure for the entire dataset, while for subsets of shallow and deep lakes, Chl-a, mean depth, TN and TN/TP explained part of the variance in CBB. Path analysis was performed and corroborated these findings. Finally, CBB was translated to a categorical variable according to risk levels for human health associated with the use of lakes for recreational activities. Several machine learning methods, namely Decision Tree, K-Nearest Neighbors, Support-vector Machine and Random Forest, were applied showing a remarkable ability to predict the risk, while Random Forest parameters were tuned and optimized, achieving a 95.81% accuracy, exceeding the performance of all other machine learning methods tested. A confusion matrix analysis is performed for all machine learning methods, identifying the potential of each method to correctly predict CBB risk levels and assessing the extent of false alarms, random forest clearly outperforms the other methods with very promising results.

Excess nutrient inputs and climate change are two of multiple stressors affecting many lakes worldwide. Lake Vansjø in southern Norway is one such eutrophic lake impacted by blooms of toxic blue-green algae (cyanobacteria), and classified as moderate ecological status under the EU Water Framework Directive. Future climate change may exacerbate the situation. Here we use a set of chained models (global climate model, hydrological model, catchment phosphorus (P) model, lake model, Bayesian Network) to assess the possible future ecological status of the lake, given the set of climate scenarios and storylines common to the EU project MARS (Managing Aquatic Ecosystems and Water Resources under Multiple Stress). The model simulations indicate that climate change alone will increase precipitation and runoff, and give higher P fluxes to the lake, but cause little increase in phytoplankton biomass or changes in ecological status. For the storylines of future management and land-use, however, the model results indicate that both the phytoplankton biomass and the lake ecological status can be positively or negatively affected. Our results also show the value in predicting a biological indicator of lake ecological status, in this case, cyanobacteria biomass with a BN model. For all scenarios, cyanobacteria contribute to worsening the status assessed by phytoplankton, compared to using chlorophyll-a alone.

The use of fish embryo toxicity (FET) data for hazard assessments of chemicals, in place of acute fish toxicity (AFT) data, has long been the goal for many environmental scientists. The FET test was first proposed as a replacement to the standardized AFT test nearly 15 y ago, but as of now, it has still not been accepted as a standalone replacement by regulatory authorities such as the European Chemicals Agency (ECHA). However, the ECHA has indicated that FET data can be used in a weight of evidence (WoE) approach, if enough information is available to support the conclusions related to the hazard assessment. To determine how such a WoE approach could be applied in practice has been challenging. To provide a conclusive WoE for FET data, we have developed a Bayesian network (BN) to incorporate multiple lines of evidence to predict AFT. There are 4 different lines of evidence in this BN model: 1) physicochemical properties, 2) AFT data from chemicals in a similar class or category, 3) ecotoxicity data from other trophic levels of organisms (e.g., daphnids and algae), and 4) measured FET data. The BN model was constructed from data obtained from a curated database and conditional probabilities assigned for the outcomes of each line of evidence. To evaluate the model, 20 data-rich chemicals, containing a minimum of 3 AFT and FET test data points, were selected to ensure a suitable comparison could be performed. The results of the AFT predictions indicated that the BN model could accurately predict the toxicity interval for 80% of the chemicals evaluated. For the remaining chemicals (20%), either daphnids or algae were the most sensitive test species, and for those chemicals, the daphnid or algal hazard data would have driven the environmental classification. Integr Environ Assess Manag 2020;16:452-460. © 2020 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Lake ecosystems across the world are under combined pressures of eutrophication and climate change, which increase the risk of harmful cyanobacteria blooms, reduced ecological status, and degraded ecosystem services. In Europe, the third cycle of river basin management plans (2021&ndash, 2027) according to the Water Framework Directive must take into account the potential impacts of climate change on water quality, including effects on relevant biological indicators. Here, we applied a Bayesian network as a meta-model for linking future climate and land-use scenarios for the time horizon 2050&ndash, 2070, via process-based catchment and lake models, to cyanobacteria abundance and ecological status of a eutrophic lake. Building upon previous applications of the model, a new version was developed to include relevant climatic variables such as wind speed. Explorative scenarios showed that the combination of low wind and high temperature gave the most synergistic effects on cyanobacteria under high levels of eutrophication (Chl-a concentration). Considering the management target of good ecological status, however, the climate-related promotion of cyanobacteria blooms contributed most to degrading the ecological status at intermediate levels of eutrophication. Future developments of this model will aim to strengthen the link between climate variables and ecological responses, to make the model also useful for seasonal forecasting.

BackgroundLong-term data from marked animals provide a wealth of opportunities for studies with high relevance to both basic ecological understanding and successful management in a changing world. The key strength of such data is that they allow to quantify individual variation in vital rates (e.g. survival, growth, reproduction) and then link it mechanistically to dynamics at the population level. However, maintaining the collection of individual-based data over long time periods comes with large logistic efforts and costs, and studies spanning over decades are therefore rare. This is the case particularly for migratory aquatic species, many of which are in decline despite their high ecological, cultural, and economical value.New informationThis paper describes two unique publicly available time series of individual-based data originating from a 51-year mark-recapture study of a land-locked population of large-sized migratory brown trout (Salmo trutta) in Norway: the Hunder trout. In the period 1966-2015, nearly 14,000 adult Hunder trout have been captured and individually marked during their spawning migration from Lake Mjøsa to the river Gubrandsdalslågen. Almost a third of those individuals were later recaptured alive during a later spawning run and/or captured by fishermen and reported dead or alive. This has resulted in the first data series: a mark-recapture-recovery dataset spanning half a century and more than 18,000 capture records. The second data series consists of additional data on juvenile and adult growth and life-history schedules from half of the marked individuals, obtained by means of scale sample analysis. The two datasets offer a rare long-term perspective on individuals and population dynamics and provide unique opportunities to gain insights into questions surrounding management, conservation, and restoration of migratory salmonid populations and freshwater ecosystems.

Marine aquaculture production has lately experienced high economic growth, but also concerns related to production and environmental contamination. For the Atlantic salmon aquaculture industry, the ectoparasitic crustacean salmon louse (Lepeophtheirus salmonis) has become a major problem. A common method to control populations of salmon lice within farm cages is treatment by various pharmaceuticals. One of the pesticides used in medicated feed for salmon is diflubenzuron (DFB), which acts as a chitin synthesis inhibitor and thereby interferes with the moulting stages during the development of this crustacean. However, DFB from fish feed may also affect non-target crustaceans such as the northern shrimp (Pandalus borealis), which is an economically and ecologically important species. Nevertheless, the actual risk posed by this chemical to shrimp populations in nature is largely unknown. Laboratory experiments have demonstrated that both larval and adult shrimp exposed to DFB through medicated fish feed have reduced survival compared to control. Moreover, the effects of DFB exposure are more severe under conditions of higher temperature and reduced pH (ocean acidification), which can be expected in a future environment. The aim of this study is to make the individual-level information from laboratory studies more relevant for risk assessment at the population level. We have developed a density-dependent age-structured population model representing a northern shrimp population located in a hypothetical Norwegian fjord containing a fish farm, under both ambient and future environments. Our model is based on thorough documentation of shrimp biology and toxicological effects from the laboratory experiments. Nevertheless, extrapolating the reported individual-level effects of DFB to the population level poses several challenges. Relevant information on shrimp populations in Norwegian fjords is sparse (such as abundances, survival and reproductive rates, and density-dependent processes). The degree of exposure to DFB at different distances from aquaculture farms is also uncertain. We have therefore developed a set of model scenarios representing different DFB application schemes and different degrees of exposure for the shrimp populations. The model predicts effects of DFB exposure on population-level endpoints such as long-term abundance, age structure and the probability of population decline below threshold abundances. These model predictions demonstrate how the risk of DFB to shrimp populations can be enhanced by factors such as the timing (season) of DFB applications, the percentage of the population affected, future environmental conditions and environmental stochasticity. publishedVersion

The presence of a multitude of bioactive organic pollutants collectively classified as pharmaceuticals and personal care products (PPCPs) in freshwaters is of concern, considering that ecological assessments of their potential impacts on natural systems are still scarce. In this field experiment we tested whether a single pulse exposure to a mixture of 12 pharmaceuticals and personal care products, which are commonly found in European inland waters, can influence the size distributions of natural lake phytoplankton communities. Size is one of the most influential determinants of community structure and functioning, particularly in planktonic communities and food webs. Using an in-situ microcosm approach, phytoplankton communities in two lakes with different nutrient levels (mesotrophic and eutrophic) were exposed to a concentration gradient of the PPCPs mixture at five levels. We tested whether sub-lethal PPCPs doses affect the scaling of organisms' abundances with their size, and the slope of these size spectra, which describe changes in the abundances of small relative to large phytoplankton. Our results showed that a large proportion (approximately 80%) of the dataset followed a power-law distribution, thus suggesting evidence of scale invariance of abundances, as expected in steady state ecosystems. PPCPs were however found to induce significant changes in the size spectra and community structure of natural phytoplankton assemblages. The two highest treatment levels of PPCPs were associated with decreased abundance of the most dominant size class (nano-phytoplankton: 2–5 μm), leading to a flattening of the size spectra slope. These results suggest that a pulse exposure to PPCPs induce changes that potentially lead to unsteady ecosystem states and cascading effects in the aquatic food webs, by favoring larger non-edible algae at the expense of small edible species. We propose higher susceptibility due to higher surface to volume ratio in small species as the likely cause of these structural changes., Science of The Total Environment, 683, ISSN:0048-9697, ISSN:1879-1026

Freshwaters are increasingly exposed to complex mixtures of pharmaceutical and personal care products (PPCPs) from municipal wastewater, which are known to alter freshwater communities’ structure and functioning. However, their interaction with other disturbances and whether their combined effects can impact ecological resilience (i.e., the ability of a system to tolerate disturbances without altering the system's original structure and processes) remain unexplored. Using in situ mesocosms in 2 lakes with different nutrient levels (mesotrophic and eutrophic), we assessed whether a pulse exposure to sublethal concentrations of 12 PPCPs affects the ecological resilience of natural phytoplankton communities that experienced an abrupt environmental change involving the destabilization of the water column through mixing. Such mixing events are predicted to increase as the effects of climate change unfold, leading to more frequent storms, which disrupt stratification in lakes and force communities to restructure. We assessed their combined effects on community metrics (biomass, species richness, and composition) and their relative resilience using 4 indicators (cross‐scale, within‐scale, aggregation length, and gap length), inferred from phytoplankton communities by discontinuity analysis. The mixing disturbance alone had negligible effects on the community metrics, but when combined with chemical contaminants significant changes were measured: reducing total biomass, species richness, and altered community composition of phytoplankton. Once these changes occurred, they persisted until the end of the experiment (day 20), when the communities’ structures from the 2 highest exposure levels diverged from the controls. The resilience indicators were not affected by PPCPs but differed significantly between lakes, with lower resilience found in the eutrophic lake. Thus, PPCPs can significantly alter community structures and reinforce mechanisms that maintain ecosystems in a “degraded state.” Environ Toxicol Chem 2019;38:2197–2208. © 2019 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC., Environmental Toxicology and Chemistry, 38 (10), ISSN:0277-2248, ISSN:1029-0486, ISSN:0092-9867

Body size can have profound impacts on survival, movement, and reproductive schedules shaping individual fitness, making growth a central process in ecological and evolutionary dynamics. Realized growth is the result of a complex interplay between life history schedules, individual variation, and environmental influences. Integrating all of these aspects into growth models is methodologically difficult, depends on the availability of repeated measurements of identifiable individuals, and consequently represents a major challenge in particular for natural populations. Using a unique 30‐yr time series of individual length measurements inferred from scale year rings of wild brown trout, we develop a Bayesian hierarchical model to estimate individual growth trajectories in temporally and spatially varying environments. We reveal a gradual decrease in average juvenile growth, which has carried over to adult life and contributed to decreasing sizes observed at the population level. Commonly studied environmental drivers like temperature and water flow did not explain much of this trend and overall persistent and among‐year individual variation dwarfed temporal variation in growth patterns. Our model and results are relevant to a wide range of questions in ecology and evolution requiring a detailed understanding of growth patterns, including conservation and management of many size‐structured populations.

Embargo until 13 July 2019 Blooms of cyanobacteria are a current threat to global water security that is expected to increase in the future because of increasing nutrient enrichment, increasing temperature and extreme precipitation in combination with prolonged drought. However, the responses to multiple stressors, such as those above, are often complex and there is contradictory evidence as to how they may interact. Here we used broad scale data from 494 lakes in central and northern Europe, to assess how cyanobacteria respond to nutrients (phosphorus), temperature and water retention time in different types of lakes. Eight lake types were examined based on factorial combinations of major factors that determine phytoplankton composition and sensitivity to nutrients: alkalinity (low and medium‐high), colour (clear and humic) and mixing intensity (polymictic and stratified). In line with expectations, cyanobacteria increased with temperature and retention time in five of the eight lake types. Temperature effects were greatest in lake types situated at higher latitudes, suggesting that lakes currently not at risk could be affected by warming in the future. However, the sensitivity of cyanobacteria to temperature, retention time and phosphorus varied among lake types highlighting the complex responses of lakes to multiple stressors. For example, in polymictic, medium‐high alkalinity, humic lakes cyanobacteria biovolume was positively explained by retention time and a synergy between TP and temperature, while in polymictic, medium‐high alkalinity, clear lakes only retention time was identified as an explanatory variable. These results show that, although climate change will need to be accounted for when managing the risk of cyanobacteria in lakes, a “one‐size fits‐all” approach is not appropriate. When forecasting the response of cyanobacteria to future environmental change, including changes caused by climate and local management, it will be important to take this differential sensitivity of lakes into account.

A model network comprising climate models, a hydrological model, a catchment-scale model for phosphorus biogeochemistry, and a lake thermodynamics and plankton dynamics model was used to simulate phosphorus loadings, total phosphorus and chlorophyll concentrations in Lake Vansjø, Southern Norway. The model network was automatically calibrated against time series of hydrological, chemical and biological observations in the inflowing river and in the lake itself using a Markov Chain Monte-Carlo (MCMC) algorithm. Climate projections from three global climate models (GCM: HadRM3, ECHAM5r3 and BCM) were used. The GCM model HadRM3 predicted the highest increase in temperature and precipitation and yielded the highest increase in total phosphorus and chlorophyll concentrations in the lake basin over the scenario period of 2031–2060. Despite the significant impact of climate change on these aspects of water quality, it is minimal when compared to the much larger effect of changes in land-use. The results suggest that implementing realistic abatement measures will remain a viable approach to improving water quality in the context of climate change.

European countries have defined >1000 national river types and >400 national lake types to implement the EU Water Framework Directive (WFD). In addition, common river and lake types have been defined within regions of Europe for intercalibrating the national classification systems for ecological status of water bodies. However, only a low proportion of national types correspond to these common intercalibration types. This causes uncertainty concerning whether the classification of ecological status is consistent across countries. Therefore, through an extensive dialogue with and data provision from all EU countries, we have developed a generic typology for European rivers and lakes. This new broad typology reflects the natural variability in the most commonly used environmental type descriptors: altitude, size and geology, as well as mean depth for lakes. These broad types capture 60–70% of all national WFD types including almost 80% of all European river and lake water bodies in almost all EU countries and can also be linked to all the common intercalibration types. The typology provides a new framework for large-scale assessments across country borders, as demonstrated with an assessment of ecological status and pressures based on European data from the 2nd set of river basin management plans. The typology can also be used for a variety of other large-scale assessments, such as reviewing and linking the water body types to habitat types under the Habitats Directive and the European Nature Information System (EUNIS), as well as comparing type-specific limit values for nutrients and other supporting quality elements across countries. Thus, the broad typology can build the basis for all scientific outputs of managerial relevance related to water body types.

Few studies have compared the response of different taxonomic groups to environmental stress across aquatic ecosystems. We regressed assemblage structure of fish, invertebrates, macrophytes, phytoplankton and benthic diatoms to total phosphorus concentration, after removing the effect of ecosystem size (stream order, lake surface area), using data from 66 streams and 45 lakes across Europe. In streams, the structure of benthic diatom assemblages, measured by nonmetric multidimensional scaling, showed the strongest correlation to elevated nutrient concentrations (adj. R2 = 0.495), followed by benthic invertebrates (0.376), fish (0.181) and macrophytes (0.153). For lakes, the patterns were less clear: fish (0.155), macrophytes (0.146) and phytoplankton (0.132). Cross-system comparison showed that stream assemblages were responding more strongly to nutrient concentrations than lake assemblages. Moreover, our results lend some support to the conjecture that response signatures are related to trophic level, with primary producers (benthic diatoms) responding more strongly than consumers (invertebrates, fish). Knowledge of differences in responses among taxonomic groups and between habitats to disturbance can be used to design more cost-effective monitoring programs.

This is the first of seven papers resulting from a Society of Environmental Toxicology and Chemistry (SETAC) international workshop titled “The Influence of Global Climate Change on the Scientific Foundations and Applications of Environmental Toxicology and Chemistry.” The workshop involved 36 scientists from 11 countries and was designed to answer the following question: How will global climate change influence the environmental impacts of chemicals and other stressors and the way we assess and manage them in the environment? While more detail is found in the complete series of articles, some key consensus points are as follows: (1) human actions (including mitigation of and adaptation to impacts of global climate change [GCC]) may have as much influence on the fate and distribution of chemical contaminants as does GCC, and modeled predictions should be interpreted cautiously; (2) climate change can affect the toxicity of chemicals, but chemicals can also affect how organisms acclimate to climate change; (3) effects of GCC may be slow, variable, and difficult to detect, though some populations and communities of high vulnerability may exhibit responses sooner and more dramatically than others; (4) future approaches to human and ecological risk assessments will need to incorporate multiple stressors and cumulative risks considering the wide spectrum of potential impacts stemming from GCC; and (5) baseline/reference conditions for estimating resource injury and restoration/rehabilitation will continually shift due to GCC and represent significant challenges to practitioners. Environ. Toxicol. Chem. 2013;32:13–19. © 2012 SETAC

Summary 1. Species responses to global warming are predicted to be manifest as poleward and upward extension of species ranges, whereas cold-adapted species experience range retractions. We report on recent range retraction of a freshwater crustacean, the Arctic fairy shrimp Branchinecta paludosa (Branchiopoda, Anostraca) from alpine ponds of southern Norway, a southernmost extension of its otherwise arctic range. 2. The species was mapped during two separate surveys, in 1970 and 2011. In 1970, it occurred in numerous ponds from the tree line at 900 m altitude to high alpine sites at 1500 m. Re-sampling of the same ponds 41 years later revealed extinction of populations from ponds along the lowest 200 m of its altitudinal range. 3. Reconstruction of summer temperatures for the periods 1965–70 and 2005–10 revealed a thermal increase for the period, corresponding to a c. 200-m upward shift of the local isotherms. More specifically, the number of warm summer days had doubled in the lowest 200 m of the region, which might be particularly detrimental for the species. 4. The extinctions at lower altitudes were not compensated for by corresponding upward colonisations. The range retraction of B. paludosa is possibly associated with oxygen stress and hypoxia, induced by increased water temperatures.

1.How climate warming may interact with other pressures on aquatic ecosystems is an important issue for research and management. We combined lake monitoring data with a palaeolimnological study to explore the combined effects of eutrophication and subsequent oligotrophication with a long-term temperature increase in epilimnetic waters. Our goals were (i) to evaluate how well sediment-based reconstructions reflect the instrumental observations, (ii) to use the palaeo-record to characterise a reference state for the lake and (iii) to explore whether data from the sediment record can aid in separating the effects of nutrient load and temperature in a large and deep lake. 2.Lake Mjosa is a large and deep lake in south-eastern Norway. Eutrophication symptoms peaked in the 1970s, which led to extensive measures to reduce the phosphorus load. A monitoring programme has run continuously from 1972. Monitoring has documented a marked decrease in phosphorus load and algal biomass and also revealed an increase in epilimnetic temperature and extended summer stratification. 3.Records of algal pigments and diatoms were extracted from sediment cores taken from 236m depth. The pigment record documented dramatic changes in lake production consistent with the monitoring record. The diatom record reflected well the eutrophication history of the lake and also demonstrated that the assemblage of the recent recovery stage differs from that of the pre-eutrophication period. 4.Ordination of diatom assemblages over time constrained by proxies for nutrient load and temperature indicated that the diatom assemblage correlated with both factors, which together accounted for 60% of the variation in diatom composition. No interaction was detected between these factors. The results suggest that the diatom assemblage has responded to varying nutrient loads as well as to changes in temperature and/or factors that correlate with temperature. 5.Reconstructions of algal biomass and total phosphorus content mirrored known changes through the monitoring period, although the absolute phosphorus estimates were too high relative to the instrumental record. The sediment record from Lake Mjosa provides a baseline for lake production in terms of algal pigments and organic contents, and for the diatom assemblage composition in a pristine stage. © 2012 Blackwell Publishing Ltd.