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Riparian forest buffers have multiple benefits for biodiversity and ecosystem services in both freshwater and terrestrial habitats but are rarely implemented in water ecosystem management, partly reflecting the lack of information on the effectiveness of this measure. In this context, social learning is valuable to inform stakeholders of the efficacy of riparian vegetation in mitigating stream degradation. We aim to develop a Bayesian belief network (BBN) model for application as a learning tool to simulate and assess the reach- and segment-scale effects of riparian vegetation properties and land use on instream invertebrates. We surveyed reach-scale riparian conditions, extracted segment-scale riparian and subcatchment land use information from geographic information system data, and collected macroinvertebrate samples from four catchments in Europe (Belgium, Norway, Romania, and Sweden). We modelled the ecological condition based on the Average Score Per Taxon (ASPT) index, a macroinvertebrate-based index widely used in European bioassessment, as a function of different riparian variables using the BBN modelling approach. The results of the model simulations provided insights into the usefulness of riparian vegetation attributes in enhancing the ecological condition, with reach-scale riparian vegetation quality associated with the strongest improvements in ecological status. Specifically, reach-scale buffer vegetation of score 3 (i.e. moderate quality) generally results in the highest probability of a good ASPT score (99–100%). In contrast, a site with a narrow width of riparian trees and a small area of trees with reach-scale buffer vegetation of score 1 (i.e. low quality) predicts a high probability of a bad ASPT score (74%). The strengths of the BBN model are the ease of interpretation, fast simulation, ability to explicitly indicate uncertainty in model outcomes, and interactivity. These merits point to the potential use of the BBN model in workshop activities, Riparian forest buffers have multiple benefits for biodiversity and ecosystem services in both freshwater and terrestrial habitats but are rarely implemented in water ecosystem management, partly reflecting the lack of information on the effectiveness of this measure. In this context, social learning is valuable to inform stakeholders of the efficacy of riparian vegetation in mitigating stream degradation. We aim to develop a Bayesian belief network (BBN) model for application as a learning tool to simulate and assess the reach-and segment-scale effects of riparian vegetation properties and land use on instream invertebrates. We surveyed reach-scale riparian conditions, extracted segment-scale riparian and subcatchment land use information from geographic information system data, and collected macroinvertebrate samples from four catchments in Europe (Belgium, Norway, Romania, and Sweden). We modelled the ecological condition based on the Average Score Per Taxon (ASPT) index, a macroinvertebrate-based index widely used in European bioassessment, as a function of different riparian variables using the BBN modelling approach. The results of the model simulations provided insights into the usefulness of riparian vegetation attributes in enhancing the ecological condition, with reach-scale riparian vegetation quality associated with the strongest improvements in ecological status. Specifically, reach-scale buffer vegetation of score 3 (i.e. moderate quality) generally results in the highest probability of a good ASPT score (99-100%). In contrast, a site with a narrow width of riparian trees and a small area of trees with reach-scale buffer vegetation of score 1 (i.e. low quality) predicts a high probability of a bad ASPT score (74%). The strengths of the BBN model are the ease of interpretation, fast simulation, ability to explicitly indicate uncertainty in model outcomes, and interactivity. These merits point to the potential use of the BBN model in workshop activiti

Long chain polyunsaturated fatty acids (LC-PUFAs) such as eicosapentaenoic acid (EPA, 20:5n-3) affect zooplankton fitness and ability to cope with environmental stressors. However, the impact of LC-PUFAs on zooplankton sensitivity to chemical stressors is unknown. Here, we aimed to document the interaction between EPA and cadmium (Cd), as model chemical stressor, in Daphnia magna. A life-history experiment was performed in which daphnid neonates were raised into adulthood on three diets of different lipid composition: (i) algae mix; (ii) algae mix supplemented with control liposomes; (iii) algae mix supplemented with liposomes containing EPA. Juveniles (3rd, 4th and 5th brood) released by daphnids during this life-history experiment were sampled, challenged with Cd during 48 h and their immobility was assessed. At the end of this life-history experiment, another immobilisation test was performed with adults from each treatment. Daphnids absorbed, incorporated and transferred ingested EPA to their offspring. Liposome feeding increased adult tolerance to Cd. The presence of EPA in liposomes did not increase adult tolerance to Cd. Offspring's tolerance to Cd was influenced by the brood number and the maternal diet. It was positively correlated with the PUFA level in body neutral lipids, especially alpha-linolenic acid (ALA, 18:3n-3) and negatively correlated with the saturated fatty acid level in body neutral lipids, especially stearic acid (18:0). Overall, these results emphasize the importance of dietary lipids and maternal transfer of body lipids in D. magna sensitivity to Cd and highlight the need to take into account these parameters in ecotoxicological studies and risk assessment.