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Climate change is increasing the proportion of river networks experiencing flow intermittence, which in turn reduces local diversity (i.e., α‐diversity) but enhances variation in species composition among sites (i.e., β‐diversity), with potential consequences on ecosystem stability. Indeed, the multiscale theory of stability proposes that regional stability can be attained not only by local processes but also by spatial asynchrony among sites. However, it is still unknown whether and how scale‐dependent changes in biodiversity associated with river flow intermittence influence stability across spatial scales. To elucidate this, we here focus on multiple metacommunities of French rivers experiencing contrasting levels of flow intermittence. We clearly show that the relative contribution of spatial asynchrony to regional stability was higher for metacommunities of intermittent than perennial rivers. Surprisingly, spatial asynchrony was mainly linked to asynchronous population dynamics among sites, but not to β‐diversity. This finding was robust for both truly aquatic macroinvertebrates and for taxa that disperse aerially during their adult stages, implying the need to conserve multiple sites across the landscape to attain regional stability in intermittent rivers. By contrast, metacommunities of truly aquatic macroinvertebrates inhabiting perennial rivers were mainly stabilized by local processes. Our study provides novel evidence that metacommunities of perennial and intermittent rivers are stabilized by contrasting processes operating at different spatial scales. We demonstrate that flow intermittence enhances spatial asynchrony among sites, thus resulting in a regional stabilizing effect on intermittent river networks. Considering that climate change is increasing the proportion of intermittent rivers worldwide, our results suggest that managers need to focus on the spatial dynamics of metacommunities more than on local‐scale processes to monitor, restore, and conserve freshwater biodiversity. [ABSTRACT FROM AUTHOR]

Successful management and protection of wild animal populations relies on good understanding of their life cycles. Because population dynamics depends on intricate interactions of biological and ecological processes at various scales, new approaches are needed that account for the variability of demographic processes and associated parameters in a hierarchy of spatial scales. A hierarchical Bayesian model for the resident brown trout (Salmo trutta) life cycle was built to assess the relative influence of local and general determinants of mortality. The model was fitted to an extensive data set collected in 40 river reaches, combining abundance and environmental data (hydraulics, water temperature). Density-dependent mortality of juveniles increased at low water temperatures and decreased with shelter availability. High water temperature increased densitydependent mortality in adults. The model could help to predict monthly juvenile and adult mortality under scenarios of global warming and changes in shelter availability due to habitat degradation or restoration. La gestion et la protection efficaces des populations d'animaux sauvages reposent sur une bonne comprehension de leurs cycles biologiques. Parce que la dynamique des populations depend des interactions complexes de processus biologiques et ecologiques a differentes echelles, de nouvelles approches sont necessaires pour expliquer la variabilite des processus demographiques et des parametres associes dans une hierarchie d'echelles spatiales. Un modele bayesien hierarchique du cycle biologique des truites fario (Salmo trutta) residentes a ete elabore pour evaluer l'influence relative de determinants locaux et generaux de la mortalite. Le modele a ete cale sur un vaste ensemble de donnees provenant de 40 troncons de riviere, qui combine des donnees d'abondance et de conditions du milieu (hydraulique, temperature de l'eau). La mortalite des juveniles dependante de la densite augmente a faibles temperatures et diminue quand la disponibilite d'abris augmente. Des temperatures de l'eau elevees accroissent la mortalite densite-dependante chez les adultes. Le modele pourrait aider a predire la mortalite mensuelle des juveniles et des adultes dans des scenarios de rechauffement climatique et de modifications de la disponibilite d'abris decoulant de la degradation ou de la restauration d'habitats. [Traduit par la Redaction], Introduction Population dynamics models (PDM) can capture the intricate demographic and ecological mechanisms that control the course of a population's life cycle. They contribute to decision-making for the management of [...]

Pesticides are priority concerns in aquatic risk assessment due to their widespread use, ongoing development of new molecules, and potential effects from short‐ and long‐term exposures to aquatic life. Water quality assessments are also challenged by contrasting pesticide behaviors (e.g., mobility, half‐life time, solubility) in different environmental contexts. Furthermore, monitoring networks are not well adapted to the pesticide media transfer dynamics and therefore fail at providing a reliable assessment of pesticides. We present here a Bayesian belief network that was developed in a cooperative process between researchers specializing in Bayesian modeling, soil sciences, agronomy, and diffuse pollutants to provide a tool for stakeholders to assess surface water contamination by pesticides. It integrates knowledge on dominant transfer pathways according to basin physical context and climate for different pesticides properties, such as half‐life duration and affinity to organic C, to develop an assessment of risks of contamination for every watershed in France. The resulting model, ARPEGES (Analyse de Risque PEsticide pour la Gestion des Eaux de Surface; trans. Risk analysis of contamination by pesticides for surface water management), was developed in R. A user‐friendly R interface was built to enable stakeholders to not only obtain ARPEGES' results, but also freely use it to test management scenarios. Though it is applicable to any chemical, its results are illustrated for S‐Metolachlor, a pesticide that was widely used on cereals crops worldwide. In addition to providing contamination potential, ARPEGES also provides a way to diagnose its main explaining factors, enabling stakeholders to focus efforts in the most potentially affected basins, but also on the most probable cause of contamination. In this context, the Bayesian belief network allowed us to use information at different scales (i.e., regional contexts for climate, pedology at the basin scale, pesticide use at the municipality scale) to provide an expert assessment of the processes driving pesticide contamination of streams and the associated uncertainties. Integr Environ Assess Manag 2021;17:188–201. © 2020 SETAC KEY POINTS: We present a Bayesian belief network, developed in a cooperative process between researchers specializing in Bayesian modelling, soil sciences, agronomy, and diffuse pollutants, to provide a tool for stakeholders to assess surface water contamination by pesticides for every watershed in France.It integrates knowledge on dominant transfer pathways according to basin physical context and climate for different pesticides properties, such as half‐life duration and affinity to organic C, to develop an assessment of potential of contamination at the watershed scale.By considering the catchment vulnerability to transfers, ARPEGES is the first process‐based model for pesticides, applicable at a national scale and filling the gap between numerical plot scale water transfers models and large scale indicators needed by stakeholders for management sakes.Taking advantage of the Bayesian principles, ARPEGES also provides an explicit measurement of the confidence associated to its predictions. [ABSTRACT FROM AUTHOR]

L’hydromorphologie, c’est-à-dire la composante physique des masses d’eau, influence fortement l’état écologique des cours d’eau. C’est aussi un des principaux leviers d’action de la directive cadre européenne sur l’eau (DCE) pour améliorer la qualité écologique d’un cours d’eau. Depuis 2012, en France, la démarche SYRAH CE, pour système relationnel d’audit de l’hydromorphologie cours d’eau, fait partie intégrante des méthodes déployées à chaque cycle d’évaluation de la DCE. Deux cent trente mille kilomètres de cours d’eau sont renseignés, et des cartes de zones à risques produites. Elle est aussi utilisée comme aide à la hiérarchisation des actions de restauration physique des cours d’eau.

Les corridors fluviaux constituent des trames naturelles aux multiples fonctions écologiques, mais ces espaces sensibles sont bien souvent menacés par les activités humaines. Identifier les trames fonctionnelles à préserver ou à restaurer est donc un enjeu essentiel pour mettre en place des stratégies de gestion pertinentes qui améliorent la connectivité écologique de ces milieux. Comment la construction d’indicateurs spatialisés peut-il aider à répondre à cet enjeu ? L’analyse de la recolonisation de la loutre sur le bassin versant de la Loire est un exemple des recherches et des analyses menées.

Pesticides are among priority concerns about water protection, due to their massive use, on-going development of new molecules and potential effects at short- and long-terms on aquatic life. The Water Framework Directive (WFD) at the European level thus incitates stakeholders to consider these pesticides in the water ecological quality assessment. Nonetheless, taking the large amount of molecules, their contrasted environmental behaviors (mobility, half-life time, solubility...) in different agro-pedo-climatic contexts was challenging them.In this context, a Bayesian Belief Network has been developed in a cooperative process between researchers into Bayesian modelling, soil sciences, agronomy and diffuse pollutions to provide such a tool to stakeholders. For each local watershed of French territory, ARPEGES (Risk Analysis of Water Surface Contamination by Pesticides) model provides an assessment of the acute and chronic risks of being contaminated. To do so, it describes the intrinsic vulnerability as the distribution of probabilities for different transfer pathways (using data on physical ground and climatology), the acute and chronic specific vulnerabilities as crossings between these pathways probabilities and molecules chemical characteristics, and finally the risk of contamination as crossings between specific vulnerabilities and quantities of used molecules. To provide complete information to stakeholders, each indicator (as a most probable state for a variable) is associated to an estimation of its confidence and a sensitivity analysis has been performed to describe the mutual importance of the variables on the risks results.Developed in 2012 and reworked since then, ARPEGES has been used by French stakeholders in the WFD assessment process in 2013 and 2019. Since 2017, a graphical friendly user version of ARPEGES is available to allow stakeholders to independently run the model with provided or newly acquired data, or to test for scenarios by introducing prospective datasets. [ABSTRACT FROM AUTHOR]