Combining expert‐based and computational approaches to design protected river networks under climate change

International audienceAim: Estimate the current and future distribution of brown trout and identify priority areas for conservation of the species.Location: Rhône River basin and Mediterranean streams.Methods: We first developed a spatially explicit species distribution model to es- timate the current and future distribution of brown trout for three time horizons (2030, 2055 and 2080) and two climate change scenarios (RCP 4.5 and RCP 8.5). We then performed a prioritization analysis to identify priority areas for brown trout conservation, accounting for: (a) spatial dependencies along the riverine system, (b) several sources of uncertainty arising from climate-related forecasts and (c) different protected area scenarios by comparing hypothetical, optimal protected networks to an actual protected network designed by regional fish experts.Results: Future projections of brown trout densities exhibited a general trend to- wards a gradual range contraction, with a significant risk of extirpation across moun- tainous regions of low to mid-elevation. Overall, the projected current and future distributions were well-covered by the existing protected network. In addition, up to 70% of the river reaches included in this expert-based protection network were also priorities in the optimal priority set (e.g. the best set of areas to maximize biodiversity protection). Finally, a large proportion of these reaches were invariably identified re- gardless of climate change scenarios and uncertainties or spatial dependencies. Main conclusions: Our analytical approach highlighted priority areas for brown trout conservation which were robust to a set of climate and connectivity assumptions. This core priority network could be further refined by taking into account key fine- scale processes like thermal refugia. Therefore, we advocate for combining computa- tional and expert-based approaches in conservation planning of riverine ecosystems to achieve a relevant consensus between regional-scale management and fine-grain ecological knowledge