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Protected areas manage synergies and trade-offs associated with core missions of nature protection while supporting education, recreation and tourism. In this paper we demonstrate how spatial modelling co-produced with managers can support the assessment of interactions between two cultural services: outdoor recreation and iconic terrestrial vertebrates. In two French national parks (Ecrins and Vanoise) we showed clear seasonal differentiation in spatial patterns for potential iconic vertebrate diversity, recreation opportunities and their interactions. Our first hypothesis that limited access and mobility of recreationists during winter would increase potential wildlife refugia was largely validated for Ecrins. Our second hypothesis that lower but spatially diffuse pressure from recreationists in Ecrins would increase potential interference as compared to more intense but directed activity in Vanoise was consistent with patterns in summer. For winter the spatial concentration of recreation around ski resorts of Vanoise was highly impactful. Across both parks concerns about the expansion of winter activities are legitimate, especially for climate-sensitive species. We also showed the critical role of refuge areas in high valleys (summer) and lower slopes away from tracks (winter), highlighting threats from off-track practices. Beyond regulation our results will support dialogue with the public and professionals based on communication and education. Addressing the challenge of co-managing multiple cultural ecosystem services requires understanding these interactions and their management implications. For this, we need evidence of the spatial distribution of their overlaps (Lautenbach et al., 2019; Plieninger et al.,

Predicting contemporary and future species distributions is relevant for science and decision making, yet the development of high-resolution spatial predictions for numerous taxonomic groups and regions is limited by the scalability of available modelling tools. Uniting species distribution modelling (SDM) techniques into one high-performance computing (HPC) pipeline, we developed N-SDM, an SDM platform aimed at delivering reproducible outputs for standard biodiversity assessments. N-SDM was built around a spatially-nested framework, intended at facilitating the combined use of species occurrence data retrieved from multiple sources and at various spatial scales. N-SDM allows combining two models fitted with species and covariate data retrieved from global to regional scales, which is useful for addressing the issue of spatial niche truncation. The set of state-of-the-art SDM features embodied in N-SDM includes a newly devised covariate selection procedure, five modelling algorithms, an algorithmspecific hyperparameter grid search, and the ensemble of small-models approach. N-SDM is designed to be run on HPC environments, allowing the parallel processing of thousands of species at the same time. All the information required for installing and running N-SDM is openly available on the GitHub repository https://github. com/N-SDM/N-SDM.

Since the late 1990s, Nature’s Contributions to People (NCPs; i.e. ecosystem services) were used as a putative leverage for fostering nature preservation. NCPs have largely been defined and mapped at the landscape level using land use and cover classifications. However, NCP mapping attempts based directly on individual species are still uncommon. Given that species shape ecosystems and ultimately deliver NCPs, mapping NCPs based on species distribution data should deliver highly meaningful results. This requires first establishing a census of the species-to-NCP relationships. However, datasets quantifying these relationships across several species and NCPs are rare. Here, we fill this gap by compiling literature and expert knowledge to establish the relationships of 1816 tracheophyte and 250 vertebrate species with 17 NCPs in the Swiss Alps. We illustrated the 31,098 identified species-NCP relationships for the two lineages and discuss why such a table is a key initial step in building spatial predictions of NCPs directly from species data, e.g. to ultimately complement spatial conservation planning.

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