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Zoos and aquaria in the European Union (EU) can play a crucial role in the conservation of EU species, as they currently hold nearly half (49%) of EU terrestrial vertebrates. In this study, we analyzed the species composition and population sizes of EU zoos and developed a framework to prioritize recommendations for additional ex-situ and in-situ interventions for 277 at-risk EU species. Our results showed that EU zoos currently hold 39% of threatened EU species, 27% of EU endemic species, 62% of EU species vulnerable to climate change, 20% of EU species listed by the Alliance for Zero Extinction (AZE), 25% of Evolutionary Distinct and Globally Endangered (EDGE) EU species, while only 5% are subject to ex-situ conservation. Using our framework, we found that additional captive breeding was recommended for 60-61%% of species while expanding protected areas was recommended for only 2–22%, as 217 out of 277 species already met habitat protection targets. Both interventions were recommended for up to 20% of species, while the remaining 18% required no interventions because captive populations and habitat protection fully met targets. Our flexible framework can support more effective integrated conservation planning decisions for EU species and help identify target species for further in-depth assessment by the IUCN Ex-situ guidelines.

Introduction: Connected landscapes can increase the effectiveness of protected areas by facilitating individual movement and gene flow between populations, thereby increasing the persistence of species even in fragmented habitats. Connectivity planning is often based on modeling connectivity for a limited number of species, i.e., “connectivity umbrellas”, which serve as surrogates for co-occurring species. Connectivity umbrellas are usually selected a priori, based on a few life history traits and often without evaluating other species. Methods: We developed a quantitative method to identify connectivity umbrellas at multiple scales. We demonstrate the approach on the terrestrial large mammal community (24 species) in continental Europe at two scales: 13 geographic biomes and 36 ecoregions, and evaluate the interaction of landscape characteristics on the selection of connectivity umbrellas. Results: We show that the number, identity, and attributes of connectivity umbrellas are sensitive to spatial scale and human influence on the landscape. Multiple species were selected as connectivity umbrellas in 92% of the geographic biomes (average of 4.15 species) and 83% of the ecoregions (average of 3.16 species). None of the 24 species evaluated is by itself an effective connectivity umbrella across its entire range. We identified significant interactions between species and landscape attributes. Species selected as connectivity umbrellas in regions with low human influence have higher mean body mass, larger home ranges, longer dispersal distances, smaller geographic ranges, occur at lower population densities, and are of higher conservation concern than connectivity umbrellas in more human-influenced regions. More species are required to meet connectivity targets in regions with high human influence (average of three species) in comparison to regions with low human influence (average of 1.67 species). Discussion: We conclude that multiple species selected in relation to l

Identifying areas that contain species assemblages not found elsewhere in a region is central to conservation planning.1,2 Species assemblages contain networks of species interactions that underpin species dynamics,3,4 ecosystem processes, and contributions to people.5,6,7 Yet the uniqueness of interaction networks in a regional context has rarely been assessed. Here, we estimated the spatial uniqueness of 10,000 terrestrial vertebrate trophic networks across Europe (1,164 species, 50,408 potential interactions8) based on the amount of similarity between all local networks mapped at a 10 km resolution. Our results revealed more unique networks in the Arctic bioregion, but also in southern Europe and isolated islands. We then contrasted the uniqueness of trophic networks with their vulnerability to human footprint and future climate change and measured their coverage within protected areas. This analysis revealed that unique networks situated in southern Europe were particularly exposed to human footprint and that unique networks in the Arctic might be at risk from future climate change. However, considering interaction networks at the level of trophic groups, rather than species, revealed that the general structure of trophic networks was redundant across the continent, in contrast to species’ interactions. We argue that proactive European conservation strategies might gain relevance by turning their eyes toward interaction networks that are both unique and vulnerable.

Aim: Forecasting changes in species distribution under future scenarios is one of the most prolific areas of application for species distribution models (SDMs). However, no consensus yet exists on the reliability of such models for drawing conclusions on species’ distribution response to changing climate. In this study, we provide an overview of common modelling practices in the field and assess the reliability of model predictions using a virtual species approach. Location: Global. Methods: We first review papers published between 2015 and 2019. Then, we use a virtual species approach and three commonly applied SDM algorithms (GLM, MaxEnt and random forest) to assess the estimated and actual predictive performance of models parameterized with different modelling settings and violations of modelling assumptions. Results: Most SDM papers relied on single models (65%) and small samples (N < 50, 62%), used presence-only data (85%), binarized models' output (74%) and used a split-sample validation (94%). Our simulation reveals that the split-sample validation tends to be over-optimistic compared to the real performance, whereas spatial block validation provides a more honest estimate, except when datasets are environmentally biased. The binarization of predicted probabilities of presence reduces models’ predictive ability considerably. Sample size is one of the main predictors of the real model accuracy, but has little influence on estimated accuracy. Finally, the inclusion of ecologically irrelevant predictors and the violation of modelling assumptions increases estimated accuracy but decreases real accuracy of model projections, leading to biased estimates of range contraction and expansion. Main conclusions: Our ability to predict future species distribution is low on average, particularly when models’ predictions are binarized. A robust validation by spatially independent samples is required, but does not rule out inflation of model accuracy by assumption violation.