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While there is a clear demand for scenarios that provide alternative states in biodiversity with respect to future emissions, a thorough analysis and communication of the associated uncertainties is still missing. Here, we modelled the global distribution of ~11,500 amphibian, bird and mammal species and project their climatic suitability into the time horizon 2050 and 2070, while varying the input data used. By this, we explore the uncertainties originating from selecting species distribution models (SDMs), dispersal strategies, global circulation models (GCMs), and representative concentration pathways (RCPs). We demonstrate the overwhelming influence of SDMs and RCPs on future biodiversity projections, followed by dispersal strategies and GCMs. The relative importance of each component varies in space but also with the selected sensitivity metrics and with species’ range size. Overall, this means using multiple SDMs, RCPs, dispersal assumptions and GCMs is a necessity in any biodiversity scenario assessment, to explicitly report associated uncertainties. Attaining global biodiversity projections requires the use of various species distribution and climate modelling and scenario approaches. Here the authors report that model choice can significantly impact results, with particularly uncertainty arising from choice of species distribution model and emission scenario.

Expanding protected areas for ecological conservation by just 5% has the potential to markedly increase terrestrial biodiversity protection. Multifaceted biodiversity gains Plans to expand protected areas around the globe could vastly increase the protection of species diversity. But whether or not the benefits extend to other facets of biodiversity is less clear. Laura Pollock et al . assess the extent to which the species, functional and phylogenetic diversity of the world's terrestrial mammals and birds is covered by existing protected areas and the benefits that expansion could bring. They find large gaps in the coverage of each facet of diversity, but show that this could be remedied by a slight expansion of protected areas. They predict that expanding these areas by just 5% has the potential to triple the protected range sizes of species, phylogenetic or functional units. The findings suggest that large gains in terrestrial biodiversity protection are possible with a relatively moderate expansion of protected areas. Different facets of biodiversity other than species numbers are increasingly appreciated as critical for maintaining the function of ecosystems and their services to humans 1 , 2 . While new international policy and assessment processes such as the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) recognize the importance of an increasingly global, quantitative and comprehensive approach to biodiversity protection, most insights are still focused on a single facet of biodiversity—species 3 . Here we broaden the focus and provide an evaluation of how much of the world’s species, functional and phylogenetic diversity of birds and mammals is currently protected and the scope for improvement. We show that the large existing gaps in the coverage for each facet of diversity could be remedied by a slight expansion of protected areas: an additional 5% of the land has the potential to more than triple the protected range of species or phylogenetic or functional units. Further, the same areas are often priorities for multiple diversity facets and for both taxa. However, we find that the choice of conservation strategy has a fundamental effect on outcomes. It is more difficult (that is, requires more land) to maximize basic representation of the global biodiversity pool than to maximize local diversity. Overall, species and phylogenetic priorities are more similar to each other than they are to functional priorities, and priorities for the different bird biodiversity facets are more similar than those of mammals. Our work shows that large gains in biodiversity protection are possible, while also highlighting the need to explicitly link desired conservation objectives and biodiversity metrics. We provide a framework and quantitative tools to advance these goals for multi-faceted biodiversity conservation.

Obtaining reliable predictions of species range shifts under climate change is a crucial challenge for ecologists and stakeholders. At the continental scale, niche-based models have been widely used in the last 10 years to predict the potential impacts of climate change on species distributions all over the world, although these models do not include any mechanistic relationships. In contrast, species-specific, process-based predictions remain scarce at the continental scale. This is regrettable because to secure relevant and accurate predictions it is always desirable to compare predictions derived from different kinds of models applied independently to the same set of species and using the same raw data. Here we compare predictions of range shifts under climate change scenarios for 2100 derived from niche-based models with those of a process-based model for 15 North American boreal and temperate tree species. A general pattern emerged from our comparisons: niche-based models tend to predict a stronger level of extinction and a greater proportion of colonization than the process-based model. This result likely arises because niche-based models do not take phenotypic plasticity and local adaptation into account. Nevertheless, as the two kinds of models rely on different assumptions, their complementarity is revealed by common findings. Both modeling approaches highlight a major potential limitation on species tracking their climatic niche because of migration constraints and identify similar zones where species extirpation is likely. Such convergent predictions from models built on very different principles provide a useful way to offset uncertainties at the continental scale. This study shows that the use in concert of both approaches with their own caveats and advantages is crucial to obtain more robust results and that comparisons among models are needed in the near future to gain accuracy regarding predictions of range shifts under climate change.

Whether species interactions influence species response to environment and species ranges has always been a central question in ecology. Joint species distribution models (JSDMs) simultaneously model the species–environment relationships of multiple species and the residual correlation between these species. These residual correlations are assumed to depict whether species co-occur less or more often than expected by the modelled species–environment relationships, which could ultimately be attributed to species interactions, or hidden environmental information. Here, we propose to specifically test the capacity of JSDMs to detect species interactions from co-occurrence data, at different scales of data aggregation. Using a recently published point-process model, we simulated equilibrium co-occurrence patterns of species pairs by varying the strength and type of interactions (e.g. competition, predator–prey, mutualism) as well as the prevalence of the interacting species in homogeneous environments (assuming the environment does not influence the species responses and co-occurrence). Then, we fitted JSDMs without environmental predictors, and compared the estimated residual correlations against the known interaction coefficients. JSDMs detected competition and mutualism well, but failed with predator–prey interactions. For the latter, JSDMs predicted both negative and positive residual correlations for these kinds of interactions, depending on the prevalence of the interacting species. Interestingly, the estimated residual correlation was strongly influenced by species’ prevalence and can thus not be translated to interaction strength. At increasingly coarser data resolution, the signals of negative and positive interactions became indiscernible by JSDMs, but – reassuringly – were rarely confounded. The underlying point-process model simulates the consequences rather than the mechanisms of interspecific interactions, and thus is better at corroborating rather than discrediting JSDMs. Nevertheless, our simple theoretical exercise pinpoints important limitations of JSDMs. In conclusion, we caution against interpreting residual correlations from JSDMs as interaction strength and against comparing these across different species and communities.

Today's scientists are facing the enormous challenge of predicting how climate change will affect species distributions and species assemblages. To do so, ecologists are widely using phenomenological models of species distributions that mainly rely on the concept of species niche and generally ignore species' demography, species' adaptive potential, and biotic interactions. This review examines the potential role of the emerging synthetic discipline of evolutionary community ecology in improving our understanding of how climate change will alter future distribution of biodiversity. We review theoretical and empirical advances about the role of niche evolution, interspecific interactions, and their interplay in altering species geographic ranges and community assembly. We discuss potential ways to integrate complex feedbacks between ecology and evolution in ecological forecasting. We also point at a number of caveats in our understanding of the eco-evolutionary consequences of climate change and highlight several challenges for future research.

Whether mammal–microbiome interactions are persistent and specific over evolutionary time is controversial. Here we show that host phylogeny and major dietary shifts have affected the distribution of different gut bacterial lineages and did so on vastly different bacterial phylogenetic resolutions. Diet mostly influences the acquisition of ancient and large microbial lineages. Conversely, correlation with host phylogeny is mostly seen among more recently diverged bacterial lineages, consistent with processes operating at similar timescales to host evolution. Considering microbiomes at appropriate phylogenetic scales allows us to model their evolution along the mammalian tree and to infer ancient diets from the predicted microbiomes of mammalian ancestors. Phylogenetic analyses support co-speciation as having a significant role in the evolution of mammalian gut microbiome compositions. Highly co-speciating bacterial genera are also associated with immune diseases in humans, laying a path for future studies that probe these co-speciating bacteria for signs of co-evolution. Both host diet and phylogeny have been argued to shape mammalian microbiome communities. Here, the authors show that diet predicts the presence of ancient bacterial lineages in the microbiome, but that co-speciation between more recent bacterial lineages and their hosts may drive associations between microbiome composition and phylogeny.

Aim Statistical species distribution models (SDMs) are the most common tool to predict the impact of climate change on biodiversity. They can be tuned to fit relationships at various levels of complexity (defined here as parameterization complexity, number of predictors, and multicollinearity) that may co‐determine whether projections to novel climatic conditions are useful or misleading. Here, we assessed how model complexity affects the performance of model extrapolations and influences projections of species ranges under future climate change. Location Europe. Taxon 34 European tree species. Methods We sampled three replicates of predictor sets for all combinations of 10 levels (n = 3–12) of environmental variables (climate, terrain, soil) and 10 levels of multicollinearity. We used these sets for each species to fit four SDM algorithms at three levels of parameterization complexity. The >100,000 resulting SDM fits were then evaluated under environmental block cross‐validation and projected to environmental conditions for 2061–2080 considering four climate models and two emission scenarios. Finally, we investigated the relationships of model design with model performance and projected distributional changes. Results Model complexity affected both model performance and projections of species distributional change. Fits of intermediate parameterization complexity performed best, and more complex parameterizations were associated with higher projected loss of current ranges. Model performance peaked at 10–11 variables but increasing number of variables had no consistent effect on distributional change projections. Multicollinearity had a low impact on model performance but distinctly increased projected loss of current ranges. Main conclusions SDM‐based climate change impact assessments should be based on ensembles of projections, varying SDM algorithms as well as parameterization complexity, besides emission scenarios and climate models. The number of predictor variables should be kept reasonably small and the classical threshold of maximum absolute Pearson correlation of 0.7 restricts collinearity‐driven effects in projections of species ranges.

AimEnvironmental DNA (eDNA) is increasingly used for analysing and modelling all‐inclusive biodiversity patterns. However, the reliability of eDNA‐based diversity estimates is commonly compromised by arbitrary decisions for curating the data from molecular artefacts. Here, we test the sensitivity of common ecological analyses to these curation steps, and identify the crucial ones to draw sound ecological conclusions.LocationValloire, French Alps.TaxonVascular plants and fungi.MethodsUsing soil eDNA metabarcoding data for plants and fungi from 20 plots sampled along a 1000‐m elevational gradient, we tested how the conclusions from three types of ecological analyses: (a) the spatial partitioning of diversity, (b) the diversity–environment relationship, and (c) the distance–decay relationship, are robust to data curation steps. Since eDNA metabarcoding data also comprise erroneous sequences with low frequencies, diversity estimates were further calculated using abundance‐based Hill numbers, which penalize rare sequences through a scaling parameter, namely the order of diversity q (Richness with q = 0, Shannon diversity with q ~ 1, Simpson diversity with q = 2).ResultsWe showed that results from different ecological analyses had varying degrees of sensitivity to data curation strategies and that the use of Shannon and Simpson diversities led to more reliable results. We demonstrated that molecular operational taxonomic unit clustering, removal of polymerase chain reaction errors and of cross‐sample contaminations had major impacts on ecological analyses.Main conclusionsIn the Era of Big Data, eDNA metabarcoding is going to be one of the major tools to describe, model and predict biodiversity in space and time. However, ignoring crucial data curation steps will impede the robustness of several ecological conclusions. Here, we propose a roadmap of crucial curation steps for different types of ecological analyses.

The distribution of living organisms on Earth is spatially structured. Early biogeographers identified the existence of multiple zoogeographical regions, characterized by faunas with homogeneous composition that are separated by biogeographical boundaries. Yet, no study has deciphered the factors shaping the distributions of terrestrial biogeographical boundaries at the global scale. Here, using spatial regression analyses, we show that tectonic movements, sharp changes in climatic conditions and orographic barriers determine extant biogeographical boundaries. These factors lead to abrupt zoogeographical transitions when they act in concert, but their prominence varies across the globe. Clear differences exist among boundaries representing profound or shallow dissimilarities between faunas. Boundaries separating zoogeographical regions with limited divergence occur in areas with abrupt climatic transitions. In contrast, plate tectonics determine the separation between deeply divergent biogeographical realms, particularly in the Old World. Our study reveals the multiple drivers that have shaped the biogeographical regions of the world. Terrestrial animals can be classified into distinct biogeographic regions, but less is known about what shapes these global boundaries. Here, the authors identify geological and climatic factors that determine the separation of realms through time.