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The ability of climatic niche models to predict species extinction risks can be hampered if niches are incompletely quantified. This can occur when niches are estimated considering only currently available climatic conditions, disregarding the fact that climate change can open up portions of the fundamental niche that are currently inaccessible to species. Using a new metric, we estimate the prevalence of potential situations of fundamental niche truncation by measuring whether current ecological niche limits are contiguous to the boundaries of currently available climatic conditions for 24,944 species at the global scale in both terrestrial and marine realms and including animals and plants. We show that 12,172 (~49%) species are showing niche contiguity, particularly those inhabiting tropical ecosystems and the marine realm. Using niche expansion scenarios, we find that 86% of species showing niche contiguity could have a fundamental niche potentially expanding beyond current climatic limits, resulting in lower-yet still alarming-rates of predicted biodiversity loss, particularly within the tropics. Caution is therefore advised when forecasting future distributions of species presenting niche contiguity, particularly towards climatic limits that are predicted to expand in the future., (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)

Mountains are hotspots of biodiversity and ecosystem services, but they are warming about twice as fast as the global average. Climate change may reduce alpine snow cover and increase vegetation productivity, as in the Arctic. Here, we demonstrate that 77% of the European Alps above the tree line experienced greening (productivity gain) and <1% browning (productivity loss) over the past four decades. Snow cover declined significantly during this time, but in <10% of the area. These trends were only weakly correlated: Greening predominated in warmer areas, driven by climatic changes during summer, while snow cover recession peaked at colder temperatures, driven by precipitation changes. Greening could increase carbon sequestration, but this is unlikely to outweigh negative implications, including reduced albedo and water availability, thawing permafrost, and habitat loss.

Assessing the degree to which climate explains the spatial distributions of different taxonomic and functional groups is essential for anticipating the effects of climate change on ecosystems. Most effort so far has focused on above-ground organisms, which offer only a partial view on the response of biodiversity to environmental gradients. Here including both above- and below-ground organisms, we quantified the degree of topoclimatic control on the occurrence patterns of >1,500 taxa and phylotypes along a c. 3,000 m elevation gradient, by fitting species distribution models. Higher model performances for animals and plants than for soil microbes (fungi, bacteria and protists) suggest that the direct influence of topoclimate is stronger on above-ground species than on below-ground microorganisms. Accordingly, direct climate change effects are predicted to be stronger for above-ground than for below-ground taxa, whereas factors expressing local soil microclimate and geochemistry are likely more important to explain and forecast the occurrence patterns of soil microbiota. Detailed mapping and future scenarios of soil microclimate and microhabitats, together with comparative studies of interacting and ecologically dependent above- and below-ground biota, are thus needed to understand and realistically forecast the future distribution of ecosystems., (© 2020 The Authors. Global Change Biology published by John Wiley & Sons Ltd.)

Demand for models in biodiversity assessments is rising, but which models are adequate for the task? We propose a set of best-practice standards and detailed guidelines enabling scoring of studies based on species distribution models for use in biodiversity assessments. We reviewed and scored 400 modeling studies over the past 20 years using the proposed standards and guidelines. We detected low model adequacy overall, but with a marked tendency of improvement over time in model building and, to a lesser degree, in biological data and model evaluation. We argue that implementation of agreed-upon standards for models in biodiversity assessments would promote transparency and repeatability, eventually leading to higher quality of the models and the inferences used in assessments. We encourage broad community participation toward the expansion and ongoing development of the proposed standards and guidelines.

Understanding the mechanisms by which the abiotic and biotic requirements of species, or ecological niches, change over time is a central issue in evolutionary biology. Niche evolution is poorly understood at both the macroecological and macroevolutionary scales, as niches can shift over short periods of time but appear to change more slowly over longer timescales. Although reconstructing past niches has always been a major concern for palaeontologists and evolutionary biologists, only a few recent studies have successfully determined the factors that affect niche evolution. Here, we compare the evolution of climatic niches in four main groups of terrestrial vertebrates using a modelling approach integrating both palaeontological and neontological data, and large-scale datasets that contain information on the current distributions, phylogenetic relationships and fossil records for a total of 11,465 species. By reconstructing historical shifts in geographical ranges and climatic niches, we show that niche shifts are significantly faster in endotherms (birds and mammals) than in ectotherms (squamates and amphibians). We further demonstrate that the diversity patterns of the four clades are directly affected by the rate of niche evolution, with fewer latitudinal shifts in ectotherms.

Improving biodiversity predictions is essential if we are to meet the challenges posed by global change. As knowledge is key to feed models, we need to evaluate how debated theory can affect models. An important ongoing debate is whether environmental constraints limit the number of species that can coexist in a community (saturation), with recent findings suggesting that species richness in many communities might be unsaturated. Here, we propose that biodiversity models could address this issue by accounting for a duality: considering communities as unsaturated but where species composition is constrained by different scale-dependent biodiversity drivers. We identify a variety of promising advances for incorporating this duality into commonly applied biodiversity modelling approaches and improving their spatial predictions., (Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Mountain ecosystems are characterized by a diverse range of climatic and topographic conditions over short distances and are known to shelter a high biodiversity. Despite important progress, still little is known on bacterial diversity in mountain areas. Here, we investigated soil bacterial biogeography at more than 100 sampling sites randomly stratified across a 700-km 2 area with 2,200-m elevation gradient in the western Swiss Alps. Bacterial grassland communities were highly diverse, with 12,741 total operational taxonomic units (OTUs) across 100 sites and an average of 2,918 OTUs per site. Bacterial community structure was correlated with local climatic, topographic, and soil physicochemical parameters with high statistical significance. We found pH (correlated with % CaO and % mineral carbon), hydrogen index (correlated with bulk gravimetric water content), and annual average number of frost days during the growing season to be among the groups of the most important environmental drivers of bacterial community structure. In contrast, bacterial community structure was only weakly stratified as a function of elevation. Contrasting patterns were discovered for individual bacterial taxa. Acidobacteria responded both positively and negatively to pH extremes. Various families within the Bacteroidetes responded to available phosphorus levels. Different verrucomicrobial groups responded to electrical conductivity, total organic carbon, water content, and mineral carbon contents. Alpine grassland bacterial communities are thus highly diverse, which is likely due to the large variety of different environmental conditions. These results shed new light on the biodiversity of mountain ecosystems, which were already identified as potentially fragile to anthropogenic influences and climate change., Importance: This article addresses the question of how microbial communities in alpine regions are dependent on local climatic and soil physicochemical variables. We benefit from a unique 700-km 2 study region in the western Swiss Alps region, which has been exhaustively studied for macro-organismal and fungal ecology, and for topoclimatic modeling of future ecological trends, but without taking into account soil bacterial diversity. Here, we present an in-depth biogeographical characterization of the bacterial community diversity in this alpine region across 100 randomly stratified sites, using 56 environmental variables. Our exhaustive sampling ensured the detection of ecological trends with high statistical robustness. Our data both confirm previously observed general trends and show many new detailed trends for a wide range of bacterial taxonomic groups and environmental parameters., (Copyright © 2016, American Society for Microbiology. All Rights Reserved.)

It remains hotly debated whether latitudinal diversity gradients are common across taxonomic groups and whether a single mechanism can explain such gradients. Investigating species richness (SR) patterns of European land plants, we determine whether SR increases with decreasing latitude, as predicted by theory, and whether the assembly mechanisms differ among taxonomic groups. SR increases towards the south in spermatophytes, but towards the north in ferns and bryophytes. SR patterns in spermatophytes are consistent with their patterns of beta diversity, with high levels of nestedness and turnover in the north and in the south, respectively, indicating species exclusion towards the north and increased opportunities for speciation in the south. Liverworts exhibit the highest levels of nestedness, suggesting that they represent the most sensitive group to the impact of past climate change. Nevertheless, although the extent of liverwort species turnover in the south is substantially and significantly lower than in spermatophytes, liverworts share with the latter a higher nestedness in the north and a higher turn-over in the south, in contrast to mosses and ferns. The extent to which the similarity in the patterns displayed by spermatophytes and liverworts reflects a similar assembly mechanism remains, however, to be demonstrated.

Considering genetic relatedness among species has long been argued as an important step toward measuring biological diversity more accurately, rather than relying solely on species richness. Some researchers have correlated measures of phylogenetic diversity and species richness across a series of sites and suggest that values of phylogenetic diversity do not differ enough from those of species richness to justify their inclusion in conservation planning. We compared predictions of species richness and 10 measures of phylogenetic diversity by creating distribution models for 168 individual species of a species-rich plant family, the Cape Proteaceae. When we used average amounts of land set aside for conservation to compare areas selected on the basis of species richness with areas selected on the basis of phylogenetic diversity, correlations between species richness and different measures of phylogenetic diversity varied considerably. Correlations between species richness and measures that were based on the length of phylogenetic tree branches and tree shape were weaker than those that were based on tree shape alone. Elevation explained up to 31% of the segregation of species rich versus phylogenetically rich areas. Given these results, the increased availability of molecular data, and the known ecological effect of phylogenetically rich communities, consideration of phylogenetic diversity in conservation decision making may be feasible and informative., (©2011 Society for Conservation Biology.)

The Mediterranean basin is considered a hotspot of biological diversity with a long history of modification of natural ecosystems by human activities, and is one of the regions that will face extensive changes in climate. For 181 terrestrial mammals (68% of all Mediterranean mammals), we used an ensemble forecasting approach to model the future (approx. 2100) potential distribution under climate change considering five climate change model outputs for two climate scenarios. Overall, a substantial number of Mediterranean mammals will be severely threatened by future climate change, particularly endemic species. Moreover, we found important changes in potential species richness owing to climate change, with some areas (e.g. montane region in central Italy) gaining species, while most of the region will be losing species (mainly Spain and North Africa). Existing protected areas (PAs) will probably be strongly influenced by climate change, with most PAs in Africa, the Middle East and Spain losing a substantial number of species, and those PAs gaining species (e.g. central Italy and southern France) will experience a substantial shift in species composition.

Understanding niche evolution, dynamics, and the response of species to climate change requires knowledge of the determinants of the environmental niche and species range limits. Mean values of climatic variables are often used in such analyses. In contrast, the increasing frequency of climate extremes suggests the importance of understanding their additional influence on range limits. Here, we assess how measures representing climate extremes (i.e., interannual variability in climate parameters) explain and predict spatial patterns of 11 tree species in Switzerland. We find clear, although comparably small, improvement (+20% in adjusted D(2), +8% and +3% in cross-validated True Skill Statistic and area under the receiver operating characteristics curve values) in models that use measures of extremes in addition to means. The primary effect of including information on climate extremes is a correction of local overprediction and underprediction. Our results demonstrate that measures of climate extremes are important for understanding the climatic limits of tree species and assessing species niche characteristics. The inclusion of climate variability likely will improve models of species range limits under future conditions, where changes in mean climate and increased variability are expected.

Vast amounts of Primary Biodiversity Data exist online (~109 records, each documenting an individual species at a point in space and time). These data hold immense but unrealized promise for science and society, including use in biogeographic research addressing issues such as zoonotic diseases, invasive species, threatened species and habitats, and climate change. Ongoing and envisioned changes in biodiversity informatics involving data providers, aggregators, and users should catalyze improvements to allow efficient use of such data for diverse analyses. We discuss relevant issues from the perspective of modeling species distributions, currently the most common use of Primary Biodiversity Data. Key cross-cutting principles for progress include harnessing feedback from users and increasing incentives for improving data quality. Critical challenges include: (1) establishing individual and collective stable unique identifiers across all of biodiversity science, (2) highlighting issues regarding data quality and representativeness, and (3) improving feedback mechanisms. Such changes should lead to ever-better data and increased utility and impact, including greater data integration with various research areas within and beyond biogeography (e.g., population demography, biotic interactions, physiology, and genetics). Building on existing pilot functionalities, biodiversity informatics could see transformative changes over the coming decade via a combination of community consensus building, coordinated efforts to justify and secure funding, and technical innovations.

The rapid loss of biodiversity in freshwater systems asks for a robust and spatially explicit understanding of species' occurrences. As two complementing approaches, habitat suitability models provide information about species' potential occurrence, while environmental DNA (eDNA) based assessments provide indication of species' actual occurrence. Individually, both approaches are used in ecological studies to characterize biodiversity, yet they are rarely combined. Here, we integrated high‐resolution habitat suitability models with eDNA‐based assessments of aquatic invertebrates in riverine networks to understand their individual and combined capacity to inform on species' occurrence. We used eDNA sampling data from 172 river sites and combined the detection of taxa from three insect orders (Ephemeroptera, Plecoptera, Trichoptera; hereafter EPT) with suitable habitat predictions at a subcatchment level (2 km2). Overall, we find congruence of habitat suitability and eDNA‐based detections. Yet, the models predicted suitable habitats beyond the number of detections by eDNA sampling, congruent with the suitable niche being larger than the realized niche. For local mismatches, where eDNA detected a species but the habitat was not predicted suitable, we calculated the minimal distance to upstream suitable habitat patches, indicating possible sources of eDNA signals from upstream sites subsequently being transported along the water flow. We estimated a median distance of 1.06 km (range 0.2–42 km) of DNA transport based on upstream habitat suitability, and this distance was significantly smaller than expected by null model predictions. This estimated transport distance is in the range of previously reported values and allows extrapolations of transport distances across many taxa and riverine systems. Together, the combination of eDNA and habitat suitability models allows larger scale and spatially integrative inferences about biodiversity, ultimately needed for the management and protection of biodiversity. [ABSTRACT FROM AUTHOR]

The Anthropocene is characterized by a rapid pace of environmental change and is causing a multitude of biotic responses, including those that affect the spatial distribution of species. Lagged responses are frequent and species distributions and assemblages are consequently pushed into a disequilibrium state. How the characteristics of environmental change—for example, gradual 'press' disturbances such as rising temperatures due to climate change versus infrequent 'pulse' disturbances such as extreme events—affect the magnitude of responses and the relaxation times of biota has been insufficiently explored. It is also not well understood how widely used approaches to assess or project the responses of species to changing environmental conditions can deal with time lags. It, therefore, remains unclear to what extent time lags in species distributions are accounted for in biodiversity assessments, scenarios and models; this has ramifications for policymaking and conservation science alike. This perspective piece reflects on lagged species responses to environmental change and discusses the potential consequences for species distribution models (SDMs), the tools of choice in biodiversity modelling. We suggest ways to better account for time lags in calibrating these models and to reduce their leverage effects in projections for improved biodiversity science and policy. [ABSTRACT FROM AUTHOR]

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 algorithm‐specific 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. [ABSTRACT FROM AUTHOR]

Spatial autocorrelation (SAC) is a common feature of ecological data where observations tend to be more similar at some geographic distance(s) than expected by chance. Despite the implications of SAC for data dependencies, its impact on the performance of species distribution models (SDMs) remains controversial, with reports of both strong and negligible impacts on inference. Yet, no study has comprehensively assessed the prevalence and the strength of SAC in the residuals of SDMs over entire geographic areas. Here, we used a large‐scale spatial inventory in the western Swiss Alps to provide a thorough assessment of the importance of SAC for (1) 850 species belonging to nine taxonomic groups, (2) six predictors commonly used for modeling species distributions, and (3) residuals obtained from SDMs fitted with two algorithms with the six predictors included as covariates. We used various statistical tools to evaluate (1) the global level of SAC, (2) the spatial pattern and spatial extent of SAC, and (3) whether local clusters of SAC can be detected. We further investigated the effect of the sampling design on SAC levels. Overall, while environmental predictors expectedly displayed high SAC levels, SAC in biodiversity data was rather low overall and vanished rapidly at a distance of ~5–10 km. We found low evidence for the existence of local clusters of SAC. Most importantly, model residuals were not spatially autocorrelated, suggesting that inferences derived from SDMs are unlikely to be affected by SAC. Further, our results suggest that the influence of SAC can be reduced by a careful sampling design. Overall, our results suggest that SAC is not a major concern for rugged mountain landscapes. [ABSTRACT FROM AUTHOR]

Aim: Bats are important components of mammalian biodiversity and strong bioindicators, but their fine‐scale distributions often remain less known than other taxa (e.g., plants, birds). Yet as highly mobile species with multiple needs in the landscape, bats impose serious modelling challenges, such as advanced use of neighbourhood analyses. The aims of this study were to test the use of a designed sampling of bats for biodiversity and conservation assessments, and to find appropriate modelling solutions for providing nature practitioners with reliable potential bat distribution maps in a mountain area of high conservation interest. Location: The western Swiss Alps of Vaud. Methods: We conducted a one‐year field survey combining passive acoustic recordings supplemented by mist net catching to collect data on bats. These data were then used to create univariate models with focal land use/cover variables using different focal window sizes to detect the optimal species‐specific scale of influence for each variable. The large number of selected variables was then used to create ensembles of small models at a 100 m × 100 m resolution, and the resulting habitat suitability maps were transformed into species distribution maps for practitioners. Results: We were able to collect data to model 14 different bat species representing 66% of the Swiss bat diversity, including four red list species. In general, the most important variables were Euclidean distance to road or water, temperature and slope, but there was large variation among species both for the variable importance and for the optimal focal window size. Main conclusion: Our study greatly increased the knowledge of bats in this region and showed that many of the red list species are nowadays disappearing from the intensively used lowland plains and restricted to the remaining forests along the slopes. Additionally, we highlighted the importance of selecting the variable scale on a species‐specific basis accounting for their mobility and range sizes. [ABSTRACT FROM AUTHOR]

Questions: Species distribution models (SDMs) based on habitat suitability and niche quantification are powerful tools in vegetation science. Recent findings suggest that they could be applied at the landscape scale as vegetation conservation tools, but that some environmental dimensions (e.g., climate) need to be considered at larger scales. What is the importance of applying hierarchical SDMs combining information from different scales to ensure consistent local vegetation management decisions? Study Site: Mainland Spain and Biosphere Reserve of Sierra del Rincón (central Spain). Methods: We generated SDMs for five tree species at the regional scale (mainland Spain) using climatic variables plus presence/absence data from the Spanish National Forest Inventory; and at the landscape scale (Sierra del Rincón Biosphere Reserve) using local environmental variables plus locally gathered vegetation presence/absence data. Predictions of both regional and landscape models were combined at the landscape scale following two different hierarchical approaches. The four resulting predictions were compared with correlation coefficients and independently evaluated with the AUC statistic and data collected in the study area. Results: The regional SDMs depict suitable climatic conditions for the tree species, while the landscape SDMs capture important local ecological drivers that influence habitat suitability at finer scales. Expectedly, the regional SDMs predict larger suitable areas than the landscape SDMs. The predictions from the hierarchical approaches are reliable and provide on average better results than non‐hierarchical ones. Conclusions: SDMs can be valuable tools for local plant conservation programs. We present examples of the applicability of a hierarchical modeling approach and conceptual and methodological solutions related to the use of these tools in local vegetation conservation programs. For example, we show that landscape SDMs could be used to determine the current distribution of endangered plant species, while a hierarchical approach would be better suited to define areas to re‐vegetate within a local restoration program. Species distribution models are powerful tools in vegetation science. Here, we highlight the importance of applying hierarchical SDMs combining information from different scales to ensure consistent local vegetation management decisions. We present examples of the applicability of a hierarchical modeling approach and conceptual and methodological solutions related to the use of these tools in local vegetation conservation programs. [ABSTRACT FROM AUTHOR]

The aim of the ecospat package is to make available novel tools and methods to support spatial analyses and modeling of species niches and distributions in a coherent workflow. The package is written in the R language (R Development Core Team) and contains several features, unique in their implementation, that are complementary to other existing R packages. Pre-modeling analyses include species niche quantifications and comparisons between distinct ranges or time periods, measures of phylogenetic diversity, and other data exploration functionalities (e.g. extrapolation detection, ExDet). Core modeling brings together the new approach of ensemble of small models (ESM) and various implementations of the spatially-explicit modeling of species assemblages (SESAM) framework. Post-modeling analyses include evaluation of species predictions based on presence-only data (Boyce index) and of community predictions, phylogenetic diversity and environmentally-constrained species co-occurrences analyses. The ecospat package also provides some functions to supplement the 'biomod2' package (e.g. data preparation, permutation tests and cross-validation of model predictive power). With this novel package, we intend to stimulate the use of comprehensive approaches in spatial modelling of species and community distributions. [ABSTRACT FROM AUTHOR]

Aim Niche-based species distribution models (SDMs) are commonly used to predict impacts of global change on biodiversity, but the reliability of these predictions in space and time depends on their transferability. We tested how the strategy used to choose predictors impacts the transferability of SDMs at a cross-continental scale. Location North America, Eurasia and Australia. Method We used a systematic approach including 50 Holarctic plant invaders and 27 initial predictor variables, considering 10 different strategies for variable selection, accounting for the proximality, multicollinearity and climate analogy of predictors. We compared the average performance of each strategy, some of which used a large number of predictor combinations. Next, we looked for the single best model for each species across all the predictor combinations retained in the analysis. Transferability was considered as the predictive success of SDMs calibrated in the native range and projected onto the invaded range. Results Two strategies showed better SDM transferability on average: a set of predictors known for their ecologically meaningful effects on plant distribution, and the two first axes of a principal component analysis calibrated on all predictor variables (Spc2). From the more than 2000 combinations of predictors per species across strategies, the best set of predictors yielded SDMs with good transferability for 45 species (90%). These best combinations consisted of eight randomly assembled (39 species) or uncorrelated predictors (6 species) and Spc2 (5 species). We also found that internal cross-validation was not sufficient to give full information about the transferability of a SDM to a distinct range. Main conclusion Transferring SDMs at the macroclimatic scale, and thus anticipating invasions, is possible for the large majority of invasive plants considered in this study, but the accuracy of the predictions relies strongly on the choice of predictors. From our results, we recommend including either proximal and state-of-the-art variables or a reduced and orthogonalized set to obtain robust SDM projections. [ABSTRACT FROM AUTHOR]

Aim Species distribution models (SDMs) are currently the most widely used tools in ecology for evaluating the suitability of environments for biodiversity in the face of future environmental change. In this study we seek to provide an assessment of the predictive performance of SDMs over time. How well do SDMs predict for future time periods and what factors influence predictive performance? Innovation We used a historical spatially explicit database of 1.8 million occurrence records for 318 tetrapod species from across continental Australia over the period 1950-2013. We fitted distribution models for each species to data from four multi-decadal time slices and used these to predict the species distributions up to 60 years after the data collection period for the fitted models. We evaluated predictions against observed data from the relevant time period. Predictions were made assuming either complete knowledge of changes in climatic and environmental conditions or assuming the environment and climate remained unchanged between the fitting and evaluation time periods. We used generalized linear mixed models to model variation in the predictive performance of SDMs over time in relation to a variety of factors, including the length of time between fitting and evaluation, species traits, taxonomic group and attributes of the dataset used to fit models. Main conclusions We found that most models provided useful predictions even when the period between model fitting and evaluation was 60 years (area under the receiver operator characteristic curve > 0.7 in 80% of the species evaluated). Variation in predictive performance over time was strongly related to the species range breadth (models for species with broad geographical ranges tended to perform worse than models for locally restricted species) and to the environmental coverage of occupancy data. Conversely, taxonomic group, habitat preferences and body size were not highly influential in describing the variation in predictive performance over time. [ABSTRACT FROM AUTHOR]

Aim The Arctic has experienced marked climatic differences between glacial and interglacial periods and is now subject to a rapidly warming climate. Knowledge of the effects of historical processes on current patterns of diversity may aid predictions of the responses of vegetation to future climate change. We aim to test whether plant species and genetic diversity patterns are correlated with time since deglaciation at regional and local scales. We also investigate whether species richness is correlated with genetic diversity in vascular plants. Location Circumarctic. Methods We investigated species richness of the vascular plant flora of 21 floristic provinces and examined local species richness in 6215 vegetation plots distributed across the Arctic. We assessed levels of genetic diversity inferred from amplified fragment length polymorphism variation across populations of 23 common Arctic species. Correlations between diversity measures and landscape age (time since deglaciation) as well as variables characterizing current climate were analysed using spatially explicit simultaneous autoregressive models. Results Regional species richness of vascular plants and genetic diversity were correlated with each other, and both showed a positive relationship with landscape age. Plot species richness showed differing responses for vascular plants, bryophytes and lichens. At this finer scale, the richness of vascular plants was not significantly related to landscape age, which had a small effect size compared to the models of bryophyte and lichen richness. Main conclusion Our study suggests that imprints of past glaciations in Arctic vegetation diversity patterns at the regional scale are still detectable today. Since Arctic vegetation is still limited by post-glacial migration lag, it will most probably also exhibit lags in response to current and future climate change. Our results also suggest that local species richness at the plot scale is more determined by local habitat factors. [ABSTRACT FROM AUTHOR]

Recent advances in remote sensing technologies have facilitated the generation of very high resolution (VHR) environmental data. Exploratory studies suggested that, if used in species distribution models (SDMs), these data should enable modelling species’ micro-habitats and allow improving predictions for fine-scale biodiversity management. In the present study, we tested the influence, in SDMs, of predictors derived from a VHR digital elevation model (DEM) by comparing the predictive power of models for 239 plant species and their assemblages fitted at six different resolutions in the Swiss Alps. We also tested whether changes of the model quality for a species is related to its functional and ecological characteristics. Refining the resolution only contributed to slight improvement of the models for more than half of the examined species, with the best results obtained at 5 m, but no significant improvement was observed, on average, across all species. Contrary to our expectations, we could not consistently correlate the changes in model performance with species characteristics such as vegetation height. Temperature, the most important variable in the SDMs across the different resolutions, did not contribute any substantial improvement. Our results suggest that improving resolution of topographic data only is not sufficient to improve SDM predictions – and therefore local management – compared to previously used resolutions (here 25 and 100 m). More effort should be dedicated now to conduct finer-scale in-situ environmental measurements (e.g. for temperature, moisture, snow) to obtain improved environmental measurements for fine-scale species mapping and management. [ABSTRACT FROM AUTHOR]

The observation of non-random phylogenetic distribution of traits in communities provides evidence for niche-based community assembly. Environment may influence the phylogenetic structure of communities because traits determining how species respond to prevailing conditions can be phylogenetically conserved. In this study, we investigate the variation of butterfly species richness and of phylogenetic α- and β-diversities along temperature and plant species richness gradients. Our study indicates that butterfly richness is independently positively correlated to temperature and plant species richness in the study area. However, the variation of phylogenetic α- and β-diversities is only correlated to temperature. The significant phylogenetic clustering at high elevation suggests that cold temperature filters butterfly lineages, leading to communities mostly composed of closely related species adapted to those climatic conditions. These results suggest that in colder and more severe conditions at high elevations deterministic processes and not purely stochastic events drive the assemblage of butterfly communities. [ABSTRACT FROM AUTHOR]

Pellissier, L., Litsios, G., Guisan, A. & Alvarez, N. (2012). Molecular substitution rate increases in myrmecophilous lycaenid butterflies (Lepidoptera). - Zoologica Scripta, 41, 651-658. Is species diversification driven by neutral- or niche-based processes? Butterflies of the Lycaenidae family have developed mutualistic interactions with ants. This biotic requirement increased the spatial fragmentation of populations of lower effective population size ( Ne) compared with autonomous species. The nearly neutral theory predicts that species with smaller Ne should fix more mutations because of the increased strength of drift. Taking into account the phylogenetic relatedness among species, this study shows that species with a stronger dependence on ants displayed more intra-specific Single Nucleotide Polymorphisms compared with species with low or no myrmecophily. This phenomenon can cause more pronounced genetic differentiation between populations and could ultimately promote speciation in a similar manner as on physical islands. The large species diversity observed in this family could be the consequence of this neutral process enhancing the diversification of lineages. [ABSTRACT FROM AUTHOR]

Global environmental changes threaten ecosystems and cause significant alterations to the supply of ecosystem services that are vital for human well-being. We provide an assessment of the potential impacts of climate change on the European diversity of vertebrates and their associated pest-control services. We modeled the distributions of the species that provide these services using forecasts from bioclimatic envelope models and then used the results to generate maps of potential species richness among vertebrate providers of pest-control services. We assessed how the potential richness of pest-control providers would change according to different climate and greenhouse emissions scenarios. We found that the potential richness of pest-control providers was likely to face substantial reductions, especially in southern European countries whose economies were highly dependent on agricultural yields. In much of central and northern Europe, where countries' economies were less dependent on agriculture, climate change was likely to benefit pest-control providers. [ABSTRACT FROM AUTHOR]

The objective of this study was to evaluate the performance of stacked species distribution models in predicting the alpha and gamma species diversity patterns of two important plant clades along elevation in the Andes. We modelled the distribution of the species in the Anthurium genus (53 species) and the Bromeliaceae family (89 species) using six modelling techniques. We combined all of the predictions for the same species in ensemble models based on two different criteria: the average of the rescaled predictions by all techniques and the average of the best techniques. The rescaled predictions were then reclassified into binary predictions (presence/absence). By stacking either the original predictions or binary predictions for both ensemble procedures, we obtained four different species richness models per taxa. The gamma and alpha diversity per elevation band (500 m) was also computed. To evaluate the prediction abilities for the four predictions of species richness and gamma diversity, the models were compared with the real data along an elevation gradient that was independently compiled by specialists. Finally, we also tested whether our richness models performed better than a null model of altitudinal changes of diversity based on the literature. Stacking of the ensemble prediction of the individual species models generated richness models that proved to be well correlated with the observed alpha diversity richness patterns along elevation and with the gamma diversity derived from the literature. Overall, these models tend to overpredict species richness. The use of the ensemble predictions from the species models built with different techniques seems very promising for modelling of species assemblages. Stacking of the binary models reduced the over-prediction, although more research is needed. The randomisation test proved to be a promising method for testing the performance of the stacked models, but other implementations may still be developed. [ABSTRACT FROM AUTHOR]

Two different approaches currently prevail for predicting spatial patterns of species assemblages. The first approach (macroecological modelling, MEM) focuses directly on realized properties of species assemblages, whereas the second approach (stacked species distribution modelling, S-SDM) starts with constituent species to approximate the properties of assemblages. Here, we propose to unify he two approaches in a single 'spatially explicit species assemblage modelling' SESAM) framework. This framework uses relevant designations of initial species source pools for modelling, macroecological variables, and ecological assembly rules to constrain predictions of the richness and composition of species assemblages obtained by stacking predictions of individual species distributions. e believe that such a framework could prove useful in many theoretical and applied disciplines of ecology and evolution, both for improving our basic understanding of species assembly across spatio-temporal scales and for anticipating expected consequences of local, regional or global environmental changes. n this paper, we propose such a framework and call for further developments and testing across a broad range of community types in a variety of environments. [ABSTRACT FROM AUTHOR]

Abstract: We present a new indicator taxa approach to the prediction of climate change effects on biodiversity at the national level in Switzerland. As indicators, we select a set of the most widely distributed species that account for 95% of geographical variation in sampled species richness of birds, butterflies, and vascular plants. Species data come from a national program designed to monitor spatial and temporal trends in species richness. We examine some opportunities and limitations in using these data. We develop ecological niche models for the species as functions of both climate and land cover variables. We project these models to the future using climate predictions that correspond to two IPCC 3rd assessment scenarios for the development of ‘greenhouse’ gas emissions. We find that models that are calibrated with Swiss national monitoring data perform well in 10-fold cross-validation, but can fail to capture the hot-dry end of environmental gradients that constrain some species distributions. Models for indicator species in all three higher taxa predict that climate change will result in turnover in species composition even where there is little net change in predicted species richness. Indicator species from high elevations lose most areas of suitable climate even under the relatively mild B2 scenario. We project some areas to increase in the number of species for which climate conditions are suitable early in the current century, but these areas become less suitable for a majority of species by the end of the century. Selection of indicator species based on rank prevalence results in a set of models that predict observed species richness better than a similar set of species selected based on high rank of model AUC values. An indicator species approach based on selected species that are relatively common may facilitate the use of national monitoring data for predicting climate change effects on the distribution of biodiversity. [Copyright &y& Elsevier]

The usefulness of species distribution models (SDMs) in predicting impacts of climate change on biodiversity is difficult to assess because changes in species ranges may take decades or centuries to occur. One alternative way to evaluate the predictive ability of SDMs across time is to compare their predictions with data on past species distributions. We use data on plant distributions, fossil pollen and current and mid-Holocene climate to test the ability of SDMs to predict past climate-change impacts. We find that species showing little change in the estimated position of their realized niche, with resulting good model performance, tend to be dominant competitors for light. Different mechanisms appear to be responsible for among-species differences in model performance. Confidence in predictions of the impacts of climate change could be improved by selecting species with characteristics that suggest little change is expected in the relationships between species occurrence and climate patterns. [ABSTRACT FROM AUTHOR]

Species distribution modelling is central to both fundamental and applied research in biogeography. Despite widespread use of models, there are still important conceptual ambiguities as well as biotic and algorithmic uncertainties that need to be investigated in order to increase confidence in model results. We identify and discuss five areas of enquiry that are of high importance for species distribution modelling: (1) clarification of the niche concept; (2) improved designs for sampling data for building models; (3) improved parameterization; (4) improved model selection and predictor contribution; and (5) improved model evaluation. The challenges discussed in this essay do not preclude the need for developments of other areas of research in this field. However, they are critical for allowing the science of species distribution modelling to move forward. [ABSTRACT FROM AUTHOR]

Aim This study used data from temperate forest communities to assess: (1) five different stepwise selection methods with generalized additive models, (2) the effect of weighting absences to ensure a prevalence of 0.5, (3) the effect of limiting absences beyond the environmental envelope defined by presences, (4) four different methods for incorporating spatial autocorrelation, and (5) the effect of integrating an interaction factor defined by a regression tree on the residuals of an initial environmental model. Location State of Vaud, western Switzerland. Methods Generalized additive models (GAMs) were fitted using the grasp package (generalized regression analysis and spatial predictions, http://www.cscf.ch/grasp). Results Model selection based on cross-validation appeared to be the best compromise between model stability and performance (parsimony) among the five methods tested. Weighting absences returned models that perform better than models fitted with the original sample prevalence. This appeared to be mainly due to the impact of very low prevalence values on evaluation statistics. Removing zeroes beyond the range of presences on main environmental gradients changed the set of selected predictors, and potentially their response curve shape. Moreover, removing zeroes slightly improved model performance and stability when compared with the baseline model on the same data set. Incorporating a spatial trend predictor improved model performance and stability significantly. Even better models were obtained when including local spatial autocorrelation. A novel approach to include interactions proved to be an efficient way to account for interactions between all predictors at once. Main conclusions Models and spatial predictions of 18 forest communities were significantly improved by using either: (1) cross-validation as a model selection method, (2) weighted absences, (3) limited absences, (4) predictors accounting for spatial autocorrelation, or (5) a factor variable accounting for interactions between all predictors. The final choice of model strategy should depend on the nature of the available data and the specific study aims. Statistical evaluation is useful in searching for the best modelling practice. However, one should not neglect to consider the shapes and interpretability of response curves, as well as the resulting spatial predictions in the final assessment. [ABSTRACT FROM AUTHOR]

Aim To assess the geographical transferability of niche-based species distribution models fitted with two modelling techniques. Location Two distinct geographical study areas in Switzerland and Austria, in the subalpine and alpine belts. Methods Generalized linear and generalized additive models (GLM and GAM) with a binomial probability distribution and a logit link were fitted for 54 plant species, based on topoclimatic predictor variables. These models were then evaluated quantitatively and used for spatially explicit predictions within (internal evaluation and prediction) and between (external evaluation and prediction) the two regions. Comparisons of evaluations and spatial predictions between regions and models were conducted in order to test if species and methods meet the criteria of full transferability. By full transferability, we mean that: (1) the internal evaluation of models fitted in region A and B must be similar; (2) a model fitted in region A must at least retain a comparable external evaluation when projected into region B, and vice-versa; and (3) internal and external spatial predictions have to match within both regions. Results The measures of model fit are, on average, 24% higher for GAMs than for GLMs in both regions. However, the differences between internal and external evaluations (AUC coefficient) are also higher for GAMs than for GLMs (a difference of 30% for models fitted in Switzerland and 54% for models fitted in Austria). Transferability, as measured with the AUC evaluation, fails for 68% of the species in Switzerland and 55% in Austria for GLMs (respectively for 67% and 53% of the species for GAMs). For both GAMs and GLMs, the agreement between internal and external predictions is rather weak on average (Kulczynski's coefficient in the range 0.3-0.4), but varies widely among individual species. The dominant pattern is an asymmetrical transferability between the two study regions (a mean decrease of 20% for the AUC coefficient when the models are transferred from Switzerland and 13% when they are transferred from Austria). Main conclusions The large inter-specific variability observed among the 54 study species underlines the need to consider more than a few species to test properly the transferability of species distribution models. The pronounced asymmetry in transferability between the two study regions may be due to peculiarities of these regions, such as differences in the ranges of environmental predictors or the varied impact of land-use history, or to species-specific reasons like differential phenotypic plasticity, existence of ecotypes or varied dependence on biotic interactions that are not properly incorporated into niche-based models. The lower variation between internal and external evaluation of GLMs compared to GAMs further suggests that overfitting may reduce transferability. Overall, a limited geographical transferability calls for caution when projecting niche-based models for assessing the fate of species in future environments. [ABSTRACT FROM AUTHOR]

The loss of biodiversity has become a matter of urgent concern and a better understanding of local drivers is crucial for conservation. Although environmental heterogeneity is recognized as an important determinant of biodiversity, this has rarely been tested using field data at management scale. We propose and provide evidence for the simple hypothesis that local species diversity is related to spatial environmental heterogeneity. Species partition the environment into habitats. Biodiversity is therefore expected to be influenced by two aspects of spatial heterogeneity: 1) the variability of environmental conditions, which will affect the number of types of habitat, and 2) the spatial configuration of habitats, which will affect the rates of ecological processes, such as dispersal or competition. Earlier, simulation experiments predicted that both aspects of heterogeneity will influence plant species richness at a particular site. For the first time, these predictions were tested for plant communities using field data, which we collected in a wooded pasture in the Swiss Jura mountains using a four-level hierarchical sampling design. Richness generally increased with increasing environmental variability and “roughness” (i.e. decreasing spatial aggregation). Effects occurred at all scales, but the nature of the effect changed with scale, suggesting a change in the underlying mechanisms, which will need to be taken into account if scaling up to larger landscapes. Although we found significant effects of environmental heterogeneity, other factors such as history could also be important determinants. If a relationship between environmental heterogeneity and species richness can be shown to be general, recently available high-resolution environmental data can be used to complement the assessment of patterns of local richness and improve the prediction of the effects of land use change based on mean site conditions or land use history. [ABSTRACT FROM AUTHOR]

We modelled the future distribution in 2050 of 975 endemic plant species in southern Africa distributed among seven life forms, including new methodological insights improving the accuracy and ecological realism of predictions of global changes studies by: (i) using only endemic species as a way to capture the full realized niche of species, (ii) considering the direct impact of human pressure on landscape and biodiversity jointly with climate, and (iii) taking species' migration into account. Our analysis shows important promises for predicting the impacts of climate change in conjunction with land transformation. We have shown that the endemic flora of Southern Africa on average decreases with 41% in species richness among habitats and with 39% on species distribution range for the most optimistic scenario. We also compared the patterns of species' sensitivity with global change across life forms, using ecological and geographic characteristics of species. We demonstrate here that species and life form vulnerability to global changes can be partly explained according to species' (i) geographical distribution along climatic and biogeographic gradients, like climate anomalies, (ii) niche breadth or (iii) proximity to barrier preventing migration. Our results confirm that the sensitivity of a given species to global environmental changes depends upon its geographical distribution and ecological proprieties, and makes it possible to estimate a priori its potential sensitivity to these changes. [ABSTRACT FROM AUTHOR]

Over the last half-century, nature conservation has shifted through several steps from 'nature for itself' to 'nature and people', corresponding to a new perspective that all species count to ensure ecosystem functioning, and with them that nature's contributions to people (NCPs) are effective and maintained. Yet, despite these conceptual shifts in the academic literature, conservation practices have remained largely focused on threatened species and protected areas. The last Global Biodiversity Framework (GBF) of the Convention on Biological Diversity insisted on the need to use biodiversity sustainably and ensure nature's contributions to people, including ecosystem functions and services for the benefit of present and future generations by 2050. Here, using recently developed tables relating a large number of species observed in the Western Swiss Alps (vascular plants and vertebrates; n = 2066) to 17 key NCPs, we show that focusing on protecting threatened species only does not ensure the maintenance of key NCPs. Our results suggest that all species (threatened or not) need to be considered, in addition to strict conservation of threatened species, to support NCP provision. Similarly, considering all species better supports existing conservation programs. Developing such direct species-NCP relationships more broadly will be needed to support spatial prioritizations and help reach the 2050 GBF goals. • We promote synergistic species-NCP relationships rather than trade-offs. • Protect threatened species doesn't ensure the protection of all NCPs and thus of human's well-being. • We need to consider non-threatened species in conservation to maintain a sustainable future with NCPs and biodiversity. • Integrating non-threatened species in conservation allows better supporting global biodiversity goals. [ABSTRACT FROM AUTHOR]

• 15 Nature's contributions to people (NCP) indicators are assessed. • Generation of a biodiversity index (BD) representing over 1400 threatened species. • Four bundles of NCP and BD display heterogeneous distribution in Switzerland. • Topography, climate and land-use are major drivers of NCP and BD bundles. Changes in climate and land use represent significant risks of biodiversity loss globally, affect ecological stability, impact nature's contributions to people (NCP, i.e. ecosystem services) and compromise human livelihood. As framings of conservation evolve to consider the interdependence between species and human needs, there is a growing recognition of the importance of NCP and biodiversity in conservation actions. However, knowledge on the interactions and spatial repartition of NCP and biodiversity remains limited. Here we show a comprehensive spatial assessment for 15 NCP and one biodiversity – distribution of threatened species – indicators in Switzerland. Indicators values were computed using a panel of mapping and modelling methods extracted from the literature, or specifically developed for this study. Through the analysis of their relationships, we reveal significant trade-offs and synergies in the spatial repartition of these indicators. Results from a spatial bundle analysis performed on the 16 indicators revealed the existence of four bundles showing a heterogeneous repartition over the Swiss landscape. Furthermore, we identified that topography (slope), climate (temperature and precipitations), and habitat (forest and meadows) were among the most influential factors to explain the spatial distribution of the four bundles. We conclude that various significant relationships exist between NCP and biodiversity indicators in Switzerland, emphasizing the importance of informed conservation approaches considering both NCP and biodiversity supply. This work helps fill the gap in our understanding of the links among different NCP, between NCP and biodiversity, and highlight their relationship to climate and land use, providing key insights for optimizing conservation efforts. [ABSTRACT FROM AUTHOR]

Scenario analyses have been used in multiple science-policy assessments to better understand complex plausible futures. Scenario archetype approaches are based on the fact that many future scenarios have similar underlying storylines, assumptions, and trends in drivers of change, which allows for grouping of scenarios into typologies, or archetypes, facilitating comparisons between a large range of studies. The use of scenario archetypes in environmental assessments foregrounds important policy questions and can be used to codesign interventions tackling future sustainability issues. Recently, scenario archetypes were used in four regional assessments and one ongoing global assessment within the Intergovernmental Science-Policy Platform for Biodiversity and Ecosystem Services (IPBES). The aim of these assessments was to provide decision makers with policy-relevant knowledge about the state of biodiversity, ecosystems, and the contributions they provide to people. This paper reflects on the usefulness of the scenario archetype approach within science-policy processes, drawing on the experience from the IPBES assessments. Using a thematic analysis of (a) survey data collected from experts involved in the archetype analyses across IPBES assessments, (b) notes from IPBES workshops, and (c) regional assessment chapter texts, we synthesize the benefits, challenges, and frontiers of applying the scenario archetype approach in a science-policy process. Scenario archetypes were perceived to allow syntheses of large amounts of information for scientific, practice-, and policy-related purposes, streamline key messages from multiple scenario studies, and facilitate communication of them to end users. In terms of challenges, they were perceived as subjective in their interpretation, oversimplifying information, having a limited applicability across scales, and concealing contextual information and novel narratives. Finally, our results highlight what methodologies, applications, and frontiers in archetype-based research should be explored in the future. These advances can assist the design of future large-scale sustainability-related assessment processes, aiming to better support decisions and interventions for equitable and sustainable futures. [ABSTRACT FROM AUTHOR]

Scenarios are a useful tool to explore possible futures of social-ecological systems. The number of scenarios has increased dramatically over recent decades, with a large diversity in temporal and spatial scales, purposes, themes, development methods, and content. Scenario archetypes generically describe future developments and can be useful in meaningfully classifying scenarios, structuring and summarizing the overwhelming amount of information, and enabling scientific outputs to more effectively interface with decision-making frameworks. The Intergovernmental Platform for Biodiversity and Ecosystem Services (IPBES) faced this challenge and used scenario archetypes in its assessment of future interactions between nature and society. We describe the use of scenario archetypes in the IPBES Regional Assessment of Europe and Central Asia. Six scenario archetypes for the region are described in terms of their driver assumptions and impacts on nature (including biodiversity) and its contributions to people (including ecosystem services): business-as-usual, economic optimism, regional competition, regional sustainability, global sustainable development, and inequality. The analysis shows that trade-offs between nature's contributions to people are projected under different scenario archetypes. However, the means of resolving these trade-offs depend on differing political and societal value judgements within each scenario archetype. Scenarios that include proactive decision making on environmental issues, environmental management approaches that support multifunctionality, and mainstreaming environmental issues across sectors, are generally more successful in mitigating tradeoffs than isolated environmental policies. Furthermore, those scenario archetypes that focus on achieving a balanced supply of nature's contributions to people and that incorporate a diversity of values are estimated to achieve more policy goals and targets, such as the UN Sustainable Development Goals and the Convention on Biological Diversity Aichi targets. The scenario archetypes approach is shown to be helpful in supporting science-policy dialogue for proactive decision making that anticipates change, mitigates undesirable trade-offs, and fosters societal transformation in pursuit of sustainable development. [ABSTRACT FROM AUTHOR]

Highlights • bS-SDMs is more accurate than SESAM in predicting present-day bird communities. • The two approaches significantly differ in their future predictions. • bS-SDMs predicts higher bird diversity in the future. • SESAM predicts lower future species richness and homogenization of bird communities. • SESAM predictions are more ecologically plausible than bS-SDM ones. Abstract The prevailing method for estimating the potential impact of future climate change on biological communities is to stack binary predictions from species distribution models (binary stacked species distribution models, bS-SDM). However, it has been argued that bS-SDM may overestimate species richness and, hence, community composition. Alternative approaches, such as SESAM ('Spatially Explicit Species Assemblage Modelling'), explicitly incorporate limits to species richness, preventing overestimation. We compared richness and taxonomic composition estimates as predicted by SESAM and bS-SDM for Mediterranean bird communities both in the present day and as projected in the future under simulated climate change scenarios. We trained single-species distribution models (S-SDM) and direct macroecological richness models (MEM) for 81 bird species, using climate, topographic, land-use and human-pressure indicators as predictors. Then, we compared and evaluated the models' predictions. Species richness as predicted by bS-SDM was more accurate than under SESAM for present-day communities. Taxonomic composition was well predicted under both methods. However, we detected significant differences in future projections. Under bS-SDM, increased suitable area for a number of species leads to important changes in community composition and predicts higher levels of diversity in the future. In stark contrast, SESAM predicts lower species richness in the future and strong homogenization of bird communities across space. This study shows how the choice of the modelling approach drives substantially different expectations about future community composition under climate change. We therefore recommend contrasting predictions generated under different modelling approaches to gain better understanding of possible future scenario of biodiversity change. In addition, trasferability tests (i.e. hindcasting to past or predicting from the past to the present) should be used to effectively compare S-SDM and SESAM predictive abilities. [ABSTRACT FROM AUTHOR]