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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]

Macroclimatic data are widely used to estimate the realized environmental niche of species and predict the current or the future spatial distribution of species. Because the realized niche is a subset of the fundamental niche—constrained by biotic interactions and dispersal limitations—proxies of the fundamental niche (e.g., thermal limits obtained from physiological experiments) are sometimes combined with macroclimatic data under the assumption that areas predicted as unsuitable from a realized niche perspective may belong to the species' fundamental niche. However, it is unclear whether this assumption is valid and whether thermal limits can be combined with macroclimatic data. Here, we explored these questions using available physiological thermal limits measured for 151 ectotherms. Specifically, we explored whether physiological thermal limits are larger than observed (realized) thermal limits measured using macroclimatic data, and what would be the effect of considering the physiological niche in addition to the realized niche for current and future predictions. Our results confirm previously raised concerns, as physiological limits can delimit a narrower range of thermal tolerance than the realized niche, particularly at the cold end of the thermal gradient where adaptive and/or facilitative mechanisms could allow species to survive in temperatures below physiological limits. These findings show that combining data on physiological thermal limits with macroclimatic data is dubious and that spatial predictions should be interpreted with caution because data on physiological thermal limits do not fit well with macroclimatic data that do not capture the conditions that organisms experience in the wild. While estimated physiological thermal limits are likely of value to complement species distribution studies, they are likely more useful in biophysical models that account for additional processes including the animal's behavior. [ABSTRACT FROM AUTHOR]

Species' distributions can take many different forms. For example, fat-tailed or skewed distributions are very common in nature, as these can naturally emerge as a result of individual variability and asymmetric environmental tolerances, respectively. Studying the basic shape of distributions can teach us a lot about the ways climatic processes and historical contingencies shape ecological communities. Yet we still lack a general understanding of how their shapes and properties compare to each other along gradients. Here, we use Bayesian nonlinear models to quantify range shape properties in empirical plant distributions. With this approach, we are able to distil the shape of plant distributions and compare them along gradients and across species. Studying the relationship between distribution properties, we revealed the existence of broad macroecological patterns along environmental gradients—such as those expected from Rapoport's rule and the abiotic stress limitation hypothesis. We also find that some aspects of the shape of observed ranges—such as kurtosis and skewness of the distributions—could be intrinsic properties of species or the result of their historical contexts. Overall, our modeling approach and results untangle the general shape of plant distributions and provide a mapping of how this changes along environmental gradients. [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]

Aim: Snow cover persistence (SCP) has significant effects on plants in high‐elevation ecosystems. It determines the length of the growing season, provides insulation against low temperatures and influences water availability, thereby shaping the vegetation mosaic. Despite its importance, SCP is rarely used in plant species distribution modelling. In this study, we examine whether incorporating SCP in plant species distribution models (SDMs) improves their predictive power. We investigate the link between species' ecology and SDM improvements by the addition of various SCP predictors. Location: Western Swiss Alps. Taxon: 206 alpine flowering plants (angiosperms). Methods: We produced three maps of landsat satellite‐based SCP indices over an entire mountain region, one of them using an online open access platform allowing quick and easy replication and used them as a predictor in plant SDMs alongside commonly used predictors. We tested whether this improved the predictive performance of plant SDMs. Results: All three SCP indices improved the overall SDM predictive accuracy, but the overall improvement was potentially limited by their correlation with other climatic predictors. Alpine plant species known for their dependence on snow benefited more from the additional snow information. Main Conclusions: SCP should be used for predicting at least the distribution of alpine, snow‐related plant species. Given that adding snow cover improves SDMs and that snow duration decreases as climate warms, future predictions of alpine plant distributions should account for both snow predictor and associated snow change scenarios. [ABSTRACT FROM AUTHOR]

The predictive power of species distribution models (SDMs) varies substantially among species depending on their ecological and life‐history traits, but which of these traits are the most relevant and how they influence species 'predictability' remains an area of debate. Here, we address these questions in bryophytes. SDMs employing macroclimatic, topographic and edaphic predictors were calibrated for 411 species in Europe and externally evaluated using an independent dataset. Regression models were implemented to determine whether species characteristics, including life‐history traits, ecological preference and niche breadth, determine the accuracy of SDMs. Variation in SDM accuracy among species was significantly explained by species characteristics, supporting the hypothesis that the strength of species–environment correlations is affected by characteristics of the species themselves. The percent variance of SDM accuracy explained by species traits, however, substantially varied between 9 and 57% depending on the evaluation metrics used. The lower correlation observed between species traits and MaxKappa and the Boyce index than with area under the curve (AUC) and MaxTSS suggests that the former are less suitable than the latter for determining species 'predictability' based on their traits. SDM accuracy decreased from species restricted to pristine habitats to species thriving in eutrophic habitats with high levels of human disturbance. The widespread distribution of man‐made habitats in fact opens the door for the spread of now ubiquitous species, even in environments that would primarily not be suitable for them. Such species, likely to occur anywhere, reach very high to full occupancy rates, thereby decreasing the accuracy of models aiming at predicting their distributions. The fact that AUC and MaxTSS were higher for species from pristine habitats is important in a conservation context, as ubiquitous species from eutrophic, disturbed environments are precisely the ones of lower conservation relevance. [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]

Habitat suitability calculated from species distribution models (SDMs) has been used to assess population performance, but empirical studies have provided weak or inconclusive support to this approach. Novel approaches measuring population distances to niche centroid and margin in environmental space have been recently proposed to explain population performance, particularly when populations experience exceptional environmental conditions that may place them outside of the species niche. Here, we use data of co‐occurring species' decay, gathered after an extreme drought event occurring in the southeast of the Iberian Peninsula which highly affected rich semiarid shrubland communities, to compare the relationship between population decay (mortality and remaining green canopy) and 1) distances between populations' location and species niche margin and centroid in the environmental space, and 2) climatic suitability estimated from frequently used SDMs (here MaxEnt) considering both the extreme climatic episode and the average reference climatic period before this. We found that both SDMs‐derived suitability and distances to species niche properly predict populations performance when considering the reference climatic period; but climatic suitability failed to predict performance considering the extreme climate period. In addition, while distance to niche margins accurately predict both mortality and remaining green canopy responses, centroid distances failed to explain mortality, suggesting that indexes containing information about the position to niche margin (inside or outside) are better to predict binary responses. We conclude that the location of populations in the environmental space is consistent with performance responses to extreme drought. Niche distances appear to be a more efficient approach than the use of climate suitability indices derived from more frequently used SDMs to explain population performance when dealing with environmental conditions that are located outside the species environmental niche. The use of this alternative metrics may be particularly useful when designing conservation measures to mitigate impacts of shifting environmental conditions. [ABSTRACT FROM AUTHOR]

The Amazon floodplains represent important surfaces of highly valuable ecosystems, yet they remain neglected from protected areas. Although the efficiency of the protected area network of the Amazon basin may be jeopardized by climate change, floodplains are exposed to important consequences of climate change but are omitted from species distribution models and protection gap analyses.The present and future (2070) distribution of the giant bony‐tongue fish Arapaima spp. (Arapaimidae) was modelled accounting for climate and habitat requirements, and with a consideration of dam presence (already existing and planned constructions) and hydroperiod (high‐ and low‐water stages). The amount of suitable environment that falls inside and outside the current network of protected areas was quantified to identify spatial conservation gaps.We predict that climate change will cause a decline in environmental suitability by 16.6% during the high‐water stage, and by 19.4% during the low‐water stage. About 70% of the suitable environments of Arapaima spp. remain currently unprotected. The gap is higher by 0.7% during the low‐water stage. The lack of protection is likely to increase by 5% with future climate change effects. Both existing and projected dam constructions may hamper population flows between the central, Bolivian and Peruvian parts of the basin.We highlight protection gaps mostly in the south‐western part of the basin and recommend the extension of the current network of protected areas in the floodplains of the upper Ucayali, Juruà and Purus rivers and their tributaries. This study has shown the importance of integrating hydroperiod and dispersal barriers in forecasting the distribution of freshwater fish species, and stresses the urgent need to integrate floodplains within the protected area networks. [ABSTRACT FROM AUTHOR]

To withstand the surge of species loss worldwide, (re)introduction of endangered plant species has become an increasingly common technique in conservation biology. Successful (re)introduction plans, however, require identifying sites that provide the optimal ecological conditions for the target species to thrive. In this study, we propose a two‐step approach to identify appropriate (re)introduction sites. The first step involves modeling the niche and distribution of the species with bioclimatic and topographical predictors, both at continental and at national scales. The second step consists of refining these bioclimatic predictions by analyzing stationary ecological parameters, such as soil conditions, and relating them to population‐level fitness values. We demonstrate this methodology using Swiss populations of the lady's slipper orchid (Cypripedium calceolus L., Orchidaceae), for which conservation plans have existed for years but have generally been unfruitful. Our workflow identified sites for future (re)introductions based on the species requirements for mid‐to‐sunny light conditions and specific soil physico‐chemical properties, such as basic to neutral pH and low soil organic matter content. Our findings show that by combining wide‐scale bioclimatic modeling with fine scale field measurements it is possible to carefully identify the ecological requirements of a target species for successful (re)introductions. [ABSTRACT FROM AUTHOR]

Many species distribution models (SDMs) are built with precise but geographically restricted presence–absence data sets (e.g., a country) where only a subset of the environmental conditions experienced by a species across its range is considered (i.e., spatial niche truncation). This type of truncation is worrisome because it can lead to incorrect predictions e.g., when projecting to future climatic conditions belonging to the species niche but unavailable in the calibration area. Data from citizen‐science programs, species range maps or atlases covering the full species range can be used to capture those parts of the species' niche that are missing regionally. However, these data usually are too coarse or too biased to support regional management. Here, we aim to (1) demonstrate how varying degrees of spatial niche truncation affect SDMs projections when calibrated with climatically truncated regional data sets and (2) test the performance of different methods to harness information from larger‐scale data sets presenting different spatial resolutions to solve the spatial niche truncation problem. We used simulations to compare the performance of the different methods, and applied them to a real data set to predict the future distribution of a plant species (Potentilla aurea) in Switzerland. SDMs calibrated with geographically restricted data sets expectedly provided biased predictions when projected outside the calibration area or time period. Approaches integrating information from larger‐scale data sets using hierarchical data integration methods usually reduced this bias. However, their performance varied depending on the level of spatial niche truncation and how data were combined. Interestingly, while some methods (e.g., data pooling, downscaling) performed well on both simulated and real data, others (e.g., those based on a Poisson point process) performed better on real data, indicating a dependency of model performance on the simulation process (e.g., shape of simulated response curves). Based on our results, we recommend to use different data integration methods and, whenever possible, to make a choice depending on model performance. In any case, an ensemble modeling approach can be used to account for uncertainty in how niche truncation is accounted for and identify areas where similarities/dissimilarities exist across methods. [ABSTRACT FROM AUTHOR]

Aim: Correlative species distribution models (SDMs) are among the most frequently used tools for conservation planning under climate and land use changes. Conservation‐focused climate change studies are often conducted on a national or local level and can use different sources of occurrence records (e.g., local databases, national biodiversity monitoring) collated at different geographic extents. However, little is known about how these restrictions in geographic space (i.e., Wallacean shortfall) can lead to restrictions in environmental space (i.e., Hutchinsonian shortfall) and accordingly affect conclusions about a species' vulnerability to climate change. Location: Americas with a focus on Mexico. Methods: We present an example study constructing SDMs for three Mexican tree species (Alnus acuminata, Liquidambar styraciflua and Quercus xalapensis) using datasets collated at a global (Americas), national (Mexico) and local (cloud forests of eastern Mexico) level to demonstrate the potential effects of a Wallacean shortfall on the estimation of the environmental niche—and thus on a Hutchinsonian shortfall—its projection in space and time and, consequently, on species' potential vulnerability to climate change. Results: The consequence of using the three datasets was species‐specific and strongly depended on the extent to which the Wallacean shortfall affected estimations of environmental niches (i.e., Hutchinsonian shortfall). Where restrictions in geographic space lead to an underestimation of the environmental niche, vulnerability to climate change was estimated to be substantially higher. Additionally, the restrictions in geographic space may increase the likelihood of issues with non‐analogue climates, increasing model uncertainty. Main conclusion: We recommend assessing the extent to which a species' entire realized environmental niche is captured within the target conservation area, and increasing the geographic extent, if needed, to account for environments and occurrences reflecting potential future conditions. This way, the risk of underestimating the climatic potential of the species (i.e., Hutchinsonian shortfall), as well as the errors induced by extrapolation into "locally novel" climates, can be minimized. [ABSTRACT FROM AUTHOR]

One key hypothesis explaining the fate of exotic species introductions posits that the establishment of a self-sustaining population in the invaded range can only succeed within conditions matching the native climatic niche. Yet, this hypothesis remains untested for individual release events. Using a dataset of 979 introductions of 173 mammal species worldwide, we show that climate-matching to the realized native climatic niche, measured by a new Niche Margin Index (NMI), is a stronger predictor of establishment success than most previously tested life-history attributes and historical factors. Contrary to traditional climatic suitability metrics derived from species distribution models, NMI is based on niche margins and provides a measure of how distant a site is inside or, importantly, outside the niche. Besides many applications in research in ecology and evolution, NMI as a measure of native climatic niche-matching in risk assessments could improve efforts to prevent invasions and avoid costly eradications. Whether invasive species must first establish in conditions within their native climatic niche before spreading remains largely untested. This study presents the Niche Margin Index for estimating climatic niche-matching of alien mammal species to a particular site, which could be used to help predict the success of invasions. [ABSTRACT FROM AUTHOR]

Aim: Niche‐based species distribution models (SDMs) have become a ubiquitous tool in ecology and biogeography. These models relate species occurrences with the environmental conditions found at these sites. Climatic variables are the most commonly used environmental data and are usually included in SDMs as averages of a reference period (30–50 years). In this study, we analyse the impact of including inter‐annual climatic variability on the estimation of species niches and predicted distributions when assessing plant demographic response to extreme climatic episodes. Location: Mediterranean basin, SE Iberian Peninsula. Methods: We first characterized species niches with inter‐annual and average climate in the same environmental space. We then compare the respective capacities of climatic suitability obtained from averaged climate‐based and from inter‐annual variability‐based niches to explain population demographic responses to extreme drought. Furthermore, we assessed the relative increase in niche size when including climatic variability for a set of Mediterranean species exhibiting a wide range of distribution areas. Results: We found that climatic suitability obtained from inter‐annual variability‐based niches showed higher explanatory capacity than average climate‐based suitability, especially for populations living in climatically marginal conditions, although both niches quantifications significantly explained species demographic responses. In addition, species with restricted distribution ranges increased relatively more their niche space when considering climatic variability, probably because in widely distributed species spatial variability compensates for temporal variability. Main conclusions: The common use of climatic averages when characterizing species niches could lead to underestimations of species distribution and misunderstanding of demographic behaviour, with implications for conservation plans derived from SDMs, for example, overestimations of species extinction risk under climate change, or underestimations of alien species invasion' risk. We highlight that including climatic variability in niche modelling can be particularly important when dealing with species with restricted distribution and populations at the margin of their species niche. [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: Mounting evidence suggests that failure of species distribution models to integrate local adaptation hinders our ability to predict distribution ranges, raising the question of whether modelling should be performed at the level of species (clade models) or intraspecific lineages (subclade models), characterized by the restricted availability of occurrence points. While Ensembles of Small Models (ESMs) offer an attractive framework for small datasets, their evaluation remains critical. We address these issues in the case of very small datasets inherent to subclade models and discuss which modelling strategy should be applied based on niche overlap among lineages. Location: Sweden. Taxon: Mosses. Methods: Ensembles of Small Models were evaluated by null models built from randomly sampled presence points. We compared the extent of suitable area predicted by the projections of clade and subclade models. Niche overlap was quantified using Schoener's D and Hellinger'sImetrics, and the significance of these metrics in terms of niche conservatism or divergence was assessed by similarity tests. Results: We introduced a simple procedure for evaluating ESMs based on the pooling of the statistics used to assess model accuracy from the replicates. Despite fairly high AUC and TSS values, 2 of the 23 subclade models did not perform better than null models and should be discarded. Combined predictions from subclade models contributed, on average, five times more than clade models to the total suitable area predicted by the combination of subclade and clade models. The D and I metrics averaged 0.45 and 0.71, with evidence for niche conservatism in half of the species and no signal for niche divergence. Main conclusions: In addition to the assessment of ESM accuracy based on the simple procedure described here, we recommend that ESMs should be systematically evaluated against null models. Lumping or splitting occurrence data at the intraspecific level substantially impacted model projections. Given the poor performance of models based on small datasets, even when employing ESMs, we pragmatically suggest that, in the absence of evidence for niche divergence during diversification of closely related intraspecific lineages, SDMs should be based on all available occurrence data at the species level. [ABSTRACT FROM AUTHOR]

Aims: Assessing climate change impacts on biodiversity is a main scientific challenge, especially in the tropics. We predicted the future of plant species and communities on the unique páramo sky islands by implementing the Spatial Explicit Species Assemblage Modelling framework. Specifically we: (a) calculated species' maximum dispersal distance; (b) modelled species' present and future distributions up to 2100; and (c) assembled models into plant communities. The final vulnerability assessment was based on a multi‐dimensional evaluation that considered the species, local plant community and sky island levels. Location: Ecuadorian super‐páramo (>4,200 m). Methods: Using species trait data, the maximum dispersal distance of 435 species was calculated. Species distribution models (SDM) were fitted to obtain current and future distribution predictions based on dispersal and bioclimatic factors. The final current assemblages and those for 2100 were achieved by stacking all probabilistic SDMs and applying the probability ranking rule. The vulnerability of each sky island was evaluated by quantifying richness and composition changes. Results: Maximum dispersal distances ranged between 0.008 m/year and 6,027 m/year, and across all scenarios, 70% of models showed a net loss in species distribution, while 9% of all species were predicted to undergo extinction in Ecuador by 2100. Local richness was estimated to decrease by 56.63% on average, and compositional changes in each sky island suggested a mean loss of 64.74% of their original species pool against a 12.97% gain. Finally, 5% of the sky island floras reconverted from high‐elevation to low‐elevation species. These numbers were usually more important for high‐elevation species and the mountains Pichincha, Ilinizas and Antisana. Conclusions: This methodological pioneer study provides novel insight into the future of páramo biodiversity. Significant losses in species distribution and changes in community richness and composition suggest drastic impacts and call for further research considering additional factors, such as land use. Finally, we recommend focusing monitoring and conservation strategies on the northern Ecuadorian sky islands as a priority. [ABSTRACT FROM AUTHOR]

Aim: The aim was to test whether species distribution models (SDMs) can reproduce major macroecological patterns in a species‐rich, tropical region and provide recommendations for using SDMs in areas with sparse biotic inventory data. Location: North‐east Brazil, including Minas Gerais. Time period: Present. Major taxa studied: Flowering plants. Methods: Species composition estimates derived from stacked SDMs (s‐SDMs) were compared with data from 1,506 inventories of 933 woody plant species from north‐east Brazil. Both datasets were used in hierarchical clustering analyses to delimit floristic units that correspond to biomes. The ability of s‐SDMs to predict the identity, functional composition and floristic composition of biomes was compared across geographical and environmental space. Results: The s‐SDMs and inventory data both resolved four major biomes that largely corresponded in terms of their distribution, floristics and function. The s‐SDMs proved excellent at identifying broad‐scale biomes and their function, but misassigned many individual sites in complex savanna–forest mosaics. Main conclusions: Our results show that s‐SDMs have a unique role to play in describing macroecological patterns in areas lacking inventory data and for poorly known taxa. s‐SDMs accurately predict floristic and functional macroecological patterns but struggle in areas where non‐climatic factors, such as fire or soil, play key roles in governing distributions. [ABSTRACT FROM AUTHOR]

A comprehensive understanding of the scale dependency of environmental filtering and biotic interactions influencing the local assembly of species is paramount to derive realistic forecasts of the future of biodiversity and efficiently manage ecological communities. A classical assumption is that environmental filters are more prevalent at coarser scales with diminishing effects towards the finest scales where biotic interactions become more decisive. Recently, a refinement was proposed stipulating that the scale dependency of biotic interactions should relate to the type of interaction. Specifically, the effect of negative interactions (e.g. competition) should diminish with coarsening scale, whereas positive interactions (i.e. facilitation) should be detected irrespective of the scale.We use multiple vascular plant species datasets sampled at nested spatial scales (plot size varying from 0.04 to 64 m2) and recently developed joint species distribution models to test the hypotheses.Our analyses indicate slightly stronger environmental filtering with increasing plot size. While the overall strength of biotic interactions did not vary consistently across scales, we found a tendency for negative interactions to fade away with increasing plot size slightly more than positive interactions.Synthesis. We provide partial, but not unambiguous, evidence of the scale dependency of ecological assembly rules. However, our correlative methodology only allows us to interpret the findings as indication of environmental filtering and biotic interactions. [ABSTRACT FROM AUTHOR]

Species distribution models (SDMs) constitute the most common class of models across ecology, evolution and conservation. The advent of ready‐to‐use software packages and increasing availability of digital geoinformation have considerably assisted the application of SDMs in the past decade, greatly enabling their broader use for informing conservation and management, and for quantifying impacts from global change. However, models must be fit for purpose, with all important aspects of their development and applications properly considered. Despite the widespread use of SDMs, standardisation and documentation of modelling protocols remain limited, which makes it hard to assess whether development steps are appropriate for end use. To address these issues, we propose a standard protocol for reporting SDMs, with an emphasis on describing how a study's objective is achieved through a series of modeling decisions. We call this the ODMAP (Overview, Data, Model, Assessment and Prediction) protocol, as its components reflect the main steps involved in building SDMs and other empirically‐based biodiversity models. The ODMAP protocol serves two main purposes. First, it provides a checklist for authors, detailing key steps for model building and analyses, and thus represents a quick guide and generic workflow for modern SDMs. Second, it introduces a structured format for documenting and communicating the models, ensuring transparency and reproducibility, facilitating peer review and expert evaluation of model quality, as well as meta‐analyses. We detail all elements of ODMAP, and explain how it can be used for different model objectives and applications, and how it complements efforts to store associated metadata and define modelling standards. We illustrate its utility by revisiting nine previously published case studies, and provide an interactive web‐based application to facilitate its use. We plan to advance ODMAP by encouraging its further refinement and adoption by the scientific community. [ABSTRACT FROM AUTHOR]

Aim: To investigate the potential of a large range of soil variables to improve topo‐climatic models of plant species distributions in a temperate mountain region encompassing complex relief. Location: The western Swiss Alps. Methods: Fitting topo‐climatic models for >60 plant species across >250 sites with and without added soil predictor variables (>30). Testing included the following: (a) which soil variables improve plant species distribution models; (b) whether an optimal subset of soil variables can improve models for the majority of species and habitat types and (c) how much variation in plant species distributions soil variables alone explain. Results: Geochemical variables (i.e. CaO, pH and inorganic carbon) and a drainage indicator (i.e. bulk soil water content) improved the predictive abilities of the models across the large majority of alpine plant species. The improvement of the models after the addition of soil information varied strongly between plant species and habitat types, but a trade‐off was found between the number of soil variables and the associated gain in model performance. Finally, across all species, one specific combination of soil variables – bulk soil water content + total phosphorus +δ13C – outperformed the commonly used topo‐climatic variables. Main conclusions: Several soil variables significantly increased the predictive power of plant species distribution models in the temperate mountain region. Geochemical and drainage variables proved most important. [ABSTRACT FROM AUTHOR]

Underpinnings of the distribution of allopolyploid species (hybrids with duplicated genome) along spatial and ecological gradients are elusive. As allopolyploid speciation combines the range of genetic and ecological characteristics of divergent diploids, allopolyploids initially show their additivity and are predicted to evolve differentiated ecological niches to establish in face of their competition. Here, we use four diploid wild wheats that differentially combined into four independent allopolyploid species to test for such additivity and assess the impact of ecological constraints on species ranges. Divergent genetic variation from diploids being fixed in heterozygote allopolyploids supports their genetic additivity. Spatial integration of comparative phylogeography and modelling of climatic niches supports ecological additivity of locally adapted diploid progenitors into allopolyploid species which subsequently colonised wide ranges. Allopolyploids fill suitable range to a larger extent than diploids and conservative evolution following the combination of divergent species appears to support their expansion under environmental changes. [ABSTRACT FROM AUTHOR]

Stacked species distribution models (S‐SDM) provide a tool to make spatial predictions about communities by first modelling individual species and then stacking the modelled predictions to form assemblages. The evaluation of the predictive performance is usually based on a comparison of the observed and predicted community properties (e.g. species richness, composition). However, the most available and widely used evaluation metrics require the thresholding of single species' predicted probabilities of occurrence to obtain binary outcomes (i.e. presence/absence). This binarization can introduce unnecessary bias and error.Herein, we present and demonstrate the use of several groups of new or rarely used evaluation approaches and metrics for both species richness and community composition that do not require thresholding but instead directly compare the predicted probabilities of occurrences of species to the presence/absence observations in the assemblages.Community AUC, which is based on traditional AUC, measures the ability of a model to differentiate between species presences or absences at a given site according to their predicted probabilities of occurrence. Summing the probabilities gives the expected species richness and allows the estimation of the probability that the observed species richness is not different from the expected species richness based on the species' probabilities of occurrence. The traditional Sørensen and Jaccard similarity indices (which are based on presences/absences) were adapted to maxSørensen and maxJaccard and to probSørensen and probJaccard (which use probabilities directly). A further approach (improvement over null models) compares the predictions based on S‐SDMs with the expectations from the null models to estimate the improvement in both species richness and composition predictions. Additionally, all metrics can be described against the environmental conditions of sites (e.g. elevation) to highlight the abilities of models to detect the variation in the strength of the community assembly processes in different environments.These metrics offer an unbiased view of the performance of community predictions compared to metrics that requiring thresholding. As such, they allow more straightforward comparisons of model performance among studies (i.e. they are not influenced by any subjective thresholding decisions). [ABSTRACT FROM AUTHOR]

Issue: Approaches to predicting species assemblages through stacking individual niche‐based species distribution models (S‐SDMs) need to account for community processes other than abiotic filtering. Such constraints have been introduced by implementing ecological assembly rules (EARs) into S‐SDMs, and can be based on patterns of functional traits in communities. Despite being logically valid, this approach has led to a limited improvement in prediction, possibly because of mismatches between the scales of measurement of niche and trait data. Evidence: S‐SDM studies have often related single values of a species' traits to environmental niches that are captured by abiotic conditions measured at a much finer spatial scale, without accounting for intraspecific trait variation along environmental gradients. Many pieces of evidence show that omitting intraspecific trait variation can hinder the proper inference of EARs from trait patterns, and we further argue that it can therefore also affect our capacity to spatially predict functional properties of communities. In addition, estimates of environmental niches and trait envelopes may vary depending on the scale at which environmental and trait measurements are made. Conclusion: We suggest that to overcome these limitations, surveys sampling both niche and trait measurements should be conducted at fine scales along wide environmental gradients, and integrated at the same scale to test and improve a new generation of spatial community models and their functional properties. [ABSTRACT FROM AUTHOR]

To protect native biodiversity and habitats from the negative impacts of biological invasions, comprehensive studies and measures to anticipate invasions are required, especially across countries in a transfrontier context. Species distribution models (SDMs) can be particularly useful to integrate different types of data and predict the distribution of invasive species across borders, both for current conditions and under scenarios of future environmental changes. We used SDMs to test whether predicting invasions and potential spatial conflicts with protected areas in a transfrontier context, under current and future climatic conditions, would provide additional insights on the patterns and drivers of invasion when compared to models obtained from predictions for individual regions/countries (different modelling strategies). The framework was tested with the invasive alien plant Acacia dealbata in North of Portugal/NW Spain Euro-region, where the species is predicted to increase its distribution under future climatic conditions. While SDMs fitted in a transfrontier context and using "the national strategy (with Portugal calibration data) presented similar patterns, the distribution of the invasive species was higher in the former. The transfrontier strategy expectedly allowed to capture a more complete and accurate representation of the species' niche. Predictions obtained in a transfrontier context are therefore more suitable to support resource prioritisation for anticipation and monitoring impacts of biological invasions, while also providing additional support for international cooperation when tackling issues of global change. Our proposed framework provided useful information on the potential patterns of invasion by A. dealbata in a transfrontier context, with an emphasis on protected areas. This information is crucial for decision-makers focusing on the prevention of invasions by alien species inside protected areas in a transfrontier context, opening a new way for collaborative management of invasions. [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]

Aim: Species distribution information is essential under increasing global changes, and models can be used to acquire such information but they can be affected by different errors/bias. Here, we evaluated the degree to which errors in species data (false presences–absences) affect model predictions and how this is reflected in commonly used evaluation metrics. Location: Western Swiss Alps. Methods: Using 100 virtual species and different sampling methods, we created observation datasets of different sizes (100–400–1,600) and added increasing levels of errors (creating false positives or negatives; from 0% to 50%). These degraded datasets were used to fit models using generalized linear model, random forest and boosted regression trees. Model fit (ability to reproduce calibration data) and predictive success (ability to predict the true distribution) were measured on probabilistic/binary outcomes using Kappa, TSS, MaxKappa, MaxTSS and Somers'D (rescaled AUC). Results: The interpretation of models' performance depended on the data and metrics used to evaluate them, with conclusions differing whether model fit, or predictive success were measured. Added errors reduced model performance, with effects expectedly decreasing as sample size increased. Model performance was more affected by false positives than by false negatives. Models with different techniques were differently affected by errors: models with high fit presenting lower predictive success (RFs), and vice versa (GLMs). High evaluation metrics could still be obtained with 30% error added, indicating that some metrics (Somers'D) might not be sensitive enough to detect data degradation. Main conclusions: Our findings highlight the need to reconsider the interpretation scale of some commonly used evaluation metrics: Kappa seems more realistic than Somers'D/AUC or TSS. High fits were obtained with high levels of error added, showing that RF overfits the data. When collecting occurrence databases, it is advisory to reduce the rate of false positives (or increase sample sizes) rather than false negatives. [ABSTRACT FROM AUTHOR]

The relationship between species' niche breadth (i.e. the range of environmental conditions under which a species can persist) and range size (i.e. the extent of its spatial distribution) has mostly been tested within geographically restricted areas but rarely at the global extent. Here, we not only tested the relationship between range size (derived from species' distribution data) and niche breadth (derived from species' distribution and co‐occurrence data) of 1255 plant species at the regional extent of the European Alps, but also at the global extent and across both spatial scales for a subset of 180 species. Using correlation analyses, linear models and variation partitioning, we found that species' realized niche breadth estimated at the regional level is a weak predictor of species' global niche breadth and range size. Against our expectations, distribution‐derived niche breadth was a better predictor for species' range size than the co‐occurrence‐based estimate, which should, theoretically, account for more than the climatically determined niche dimensions. Our findings highlight that studies focusing on the niche breadth vs range size relationship must explicitly consider spatial mismatches that might have confounded and diminished previously reported relationships. [ABSTRACT FROM AUTHOR]

Abstract Correlative species distribution models (SDMs) are widely used to predict species distributions and assemblages, with many fundamental and applied uses. Different factors were shown to affect SDM prediction accuracy. However, real data cannot give unambiguous answers on these issues, and for this reason, artificial data have been increasingly used in recent years. Here, we move one step further by assessing how different factors can affect the prediction accuracy of virtual assemblages obtained by stacking individual SDM predictions (stacked SDMs, S-SDM). We modelled 100 virtual species in a real study area, testing five different factors: sample size (200-800-3200), sampling method (nested, non-nested), sampling prevalence (25%, 50%, 75% and species true prevalence), modelling technique (GAM, GLM, BRT and RF) and thresholding method (ROC, MaxTSS, and MaxKappa). We showed that the accuracy of S-SDM predictions is mostly affected by modelling technique followed by sample size. Models fitted by GAM/GLM had a higher accuracy and lower variance than BRT/RF. Model accuracy increased with sample size and a sampling strategy reflecting the true prevalence of the species was most successful. However, even with sample sizes as high as >3000 sites, residual uncertainty remained in the predictions, potentially reflecting a bias introduced by creating and/or resampling the virtual species. Therefore, when evaluating the accuracy of predictions from S-SDMs fitted with real field data, one can hardly expect reaching perfect accuracy, and reasonably high values of similarity or predictive success can already be seen as valuable predictions. We recommend the use of a 'plot-like' sampling method (best approximation of the species' true prevalence) and not simply increasing the number of presences-absences of species. As presented here, virtual simulations might be used more systematically in future studies to inform about the best accuracy level that one could expect given the characteristics of the data and the methods used to fit and stack SDMs. Highlights • Virtual species were used to test factors affecting spatial assemblage predictions. • Modelling technique and sample size proved to be the most important factors. • Sampling procedures caused uncertainty in predictions even under a "known" truth. • A 'plot-like' sampling method ensures accurate predictions even at small samples. • Confirmed the importance of evaluating relative effects of factors affecting SDMs. [ABSTRACT FROM AUTHOR]

Aim: Understanding the relative roles of geography and ecology in driving speciation, population divergence and maintenance of species cohesion is of great interest to molecular ecology. Closely related species that are parapatrically distributed in mountainous areas provide an ideal model to evaluate these key issues, especially when genomic data are analyzed within a spatially and ecologically explicit context. Here, we used three closely related species of Primula that occur in the Himalayas, the Hengduan Mountains and north‐east Qinghai–Tibet Plateau (QTP) to examine spatial and ecological effects on interspecific divergence and maintenance of species cohesion. Location: Himalayas, the Hengduan Mountains and north‐east QTP. Methods: We used genomic data for 770 samples of the three species using double‐digest restriction site‐associated DNA (ddRAD) sequencing and combined approximate Bayesian computation (ABC) modeling, generalized linear mixed modeling (GLMM) and niche‐based species distribution modeling (SDM). Results: The three species are clearly delimited by the RADseq data. Further ABC modeling indicates that Primula tibetica diverged first followed by a later divergence between Primula nutans and Primula fasciculata. The time frames of the divergences among the three species coincide with the uplifts of the Hengduan Mountains and the northern QTP during the late Miocene and Pliocene followed by a long period of founder events. SDMs indicate that the three species might have survived in different refugia during glaciations and came into secondary contact during the postglacial expansions but with no significant introgression. Finally, GLMM suggests that both the geographical and ecological factors play roles in population differentiation in P. fasciculata and P. tibetica, while for P. nutans, geography is the major driver of genomic variation. The different roles played by geographical and ecological factors in the three species may have affected the maintenance of species cohesion. Main conclusion: Our results provide insights of unprecedented details into the start and maintenance of interspecific divergence in the context of changing environments in mountains. Our findings highlight the significance of combining population genomics with environmental data when evaluating the effects of geography and ecology on interspecific divergence and maintenance of closely related species. [ABSTRACT FROM AUTHOR]

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Sample size is a key issue in species distribution modelling. While many studies focused on the relevance of sample size for model calibration, the importance of the size of the dataset used for model evaluation has received much less attention. Here, we highlight two previously published approaches to address the problem, and which are relatively simple to implement: the pooling evaluation and the implementation of null models. We discuss the importance of these or other potential approaches that are critical for model evaluation in rare species, which represent the bulk of biodiversity, and for which accurate models are most necessary in a conservation context. [ABSTRACT FROM AUTHOR]

1. Selecting the best subset of covariates out of a panel of many candidates is a key and highly influential stage of the species distribution modelling process. Yet, there is currently no commonly accepted and widely adopted standard approach by which to perform this selection. 2. We introduce a two-step "embedded" covariate selection procedure aimed at optimizing the predictive ability and parsimony of species distribution models fitted in a context of high-dimensional candidate covariate space. The procedure combines a collinearity-filtering algorithm (Step A) with three model-specific embedded regularization techniques (Step B), including generalized linear model with elastic net regularization, generalized additive model with null-space penalization, and guided regularized random forest. 3. We evaluated the embedded covariate selection procedure through an example application aimed at modelling the habitat suitability of 50 species in Switzerland from a suite of 123 candidate covariates. We demonstrated the ability of the embedded covariate selection procedure to provide significantly more accurate species distribution models as compared to models obtained with alternative procedures. Model performance was independent of the characteristics of the species data, such as the number of occurrence records or their spatial distribution across the study area. 4. We implemented and streamlined our embedded covariate selection procedure in the covsel R package, paving the way for a ready-to-use, automated, covariate selection tool that was missing in the field of species distribution modelling. All the information required for installing and running the covsel R package is openly available on the GitHub repository https://github.com/N-SDM/covsel. • covariate selection is a key stage of the species distribution modelling process • we introduce covsel: an R package for automated covariate selection • covsel (Step A) applies a collinearity-filtering algorithm • covsel (Step B) applies model-specific embedded regularization techniques • covsel is openly available on GitHub (https://github.com/N-SDM/covsel) [ABSTRACT FROM AUTHOR]

A key focus in ecology is to search for community assembly rules. Here we compare two community modelling frameworks that integrate a combination of environmental and spatial data to identify positive and negative species associations from presence–absence matrices, and incorporate an additional comparison using joint species distribution models (JSDM). The frameworks use a dichotomous logic tree that distinguishes dispersal limitation, environmental requirements, and interspecific interactions as causes of segregated or aggregated species pairs. The first framework is based on a classical null model analysis complemented by tests of spatial arrangement and environmental characteristics of the sites occupied by the members of each species pair (Classic framework). The second framework, (SDM framework) implemented here for the first time, builds on the application of environmentally‐constrained null models (or JSDMs) to partial out the influence of the environment, and includes an analysis of the geographical configuration of species ranges to account for dispersal effects. We applied these approaches to examine plot‐level species co‐occurrence in plant communities sampled along a wide elevation gradient in the Swiss Alps. According to the frameworks, the majority of species pairs were randomly associated, and most of the non‐random positive and negative species associations could be attributed to environmental filtering and/or dispersal limitation. These patterns were partly detected also with JSDM. Biotic interactions were detected more frequently in the SDM framework, and by JSDM, than in the Classic framework. All approaches detected species aggregation more often than segregation, perhaps reflecting the important role of facilitation in stressful high‐elevation environments. Differences between the frameworks may reflect the explicit incorporation of elevational segregation in the SDM framework and the sensitivity of JSDM to the environmental data. Nevertheless, all methods have the potential to reveal general patterns of species co‐occurrence for different taxa, spatial scales, and environmental conditions. [ABSTRACT FROM AUTHOR]

Abstract: Ensembles of Small Models (ESM) represent a novel strategy for species distribution modelling with few observations. ESMs are built by calibrating many small models and then averaging them into an ensemble model where the small models are weighted by their cross‐validated scores of predictive performance. In a previous paper (Breiner, Guisan, Bergamini, & Nobis, Methods in Ecology and Evolution, 6, 1210–1218, 2015), we reported two major findings. First, ESMs proved largely superior to standard models in terms of model performance and transferability. Second, ESMs including different modelling techniques did not clearly improve model performance compared to single‐technique ESMs. However, ESMs often require a large computation effort, which can become problematic when modelling large numbers of species. Given the appealing new perspectives offered by ESMs, it is especially important to investigate if some techniques yield increased performance while saving computation time and thus could be predominantly used for building ESMs. Here, we present results from a reanalysis of a subset of the data used in Breiner et al. (2015). More specifically, we ran ESMs: (1) fitted with 10 modelling techniques separately (in Breiner et al., 2015 we used only three techniques); and (2) using various parameter options for each modelling technique (i.e., model tuning). We show that ESMs vary in model performance and computation time across techniques, and some techniques are advantageous in terms of optimizing model performance and computation time (i.e., GLM, CTA and ANN). Including one of these modelling techniques could thus optimize computation time compared to using more computing‐intensive techniques like GBM. Next, we show that parameter tuning can improve performance and transferability of ESMs, but often at the cost of computation time. Parameter tuning could therefore be used when computing resources are not a limiting factor. These findings help improve the applicability and performance of ESMs when applied to large numbers of species. [ABSTRACT FROM AUTHOR]

In this study, we compare two community modelling approaches to determine their ability to predict the taxonomic, functional and phylogenetic properties of plant assemblages along a broad elevation gradient and at a fine resolution. The first method is the standard stacking individual species distribution modelling ( SSDM) approach, which applies a simple environmental filter to predict species assemblages. The second method couples the SSDM and macroecological modelling ( MEM- SSDM- MEM) approaches to impose a limit on the number of species co-occurring at each site. Because the detection of diversity patterns can be influenced by different levels of phylogenetic or functional trees, we also examine whether performing our analyses from broad to more exact structures in the trees influences the performance of the two modelling approaches when calculating diversity indices., We found that coupling the SSDM with the MEM improves the overall predictions for the three diversity facets compared with those of the SSDM alone. The accuracy of the SSDM predictions for the diversity indices varied greatly along the elevation gradient, and when considering broad to more exact structure in the functional and phylogenetic trees, the SSDM- MEM predictions were more stable., SSDM- MEM moderately but significantly improved the prediction of taxonomic diversity, which was mainly driven by the corrected number of predicted species. The performance of both modelling frameworks increased when predicting the functional and phylogenetic diversity indices. In particular, fair predictions of the taxonomic composition by SSDM- MEM led to increasingly accurate predictions of the functional and phylogenetic indices, suggesting that the compositional errors were associated with species that were functionally or phylogenetically close to the correct ones; however, this did not always hold for the SSDM predictions., Synthesis. In this study, we tested the use of a recently published approach that couples species distribution and macroecological models to provide the first predictions of the distribution of multiple facets of plant diversity: taxonomic, functional and phylogenetic. Moderate but significant improvements were obtained; thus, our results open promising avenues for improving our ability to predict the different facets of biodiversity in space and time across broad environmental gradients when functional and phylogenetic information is available. [ABSTRACT FROM AUTHOR]

Explanatory studies suggest that using very high resolution (VHR, 1-5 m resolution) topo-climatic predictors may improve the predictive power of plant species distribution models (SDMs). However, the use of VHR topo-climatic data alone was recently shown not to significantly improve SDM predictions. This suggests that new ecologically-meaningful VHR variables based on more direct field measurements are needed, especially since non topo-climatic variables, such as soil parameters, are important for plants. In this study, we investigated the effects of adding mapped VHR predictors at a 5 m resolution, including field measurements of temperature, carbon isotope composition of soil organic matter (δ13CSOM values) and soil pH, to topo-climatic predictors in SDMs for the Swiss Alps. We used data from field temperature loggers to construct temperature maps, and we modelled the geographic variation in δ13CSOM and soil pH values. We then tested the effect of adding these VHR mapped variables as predictors into 154 plant SDMs and assessed the improvement in spatial predictions across the study area. Our results demonstrate that the use of VHR predictors based on more proximal field measurements, particularly soil parameters, can significantly increase the predictive power of models. Predicted soil pH was the second most important predictor after temperature, and predicted δ13CSOM was fourth. The greatest increase in model performance was for species found at high elevation (i.e. 1500-2000 m a.s.l.). Addition of predicted soil factors thus allowed better capturing of plant requirements in our models, showing that these can explain species distributions in ways complementary to topo-climatic variables. Modelling techniques to generalize edaphic information in space and then predict plant species distributions revealed a great potential in complex landscapes such as the mountain region considered in this study. [ABSTRACT FROM AUTHOR]

Blonder et al. (, Global Ecology and Biogeography, 23, 595-609) introduced a new multivariate kernel density estimation (KDE) method to infer Hutchinsonian hypervolumes in the modelling of ecological niches. The authors argued that their KDE method matches or outperforms several methods for estimating hypervolume geometries and for conducting species distribution modelling. Further clarification, however, is appropriate with respect to the assumptions and limitations of KDE as a method for species distribution modelling. Using virtual species and controlled environmental scenarios, we show that KDE both under- and overestimates niche volumes depending on the dimensionality of the dataset and the number of occurrence records considered. We suggest that KDE may be a viable approach when dealing with large sample sizes, limited sampling bias and only a few environmental dimensions. [ABSTRACT FROM AUTHOR]

Aim International Union for Conservation of Nature (IUCN) expert maps are increasingly used in macroecological research. However, they have not been produced for this purpose. Macroecological insights based exclusively on this type of data could therefore be misleading. Here we compare, for a large taxonomic group (bats) and an entire biogeographical realm (Africa), the species-specific discrepancies between IUCN maps and species distribution models (SDMs) that approximate the complete geographical range of species. We then examine the implications for a typical macroecological analysis that explores environmental correlates of species richness. Location Continental Africa. Time period Around 2000. Taxa Bats (Chiroptera). Methods We measure disagreement between IUCN expert maps and SDMs at both the species (geographical ranges) and the aggregated level (range size-frequency distributions and species richness). We further quantify the difference in absolute and relative weight assigned to three variables hypothesized to drive species richness: primary productivity, climatic seasonality and environmental heterogeneity. Results Location, shape and size of individual species' ranges, derived richness patterns and range size-frequency distributions differ substantially. SDMs predict larger and more complex geographical ranges, and species' range sizes vary less. The spatial congruence of richness hotspots among both datasets is only 42%. These discrepancies are large enough to alter the absolute explanatory power of environmental correlates, whereas the redundancy in the variation explained increases markedly when richness is inferred using SDM-based estimates of complete species ranges. Main conclusions IUCN expert maps differ considerably and systematically from SDMs built to estimate complete species ranges, primarily because of their intentional greater sensitivity to geographical sampling bias. This property is desirable in a conservation context but unwanted in most macroecological analyses. We therefore caution against the use of IUCN expert maps in macroecology and recommend at least gauging the robustness of results using additional range estimates designed to approximate the complete geographical range of species. [ABSTRACT FROM AUTHOR]

Correlative species distribution models are based on the observed relationship between species' occurrence and macroclimate or other environmental variables. In climates predicted less favourable populations are expected to decline, and in favourable climates they are expected to persist. However, little comparative empirical support exists for a relationship between predicted climate suitability and population performance. We found that the performance of 93 populations of 34 plant species worldwide - as measured by in situ population growth rate, its temporal variation and extinction risk - was not correlated with climate suitability. However, correlations of demographic processes underpinning population performance with climate suitability indicated both resistance and vulnerability pathways of population responses to climate: in less suitable climates, plants experienced greater retrogression (resistance pathway) and greater variability in some demographic rates (vulnerability pathway). While a range of demographic strategies occur within species' climatic niches, demographic strategies are more constrained in climates predicted to be less suitable. [ABSTRACT FROM AUTHOR]

Aim Spatial predictions of future communities under climate change can be obtained by stacking species distribution models ( S- SDM), but proper evaluation of community S- SDM predictions across time with fully independent data has rarely been carried out. The aim of this study was to evaluate the predictive abilities of S- SDMs for whole forest communities across the last 25 years in a mountain region. Location The western Swiss Alps. Methods We used past vegetation surveys (2,984 plots) and environmental data from the 1990s to calibrate SDMs for 364 plant species and predict changes in forest composition under contemporary conditions. These projections were then evaluated by resurveying a random subset of 92 forest plots in summer 2014. Results Species distribution models showed the same accuracy in the past (calibration data) and present (evaluation data). The S- SDMs correctly predicted the general trends in species richness and shift of ecological conditions (i.e., temperature, moisture) at the regional level. However, it proved more difficult to identify precisely which forest communities or areas are most or least affected by climate change. Main conclusion Our results show that, across a period of a few decades, S-SDMs can usefully predict trends in macroecological properties such as richness or average ecological conditions, but fail to accurately predict changes in composition. This is likely due to the combined effects of the stochasticity of local colonization and extinction events, dispersal limitations, community assembly rules (e.g., competition), observer bias, model and location errors and interannual variation. Furthermore, these models cannot account for potential species adaptations leading to persistence in sites predicted unsuitable. This highlights the need for developing more accurate forest community predictions as support to help prioritizing conservation actions. [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]

ABSTRACT A fundamental goal of ecological research is to understand and model how processes generate patterns so that if conditions change, changes in the patterns can be predicted. Different approaches have been proposed for modelling species assemblage, but their use to predict spatial patterns of species richness and other community attributes over a range of spatial and temporal scales remains challenging. Different methods emphasize different processes of structuring communities and different goals. In this review, we focus on models that were developed for generating spatially explicit predictions of communities, with a particular focus on species richness, composition, relative abundance and related attributes. We first briefly describe the concepts and theories that span the different drivers of species assembly. A combination of abiotic processes and biotic mechanisms are thought to influence the community assembly process. In this review, we describe four categories of drivers: ( i) historical and evolutionary, ( ii) environmental, ( iii) biotic, and ( iv) stochastic. We discuss the different modelling approaches proposed or applied at the community level and examine them from different standpoints, i.e. the theoretical bases, the drivers included, the source data, and the expected outputs, with special emphasis on conservation needs under climate change. We also highlight the most promising novelties, possible shortcomings, and potential extensions of existing methods. Finally, we present new approaches to model and predict species assemblages by reviewing promising 'integrative frameworks' and views that seek to incorporate all drivers of community assembly into a unique modelling workflow. We discuss the strengths and weaknesses of these new solutions and how they may hasten progress in community-level modelling. [ABSTRACT FROM AUTHOR]

Aims The choice of environmental predictor variables in correlative models of plant species distributions (hereafter SDMs) is crucial to ensure predictive accuracy and model realism, as highlighted in multiple earlier studies. Because variable selection is directly related to a model's capacity to capture important species' environmental requirements, one would expect an explicit prior consideration of all ecophysiologically meaningful variables. For plants, these include temperature, water, soil nutrients, light, and in some cases, disturbances and biotic interactions. However, the set of predictors used in published correlative plant SDM studies varies considerably. No comprehensive review exists of what environmental predictors are meaningful, available (or missing) and used in practice to predict plant distributions. Contributing to answer these questions is the aim of this review. Methods We carried out an extensive, systematic review of recently published plant SDM studies (years 2010-2015; n = 200) to determine the predictors used (and not used) in the models. We additionally conducted an in-depth review of SDM studies in selected journals to identify temporal trends in the use of predictors (years 2000-2015; n = 40). Results A large majority of plant SDM studies neglected several ecophysiologically meaningful environmental variables, and the number of relevant predictors used in models has stagnated or even declined over the last 15 yr. Conclusions Neglecting ecophysiologically meaningful predictors can result in incomplete niche quantification and can thus limit the predictive power of plant SDMs. Some of these missing predictors are already available spatially or may soon become available (e.g. soil moisture). However, others are not yet easily obtainable across whole study extents (e.g. soil pH and nutrients), and their development should receive increased attention. We conclude that more effort should be made to build ecologically more sound plant SDMs. This requires a more thorough rationale for the choice of environmental predictors needed to meet the study goal, and the development of missing ones. The latter calls for increased collaborative effort between ecological and geo-environmental sciences. [ABSTRACT FROM AUTHOR]

The importance of monitoring biodiversity to detect and understand changes throughout time and to inform management is increasingly recognized. Monitoring schemes should be globally unified, spatially integrated across scales, long term, and cost-efficient., We propose a framework to design optimized multispecies-targeted monitoring networks over large areas. The method builds upon previous developments on systematic conservation planning in terms of optimizing resource allocation in space, and comprises seven steps: (a) determine which questions will be addressed, (b) define species to be monitored, (c) compile occurrence data for all defined species, (d) predict the overall distribution of each species, (e) collect relevant environmental data and identify homogeneous strata, (f) set targets for the minimum number of monitoring sites per species and/or stratum and (g) identify optimal monitoring sites., We tested whether the monitoring networks designed with our framework have increased performance when compared to networks obtained with simple-random or stratified-random sampling by using a set of different indicators. To that end, we designed monitoring networks using optimized and non-optimized sampling schemes, applied to a case study in Portugal, where the goal was to design a monitoring network for amphibians and reptiles, to complement the one currently established in Spain., Results allowed us to conclude that monitoring networks designed with our method tend to outperform the non-optimized ones, in terms of higher species diversity (i.e. higher number of species and equity across monitoring sites), higher representation of environmental strata, and particularly higher coverage of rare species, with less survey effort., Synthesis and applications. We developed a framework to allocate monitoring sites for multiple species at broad scales using predictive models and optimization algorithms currently applied in systematic conservation planning. This framework presents field survey cost-efficiency advantages when compared to other standard sampling designs and can significantly contribute to improving the design of monitoring schemes. Thus, we recommend its application to design new multispecies monitoring networks or to extend existing ones. [ABSTRACT FROM AUTHOR]

Aim Ecological niche theory states that realized niche breadth should increase with population growth. This relationship has been studied extensively in the context of density-dependent habitat selection, and there is evidence that animal populations at higher density occupy a wider range of vegetation types. To our knowledge, no previous studies have investigated the relationship between population growth and climate niche breadth (i.e. the range of climatic conditions occupied). Here we aim to estimate the influence of population trend, as well as changes in distribution, on realized climate niche breadth. Location North America. Methods We estimated changes in realized climate niche breadth and distribution between 1980 and 2012 for 46 bird species using data from the North American Breeding Bird Survey (BBS) and standard ecological niche modelling techniques. We analysed changes in niche breadth in relation to population trends and distributional changes from the BBS for these same species. Results Changes in realized climate niche breadth were significantly and positively associated with population growth, as reflected by BBS population trends, and with changes in distributional extent. Using variance partitioning, we showed that 44.2% of the variation in change in niche breadth can be explained by population trend, and that roughly half of this was independent of changes in distribution. Conclusions Realized climate niche breadth is variable on an ecological time-scale as a function of population trend. Mechanisms associated with changes in distribution and those acting within current species range limits appear to be equally important in driving this relationship. Observed changes in niche breadth may violate distribution modelling assumptions of niche conservatism. Studying how population growth influences realized climate niche breadth is therefore important for understanding dynamic species distributions, responses to climate change and our ability to model future species distributions. [ABSTRACT FROM AUTHOR]