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A large array of species distribution model (SDM) approaches has been developed for explaining and predicting the occurrences of individual species or species assemblages. Given the wealth of existing models, it is unclear which models perform best for interpolation or extrapolation of existing data sets, particularly when one is concerned with species assemblages. We compared the predictive performance of 33 variants of 15 widely applied and recently emerged SDMs in the context of multispecies data, including both joint SDMs that model multiple species together, and stacked SDMs that model each species individually combining the predictions afterward. We offer a comprehensive evaluation of these SDM approaches by examining their performance in predicting withheld empirical validation data of different sizes representing five different taxonomic groups, and for prediction tasks related to both interpolation and extrapolation. We measure predictive performance by 12 measures of accuracy, discrimination power, calibration, and precision of predictions, for the biological levels of species occurrence, species richness, and community composition. Our results show large variation among the models in their predictive performance, especially for communities comprising many species that are rare. The results do not reveal any major trade-offs among measures of model performance; the same models performed generally well in terms of accuracy, discrimination, and calibration, and for the biological levels of individual species, species richness, and community composition. In contrast, the models that gave the most precise predictions were not well calibrated, suggesting that poorly performing models can make overconfident predictions. However, none of the models performed well for all prediction tasks. As a general strategy, we therefore propose that researchers fit a small set of models showing complementary performance, and then apply a cross-validation procedure involving separate data to establish which of these models performs best for the goal of the study.

An ability to predict the ranges and dynamics of species is central to ecological and evolutionary research. Despite species range modelling being an active area of research for the past three to four decades, methods for predicting species ranges and their dynamics are still diversifying. One promising avenue for improvement lies in process‐based models. While the potential advantages of using process‐based models are apparent to both practitioners and researchers, convenient software for implementing process models is often lacking. Here we present an R package for modelling the ranges and dynamics of plant species using a well established plant growth model, the Thornley transport resistance model. The R package provides access to the code used in previous studies and to new features. In this manuscript, we demonstrate two applications of the model, a species distribution model using occurrence data and a time series analysis of vegetation activity using remotely sensed solar induced chlorophyll‐fluorescence (SIF) data.

Released 4 years ago, the Wallace EcoMod application (R package wallace) provided an open‐source and interactive platform for modeling species niches and distributions that served as a reproducible toolbox and educational resource. wallace harnesses R package tools documented in the literature and makes them available via a graphical user interface that runs analyses and returns code to document and reproduce them. Since its release, feedback from users and partners helped identify key areas for advancement, leading to the development of wallace 2. Following the vision of growth by community expansion, the core development team engaged with collaborators and undertook a major restructuring of the application to enable: simplified addition of custom modules to expand methodological options, analyses for multiple species in the same session, improved metadata features, new database connections, and saving/loading sessions. wallace 2 features nine new modules and added functionalities that facilitate data acquisition from climate‐simulation, botanical and paleontological databases; custom data inputs; model metadata tracking; and citations for R packages used (to promote documentation and give credit to developers). Three of these modules compose a new component for environmental space analyses (e.g., niche overlap). This expansion was paired with outreach to the biogeography and biodiversity communities, including international presentations and workshops that take advantage of the software's extensive guidance text. Additionally, the advances extend accessibility with a cloud‐computing implementation and include a suite of comprehensive unit tests. The features in wallace 2 greatly improve its expandability, breadth of analyses, and reproducibility options, including the use of emerging metadata standards. The new architecture serves as an example for other modular software, especially those developed using the rapidly proliferating R package shiny, by showcasing straightforward module ingestion and unit testing. Importantly, wallace 2 sets the stage for future expansions, including those enabling biodiversity estimation and threat assessments for conservation.

The contributions of species to ecosystem functions or services depend not only on their presence but also on their local abundance. Progress in predictive spatial modelling has largely focused on species occurrence rather than abundance. As such, limited guidance exists on the most reliable methods to explain and predict spatial variation in abundance. We analysed the performance of 68 abundance‐based species distribution models fitted to 800 000 standardised abundance records for more than 800 terrestrial bird and reef fish species. We found a large amount of variation in the performance of abundance‐based models. While many models performed poorly, a subset of models consistently reconstructed range‐wide abundance patterns. The best predictions were obtained using random forests for frequently encountered and abundant species and for predictions within the same environmental domain as model calibration. Extending predictions of species abundance outside of the environmental conditions used in model training generated poor predictions. Thus, interpolation of abundances between observations can help improve understanding of spatial abundance patterns, but our results indicate extrapolated predictions of abundance under changing climate have a much greater uncertainty. Our synthesis provides a road map for modelling abundance patterns, a key property of species distributions that underpins theoretical and applied questions in ecology and conservation.

As climate change continues to impact the planet, the importance of forests is becoming increasingly emphasized. The International Co-operative Program on the Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests) has been monitoring and assessing forests in 40 countries since 1985. In Republic of Korea, the first Forest Health Management (FHM) survey was a nationwide sample point assessment conducted between 2011 and 2015. However, there are limitations in representing the health of forests that occupy 63.7% of Korea’s land area due to the nature of sample point surveys, which survey a relatively small area. Accordingly, a species richness map was created to promote species diversity in forest health evaluations in Republic of Korea. The map was created using data from the first FHM survey, which examined 28 factors with 12 survey indicators in four categories: tree health, vegetation health, soil health, and atmospheric health. We conducted an ensemble modeling of species distribution for woody plant species that are major habitats in Republic of Korea. To select the species, we used the first FHM survey data and chose those with more than 100 sample points, resulting in a total of 11 species. We then created the species richness map of Republic of Korea by overlaying their distributions. To verify the accuracy of the derived map, an independent verification was conducted using statistical verification and external data from the National Natural Environment Survey. To support forest management that accounts for climate change adaptation, the derived species richness map was validated based on the vegetation climate distribution map of the Korean Peninsula, which was published by the Korea National Arboretum. The map confirmed that species richness is highest around the boundary of the deciduous forest in the central temperate zone and lowest around the evergreen and deciduous mixed forest in the southern temperate zone. By establishing this map, it was possible to confirm the spatial distribution of species by addressing the limitations of direct surveys, which are unable to represent all forests. However, it is important to note that not all factors of the first FHM survey were considered during the spatialization process, and the target area only includes Republic of Korea. Thus, further research is necessary to expand the target area and include additional items.

Aim To provide distribution information and preliminary conservation assessments for all species of the pineapple family (Bromeliaceae), one of the most diverse and ecologically important plant groups of the American tropics—a global biodiversity hotspot. Furthermore, we aim to analyse patterns of diversity, endemism and the conservation status of the Bromeliaceae on the continental level in the light of their evolutionary history. Location The Americas. Methods We compiled a dataset of occurrence records for 3,272 bromeliad species (93.4% of the family) and modelled their geographic distribution using either climate‐based species distribution models, convex hulls or geographic buffers dependent on the number of occurrences available. We then combined this data with information on taxonomy and used the ConR software for a preliminary assessment of the conservation status of all species following Criterion B of the International Union for the Conservation of Nature (IUCN). Results Our results stress the Atlantic Forest in eastern Brazil, the Andean slopes, Central America and the Guiana Highlands as centres of bromeliad diversity and endemism. Phylogenetically ancient subfamilies of bromeliads are centred in the Guiana highlands whereas the large radiations of the group spread across different habitats and large geographic area. A total of 81% of the evaluated bromeliad species are Possibly Threatened with extinction. We provide range polygons for 3,272 species, as well as newly georeferenced point localities for 911 species in the novel “bromeliad” r package, together with functions to generate diversity maps for individual taxonomic or functional groups. Main conclusions Diversity centres of the Bromeliaceae agreed with macroecological patterns of other plant and animal groups, but show some particular patterns related to the evolutionary origin of the family, especially ancient dispersal corridors. A staggering 2/3rds of Bromeliaceae species might be threatened with extinction, especially so in tropical rain forests, raising concerns about the conservation of the family and bromeliad‐dependent animal species.

Species Distribution Models (SDMs) are vital tools for predicting species occurrences and are used in many practical tasks including conservation and biodiversity management. However, the expanding minefield of SDM methodologies makes it difficult to select the most reliable method for large co‐occurrence datasets, particularly when time constraints make designing a bespoke model challenging. To facilitate model selection for practical out‐of‐sample prediction, we consider three major challenges: (a) the difficulty of incorporating multiple functional forms for species associations; (b) the limited knowledge on how characteristics of co‐occurrence data impact model performance; and (c) whether individual model predictions could be combined to obtain optimised community predictions without the need for bespoke models. To address these gaps, we propose an ensemble method that uses descriptive features of binary co‐occurrence datasets to predict model weightings for a set of candidate SDMs. We demonstrate how this method may be applied through a simple case study that uses five independent Joint Species Distribution Models (JSDMs) and Stacked Species Distribution Models (SSDMs) to predict out‐of‐sample observations for a diversity of co‐occurrence datasets. Moreover, we introduce a novel SSDM that offers the potential to include multiple functional forms for each species while delivering robust community predictions. Our case study highlights two major findings. First, the ability for the feature‐based ensemble to offer more robust species co‐occurrence predictions compared to other candidate SDMs while providing insights into the data features that impact model performance. Second, the competitiveness of the novel SSDM method for forecasting species co‐occurrences, even when using a simple univariate generalised linear model (GLM) as the base model prior to stacking. We conclude that feature‐based ensembles can provide ecologists with a useful tool for generating species distribution predictions in a way that is reliable and informative. Moreover, the flexibility of the ensemble and the novel SSDM method both offer exciting prospects for incorporating a diversity of functional forms while prioritising out‐of‐sample prediction.

Correlative species distribution models (SDMs) are increasingly being used to predict suitable insect habitats. There is also much criticism of prediction discrepancies among different SDMs for the same species and the lack of effective communication about SDM prediction uncertainty. In this paper, we undertook a factorial study to investigate the effects of various modeling components (species-training-datasets, predictor variables, dimension-reduction methods, and model types) on the accuracy of SDM predictions, with the aim of identifying sources of discrepancy and uncertainty. We found that model type was the major factor causing variation in species-distribution predictions among the various modeling components tested. We also found that different combinations of modeling components could significantly increase or decrease the performance of a model. This result indicated the importance of keeping modeling components constant for comparing a given SDM result. With all modeling components, constant, machine-learning models seem to outperform other model types. We also found that, on average, the Hierarchical Non-Linear Principal Components Analysis dimension-reduction method improved model performance more than other methods tested. We also found that the widely used confusion-matrix-based model-performance indices such as the area under the receiving operating characteristic curve (AUC), sensitivity, and Kappa do not necessarily help select the best model from a set of models if variation in performance is not large. To conclude, model result discrepancies do not necessarily suggest lack of robustness in correlative modeling as they can also occur due to inappropriate selection of modeling components. In addition, more research on model performance evaluation is required for developing robust and sensitive model evaluation methods. Undertaking multi-scenario species-distribution modeling, where possible, is likely to mitigate errors arising from inappropriate modeling components selection, and provide end users with better information on the resulting model prediction uncertainty.

Aim: Modelling species distributions at the community level is required to make effective forecasts of global change impacts on diversity and ecosystem functioning. Community predictions may be achieved using macroecological properties of communities (macroecological models, MEM), or by stacking of individual species distribution models (stacked species distribution models, SSDMs). To obtain more realistic predictions of species assemblages, the SESAM (spatially explicit species assemblage modelling) framework suggests applying successive filters to the initial species source pool, by combining different modelling approaches and rules. Here we provide a first test of this framework in mountain grassland communities. Location: The western Swiss Alps. Methods: Two implementations of the SESAM framework were tested: a 'probability ranking' rule based on species richness predictions and rough probabilities from SDMs, and a 'trait range' rule that uses the predicted upper and lower bound of community-level distribution of three different functional traits (vegetative height, specific leaf area, and seed mass) to constrain a pool of species from binary SDMs predictions. Results: We showed that all independent constraints contributed to reduce species richness overprediction. Only the 'probability ranking' rule allowed slight but significant improvements in the predictions of community composition. Main conclusions: We tested various implementations of the SESAM framework by integrating macroecological constraints into S-SDM predictions, and report one that is able to improve compositional predictions. We discuss possible improvements, such as further understanding the causality and precision of environmental predictors, using other assembly rules and testing other types of ecological or functional constraints.

Presently, it is debated if regional conservation efforts can alone resolve the ecological problems that global climatic changes could bring. Biological invasion is one of such concerns. In the present study, we modeled how change in global climate and regional anthropogenic pressure can impact the distribution of invasive Lantana camara in the Upper Ganga valley of the Western Himalaya (India). The forest in the study area was stratified into 1 km2 grid and two 15 m radius plot were located in each of the forest types in the grid, for recording Lantana presence. In total, 2221 plots were sampled covering 22% of forest. We used predictors representing the climate, forest patch size, fire and natural disaster variables for modeling the species distribution using maximum entropy algorithm. We further simulated 12 future landscape scenarios based on the global trends of these parameters. The present species—environment relationship was projected to these future landscape scenarios. Lantana was presently estimated to spread in 231 km2 of the study area. It invaded larger forest patches in the sub-tropical region, and smaller disturbed forest patches in the warm and cold temperate region. Increased distribution of Lantana was projected across all the future scenarios. The study revealed how global climate changes and regional anthropogenic pressure can have a synergistic effect on the expansion of invasive species in the future. It thus questions the efficiency of conducting only regional efforts in absence of global initiative to reduce the greenhouse gases emission.