Species distribution models (SDMs) relate species observations to mapped environmental variables to estimate the realized niche of species and predict their distribution. SDMs are key tools for projecting the impact of climate change on species and have been used in many biodiversity assessments. However, when fitted within spatial extents that do not encompass the whole species range (i.e. subrange), the estimated realized environmental niche can be truncated, which can lead to wrong or inaccurate predictions. A simple solution to this niche truncation consists in fitting SDMs at a spatial extent that encompasses the whole species range, but this often implies using a spatial resolution too coarse for local conservation assessments. To keep a fine resolution, a solution is to fit spatially nested SDMs (N‐SDMs), where a whole range, coarse‐grain SDM is combined with a subrange, fine‐grain SDM. N‐SDMs have demonstrated superior performance to subrange (truncated) SDMs in projecting species distributions under climate change and have accordingly regained considerable interest. Here, we review developments, applications and effectiveness of N‐SDMs. We present and discuss existing methods and tools to fit N‐SDMs, and assess when N‐SDMs are not needed. We highlight strengths and weaknesses of N‐SDMs, underline their importance in reducing niche truncation, and identify remaining challenges and future perspectives. Our review highlights that subrange SDMs most often lead to niche truncation and thus to incorrect spatial projections, a problem that can be overcome by using N‐SDMs. We show that the various N‐SDM methods come with their strengths and weaknesses and should be selected depending on the intended goal of the study. Synthesis. N‐SDMs are key tools to develop untruncated regional climate change forecasts of species distributions at fine resolution over restricted extent. While several N‐SDM approaches were proposed, there is currently no universal solution suggesting that further developments and testing are crucial if we are to derive robust future projections of species distributions, at least until SDMs can be applied for most species at high resolution over large geographic extents.

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 .

Spatial truncation in species distribution models (SDMs) might cause niche truncation and model transferability issues, particularly when extrapolating models to non‐analog environmental conditions. While broad calibration extents reduce truncation issues, they usually overlook local ecological factors driving species distributions at finer resolution. Spatially‐nested hierarchical SDMs (HSDMs) address truncation by merging (a) a global model calibrated with broadly extended, yet typically low‐resolution, basic, and imprecise data; and (b) a regional model calibrated with spatially restricted but more precise and reliable data. This study aimed to examine HSDMs' efficacy to overcome spatial truncation in national‐scale studies. We compared two hierarchical strategies (‘covariate', which uses the global model output as a covariate for the regional model, and ‘multiply', which calculates the geometric mean of the global and regional models) and a non‐hierarchical strategy. The three strategies were compared in terms of niche truncation, environmental extrapolation, model performance, species' predicted distributions and shifts, and trends in species richness. We examined the consistency of the results over two study areas (Spain and Switzerland), 108 tree species, and four future climate scenarios. Only the non‐hierarchical strategy was susceptible to niche truncation, and environmental extrapolation issues. Hierarchical strategies, particularly the ‘covariate' one, presented greater model accuracy than non‐hierarchical strategies. The non‐hierarchical strategy predicted the highest overall values and the lowest decreases over time in species distribution ranges and richness. Differences between strategies were more evident in Switzerland, which was more affected by niche truncation issues. Spain was more negatively affected by climate change and environmental extrapolation. The ‘covariate' strategy exhibited higher model performance than the ‘multiply' one. However, uncertainties regarding model temporal transferability advocate for adopting and further examining multiple hierarchical approaches. This research underscores the importance of adopting spatially‐nested hierarchical SDMs given the compromised reliability of non‐hierarchical approaches due to niche truncation and extrapolation issues.

Standard and easily accessible cross-thematic spatial databases are key resources in ecological research. In Switzerland, as in many other countries, available data are scattered across computer servers of research institutions and are rarely provided in standard formats (e.g., different extents or projections systems, inconsistent naming conventions). Consequently, their joint use can require heavy data management and geomatic operations. Here, we introduce SWECO25, a Swiss-wide raster database at 25-meter resolution gathering 5,265 layers. The 10 environmental categories included in SWECO25 are: geologic, topographic, bioclimatic, hydrologic, edaphic, land use and cover, population, transportation, vegetation, and remote sensing. SWECO25 layers were standardized to a common grid sharing the same resolution, extent, and geographic coordinate system. SWECO25 includes the standardized source data and newly calculated layers, such as those obtained by computing focal or distance statistics. SWECO25 layers were validated by a data integrity check, and we verified that the standardization procedure had a negligible effect on the output values. SWECO25 is available on Zenodo and is intended to be updated and extended regularly.

Although the exceptional spatiotemporal extent of the Waterfowl Breeding Population and Habitat Survey (WBPHS) has substantially contributed to our understanding of the ecology of North American waterfowl, vast geographical areas remain excluded from the survey. The unprecedented number of observations generated by the recent boom in citizen science initiatives could help resolve these spatial gaps and increase the density of records in regions already covered. The study objective was to assess the value of the integrated species distribution modeling (ISDM) approach for integrating WBPHS and eBird data to model waterfowl distribution across the Canadian western boreal forest, where WBPHS data are sparse. Following the ISDM approach, we used a state‐space point process formulation that combined a model for the “true” species distribution and two observation models for how WBPHS and eBird data were generated. Our results highlighted the importance of observational processes related to sampling effort and site accessibility for modeling eBird data. In addition, our models allowed identifying waterfowl–habitat associations related to geoclimatic, forest, and hydrological factors that explained the distribution of target species. To assess the individual contribution of WBPHS and eBird data, we re‐fitted the models using only one of the two data sets and compared the results obtained against those from the integrated approach. Waterfowl–habitat associations and predictions derived from the models using both data sets and those fitted with WBPHS data only were close and consistent with the observed species distribution. However, it was more difficult to extract an ecological signal from models fitted with eBird data only. Interestingly, predictions from models combining both data sets were closer to the WBPHS records than the predictions from models fitted with WBPHS data only. By facilitating the combination of all available data sources, we demonstrated the potential of the ISDM approach for modeling and mapping species distributions. We encourage future North American waterfowl modeling attempts to use this method, especially for resolving gaps in the WBPHS coverage. As multiple data sets can be added to the original framework, integration efforts must not be restricted to the additional contribution of eBird data alone and could consider, for example, provincial atlases and regional helicopter surveys.

Climate projections for continental Europe indicate drier summers, increased annual precipitation, and less snowy winters, which are expected to cause shifts in species' distributions. Yet, most regions/countries currently lack comprehensive climate‐driven biodiversity projections across taxonomic groups, challenging effective conservation efforts. To address this gap, our study evaluated the potential effects of climate change on the biodiversity of an alpine country of Europe, Switzerland. We used a state‐of‐the art species distribution modeling approach and species occurrence data that covered the climatic conditions encountered across the full species' ranges to help limiting niche truncation. We quantified the relationship between baseline climate and the spatial distribution of 7291 species from 12 main taxonomic groups and projected future climate suitability for three 30‐year periods and two greenhouse gas concentration scenarios (RCP4.5 and 8.5). Our results indicated important effects of projected climate changes on species' climate suitability, with responses varying by the taxonomic and conservation status group. The percentage of species facing major changes in climate suitability was higher under RCP8.5 (68%) compared to RCP4.5 (66%). By the end of the century, decreases in climate suitability were projected for 3000 species under RCP8.5 and 1758 species under RCP4.5. The most affected groups under RCP8.5 were molluscs, algae, and amphibians, while it was molluscs, birds, and vascular plants under RCP4.5. Spatially, by 2070–2099, we projected an overall decrease in climate suitability for 39% of the cells in the study area under RCP8.5 and 10% under RCP4.5, while projecting an increase for 50% of the cells under RCP8.5 and 73% under RCP4.5. The most consistent geographical shifts were upward, southward, and eastward. We found that the coverage of high climate suitability cells by protected areas was expected to increase. Our models and maps provide guidance for spatial conservation planning by pointing out future climate‐suitable areas for biodiversity.