Species distribution models (SDMs) have proven valuable in filling gaps in our knowledge of species occurrences. However, despite their broad applicability, SDMs exhibit critical shortcomings due to limitations in species occurrence data. These limitations include, in particular, issues related to sample size, positional uncertainty, and sampling bias. In addition, it is widely recognised that the quality of SDMs as well as the approaches used to mitigate the impact of the aforementioned data limitations depend on species ecology. While numerous studies have evaluated the effects of these data limitations on SDM performance, a synthesis of their results is lacking. However, without a comprehensive understanding of their individual and combined effects, our ability to predict the influence of these issues on the quality of modelled species–environment associations remains largely uncertain, limiting the value of model outputs. In this paper, we review studies that have evaluated the effects of sample size, positional uncertainty, sampling bias, and species ecology on SDMs outputs. We build upon their findings to provide recommendations for the critical assessment of species data intended for use in SDMs.

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.

Many large carnivore populations are expanding into human-modified landscapes and the subsequent increase in coexistence between humans and large carnivores may intensify various types of conflicts. A proactive management approach is critical to successful mitigation of such conflicts. The Cantabrian Mountains in Northern Spain are home to the last remaining native brown bear (Ursus arctos) population of the Iberian Peninsula, which is also amongst the most severely threatened European populations, with an important core group residing in the province of Asturias. There are indications that this small population is demographically expanding its range. The identification of the potential areas of brown bear range expansion is crucial to facilitate proactive conservation and management strategies towards promoting a further recovery of this small and isolated population. Here, we used a presence-only based maximum entropy (MaxEnt) approach to model habitat suitability and identify the areas in the Asturian portion of the Cantabrian Mountains that are likely to be occupied in the future by this endangered brown bear population following its range expansion. We used different spatial scales to identify brown bear range suitability according to different environmental, topographic, climatic and human impact variables. Our models mainly show that: (1) 4977 km² are still available as suitable areas for bear range expansion, which represents nearly half of the territory of Asturias; (2) most of the suitable areas in the western part of the province are already occupied (77% of identified areas, 2820 km²), 41.4% of them occurring inside protected areas, which leaves relatively limited good areas for further expansion in this part of the province, although there might be more suitable areas in surrounding provinces; and (3) in the eastern sector of the Asturian Cantabrian Mountains, 62% (2155 km²) of the land was classified as suitable, and this part of the province hosts 44.3% of the total area identified as suitable areas for range expansion. Our results further highlight the importance of increasing: (a) the connectivity between the currently occupied western part of Asturias and the areas of potential range expansion in the eastern parts of the province; and (b) the protection of the eastern sector of the Cantabrian Mountains, where most of the future population expansion may be expected.

The performance of species distribution models (SDMs) is known to be affected by analysis grain and positional error of species occurrences. Coarsening of the analysis grain has been suggested to compensate for positional errors. Nevertheless, this way of dealing with positional errors has never been thoroughly tested. With increasing use of fine‐scale environmental data in SDMs, it is important to test this assumption. Models using fine‐scale environmental data are more likely to be negatively affected by positional error as the inaccurate occurrences might easier end up in unsuitable environment. This can result in inappropriate conservation actions. Here, we examined the trade‐offs between positional error and analysis grain and provide recommendations for best practice. We generated narrow niche virtual species using environmental variables derived from LiDAR point clouds at 5 × 5 m fine‐scale. We simulated the positional error in the range of 5 m to 99 m and evaluated the effects of several spatial grains in the range of 5 m to 500 m. In total, we assessed 49 combinations of positional accuracy and analysis grain. We used three modelling techniques (MaxEnt, BRT and GLM) and evaluated their discrimination ability, niche overlap with virtual species and change in realized niche. We found that model performance decreased with increasing positional error in species occurrences and coarsening of the analysis grain. Most importantly, we showed that coarsening the analysis grain to compensate for positional error did not improve model performance. Our results reject coarsening of the analysis grain as a solution to address the negative effects of positional error on model performance. We recommend fitting models with the finest possible analysis grain and as close to the response grain as possible even when available species occurrences suffer from positional errors. If there are significant positional errors in species occurrences, users are unlikely to benefit from making additional efforts to obtain higher resolution environmental data unless they also minimize the positional errors of species occurrences. Our findings are also applicable to coarse analysis grain, especially for fragmented habitats, and for species with narrow niche breadth.

Coexistence of humans and large carnivores is a major challenge for conservation and management, especially in human‐modified landscapes. Ongoing recovery of some large carnivore populations is good conservation news, but it also brings about increased levels of conflict with humans. Compensation payments and preventive measures are used worldwide as part of conservation programmes with the aim of reducing such conflicts and improving public attitude towards large carnivores. However, understanding the drivers triggering conflicts is a conservation priority, which helps prevent and reduce damages. Here, we have analysed the spatio‐temporal patterns of brown bear Ursus arctos damages to apiaries, crops and livestock in the two small, isolated and endangered bear populations in northern Spain. The increase in the number of damages varied in parallel with the increase in bear numbers, which is probably a primary cause determining the occurrence on damages. Damages also varied among years, seasons and bear populations and seemed to mainly depend on the local availability of natural food items, weather conditions and the availability of apiaries and livestock. Fluctuating availability of food items may explain the frequency of conflicts, which is yet another call to apply preventive measures in carnivore damage to human property in seasons and years when natural food availability is lower than usual. Understanding and preventing damage is in turn essential to mitigate conflicts where humans and large carnivores share the same landscape.