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Simple Summary: Human activities have led to significant global habitat degradation and fragmentation. However, some carnivores have adapted to these conditions and are expanding, leading to closer coexistence with humans and potential conflicts. This study analysed over 350 sightings of the European wildcat (Felis silvestris) in NW Spain over 17 years to develop suitability models based on environmental, topographic, climatic, and human impact factors. Using MaxEnt, the study predicted the species' potential regional distribution. The results revealed that less than a third of the suitable areas for wildcats had confirmed their presence. Elevation, forested area percentage, and footpath density were key factors influencing wildcat presence, with the first two having positive effects and footpath density having a negative impact. The wildcats' preference for high and forested areas likely relates to food availability, while avoiding footpaths is linked to human-related mortality. These findings provide insights for conservation strategies to protect the species. Human activities have resulted in severe habitat degradation and fragmentation at a global scale. Despite this scenario, some carnivore species that adapted to the new conditions are expanding, leading to close coexistence with humans and the emergence of potential conflicts. In this work, we used a European wildcat (Felis silvestris) observations database of more than 350 sightings over 17 years in NW Spain to build suitability models based on environmental, topographic, climatic, and human impact variables. MaxEnt was used to analyse the availability of suitable habitats for the species at a regional scale. Our results showed that less than one third of the suitable area for the species had confirmed wildcat presence. Elevation, the percentage of forested area, and footpath density were the three main variables conditioning wildcat presence, with the first two variables having positive effects and footpath density negatively affecting wildcat presence. The selection of high areas and forest areas by the species seems to be related to food availability, while the avoidance of footpaths seems to be related to the fact that main mortality causes are linked to human disturbances. The results enhance the understanding of the European wildcat ecology and provide insight into potential management plans to ensure the conservation of one of the main populations of the species throughout its range. [ABSTRACT FROM AUTHOR]

Species distribution models (SDMs) have become a common tool in studies of species–environment relationships but can be negatively affected by positional uncertainty of underlying species occurrence data. Previous work has documented the effect of positional uncertainty on model predictive performance, but its consequences for inference about species–environment relationships remain largely unknown. Here we use over 12 000 combinations of virtual and real environmental variables and virtual species, as well as a real case study, to investigate how accurately SDMs can recover species–environment relationships after applying known positional errors to species occurrence data. We explored a range of environmental predictors with various spatial heterogeneity, species' niche widths, sample sizes and magnitudes of positional error. Positional uncertainty decreased predictive model performance for all modeled scenarios. The absolute and relative importance of environmental predictors and the shape of species–environmental relationships co‐varied with a level of positional uncertainty. These differences were much weaker than those observed for overall model performance, especially for homogenous predictor variables. This suggests that, at least for the example species and conditions analyzed, the negative consequences of positional uncertainty on model performance did not extend as strongly to the ecological interpretability of the models. Although the findings are encouraging for practitioners using SDMs to reveal generative mechanisms based on spatially uncertain data, they suggest greater consequences for applications utilizing distributions predicted from SDMs using positionally uncertain data, such as conservation prioritization and biodiversity monitoring. [ABSTRACT FROM AUTHOR]

There is a lack of guidance on the choice of the spatial grain of predictor and response variables in species distribution models (SDM). This review summarizes the current state of the art with regard to the following points: (i) the effects of changing the resolution of predictor and response variables on model performance; (ii) the effect of conducting multi-grain versus single-grain analysis on model performance; and (iii) the role of land cover type and spatial autocorrelation in selecting the appropriate grain size. In the reviewed literature, we found that coarsening the resolution of the response variable typically leads to declining model performance. Therefore, we recommend aiming for finer resolutions unless there is a reason to do otherwise (e.g. expert knowledge of the ecological scale). We also found that so far, the improvements in model performance reported for multi-grain models have been relatively low and that useful predictions can be generated even from single-scale models. In addition, the use of high-resolution predictors improves model performance; however, there is only limited evidence on whether this applies to models with coarser-resolution response variables (e.g. 100 km2 and coarser). Low-resolution predictors are usually sufficient for species associated with fairly common environmental conditions but not for species associated with less common ones (e.g. common vs rare land cover category). This is because coarsening the resolution reduces variability within heterogeneous predictors and leads to underrepresentation of rare environments, which can lead to a decrease in model performance. Thus, assessing the spatial autocorrelation of the predictors at multiple grains can provide insights into the impacts of coarsening their resolution on model performance. Overall, we observed a lack of studies examining the simultaneous manipulation of the resolution of predictor and response variables. We stress the need to explicitly report the resolution of all predictor and response variables. [ABSTRACT FROM AUTHOR]

Background: We compiled a database of firefly species records from the Atlantic Forest hotspot in Brazil and made it available at GBIF. Data were gathered from literature and from several key entomological collections, including: Coleção entomológica Prof. José Alfredo Pinheiro Dutra (DZRJ/UFRJ) and Coleção do Laboratório de Ecologia de Insetos from Universidade Federal do Rio de Janeiro (CLEI/UFRJ); Coleção Entomológica do Instituto Oswaldo Cruz (CEIOC); Museu de Zoologia da Universidade de São Paulo (MZSP); Coleção Entomológica Pe. Jesus Santiago Moure from Universidade Federal do Paraná (DZUP/UFPR); and Coleção Entomológica from Universidade Federal Rural de Pernambuco (UFRPE). This database represents the largest contribution to a public repository of recorded occurrences from Neotropical fireflies. New information: This dataset shows the occurrence and abundance of firefly species in the Atlantic Forest hotspot. Firefly species endemic to this biome are also present and considered in the study. These data can assist scientific and societal needs, by supporting future research projects and conservation decision-making. [ABSTRACT FROM AUTHOR]

Species distribution models (SDMs) are powerful tools in ecology and conservation. Choosing the right environmental drivers and filtering species' occurrences taking their biases into account are key factors to consider before modeling. In this case study, we address five common problems arising during the selection of input data for presence‐only SDMs on an example of a generalist species: the endangered Cantabrian brown bear. First, we focus on the selection of environmental variables that may drive its distribution, testing if climatic variables should be considered at a 1‐km analysis grain. Second, we investigate how filtering the species' data in view of (1) their collection procedures, (2) different time frames, (3) dispersal areas, and (4) subpopulations affects the performance and outputs of the models at three different spatial analysis grains (500 m, 1 km, and 5 km). Our results show that models with different input data yielded only minor differences in performance and behaved properly in terms of model validation, although coarsening the analysis grain deteriorated model performance. Still, the contribution of individual variables and the habitat suitability predictions differed among models. We show that a combination of limited data availability and poor selection of environmental variables can lead to inaccurate predictions. Specifically for the brown bear, we conclude that climatic variables should not be considered for exploring habitat suitability and that the best input data for modeling habitat suitability in the study area originate from (1) observations and traces from the (2) most recent period (2006–2019) in which the population is expanding, (3) not considering cells of dispersing bear occurrences and (4) modeling subpopulations independently (as they show distinct habitat preferences). In conclusion, SDMs can serve as a useful tool for generalist species including all available data; still, expert evaluation from the perspective of data suitability for the purpose of modeling and possible biases is recommended. This is especially important when the results are intended for management and conservation purposes at the local level, and for species that respond to the environment at coarse analysis grains. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Large carnivores are essential keystone species in the ecosystems that they inhabit, and the warming climate is harming a majority of the species. Here, we review the literature that spanned the years 1991-2022 on fifteen large carnivore species and their response to climate change via the proxies of (1) habitat alterations; (2) diet profile changes; and (3) behavioural changes. The literature review highlighted that 15 large carnivore species had been taken into account by 164 studies (87 on habitat, 59 on diet, 18 on behaviour) on potential climate change effects in five continents. Eightyseven studies featured projected or current changes in suitable habitat due to climate change, 59 studies featured projected or current changes in preferred diet due to climate change, and 18 studies covered proposed or current behaviour changes in response to climate change. Of the 87 suitable habitat studies, 66 (78 %) were categorized as negative, i.e., when a potential reduction in resources has been projected. Of the 59 preferred diet studies, 39 (66 %) were categorized as negative. Despite the evidence that information on how large carnivore habitats, diets, and behaviours might be affected by climate change are still scarce for several species and/or geographical areas, most of the available predictions point to an unfortunate truth. Species with habitats susceptible to considerable alterations will probably experience a severe local decline in the next few decades. Loss of suitable habitats and decreased food availability, which has been forecasted for most large carnivores, might also induce these species to shift their home ranges in search of alternative food sources. These may include areas where they are more likely to experience more conflict with humans. Large carnivores require long-term conservation, management strategies, and more research to develop a deeper understanding of climate change's impacts and establish pre-emptive measures ensuring population viability in the following decades. [ABSTRACT FROM AUTHOR]

The representation of a land cover type (i.e. habitat) within an area is often used as an explanatory variable in species distribution models. However, it is possible that a simple binary presence/absence of the suitable habitat might be the most important determinant of the presence/absence of some species and, thus, be a better predictor of species occurrence than the continuous parameter (area). We hypothesize that the binary predictor is more suitable for relatively rare habitats (e.g. wetlands) while for common habitats (e.g. forests) the amount of the focal habitat is a better predictor. We used the Third Atlas of Breeding Birds in the Czech Republic as the source of species distribution data and CORINE Land Cover inventory as the source of the landcover information. To test our hypothesis, we fitted generalized linear models of 32 water and 32 forest bird species. Our results show that for water bird species, models using binary predictors (presence/absence of the habitat) performed better than models with continuous predictors (i.e. the amount of the habitat); for forest species, however, we observed the opposite. Thus, future studies using habitats as predictors of species occurrences should consider the prevalence of the habitat in the landscape, and the biological role of the habitat type in the particular species' life history. In addition, performing a preliminary comparison of the performance of the binary and continuous versions of habitat predictors (e.g. using information criteria) prior to modelling, during variable selection, can be beneficial. These are simple steps that will improve explanatory and predictive performance of models of species distributions in biogeography, community ecology, macroecology and ecological conservation. [ABSTRACT FROM AUTHOR]

Scent-mediated communication is considered the principal communication channel in many mammal species. Compared with visual and vocal communication, odors persist for a longer time, enabling individuals to interact without being in the same place at the same time. The brown bear (Ursus arctos), like other mammals, carries out chemical communication, for example, by means of scents deposited on marking (or rub) trees. In this study, we assessed rub tree selectivity of the brown bear in the predominantly deciduous forests of the Cantabrian Mountains (NW Spain). We first compared the characteristics of 101 brown bear rub trees with 263 control trees. We then analyzed the potential factors affecting the density of rub trees along 35 survey routes along footpaths. We hypothesized that: (1) bears would select particular trees, or tree species, with characteristics that make them more conspicuous; and (2) that bears would select trees located in areas with the highest presence of conspecifics, depending on the population density or the position of the trees within the species' range. We used linear models and generalized additive models to test these hypotheses. Our results showed that brown bears generally selected more conspicuous trees with a preference for birches (Betula spp.). This choice may facilitate the marking and/or detection of chemical signals and, therefore, the effectiveness of intraspecific communication. Conversely, the abundance of rub trees along footpaths did not seem to depend on the density of bear observations or their relative position within the population center or its border. Our results suggest that Cantabrian brown bears select trees based on their individual characteristics and their location, with no influence of characteristics of the bear population itself. Our findings can be used to locate target trees that could help in population monitoring. [ABSTRACT FROM AUTHOR]

The survival of an increasing number of species is threatened by climate change: 20%–30% of plants and animals seem to be at risk of range shift or extinction if global warming reaches levels projected to occur by the end of this century. Plant range shifts may determine whether animal species that rely on plant availability for food and shelter will be affected by new patterns of plant occupancy and availability. Brown bears in temperate forested habitats mostly forage on plants and it may be expected that climate change will affect the viability of the endangered populations of southern Europe. Here, we assess the potential impact of climate change on seven plants that represent the main food resources and shelter for the endangered population of brown bears in the Cantabrian Mountains (Spain). Our simulations suggest that the geographic range of these plants might be altered under future climate warming, with most bear resources reducing their range. As a consequence, this brown bear population is expected to decline drastically in the next 50 years. Range shifts of brown bear are also expected to displace individuals from mountainous areas towards more humanized ones, where we can expect an increase in conflicts and bear mortality rates. Additional negative effects might include: (a) a tendency to a more carnivorous diet, which would increase conflicts with cattle farmers; (b) limited fat storage before hibernation due to the reduction of oak forests; (c) increased intraspecific competition with other acorn consumers, that is, wild ungulates and free‐ranging livestock; and (d) larger displacements between seasons to find main trophic resources. The magnitude of the changes projected by our models emphasizes that conservation practices focused only on bears may not be appropriate and thus we need more dynamic conservation planning aimed at reducing the impact of climate change in forested landscapes. The brown bear Ursus arctos range in the Cantabrian Mountains (NW Spain) is expected to reduce in the next 50 years, mostly due to the effect of climate change on vegetation range shifts. Conservation plans that overlook potential range shifts have poor expected outcomes for most species. Indeed, projecting future scenarios of forest shifts given climate change predictions can help inform conservation planning to mitigate bear food and shelter range contractions. Conservation practices only focused on the Cantabrian brown bear population may not be appropriate; rather, we also need more dynamic conservation planning aimed to reduce the impact of climate change in forested landscapes. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract: Evolutionary traps, and their derivative, ecological traps, occur when animals make maladaptive decisions based on seemingly reliable environmental cues, and are important mechanistic explanations for declines in animal populations. Despite the interest in large carnivore conservation in human‐modified landscapes, the emergence of traps and their potential effects on the conservation of large carnivore populations has frequently been overlooked. The brown bear Ursus arctos typifies the challenges facing large carnivore conservation and recent research has reported that this species can show maladaptive behaviours in human‐modified landscapes. Here we review, describe and discuss scenarios recognised as evolutionary or ecological traps for brown bears, and propose possible trap scenarios and mechanisms that have the potential to affect the dynamics and viability of brown bear populations. Six potential trap scenarios have been detected for brown bears in human‐modified landscapes: 1) food resources close to human settlements; 2) agricultural landscapes; 3) roads; 4) artificial feeding sites; 5) hunting by humans; and 6) other human activities. Because these traps are likely to be of contrasting relevance for different demographic segments of bear populations, we highlight the importance of evaluations of the relative demographic consequences of different trap types for wildlife management. We also suggest that traps may be behind the decreases in brown bear and other large carnivore populations in human‐modified landscapes. [ABSTRACT FROM AUTHOR]