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Here we present annual nearest-neighbour distances (as a proxy of density) between females with cubs-of-the-year (hereafter FCOY) and reproductive characteristics of brown bears Ursus arctos in the Cantabrian Mountains (NW Spain), from 1989 to 2017. FCOY nearest-neighbour distances and reproduction parameters of 19 focal females followed over several consecutive years (from 2004 to 2017) were obtained from bears inhabiting the western sector of the Cantabrian Mountains, where most of the bear population resides. In contrast, general reproductive characteristics were studied in the whole Cantabrian Mountains (western and eastern sectors together) on a sample of 362 litter sizes and 695 cubs. Mean nearest-neighbour distance between FCOY was 2559 ± 1222 m (range = 1305–4757 m). Mean litter size was significantly larger in the west (1.8 ± 0.2 cubs) than in the east (1.3 ± 0.6 cubs). Mean litter size for the whole of the Cantabrian Mountains was 1.6 ± 0.3 cubs. Litter sizes of one, two and three cubs represented 33.4, 56.1 and 10.5% of observed family groups, respectively. Interannual variations in litter size were not significant for both the western and the eastern areas. Mean cub mortality was 0.2 ± 0.5 cubs and did not vary among years. Cub mortality per litter size was 3.9% for one cub, 69.2% for two cubs and 26.9% for three cubs. Mean reproductive rate of the 19 focal females was 1.5 ± 0.6 cubs (n = 58 litters). Litter size of focal FCOY did not differ from the litter size obtained from systematic observations in the whole Cantabrian Mountains. During this period, cub mortality occurred in 24.1% of the 58 litters. Females usually bred every second year (average litter interval = 2.2 years). The estimated reproductive rate for the bear population was 0.7 young born/year/reproductive adult female.

Abstract Species distribution models have evolved to combine species‐environment interactions across multiple scales. Spatially nested hierarchical models (NSDMs) offer a promising avenue by integrating datasets and predictive models from broad to fine scales. But a user‐friendly tool to execute these models remains an important ongoing challenge. To address this gap, we introduce the sabinaNSDM R package that provides a straightforward approach to develop NSDMs. This package merges global scale models, capturing extensive ecological niches, with regional scale models featuring high‐resolution covariates, to form a unified hierarchical modelling framework. This toolkit is designed to facilitate the implementation of NSDMs for ecologists, conservationists and researchers aiming to produce more reliable species distribution predictions. sabinaNSDM streamlines the data preparation, calibration, integration and projection of models across two scales. It automates (if necessary) the generation of background points, spatial thinning of species occurrence and absence (if available) data, covariate selection and the generation of NSDMs. This paper outlines the workflow and functions integrated into the sabinaNSDM package, complemented by an applied case study involving a pool of 76 tree species. Consistent with previous publications, the generated NSDMs facilitated precise predictions (mean AUC value through independent evaluation higher than 0.88) of species distributions under current and future environmental scenarios.

The Atlantic Forest holds the greatest diversity of fireflies on Earth. Although the knowledge about lampyrids in the Atlantic Forest has increased in the last 10 years thanks to the efforts of new taxonomists and ecologists, both the total number of species and their geographic ranges remain largely unknown. Furthermore, the Atlantic Forest has been facing intense deforestation since the XV century, and now only 23 % of the natural vegetation cover remains, resulting in high vulnerability of its endemic species. Here, we assess the quality and coverage of the information available on the distribution of Lampyridae fireflies; i.e. the extent of the so-called Wallacean shortfall. To do this, we used a database that comprises 3010 records with geospatial and taxonomic information to evaluate the overall coverage of the geographical and environmental distribution of fireflies in the Atlantic Forest. Our assessment shows that the knowledge of Atlantic Forest's firefly diversity and distribution presents significant spatial and climatic biases. Firefly species remain largely under-collected in the Atlantic Forest; only around 16 % of its grid cells at a 50 km scale are well-sampled, and most of them are located in the Serra do Mar bioregion. The location of well-sampled cells is conditioned by recorders’ survey bias and is particularly biased towards protected areas, large forest remnants, the proximity to institutions holding entomological collections, and light-polluted sites surrounding urban spaces. Our findings also highlight that a considerable number of firefly species may remain undiscovered, which adds to the lack of reliable information on the geographic location of many valid species. Based on this assessment, we further point to areas where more sampling effort would provide maximum benefits in terms of geographical and environmental coverage, thus maximizing its value for biodiversity knowledge and conservation.

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.

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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]

Climate change is an important risk factor for the survival of butterflies and other species. In this study, we developed predictive models that show the potentially favourable areas for a lepidopteran endemic to the Iberian Peninsula, the Spanish argus (Aricia morronensis), and its larval food plants, the storksbill (genus Erodium). We used species distribution modelling software (MaxEnt) to perform the models in the present and in the future in two climatic scenarios based on climatic and topographic variables. The results show that climate change will not significantly affect A. morronensis distribution, and may even slightly favour its expansion. Some plants may undergo a small reduction in habitat favourability. However, it seems that the interaction between this butterfly and its food plants is unlikely to be significantly affected by climate change.

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.

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]

Roads are human infrastructure that heavily affect wildlife, often with marked impacts on carnivores, including brown bears Ursus arctos. Here, we assessed the potential impact of road networks on the distribution of brown bears in the small, isolated and endangered Cantabrian population of north‐western Spain. To ascertain whether local road networks affect brown bear spatial distribution, we first assessed potential influences on the distance of bear locations to roads using candidate models which included topographic variables, landcover types, bear age and reproductive status, traffic volume and road visibility. Then, we built two sets of habitat suitability models, both with and without roads, to discern the possible loss of habitat suitability caused by roads. The mean distance of bear locations to the nearest road was 968 ± 804 m and the closest road was a low traffic road in 72.5% of cases. Candidate models showed little influence of our variables on bear distance to the nearest road, with the exception of elevation. Habitat suitability models revealed that road networks in our study area seem to have almost no effect on brown bear habitat suitability, except for females with yearlings during the denning season. However, this result may also be a consequence of the fact that only a small proportion (16.5%) of the cells classified as suitable bear habitats were crossed by roads, that is, most of the roads are primarily located in unsuitable bear habitats in the Cantabrian Mountains. Compared to previous studies conducted in other populations, mainly North American ones, our findings might suggest a different response of Eurasian brown bears to roads due to a longer bear‐human coexistence in Europe versus North America. However, the indirect approach used in our study does not exclude other detrimental effects, for example, road mortality, increased stress and movement pattern disruption, only detectable by more direct approaches such as telemetry. [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]

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. [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]

Here we present annual nearest-neighbour distances (as a proxy of density) between females with cubs-of-the-year (hereafter FCOY) and reproductive characteristics of brown bears Ursus arctos in the Cantabrian Mountains (NW Spain), from 1989 to 2017. FCOY nearest-neighbour distances and reproduction parameters of 19 focal females followed over several consecutive years (from 2004 to 2017) were obtained from bears inhabiting the western sector of the Cantabrian Mountains, where most of the bear population resides. In contrast, general reproductive characteristics were studied in the whole Cantabrian Mountains (western and eastern sectors together) on a sample of 362 litter sizes and 695 cubs. Mean nearest-neighbour distance between FCOY was 2559 ± 1222 m (range = 1305-4757 m). Mean litter size was significantly larger in the west (1.8 ± 0.2 cubs) than in the east (1.3 ± 0.6 cubs). Mean litter size for the whole of the Cantabrian Mountains was 1.6 ± 0.3 cubs. Litter sizes of one, two and three cubs represented 33.4, 56.1 and 10.5% of observed family groups, respectively. Interannual variations in litter size were not significant for both the western and the eastern areas. Mean cub mortality was 0.2 ± 0.5 cubs and did not vary among years. Cub mortality per litter size was 3.9% for one cub, 69.2% for two cubs and 26.9% for three cubs. Mean reproductive rate of the 19 focal females was 1.5 ± 0.6 cubs (n = 58 litters). Litter size of focal FCOY did not differ from the litter size obtained from systematic observations in the whole Cantabrian Mountains. During this period, cub mortality occurred in 24.1% of the 58 litters. Females usually bred every second year (average litter interval = 2.2 years). The estimated reproductive rate for the bear population was 0.7 young born/year/reproductive adult female. [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]

Cannabinoids have shown to exert their therapeutic actions through a variety of targets. These include not only the canonical cannabinoid receptors CB1R and CB2R but also related orphan G protein-coupled receptors (GPCRs), ligand-gated ion channels, transient receptor potential (TRP) channels, metabolic enzymes, and nuclear receptors. In this review, we aim to summarize reported compounds exhibiting their therapeutic effects upon the modulation of CB1R and/or CB2R and the nuclear peroxisome proliferator-activated receptors (PPARs). Concomitant actions at CBRs and PPARα or PPARγ subtypes have shown to mediate antiobesity, analgesic, antitumoral, or neuroprotective properties of a variety of phytogenic, endogenous, and synthetic cannabinoids. The relevance of this multitargeting mechanism of action has been analyzed in the context of diverse pathologies. Synergistic effects triggered by combinatorial treatment with ligands that modulate the aforementioned targets have also been considered. This literature overview provides structural and pharmacological insights for the further development of dual cannabinoids for specific disorders. [ABSTRACT FROM AUTHOR]

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.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 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.

Species Distribution Models (SDMs) are essential tools for predicting climate change impact on species’ distributions and are commonly employed as an informative tool on which to base management and conservation actions. Focusing only on a part of the entire distribution of a species for fitting SDMs is a common approach. Yet, geographically restricting their range can result in considering only a subset of the species’ ecological niche (i.e., niche truncation) which could lead to biased spatial predictions of future climate change effects, particularly if future conditions belong to those parts of the species ecological niche that have been excluded for model fitting. The integration of large-scale distribution data encompassing the whole species range with more regional data can improve future predictions but comes along with challenges owing to the broader scale and/or lower quality usually associated with these data. Here, we compare future predictions obtained from a traditional SDM fitted on a regional dataset (Switzerland) to predictions obtained from data integration methods that combine regional and European datasets for several bird species breeding in Switzerland. Three models were fitted: a traditional SDM based only on regional data and thus not accounting for niche truncation, a data pooling model where the two datasets are merged without considering differences in extent or resolution, and a downscaling hierarchical approach that accounts for differences in extent and resolution. Results show that the traditional model leads to much larger predicted range changes (either positively or negatively) under climate change than both data integration methods. The traditional model also identified different variables as main drivers of species’ distribution compared to data-integration models. Differences between models regarding predicted range changes were larger for species where future conditions were outside the range of conditions existing in the regional dataset (i.e., when future conditions implied extrapolation). In conclusion, we showed that (i) models calibrated on a geographically restricted dataset provide markedly different predictions than data integration models and (ii) that these differences are at least partly explained by niche truncation. This suggests that using data integration methods could lead to more accurate predictions and more nuanced range changes than regional SDMs through a better characterization of species’ entire realized niches.