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[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.

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 resol

Natural habitats become increasingly degraded and fragmented due to rapid human expansion. The decreasing availability of high-quality habitats combined with a lack of connectivity among suitable patches and the low permeability of human-transformed landscapes endangers the survival of many species. Understanding the environmental conditions favoring a species’ distribution and the identification of movement corridors between populations is crucial for sustainable conservation and management. Serbia is the only European country inhabited by three different brown bear metapopulations, highlighting its crucial geographical position for establishing functional connections among these metapopulations. We used species distribution modeling to predict suitable habitats for the three bear metapopulations in Serbia at two spatial scales (5 and 1 km2). We combined the predictions from each metapopulation to define suitable habitats for range expansion. Further, we created landscape resistance maps to identify possible connectivity areas to promote gene flow between these metapopulations. Our results highlight that 1) the underlying processes of bear habitat selection at the coarse scale differ between metapopulations, mainly due to the differences in habitat availability; 2) > 60% of areas predicted as suitable for bears in Serbia are currently still unoccupied; 3) the south-eastern part of Serbia represents a key area for the connectivity between bear metapopulations in the future. However, the presence of several movement barriers, such as highways, highlights the need to implement adequate mitigation measures to increase habitat permeability. Because bears are a useful umbrella species for conservation actions, improvement of habitat quality and permeability will also positively affect many other species in this region.

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.

Areas used by female brown bears (Ursus arctos) with cubs-of-the-year (hereafter, FCOY) during the first months after den exit are crucial for offspring survival, primarily because of the risk of infanticide by male bears. Therefore, FCOY may avoid areas frequented by adult males during the mating season. The main aim of this study was to identify landscape features (i.e., structure, composition, and human footprint) that may differentiate the habitat use of FCOY in the small bear population of the Cantabrian Mountains (northwestern Spain; 2001–2016) from (a) areas frequented by females with yearlings, because older cubs are at less risk of infanticide than cubs-of-the-year, and (b) bear mating areas (i.e., the riskiest areas for FCOY because of the presence of adult males). During the first months after den emergence (Apr–Jun), FCOY settled in the roughest areas of the Cantabrian Mountains at most spatial scales. This settlement pattern might represent a behavioral adaptation of FCOY to reduce the risk of encounters with males during the mating season. However, FCOY also settled in similar landscapes to those used by adult bears during the mating season, which may increase the likelihood of risky encounters. Indeed, we observed a spatial overlap between observed locations of FCOY and mating areas, which may help explain the high frequency of infanticide observed in the Cantabrian Mountains. We hypothesize that the need for both shelter and food in early spring may ‘trap’ FCOY in otherwise favorable areas that overlap with mating areas. This may occur predominately in small and confined bear populations, such as the one in the Cantabrian Mountains, where a restricted range and limited habitat availability might prevent FCOY from avoiding risky areas successfully.

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 region

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 sui

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 fro

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 regiona

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.

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.

Brown bears Ursus arctos were historically persecuted and almost eradicated from southern Europe in the twentieth century as a result of hunting and direct persecution. The effects of human-induced mortality were exacerbated by other threats, such as habitat loss and fragmentation, due to the expansion of human populations. As a result, nowadays there are only small fragmented populations of bears in southern Europe. Brown bears in the Cantabrian (north-western Spain), Apennine (central Italy), and Pindos (north-western Greece) mountains represent three examples of small and threatened bear populations in human-modified landscapes. Most of their range is characterized by high human densities, widespread agricultural activities, livestock raising and urban development, connected by dense networks of transport infrastructures. This has resulted in a reduction of continuous habitat suitable for the species. Here, we summarize the past and present histories and fates of these three populations as examples on how the coexistence of bears and people in human-modified landscapes can take different turns depending on human attitudes.

Conflicts between humans and bears have occurred since prehistory. Through time, the catalogue of human–bear conflicts (HBC) has been changing depending on the values and needs of human societies and their interactions with bears. Even today, conflict situations vary among the eight species of bears and geographically across these species’ ranges. This results in a broad range of interactions between bears and humans that may be considered as conflicts, including: (1) predation of domestic or semiwild animals, including bees, hunting dogs, and pet animals; (2) damage due to foraging on cultivated berries, fruits, agricultural products, and the tree bark in forest plantations; (3) economic loss due to destruction of beehives, fences, silos, houses, and other human property; (4) bear attacks on humans causing mild or fatal trauma; (5) bluff charges, bear intrusions into residential areas; and (6) vehicle collisions with bears and traffic accidents. In this chapter we aim to outline the principal types of HBC and geographical differences in the occurrence of conflicts and the coexistence between people and bears.

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.

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 km(2) 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 km(2)), 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 km(2)) of the land was classified as suitable, and this part of the province hos

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.