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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., Permission to undertake research in the protected areas mentioned in this paper was granted by the Instituto Chico Mendes de Conservação da Biodiversidade, Ministério do Meio Ambiente, Brazil. We also thank the staff at Parque Estadual da Pedra Branca and our dear friend André Luiz Diniz Ferreira (PEPB). We thank the staff at the Parque Nacional da Serra dos Órgãos and Reserva Ecológica de Guapiaçu for supporting our fieldwork and the staff at Laboratório de Ecologia de Insetos/UFRJ and Laboratório de Entomologia/UFRJ for field assistance. We thank all the curators of DZRJ, DZUP, MZSP, CLEI, UFRPE and CEIOC, for their support during our visits to their respective institutions and Programa de Pós-Graduação em Ecologia for supporting the visitation to the collections. We would like to thank SIBBr for supporting our dataset and to provide precious technical information to provide the firefly assessments online. S.V. was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES). We acknowledge support of the publication fee by the CSIC Open Access Publication Support Initiative through its Unit of Information Resources for Research (URICI).

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., This research was funded by the Technological grant agency of the Czech Republic (grant no. SS02030018 DivLand) and by OP RDE Improvement in Quality of the Internal Grant Scheme at CZU, reg. no. CZ.02.2.69/0.0/0.0/19_073/0016944 (grant no. 43/2021). In addition, this paper was made possible by generous support from the Fulbright-Masaryk program sponsored by US and Czech governments, which provided Lukáš Gábor with the opportunity to conduct research at Yale University.

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., During this research, EG-B was financially supported by a FPU grant (FPU15-03429) from the Spanish Ministry of Science, Innovation and Universities. VP, EG-B, AMG and HRV were financially supported by the I + D + I Project PID2020-114181GB-I00 financed by the Spanish Ministry of Science and Innovation, the Agencia Estatal de Investigación (AEI) and the Fondo Europeo de Desarrollo Regional (FEDER, EU). AMG was supported by the Predoctoral Fellowship PRE2018-086102. AZA was financially supported by a Margarita Salas contract financed by the European Union-NextGenerationEU, Ministerio de Universidades y Plan de Recuperación, Transformación y Resiliencia, through the call of the Universidad de Oviedo (Asturias).

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., European Union-Next Generation EU, Ministerio de Universidades y Plan de Recuperación, Transformación y Resiliencia, Universidad de Oviedo, Grant/Award Number: Margarita Salas; Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Grant/Award Number: 2022B0012; Spanish Ministry of Science and Innovation, the Agencia Estatal de Investigación (AEI), and the Fondo Europeo de Desarrollo Regional (FEDER, EU), Grant/Award Number: PID2020-114181GB-I00. Lukáš Gábor was supported by the Faculty of Environmental Sciences, Czech University of Life Sciences Prague (Internal Grant Agency project no. 2022B0012).

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., During this research, AG was financially supported by the ValPar.ch project funded by the Swiss Office of the Environment and AZ-A was financially supported by a Margarita Salas contract financed by the European Union Next Generation EU, Ministerio de Universidades y Plan de Recuperación, Transformación y Resiliencia, through the call of the Universidad de Oviedo (Asturias).

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.

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., This research was funded by the Internal Grant Agency of Faculty of Environmental Sciences, Czech Univ. of Life Sciences Prague, grant no. 2020B0009 and by the Technological grant agency of the Czech Republic (grant no. SS02030018 DivLand). In addition, this paper was made possible by generous support from Fulbright-Masaryk program sponsored by U.S. and Czech governments, which provided Lukáš Gábor with the opportunity to conduct research at Yale University.