Occurrence Dataset Research Articles

Sentinel-2 Time Series and Classifier Fusion to Map an Aquatic Invasive Plant Species along a River—The Case of Water-Hyacinth

Freshwater ecosystems host high levels of biodiversity but are also highly vulnerable to biological invasions. Aquatic Invasive Alien Plant Species (aIAPS) can cause detrimental effects on freshwater ecosystems and their services to society, raising challenges to decision-makers regarding their correct management. Spatially and temporally explicit information on the occurrence of aIAPS in dynamic freshwater systems is essential to implement efficient regional and local action plans. The use of unmanned aerial vehicle imagery synchronized with free Sentinel-2 multispectral data allied with classifier fusion techniques may support more efficient monitoring actions for non-stationary aIAPS. Here, we explore the advantages of such a novel approach for mapping the invasive water-hyacinth (Eichhornia crassipes) in the Cávado River (northern Portugal). Invaded and non-invaded areas were used to explore the evolution of spectral attributes of Eichhornia crassipes through a time series (processed by a super-resolution algorithm) that covers March 2021 to February 2022 and to build an occurrence dataset (presence or absence). Analysis of the spectral behavior throughout the year allowed the detection of spectral regions with greater capacity to distinguish the target plant from the surrounding environment. Classifier fusion techniques were implemented in the biomod2 predictive modelling package and fed with selected spectral regions to firstly extract a spectral signature from the synchronized day and secondly to identify pixels with similar reflectance values over time. Predictions from statistical and machine-learning algorithms were ensembled to map invaded spaces across the whole study area during all seasons with classifications attaining high accuracy values (True Skill Statistic, TSS: 0.932; Area Under the Receiver Operating Curve, ROC: 0.992; Kappa: 0.826). Our results provide evidence of the potential of our approach to mapping plant invaders in dynamic freshwater systems over time, applicable in the assessment of the success of control actions as well as in the implementation of long-term strategic monitoring.

Bats of Georgia - an occurrence dataset from 1835 through 2022.

In Georgia, currently, 30 species of bats are recorded from four families and eleven genera. Although the oldest record of bats is from 1835 and continues until today, there are no comprehensive data available for bat diversity and distribution in Georgia. Thus, we aimed to fill that gap and make complete, expertly curated literature and our own published data openly available (through GBIF) for researchers and conservationists. In this publication, out of 1987 records, 1243 (62.4%) are new and unpublished data. Generally, out of all records, 34% are literature and museum data and 66% are data collected by us. Additionally, for the first time in the history of the study of bats in Georgia, we initiated surveys in forested areas of the country.

Analysis of the environmental impacts affecting Cambrian reef building and carbonate settings during the Miaolingian and Furongian epochs: A hypothesis for consideration

The Miaolingian and Furongian epochs of the Cambrian period have been identified as a time of limited metazoan reef development. The aim of this paper is to improve understanding of the biological and geochemical conditions that affected reefs during this interval, and to propose a hypothesis for understanding why metazoan reef development was inhibited. To address these issues, a global dataset of fossil occurrences (N ​= ​25,307) spanning Cambrian Stage 4 to the early Ordovician (Tremadocian) was extracted from the Paleobiology Database, Paleoreef Database, and a review of the primary literature. Findings show that the proportion of reefs constructed by metazoans fell from 40% in the Wuliuan age to 0% in the Drumian age, with reefs being overwhelmingly dominated by microbial ecosystems through the remainder of the Cambrian. The proportion of skeletal material constructed from carbonate fell from 85% in the Wuliuan age to 63% in the Drumian age across all the fossil occurrence data, before recovering. These findings suggest that environmental conditions may have not been favorable to carbonate organisms, but this does not fully explain the prolonged reduction of metazoans within reefs throughout this interval. A hypothesis proposed here is that Miaolingian to Furongian metazoan reef abundances were low because of two factors: (1) shallow water anoxia – and other factors such as elevated temperatures and ocean acidification – caused the extinction of metazoan reef builders in the late-early Cambrian and (2) deep water anoxia and marine regression, resulted in a loss of habitat. These inhibiting conditions were not necessarily concurrent but are inferred to have collectively suppressed the growth of metazoan reefs until the Early Ordovician when more shelf space for new reef development occurred. This hypothesis provides a first step in exploration of these conditions during the middle and late Cambrian and for reef development in general.

Harnessing iNaturalist to quantify hotspots of urban biodiversity: the Los Angeles case study

IntroductionA major goal for conservation planning is the prioritized protection and management of areas that harbor maximal biodiversity. However, such spatial prioritization often suffers from limited data availability, resulting in decisions driven by a handful of iconic or endangered species, with uncertain benefits for co-occurring taxa. We argue that multi-species habitat preferences based on field observations should guide conservation planning to optimize the long-term persistence of as many species as possible.MethodsUsing habitat suitability modeling techniques and data from the community-science platform iNaturalist, we provide a strategy to develop spatially explicit models of habitat suitability that enable better informed, place-based conservation prioritization. Our case study in Greater Los Angeles used Maxent and Random Forests to generate suitability models for 1,200 terrestrial species with at least 25 occurrence records, drawn from plants (45.5%), arthropods (27.45%), vertebrates (22.2%), fungi (3.2%), molluscs (1.3%), and other taxonomic groups (< 0.3%). This modeling strategy further compared spatial thinning and taxonomic bias file corrections to account for the biases inherent to the iNaturalist dataset, modeling species jointly and separately in wildland and urban sub-regions and validated model performance using null models and a “test” dataset of species and occurrences that were not used to train models.ResultsMean models of habitat suitability of all species combined were similar across model settings, but the mean Random Forest model received the highest median AUCROC and AUCPRG scores in model evaluation. Taxonomic groups showed relatively modest differences in their response to the urbanization gradient, while native and non-native species showed contrasting patterns in the most urban and the most wildland habitats and both peaked in mean habitat suitability near the urban-wildland interface.DiscussionOur modeling framework is based entirely on open-source software and our code is provided for further use. Given the increasing availability of urban biodiversity data via platforms such as iNaturalist, this modeling framework can easily be applied to other regions. Quantifying habitat suitability for a large, representative subset of the locally occurring pool of species in this way provides a clear, data-driven basis for further ecological research and conservation decision-making, maximizing the impact of current and future conservation efforts.

‘The human shield effect’: Human-wildlife co-occurrence patterns in the coffee forests of southwestern Ethiopia

Understanding species (co)-occurrence patterns and how these are affected and mediated by humans is essential for the development of management plans to guide coexistence between humans and wildlife. Here, we evaluated two opposing hypotheses regarding the effects of humans on species occurrence: “humans as super-predators” and “humans as shield”, using an existing camera-trap dataset of mammal species occurrence collected in the coffee forests of southwestern of Ethiopia. We applied a multispecies occupancy modelling framework to explicitly examine co-occurrence patterns between humans, top-predators, prey, and crop-raiders, along a gradient of forest integrity (characterized by forest cover and fragmentation). We examined co-occurrence patterns during both coffee and non-coffee harvest seasons. Our results show partial support for the “humans as shield” hypothesis. We found (i) signs of co-occurrence between humans and prey in areas of low forest integrity during both survey seasons, and between humans and raiders during the coffee-season, (ii) signs of co-occurrence between prey and raiders during both seasons, (iii) no signs of negative or positive co-occurrence between humans and top-predators. Our findings indicate that a possible “shield effect” between humans and prey within a predator space, might be undergoing at the edges of coffee forests. Our findings suggest that incorporating humans as one more species in the ecological system can contribute to shed light into the effects of humans on species occurrence and ultimately contribute to inform management for coexistence.

Occurrence dataset of birds in the Xinjizhou National Wetland Park, Nanjing, China.

Xinjizhou National Wetland Park is located in Jiangning District, Nanjing, Jiangsu, China. With diverse wetland landscape types, Xinjizhou National Wetland Park maintains high biodiversity all year around. Meanwhile, as an important hub on the East Asian-Australasian Flyway, Xinjizhou National Wetland Park also occupies a core ecological position in the middle and lower reaches of the Yangtze River in Jiangsu Province. Therefore, carrying out systematic bird surveys and consequently understanding the distribution of birds and the seasonal variation of their communities can provide important insights for conservation. We conducted a one-year bird survey in the Xinjizhou National Wetland Park from 2021 to 2022 and provided occurrence datasets, including detailed species and geographic information.This dataset fills the knowledge gap in avian community composition for the Wetland Park and more importantly provides a basis to assess the conservation effectiveness of conservation measures taken in the Wetland Park. This occurrence dataset is the first public record of birds in Xinjizhou National Wetland Park. All data have been published on GBIF.

Assisted tree migration can reduce but not avert the decline of forest ecosystem services in Europe

European forests are facing multiple natural and anthropogenic pressures that are expected to become more severe in the next decades. Tree diversity is projected to decline in many areas across the continent. How this will affect the provision of forest services remains an open question, whose answer depends, among others, on the practical and theoretical challenges of incorporating assisted migration into climate adaptation strategies. Here, we tackle the issue by combining a large dataset of tree species occurrences, future climatic projections, and data on tree functional traits and tree-specific forest services into a novel modelling framework. We estimate that, by the end of the century and under a natural dispersal scenario, the provision of forest services would decrease on average by 15% in Europe (for RCP 4.5; 23% for RCP 8.5), and up to 52% (70% for RCP 8.5) in the Mediterranean. To explore if and how management could reduce the projected losses, we simulated a suite of alternative assisted migration strategies aimed at identifying, for each locality, the tree species communities offering the best compromise in terms of resilience to climate change and delivery of specific combinations of ecosystem services. Such strategies could reduce losses of services by 10% (15%) on average in Europe, and even increase service availability in the Alpine and Boreal regions but not in the Mediterranean, where losses will remain as high as 33% (54% for RCP 8.5). Our findings highlight how science-driven management strategies could be vital to reduce an otherwise dramatic, European-wide decline of forest services. Our results are qualitatively robust to different assumptions on future carbon emissions and related climate trajectories. That is, our simulated assisted migration strategies identify similar tree species communities under different pathways (RCP 4.5 vs RCP 8.5). This makes our approach a powerful tool for forest management, as it generates advice that is valid regardless of whether, and to what extent, human society will steer away from business-as-usual emission trajectories.

Multiple invasion trajectories induce niche dynamics inconsistency and increase risk uncertainty of a plant invader

AbstractOur knowledge of how niche dynamic patterns respond to invasion trajectories and influence invasion risk prediction is elusive for the majority of notorious invaders, hindering scientific understanding, biosecurity planning and practice, and management implementation of biological invasions. We used Mikania micrantha, one of the world's worst invasive alien species (IAS), to test the hypothesis that multiple invasion trajectories could induce niche dynamics inconsistency and increase risk uncertainty of IAS. We compiled a robust database of M. micrantha occurrence across its native range in Central and South America and invaded range in China. This database was used to clarify different invaded ranges and invasion trajectories of M. micrantha in China. Principal components analysis of climatic variables associated with the database was used to explore the niche dynamic patterns associated with multiple invasion trajectories of M. micrantha. Maximum entropy algorithm was used to predict the high‐risk area of M. micrantha invasion using occurrence datasets for invaded ranges where niches remained conservative, and to detect area changes with the inclusion of occurrence datasets for invaded ranges where niche shifts occurred. M. micrantha invasion occurred in three geographically distinct regions, with conservative climate niches in southern and southeastern China and climatic niche shifts in southwestern China. A high‐risk area for M. micrantha invasion spanned multiple provinces and cities and expanded considerably with the inclusion of the occurrence dataset for southwestern China. Our findings contribute to the theoretical understanding of invasion mechanisms and the practical optimization of biosecurity planning and implementation.

Natural Disturbances are Essential Determinants of Tree-Related Microhabitat Availability in Temperate Forests

Assessing the impacts of natural disturbance on the functioning of complex forest systems are imperative in the context of global change. The unprecedented rate of contemporary species extirpations, coupled with widely held expectations that future disturbance intensity will increase with warming, highlights a need to better understand how natural processes structure habitat availability in forest ecosystems. Standardised typologies of tree-related microhabitats (TreMs) have been developed to facilitate assessments of resource availability for multiple taxa. However, natural disturbance effects on TreM diversity have never been assessed. We amassed a comprehensive dataset of TreM occurrences and a concomitant 300-year disturbance history reconstruction that spanned large environmental gradients in temperate primary forests. We used nonlinear analyses to quantify relations between past disturbance parameters and contemporary patterns of TreM occurrence. Our results reveal that natural forest dynamics, characterised by fluctuating disturbance intervals and variable severity levels, maintained structurally complex landscapes rich in TreMs. Different microhabitat types developed over time in response to divergent disturbance histories. The relative abundance of alternate TreMs was maximised by unique interactions between past disturbance severity and elapsed time. Despite an unequal distribution of individual TreMs, total microhabitat diversity was maintained at constant levels, suggesting that spatially heterogeneous disturbances maintained a shifting mosaic of habitat types over the region as a whole. Our findings underscore the fundamental role of natural processes in promoting conditions that maximise biodiversity potential. Strict conservation and management systems that preserve natural disturbance outcomes, including associated biological legacies, may therefore safeguard biodiversity at large scales.

Forest and tree species distribution on the ultramafic substrates of New Caledonia

ABSTRACT About a third of New Caledonia is covered with ultramafic soils (UM) which hosts a rich flora (endemism > 85%) threatened by mining activities. This combination makes the ultramafic vegetation a floristic hotspot within a biodiversity hotspot. UM soils are distributed from sea level to 1618 m elevation with about two-thirds forming a large continuous unit while the remaining forms numerous relatively small isolated units. Here, we provide a synthesis of the distribution of forest and tree species across 22 UM units. We compiled an extensive tree occurrence dataset (109,896 occurrences and 1,065 species) and a new expert-based forest map at a 1:3000 spatial resolution. Only 10% of these species represented more than 50% of the occurrences, while 10% of the species had only one or two occurrences. A quarter of the UM area did not contain any occurrences, and we estimated that on average a quarter of the species remained to be inventoried in the 22 units. Forest covers about one-third of the UM areas with forest coverage ranging from 1.7% to 72.3% in the different UM units. Forest coverage increased from 14.6% on sea level to 93.3% at 1,200 m of elevation. About 30% of the forest and 90% of the species were located within mining concessions, while 14.5% and 73%, respectively, were located within protected areas. We recommend setting up new protected areas on ultramafic substrate, specially in the Northern province, to protect more forest and the diversity it harbours.

NEOTROPICAL FRESHWATER FISHES: A dataset of occurrence and abundance of freshwater fishes in the Neotropics.

The Neotropical region hosts 4225 freshwater fish species, ranking first among the world's most diverse regions for freshwater fishes. Our NEOTROPICAL FRESHWATER FISHES data set is the first to produce a large-scale Neotropical freshwater fish inventory, covering the entire Neotropical region from Mexico and the Caribbean in the north to the southern limits in Argentina, Paraguay, Chile, and Uruguay. We compiled 185,787 distribution records, with unique georeferenced coordinates, for the 4225 species, represented by occurrence and abundance data. The number of species for the most numerous orders are as follows: Characiformes (1289), Siluriformes (1384), Cichliformes (354), Cyprinodontiformes (245), and Gymnotiformes (135). The most recorded species was the characid Astyanax fasciatus (4696 records). We registered 116,802 distribution records for native species, compared to 1802 distribution records for nonnative species. The main aim of the NEOTROPICAL FRESHWATER FISHES data set was to make these occurrence and abundance data accessible for international researchers to develop ecological and macroecological studies, from local to regional scales, with focal fish species, families, or orders. We anticipate that the NEOTROPICAL FRESHWATER FISHES data set will be valuable for studies on a wide range of ecological processes, such as trophic cascades, fishery pressure, the effects of habitat loss and fragmentation, and the impacts of species invasion and climate change. There are no copyright restrictions on the data, and please cite this data paper when using the data in publications.

The diversity of cultivable endophytic fungi of the sand coast plant Ipomoeapes-caprae in Taiwan.

Ipomoeapes-caprae is a plant of sand coasts and it can tolerate stresses, such as high salinity, strong wind and sand movements and lack of nutrients. It plays an important role in coast protection and preventing erosion. Fungal endophytes show high biodiversity and have a strong influence on the survival of plants under different stress factors. Although this plant is important for sand coast ecosystems, little is known about the associated fungi. In this study, we isolated and identified endophytic fungi of Ipomoeapes-caprae, a dominant plant along the shore of Taiwan. The dataset contains 896 records, which correspond to 177 species. The geographical scope of the dataset covers the northern subtropical area of the main island of Taiwan, with its sand coasts in New Taipei, Taoyuan, Hsinchu and Taichung and two botanical gardens in Taipei and Taichung. The detailed original data of fungal diversity are rarely publicly shared under strictly formalised and, thus, reusable standards. As an example for such an approach, the complete occurrence dataset was made available in the Darwin Core Archive format via the Global Biodiversity Information Facility (GBIF) under Version 1.13, Taiwan Biodiversity Information Facility (TaiBIF) https://doi.org/10.15468/9h9rcg. In this first data paper on endophytic fungi, the scientific name and associated DNA sequence in the dataset were directly linked to other free online resource (Index Fungorum, GenBank), which shows the potential of GBIF for linking together different online data repositories. We describe a dataset, in which the diversity of endophytic fungi of the sand coast plant Ipomoeapes-caprae in Taiwan was investigated.

AMI-KMNP dataset: Occurrence records of aquatic macroinvertebrate species from a 10-year-long biodiversity survey in SE Hungary

We outline a 100% georeferenced dataset of aquatic macroinvertebrate occurrence records collected from the operational area of the Körös-Maros National Park Directorate (SE Hungary) between 2012 and 2021. The species-level dataset includes 25,935 records of 644 taxa from 625 localities of wide variety of freshwater habitats from soda pans to lowland marshes and small watercourses to medium-sized and larger rivers. Four non-biting midge species are reported for the first time from the Hungarian fauna. The dataset is available through the Global Biodiversity Information Facility (GBIF).

A georeferenced dataset of Italian occurrence records of the phylum Rotifera

We report a dataset of known and published occurrence records of Italian taxa from species (and subspecies) to family rank of the phylum Rotifera; we considered only Bdelloidea, Monogononta, and Seisonacea, and did not include Acanthocephala. The dataset includes 15,525 records (12,015 of which with georeferenced coordinates) of 584 valid species and subspecies names, gathered from 332 published papers. The published literature spans the period from 1838 to 2022, with the lowest number of papers published during the Second World War followed by an increasing number of papers, from 20 to more than 60 in each decade. The Italian regions with the highest number of records and species are Emilia-Romagna, Lombardy, and Piedmont, whereas no records are known for Molise. The number of species known from each region mostly mirrors sampling efforts, measured as the number of publications per region. The dataset is available through the Open Science Framework (OSF), and all the georeferenced occurrence data have been uploaded to the Global Biodiversity Information Facility (GBIF).

Itsdm: Isolation forest‐based presence‐only species distribution modelling and explanation in r

Abstract Multiple statistical algorithms have been used for species distribution modelling (SDM). Due to shortcomings in species occurrence datasets, presence‐only methods (such as MaxEnt) have become increasingly widely used. However, sampling bias remains a challenging issue, particularly for density‐based approaches. The Isolation Forest (iForest) algorithm is a presence‐only method less sensitive to sampling patterns and over‐fitting because it fits the model by describing the unsuitable instead of suitable conditions. Here, we present the itsdm package for species distribution modelling with iForest, which provides a workflow wrapper for the algorithms in iForest family and convenient tools for model diagnostic and post‐modelling analysis. itsdm allows users to fit and evaluate an iForest SDM using presence‐only occurrence data. It also helps the users to understand relationships between species and the living environment using Shapley values, a suggested technique in explainable artificial intelligence (xAI). Additionally, itsdm can make spatial response maps that indicate how species respond to environmental variables across space and detect areas potentially affected by a changing environment. We demonstrated the usage of the itsdm package and compared iForest with other mainstream SDMs using virtual species. The results enlightened that iForest is an advantageous presence‐only SDM when the actual distribution range is unclear.

Stable variable ranking and selection in regularized logistic regression for severely imbalanced big binary data.

We develop a novel covariate ranking and selection algorithm for regularized ordinary logistic regression (OLR) models in the presence of severe class-imbalance in high dimensional datasets with correlated signal and noise covariates. Class-imbalance is resolved using response-based subsampling which we also employ to achieve stability in variable selection by creating an ensemble of regularized OLR models fitted to subsampled (and balanced) datasets. The regularization methods considered in our study include Lasso, adaptive Lasso (adaLasso) and ridge regression. Our methodology is versatile in the sense that it works effectively for regularization techniques involving both hard- (e.g. Lasso) and soft-shrinkage (e.g. ridge) of the regression coefficients. We assess selection performance by conducting a detailed simulation experiment involving varying moderate-to-severe class-imbalance ratios and highly correlated continuous and discrete signal and noise covariates. Simulation results show that our algorithm is robust against severe class-imbalance under the presence of highly correlated covariates, and consistently achieves stable and accurate variable selection with very low false discovery rate. We illustrate our methodology using a case study involving a severely imbalanced high-dimensional wildland fire occurrence dataset comprising 13 million instances. The case study and simulation results demonstrate that our framework provides a robust approach to variable selection in severely imbalanced big binary data.

Ancient settlements as natural heritage sites: the first occurrence dataset on vascular plant species from ancient settlements in the Lower Dnipro region (Ukraine).

This work is a long-term outcome of an international Ukrainian-Polish teamwork, aiming to assess the role of ancient settlements for steppe conservation and protection. The dataset contains georeferenced occurrences of vascular plant species on 18 ancient settlements (Lower Dnipro, southern Ukraine), collected during the 2015-2020 period. Additionally, to the total species list, the publication presents the taxonomic coverage (according to GBIF Backbone Taxonomy), the frequency classes of occurrences of the total taxa and the floristic differences amongst studied sites. The report also shows the high sozological value of the studied ancient settlements, the high levels of vascular plant species richness and the various means of the plant species protection (according to the Bern Convention, the Red Data Book of Ukraine and regional Red Lists). This work provides the first occurrence dataset from ancient settlements in Ukraine. The dataset includes 3,210 occurrences of vascular plants recorded during the study period of 2015-2020 conducted in the Lower Dnipro region. As ancient settlements were generally considered as steppe refuges, great attention was paid to the native steppe species, as well as to the rare components of the flora. The dataset includes 1,525 occurrences of steppe species and 87 occurrences of rare species, respectively. The dataset could be useful for further research of ancient settlements` floristic richness, but also analyses and comparison with other objects of cultural origin (e.g. kurgans, hillforts, old cemeteries, forgotten parks, sacred groves etc.).

A data-driven model for Fennoscandian wildfire danger

Abstract. Wildfires are recurrent natural hazards that affect terrestrial ecosystems, the carbon cycle, climate and society. They are typically hard to predict, as their exact location and occurrence are driven by a variety of factors. Identifying a selection of dominant controls can ultimately improve predictions and projections of wildfires in both the current and a future climate. Data-driven models are suitable for identification of dominant factors of complex and partly unknown processes and can both help improve process-based models and work as independent models. In this study, we applied a data-driven machine learning approach to identify dominant hydrometeorological factors determining fire occurrence over Fennoscandia and produced spatiotemporally resolved fire danger probability maps. A random forest learner was applied to predict fire danger probabilities over space and time, using a monthly (2001–2019) satellite-based fire occurrence dataset at a 0.25∘ spatial grid as the target variable. The final data-driven model slightly outperformed the established Canadian Forest Fire Weather Index (FWI) used for comparison. Half of the 30 potential predictors included in the study were automatically selected for the model. Shallow volumetric soil water anomaly stood out as the dominant predictor, followed by predictors related to temperature and deep volumetric soil water. Using a local fire occurrence record for Norway as target data in a separate analysis, the test set performance increased considerably. This demonstrates the potential of developing reliable data-driven models for regions with a high-quality fire occurrence record and the limitation of using satellite-based fire occurrence data in regions subject to small fires not identified by satellites. We conclude that data-driven fire danger probability models are promising, both as a tool to identify the dominant predictors and for fire danger probability mapping. The derived relationships between wildfires and the selected predictors can further be used to assess potential changes in fire danger probability under different (future) climate scenarios.

Widespread habitat for Europe's largest herbivores, but poor connectivity limits recolonization

AbstractAimSeveral large‐mammal species in Europe have recovered and recolonized parts of their historical ranges. Knowing where suitable habitat exists, and thus where range expansions are possible, is important for proactively promoting coexistence between people and large mammals in shared landscapes. We aimed to assess the opportunities and limitations for range expansions of Europe's two largest herbivores, the European bison (Bison bonasus) and moose (Alces alces).LocationCentral Europe.MethodsWe used large occurrence datasets from multiple populations and species distribution models to map environmentally suitable habitats for European bison and moose across Central Europe, and to assess human pressure inside the potential habitat. We then used circuit theory modeling to identify potential recolonization corridors.ResultsWe found widespread suitable habitats for both European bison (>120,000 km2) and moose (>244,000 km2), suggesting substantial potential for range expansions. However, much habitat was associated with high human pressure (37% and 43% for European bison and moose, respectively), particularly in the west of Central Europe. We identified a strong east–west gradient of decreasing connectivity, with major barriers likely limiting natural recolonization in many areas.Main conclusionsWe identify major potential for restoring large herbivores and their functional roles in Europe's landscapes. However, we also highlight considerable challenges for conservation planning and wildlife management, including areas where recolonization likely leads to human–wildlife conflict and where barriers to movement prevent natural range expansion. Conservation measures restoring broad‐scale connectivity are needed in order to allow European bison and moose to recolonize their historical ranges. Finally, our analyses and maps indicate suitable but isolated habitat patches that are unlikely to be colonized but are candidate locations for reintroductions to establish reservoir populations. More generally, our work emphasizes that transboundary cooperation is needed for restoring large herbivores and their ecological roles, and to foster coexistence with people in Europe's landscapes.

Atlantic flower-invertebrate interactions: A data set of occurrence and frequency of floral visits.

Encounters between flowers and invertebrates are key events for the functioning of tropical forests. Assessing the structure of networks composed of the interactions between those partners leads to a better understanding of ecosystem functioning and the effects of environmental factors on ecological processes. Gathering such data is, however, costly and time-consuming, especially in the highly diverse tropics. We aimed to provide a comprehensive repository of available flower-invertebrate interaction information for the Atlantic Forest, a South American tropical forest domain. Data were obtained from published works and "gray literature," such as theses and dissertations, as well as self-reports by co-authors. The data set has ~18,000 interaction records forming 482 networks, each containing between one and 1061 interaction links. Each network was sampled for about 200 h or less, with few exceptions. A total of 641 plant genera within 136 different families and 39 orders were reported, with the most abundant and rich families being Asteraceae, Fabaceae, and Rubiaceae. Invertebrates interacting with these plants were all arthropods from 10 orders, 129 families, and 581 genera, comprising 2419 morphotypes (including 988 named species). Hymenoptera was the most abundant and diverse order, with at least six times more records than the second-ranked order (Lepidoptera). The complete data set shows Hymenoptera interacting with all plant orders and also shows Diptera, Lepidoptera, Coleoptera, and Hemiptera to be important nodes. Among plants, Asterales and Fabales had the highest number of interactions. The best sampled environment was forest (~8000 records), followed by pastures and crops. Savanna, grasslands, and urban environments (among others) were also reported, indicating a wide range of approaches dedicated to collecting flower-invertebrate interaction data in the Atlantic Forest domain. Nevertheless, most reported data were from forest understory or lower strata, indicating a knowledge gap about flower-invertebrate interactions at the canopy. Also, access to remote regions remains a limitation, generating sampling bias across the geographical range of the Atlantic Forest. Future studies in these continuous and hard-to-access forested areas will yield important new information regarding the interactions between flowers and invertebrates in the Atlantic Forest. There are no copyright restrictions on the data set. Please cite this data paper if the data are used in publications and teaching events.