www.nature.com/scientificdata OPEN DATA DESCRIPTOR DarkCideS 1.0, a global database for bats in karsts and caves Krizler C. Tanalgo et al.# Understanding biodiversity patterns as well as drivers of population declines, and range losses provides crucial baselines for monitoring and conservation. However, the information needed to evaluate such trends remains unstandardised and sparsely available for many taxonomic groups and habitats, including the cave-dwelling bats and cave ecosystems. We developed the DarkCideS 1.0 (https://darkcides.org/), a global database of bat caves and species synthesised from publicly available information and datasets. The DarkCideS 1.0 is by far the largest database for cave-dwelling bats, which contains information for geographical location, ecological status, species traits, and parasites and hyperparasites for 679 bat species are known to occur in caves or use caves in part of their life histories. The database currently contains 6746 georeferenced occurrences for 402 cave-dwelling bat species from 2002 cave sites in 46 countries and 12 terrestrial biomes. The database has been developed to be collaborative and open-access, allowing continuous data-sharing among the community of bat researchers and conservation biologists to advance bat research and comparative monitoring and prioritisation for conservation. Background & Summary Human civilization has left its footprint on every part of the planet, in the process driving what is frequently referred to as the sixth mass extinction1,2. Conservation prioritisation requires a rigorous assessment of vulnerable species as well as their habitats to develop priorities for conservation. Biodiversity data integration and synthesis are significant empirical steps to identify priorities in strategically using the limited funds allocated to conservation3. However, the data needed to develop such priorities with rigour are often lacking. The diversity and distribution of a subset of terrestrial vertebrates have become an umbrella for taxonomic and spatial conservation, despite the known biases present in popular open datasets4,5. Efforts to mitigate extinction risks or protect key habitats often disproportionately focus on particular taxa, ecosystems, or regions6,7. This approach neglects many other equally important species and their habitats and compromises the maintenance of ecosystem services provided by diverse functional groups8,9. Cave ecosystems are critical for bats, with around half of all bat species reliant on caves, with a high rate of endemism10,11. Of the more than 1400 known extant bat species distributed across almost all terrestrial habitats around the globe, at least 679 species are known to be cave-dwelling11–13. Many of these species occur in biodiversity hotspots that are threatened by varying anthropogenic and natural threats13,14. Caves are important habitats for bats and other unique species but are nonetheless threatened and in need of urgent conservation10. Despite hosting high endemism, cave ecosystems receive little attention in terms of fund allocation and appropriate priorities for scientific studies and conservation compared to their surface counterparts such as agricultural and forest ecosystems10,13,15–18. Cave taxa are adapted to light-limited underground environments and most of them are dependent on mobile species such as bats to transport organic nutrients into these environments19–21. Bats are keystone species in karst ecosystems and ideal cave conservation surrogates, delivering vital energy sources into caves as they regularly forage from outside ecosystems22. Nevertheless, conservation attention towards cave-dwelling bats remains limited compared to other mammalian taxa. Thus, there is an urgent need for better data to develop effective conservation strategies for bats13. Effective conservation decision-making relies on the accuracy and precision of the data used to design present and future management strategies5,7. Identifying priority caves for conservation requires an understanding of species diversity, endemism patterns, interactions with other organisms, and threats within and outside these systems17,23. Additionally, while numerous organisations and collaborative efforts aim to database bat distributions, comprehensive and specific datasets for cave-dwelling bats, including their distributions and ecological A full list of authors and their affiliations appears at the end of the paper. # SCIENTIFIC DATA | (2022) 9:155 | https://doi.org/10.1038/s41597-022-01234-4 1 www.nature.com/scientificdata/ www.nature.com/scientificdata Fig. 1 A schematic diagram showing the features, contents, and potential applications of the DarkCideS 1.0 database. The database is a centralised, collaborative, and open-access platform that contains information on cave-dwelling bat species and their distribution. traits, are currently lacking. Large databases for species distributions such as the Global Biodiversity Information Facility (GBIF) exist and openly provide distribution data for bats. However, due to the enormous amount of information within these databases, it is challenging to selectively evaluate data for specific ecosystems such as caves, and thus more specialist datasets are needed to facilitate appropriate habitat-based prioritisation. To address this knowledge gap, we created DarkcideS 1.0 (https://darkcides.org/), a global database for bats in karsts and caves, to advance global bat cave vulnerability and conservation mapping initiatives. The creation of the dataset primarily aims to map and digitise the distribution of cave-dwelling bats to facilitate the assessment of their vulnerability to landscape threats. DarkCideS 1.0 represents a publicly available database of cave-dwelling bats across time and space, including their estimated population (e.g., counts), geographical distribution (latitude and longitude), ecological traits, levels of endemism, conservation status, and threatening processes. The purpose of the DarkCideS 1.0 initiative is to centralise and develop an open-access platform for information exchange among bat researchers and conservation biologists to advance the development of targeted conservation measures and macroecological studies (Fig. 1). Potential applications of the database include assessing species conservation status and extinction risks; understanding drivers of extinction, cave conditions, and landscape threats; accurately developing species distribution models; and determining long-term cave conservation priorities at regional to global scales. Methods The DarkCideS database was initially conceptualised and developed by KCT, JAG, and ACH as part of the “Global Bat Cave Vulnerability and Conservation Mapping Initiative” in 2014, and later with the “Mapping Karst Biodiversity in Yunnan” and the “Southeast Asian Atlas of Biodiversity” projects. The initiative includes developing tools and methods (e.g., the Bat Cave Vulnerability Index14) and synthesis (e.g., the global bat cave vulnerability assessment11) to identify conservation priorities and important bat caves in the tropics. Since 2019, the initiative has expanded and potential collaborators and contributors were invited through scientific conferences (Association for Tropical Biology and Conservation 2018, International Bat Research Conference 2019), social media platforms, and personal correspondences. At present, the database has 36 collaborators from twenty countries on six continents with expertise and research interests in bat conservation. Four main datasets for all known cave-dwelling bats were built for the DarkCideS database version 1.0. Datasets and compilation for species checklist. The first dataset contains taxonomic checklists for all extant cave-dwelling bats species extracted from the expert-based International Union for the Conservation Union (IUCN) Red List database version 2020-1 (Table 1). We screened and included all bat species that were reported to use, roost in, or aggregate in “Caves”, “Underground”, and “Karsts” habitats in any part of their life histories. We also scanned major publicly available bat cave databases from expeditions such as “Bats in China” (http://www.bio.bris.ac.uk/research/bats/China/) and UNEP-EUROBATS (https://www.eurobats.org/) for European bats24 for additional information and datasets. In addition, the first dataset contains species ecological traits, distribution range, and threatening processes (Table 1). Information per species was pooled from the IUCN Red List versions 2020-125. Species taxonomy was then curated and updated (e.g., synonyms or merged species) using the nomenclature from Simmons and SCIENTIFIC DATA | (2022) 9:155 | https://doi.org/10.1038/s41597-022-01234-4 2 www.nature.com/scientificdata www.nature.com/scientificdata/ Trait category Habitat preference Trait (Data name) Variable type Data filters 586 Savanna 140 Desert Urban Binomial Yes = 1, No = 0 Underground Conservation.status Geopolitical.endemism Nominal Island.endemism Biogeographic.breadth Geographical range Biological traits Direct threats Feeding.groups 150 Stable 161 Increasing 6 Unknown 362 Data.Deficient 83 Least.Concern 452 Near.Threatened 54 Vulnerable 54 Endangered 25 Critically.Endangered 11 Non.Endemic 459 Endemic 220 Island.Endemic 159 Mainland 520 Afrotropical 102 Indomalayan 184 Austral-Oceania 49 Neotropical 173 Palearactic 85 Neactic 18 Cosmopolitan 68 Carnivore 553 Frugi-nectarivore 60 Omnivore 66 IUCN Red List database Phylacine 1.2 EltonTraits 1.0 Islandic 160 Non-islandic 521 Continuous N/A 679 Continuous N/A 679 Generation.length Continuous N/A 679 Pacifi et al. (2013) Body.mass (grams) Continuous N/A 679 Phylacine 1.2 Island.endemism Nominal Current.range Natural.range Mining.quarrying 155 Sacred.activities 11 Tourism.caving 226 Guano.extraction 69 Vandalism 106 Nest.harvesting 5 Hunting.bushmeat 109 Intensional.killings 48 Gating 7 Agricultural.conversion Natural threats 16 Decreasing Scientific.research Indirect threats 45 56 Population.status Feeding groups Sources 523 Wetlands Ecological status and distribution N species Forest 7 Binomial Yes = 1, No = 0 155 Urbanisation 76 Deforestation 284 Pollution 65 Road.kills 12 Disease.parasites 5 Invasive.species 21 Fires 36 Drought 9 Extreme.cold 1 Storm 17 Phylacine 1.2 Phylacine 1.2 IUCN Red List database Table 1. DarkCideS 1.0 includes key traits for all living cave-dwelling bat species (N = 679). General metadata for traits included in the current version of the database: habitat preference, ecological status, feeding groups, geographical range, island endemism, geopolitical endemism, distribution range, biogeographical breadth, generation length, body mass, and threatening process. SCIENTIFIC DATA | (2022) 9:155 | https://doi.org/10.1038/s41597-022-01234-4 3 www.nature.com/scientificdata/ www.nature.com/scientificdata Fig. 2 Percentage of species data completeness according to biogeographical realm (a) and family-level (b) between IUCN estimates (red bars) and sampled caves from DarkCideS 1.0 (black bars) species richness, the proportion of endemism, and proportion of threatened species worldwide. Cirranello12. The “checklist for global cave-dwelling bats” derived from the IUCN Red List includes 679 species. Meanwhile, the DarkCideS 1.0 dataset contains occurrence data for 402 species from 16 families representing 59% of all cave-dwelling species11 (Fig. 2). We found a marginally significant relationship between the species richness and proportion of threatened species between the IUCN-based global cave-dwelling bat and DarkCideS datasets (Kendall’s τ b = 0.60, P = 0.07). The highest completeness of sampled species is in the Neotropics (67.38%) and Indomalayan region (66.08%), and the greatest gaps are in Austral-Oceania (40.28%). Highest endemism was recorded in Austral-Oceania (58.62%) (χ2 = 227.32, df = 5, P < 0.001) (Fig. 2a). The proportion of threatened species is highest in the Indomalayan region (16%) realm (χ2 = 281.18, df = 5, P < 0.01) (Fig. 2a). Most bat families have a coverage of 30 to 60% of species, but four families had all cave-dwelling species in the DarkCideS database, and three smaller families had no species included (Fig. 2b). Habitat preference, distribution, ecological status, and traits. We classified species distribution by biogeographical realm (Indomalaya, Austral-Oceania, Afrotropical, Neotropical, Palearctic, and Nearctic) and terrestrial biomes following Olson et al.26. We described species’ major habitat breadth based on IUCN Level 1 classification https://www.iucnredlist.org/resources/habitat-classification-scheme (Caves, Forests, Savanna, Desert, Urban, Artificial, and Wetlands). Species current conservation status (Data Deficient, Least Concern, Near Threatened, Vulnerable, Endangered, and Critically Endangered) and population trends (e.g., Unknown, Decreasing, Stable, Increasing) were categorised using standard IUCN Red List assessments. Using the same criteria, we categorised species endemism as geopolitically endemic (e.g., country-endemic, and non-endemic) when a species occurs only in a single country or state territory27, and island endemism was classified as island-restricted or predominantly mainland28. The highest country endemism was in the Eastern Hemisphere with the highest in the Austral-Oceania (40%) region, followed by the Afrotropical (21%), then the Indomalayan region (16%). However, the highest proportion of threatened species was in the Indomalayan region (43%) and the Neotropics (22%) (Fig. 2a). SCIENTIFIC DATA | (2022) 9:155 | https://doi.org/10.1038/s41597-022-01234-4 4 www.nature.com/scientificdata www.nature.com/scientificdata/ Data Column Data type Data filters Afrotropical Indomalayan Biogeographical.realm Nominal Austral-Oceania Neotropical Palearctic Nearctic Deserts & Xeric Shrublands = DES Flooded Grasslands & Savannas = FLO Mangroves = MAN Mediterranean Forests, Woodlands & Scrub = MFWS Montane Grasslands & Shrublands = MGS Biome.classification Nominal Temperate Broadleaf & Mixed Forests = TBMF Temperate Conifer Forests = TCF Temperate Grasslands, Savannas & Shrublands = TGSS Tropical & Subtropical Coniferous Forests = TSCF Tropical & Subtropical Dry Broadleaf Forests = TSDB Tropical & Subtropical Grasslands, Savannas & Shrublands = TSGS Tropical & Subtropical Moist Broadleaf Forests = TSMB Country.record Nominal All countries with records Latitude Continuous (WGS 84 in DD) N/A Longitude Continuous (WGS 84 in DD) N/A Table 2. Metadata of the georeferenced information of cave-dwelling bats and caves. Fig. 3 The geographical data turnover of the current database version: (a) geographical locations of all bat caves included in the database, (b) percent distribution of species occurrence in terms of the biogeographical realm and terrestrial biome, (c) country-level turnover. Furthermore, current geographical ranges were assembled from the Phylacine 1.2 database28 based on IUCN species ranges. Three species traits were included: the adult body mass (in grams) per species were derived from Phylacine 1.228 and generation length from Pacifici et al.29. For trophic groups, we derived diet information from EltonTraits 1.030. We grouped species as frugi-nectarivorous for all species that forage on plant-based resources (e.g., fruits, leaves, and nectars). As species foraging smaller vertebrates (i.e., fish, birds, and rodents) and larger invertebrates are very few, we classified them as carnivores along with insectivorous bats. Species that forage on both resources were grouped into omnivores (Table 1). Species threatening process. We identified potential threats for each bat species listed in the checklist using the information from the IUCN Red List assessments (version 2020-1) in addition to threats highlighted SCIENTIFIC DATA | (2022) 9:155 | https://doi.org/10.1038/s41597-022-01234-4 5 www.nature.com/scientificdata www.nature.com/scientificdata/ Variables Variable type Biogeographical. realm Nominal Region Data Filters Description Afrotropical N/A Indomalayan N/A Austral-Oceania N/A Neotropical N/A Palearctic N/A Nearctic N/A All continents entered N/A Country All countries entered N/A Cave_Name All cave names entered N/A Latitude Continuous (WGS84 DD) N/A N/A Longitude Continuous (WGS84 DD) Sources N/A N/A N/A Canopy cover height Canopy.cov A wall-to-wall, global map of canopy height at 1-km spatial resolution Simard et al.34 Tree density Tree.dens A spatially continuous map of forest tree density based in global scale. Crowther et al.35 Distance to freshwater bodies Freshwater.dist A global 3arc-second Water Body Map (G3WBM) Yamazaki et al.36 Bare ground cover change Bareground.change Continuous global vegetation for tall vegetation ( ≥ 5 m in height; hereafter referred to as tree canopy (TC)) cover, short vegetation (SV) cover and bare ground (BG) cover, at 0.05° × 0.05° spatial resolution Song et al.37 Short vegetation cover change Shortveg.change Continuous global vegetation for tall vegetation ( ≥ 5 m in height; hereafter referred to as tree canopy (TC)) cover, short vegetation (SV) cover and bare ground (BG) cover, at 0.05° × 0.05° spatial resolution Song et al.37 Tall tree cover change Talltree.change Continuous global vegetation for tall vegetation ( ≥ 5 m in height; hereafter referred to as tree canopy (TC)) cover, short vegetation (SV) cover and bare ground (BG) cover, at 0.05° × 0.05° spatial resolution Song et al.37 Urban.dist Continuous global vegetation for tall vegetation ( ≥ 5 m in height; hereafter referred to as tree canopy (TC)) cover, short vegetation (SV) cover and bare ground (BG) cover, at 0.05° × 0.05° spatial resolution Song et al.37 Distance to roads Road.dist A globally harmonised map for road networks and road density at a 5 arcminutes resolution (~8x8km) based on Global Road Inventory Project Meijer et al.38 Mine density Mine.dens A global distribution of selected critical mineral resources in mines, deposits, districts, and regions Labay et al.39 Nightlight Nightlight Satellite images of Earth at night based on 2016 cloud-free observations over land mass. The image is divided in to Earth at Night40 three different resolutions: 0.1 degrees (3600 × 1800), 3 km (13500 × 6750), and 500 m (86400 × 43200). Relative pesticide exposure Pesticide.exp A database of the 20 most used pesticide active ingredients on 6 dominant crops and 4 aggregated crop classes at 5 Maggi et al.41 arc-min resolution (about 10 km at the equator) projected from 2015 to 2025 Pop.dens Population input data are collated from the 2010 round of Population and Housing Censuses, from 2005 and 2014 data. The input data are extrapolated to produce population estimates for the years 2000, 2005, 2010, 2015, and 2020. GPWv4 is gridded with an output resolution of 30 arc-seconds (approximately 1 km at the equator). Distance to urban areas Population density Continuous (see source for units) (in 1-km distance resolution) Hughes42; SEDAC43 Table 3. Bat cave distance at 1-km resolution to landscape features included in the current version of the database. in the literature. The IUCN Red List standardised its classification based on Salafsky et al.31, but we reclassified the threatening process into three key categories: Direct, Indirect, and Natural (Table 1) based on the drivers of threat10,14,32. Direct threats (Tdir) refer to the threats or risks that are direct to or in cave systems with immediate and perceivable impacts on populations or behaviour of species. This category includes direct human impacts (e.g., persecution, eviction, and cave closures) and the use of caves for harvesting bats, tourism, religious visits, and mining (minerals or guano). Indirect threats (Tind) refer to the threats outside cave systems or within cave proximity, of which the impacts to populations are secondary or non-immediate but otherwise detrimental. Examples include deforestation, agriculture, and urbanisation. Lastly, Natural threats (Tnat) refer to threats that are natural in origin, though their frequency may be impacted by human activities, and that may directly or indirectly impact populations, such as diseases (e.g., White-nose syndrome) and climate-driven risks (e.g., drought, extreme cold) (Table 1). Bat cave georeferencing. The second dataset contains the bat cave geographical location (latitude/ longitude) and recorded species (Table 2, Fig. 3a). We used the Web of Science and Google Scholar to search SCIENTIFIC DATA | (2022) 9:155 | https://doi.org/10.1038/s41597-022-01234-4 6 www.nature.com/scientificdata/ www.nature.com/scientificdata Fig. 4 Biogeographical comparison (mean, 95% CI) of landscape parameters at 1-km resolution. online literature, databases, and repositories for published information on cave-dwelling bats from 1990 to 2021. We used the following combination of keywords: (Bat* OR Chiroptera OR Chiroptera fauna*) AND (Diversity OR “Species richness” OR abundance OR distribution OR conservation OR ecology) AND (Cave* OR Cave-dwelling OR Cave-roosting OR underground* OR subterranean OR karst* OR Limestone). We also set a “create alert” in Google Scholar whenever new related papers were published. The data mining process for version 1.0 ended in June 2021. Our search returned 753 papers. We also searched using the Baidu Research engine for Chinese literature and self-archived ResearchGate to maximise search results. To ensure the precision of the datasets included in DarkCideS 1.0, we filtered all published literature to only include those papers or reports with complete species names and geographical records. We contacted corresponding authors with requests to provide us with geographical data when these were missing from their papers or supplementary materials. In the circumstance that we were unable to find the data, and the corresponding author did not respond to our request, that “cave site” was excluded from the database. We converted all species and cave latitude and longitude into WG8 84 decimal degrees with five significant figures. The second dataset of DarkCideS 1.0 contains 6746 georeferenced occurrences for 402 species11 from 2002 cave sites (Fig. 3a). Cave sites occur in all continents except Antarctica, with most of the data originating from tropical and temperate biomes (Fig. 3b). We have cave records from 46 countries of which China and Brazil have the highest number of caves recorded (Fig. 3c). Cave landscape features and vulnerabilities. The condition of surface ecosystems and the extent of threats are significant determinants of cave-dwelling bat diversity11. Yet, standardising the vulnerability of caves and underground ecosystems from threats on a global scale is challenging11,14. To address this, the surface SCIENTIFIC DATA | (2022) 9:155 | https://doi.org/10.1038/s41597-022-01234-4 7 www.nature.com/scientificdata/ www.nature.com/scientificdata Fig. 5 Schematic diagram showing the updating workflow of the database from new data entry. The DarkCideS database aims to be a long-term biodiversity data exchange platform by including new data from fieldwork and assessments. Authors can upload their dataset containing species records, geographical information, and landscape threats on the web page. The corresponding authors will receive new data entries for validation before being merged into the database. ecosystem was mapped as a proxy to assay cave vulnerability to threats using remotely sensed landscape features. The third dataset included in the database contains the measured land-use and landscape features of the cave surroundings using the georeferenced data from the second dataset (Table 3; Fig. 4). The selected landscape features measurements of the 2002 cave sites were selected based on Tanalgo et al.11. We included the estimated distance and measures of twelve landscape variables in the database, including canopy cover height33, tree density34, distance to freshwater bodies35, bare ground cover change36, short vegetation cover change36, tall tree cover change36, for vulnerabilities we included distance to urban areas36, distance to roads37, mine density38, night light39, relative pesticide exposure40, and population density41,42. For distance variables, the “distance to feature” tool was used in ArcMap 10.3 and distances were mapped at a 1-km resolution. Cave bat parasites and hyperparasites. Parasites, while being among the most diverse modes of life, are often disregarded in conservation strategies43. It is well established that parasites affect the stability of food webs and ecosystem health, but hyperparasites have thus far been severely understudied. For future studies on host associations across multiple trophic levels and on the effects of climatic conditions and land-use changes, parasites and hyperparasites are part of our DarkCideS 1.0 database. The fourth dataset lists the parasitic bat flies and their Laboulbeniales fungal hyperparasites associated with cave bats. Data were collected from several sources, including our fieldwork data36, Haelewaters et al.44, and de Groot et al.45. Bat fly taxonomy followed Dick and Graciolli46 and Graciolli and Dick47 and fungal taxonomy followed Index Fungorum48. In addition to the conspicuous bat flies, bats are host to several other lineages of parasites mites and ticks, lice, fleas, bugs, and earwigs49,50. Consequently, the fourth dataset will be expanded on in future versions of DarkCideS with data on these parasitic organisms. A recent call for global collaborations among bat scientists and collaborations to generate multitrophic data of bats, bat flies, and fungi50 along with the current DarkCideS 1.0 initiative will contribute to a general understanding of how ecological and life-history traits are correlated with bat parasitism and how host associations may change under changing conditions. Data Records The complete database for global cave-dwelling bats was organised in four main datasets stored in separate Excel workbooks (.csv file format). Each dataset contains unique sequential name IDs that correspond to metadata, variables, and references. All datasets included in the database are available and open-access from Figshare online repository51 and through a public website (https://darkcides.org/). The resolution of the publicly available cave and species occurrences were reduced for the protection of caves and to prevent hunting and harvesting. Database users can request high-resolution data of georeferenced species occurrence and cave sites from the corresponding authors. When a substantial dataset is available, all additional datasets will be updated in Figshare. Technical Validation The data included in this database are mainly derived from public, expert-based databases, published material and bat researchers, therefore ensuring the accuracy of the included data. We provided the corresponding reference (when applicable) for each cave record for cross-referencing and data validation purposes. When published “cave datasets” were unclear or lacked detailed information, we communicated with the corresponding authors. SCIENTIFIC DATA | (2022) 9:155 | https://doi.org/10.1038/s41597-022-01234-4 8 www.nature.com/scientificdata/ www.nature.com/scientificdata We encourage continued contributions to the DarkCideS database as we aim to regularly update the entries for species checklists, traits, geographical locations of caves, and species occurrence data. For species ecological status (e.g., current conservation status, population trends, geopolitical endemism), we will update entries after every IUCN Red List assessment cycle. The database will be updated when new data are contributed and corrected when an error in the data entry is reported to any of the corresponding authors. New entries will be quality screened based on the criteria listed above before adding to the database (Fig. 5). Once an update is made, a release note will be published on the database website. When updating new versions of DarkCideS, we will continue to make available previous releases. Contributors will be included as co-authors when the next version of the database is published. Furthermore, as each cave has a unique ID, additional surveys of other taxa at the same locality can be integrated into the database, to provide a backbone for enhancing our understanding of cave biodiversity through time. Usage Notes All datasets included in DarkCideS are publicly available under a Creative Commons Attribution 4.0 International Public Licences (https://creativecommons.org/licenses/by/4.0/), where users and authors may freely use our datasets, with the condition that the sources are credited and acknowledged, the original license is linked, and any modifications and treatments to our data are indicated in the final work or material. Although we aim to maximise spatial coverage with datasets from across the globe, we acknowledge that geographical biases inevitably exist52. For example, we have multiple datasets from the Palearctic, Indomalayan, and Neotropical realms, whereas very little data, originated from the Afrotropical region (see Fig. 3). We also encountered similar coverage bias in country-level data richness. For example, Indonesia is one of the most diverse countries for estimated cave-dwelling bat species richness11, but a very small number of species were included in the current version of the database. The database is intended as a long-term data-sharing platform, and we hope to fill these gaps in the next versions of the database. Further data and better coverage will provide a better index for regional prioritisation in addition to further research on bat diversity patterns and threats. Consortia authorship. The DarkCideS database is a continuous project. To promote global collaboration and equitability, all present and future members of the DarkCideS initiative and consortia (https://darkcides.org/ our-team/) will be considered bona fide authors of the current and future versions of the database. Code availability No code was used to generate the data presented in this data paper. Received: 1 October 2021; Accepted: 24 February 2022; Published: xx xx xxxx References 1. Brooks, T. M. et al. Habitat Loss and Extinction in the Hotspots of Biodiversity. Conserv. Biol. 16, 909–923 (2002). 2. Vos, J. M. D., Joppa, L. N., Gittleman, J. L., Stephens, P. R. & Pimm, S. L. Estimating the normal background rate of species extinction. Conserv. Biol. 29, 452–462 (2015). 3. Heberling, J. M., Miller, J. T., Noesgaard, D., Weingart, S. B. & Schigel, D. Data integration enables global biodiversity synthesis. Proc. Natl. Acad. Sci. 118 (2021). 4. Ripple, W. J. et al. Extinction risk is most acute for the world’s largest and smallest vertebrates. Proc. 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This work is part of the Doctoral project of KCT supported by the University of Chinese Academy of Sciences and Chinese Government Scholarship council, P.R. China (CGS No. 2018SLJ023249) and the Zukunftskolleg Connect Fellowship at the University of Konstanz. DH is supported by the U.S. National Science Foundation (Grant No. DEB-2127290), and by the Research Foundation–Flanders (Junior Postdoctoral Fellowship No. 1206620 N) for his project “Laboulbeniales hyperparasitic fungi of bat flies: host specificity and patterns of speciation”. FG was supported by the São Paulo Research FoundationFAPESP (Grant No. 2017/24252–0, 2019/00648–7). RLM was supported by Bryce Carmine and Anne Carmine (née Percival), through the Massey University Foundation. AOA was financed by a postdoctoral grant from Coordenação de Aperfeiçoamento de Nível Superior (CAPES) and Fundacão de Amparo à Ciência e Tecnologia do Estado do Pernambuco (FACEPE). PWW is supported by the U.S. Agency for International Development (USAID) and the US National Academies of Sciences, Engineering, and Medicine under the Partnerships for Enhanced Engagement in Research (PEER) Program. JB is supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior- Brasil (CAPES) (Finance Code 001). Proyecto CUBABAT is supported by the participants of Animal Experience International (http://www.animalexperienceinternational.com/batsincuba) along with funding from The Antonio Núñez Jiménez Foundation for Nature and Humanity (FANJ); Empresa Nacional para la Protección de la Flora y la Fauna (ENPFF), Cuba; el Centro de Servicios Ambientales de Matanzas (CSAM); and The Ministry of Science, Technology and Environment of the Republic of Cuba (CITMA). HFMO was supported by a PhD scholarship and a postdoctoral fellowship from the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-CAPES (Coordination for the Improvement of Higher Education Personnel; CAPES and CAPES-PRINT). LHDS was supported by a MSc scholarship from the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-CAPES. The field work was supported by Fundação de Amparo a Pesquisa de Minas Gerais, Spelayon Consultoria, Carste Ciência Ambiental and Anglo-American Brasil. To the Big Bat Theory research group and to the Instituto Nacional de Conservación y Desarrollo Forestal, Áreas Protegidas y Vida Silvestre (ICF) for the research permit in Honduras. We thank Fu Wuxiang and Chen Huanhua (Landscape Ecology Group, XTBG) for organising our datasets from Chinese literature. Open Access funding was provided by the University of Konstanz and the Zukunftskolleg to KCT through the German Projekt DEAL. SCIENTIFIC DATA | (2022) 9:155 | https://doi.org/10.1038/s41597-022-01234-4 10 www.nature.com/scientificdata/ www.nature.com/scientificdata Author contributions K.C.T., J.A.G. and A.C.H. conceived and primarily developed the database, with funding acquired by A.C.H. Data collection, organisation, and formatting were led by K.C.T. and A.C.H. A.C.H. performed the landscape feature mapping and analyses. Data on bat parasites and hyperparasites were compiled and curated by D.H. The first draft of the manuscript was written by K.C.T. and A.C.H. K.C.T. performed data visualisation. All authors provided inputs and suggestions on the draft and approved the final manuscript. Most of the authors provided data to at least one of the DarkCideS datasets. Funding Open Access funding enabled and organized by Projekt DEAL. Competing interests The authors declare no competing interests. Additional information Correspondence and requests for materials should be addressed to K.C.T., D.H. or A.C.H. 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To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. © The Author(s) 2022 Krizler C. Tanalgo 1,2,3,4 ✉, John Aries G. Tabora3,5, Hernani Fernandes Magalhães de Oliveira6, Danny Haelewaters 7,8,9 ✉, Chad T. Beranek 10,11, Aída Otálora-Ardila12,13, Enrico Bernard13, Fernando Gonçalves 14,15, Alan Eriksson16, Melissa Donnelly 17, Joel Monzón González17,18,19, Humberto Fernández Ramos17,19, Alberto Clark Rivas17,19, Paul W. Webala20, Stanimira Deleva21,22, Ridha Dalhoumi23, Jaycelle Maula23, Dennis Lizarro25,26,37, Luis F. Aguirre 26,27, Nils Bouillard28, Ma. Niña Regina M. Quibod1,2,29, Jennifer Barros 13, Manfredo Alejandro Turcios-Casco30, Marcio Martínez30, Diego Iván Ordoñez-Mazier30, José Alejandro Soler Orellana30, Eduardo J. Ordoñez-Trejo30, Danny Ordoñez30, Ada Chornelia1,2, Jian Mei Lu1, Chen Xing31, Sanjeev Baniya32, Renata L. Muylaert33, Leonardo Henrique Dias-Silva34, Nittaya Ruadreo35 & Alice Catherine Hughes 1,2,36 ✉ 1 Center for Integrative Conservation, Xishuangbanna Tropical Botanical Garden, and the Center for Conservation Biology, Core Botanical Gardens, Chinese Academy of Sciences, Yunnan, P.R. China. 2International College of the Chinese Academy of Sciences, Beijing, P.R. China. 3Ecology and Conservation Research Lab (Eco/Con Lab), Department of Biological Sciences, College of Science and Mathematics, University of Southern Mindanao, Kabacan, North Cotabato, Philippines. 4Zukunftskolleg and the Center for Advanced Study of Collective Behaviour, University of Konstanz, Universitätsstrasse 10, Baden-Württemberg, Konstanz, Germany. 5School of Environmental Science and Management, University of the Philippines, Los Banos, Laguna, Philippines. 6Department of Zoology, Federal University of Paraná, Curitiba, PR, Brazil. 7Research Group Mycology, Department of Biology, Ghent University, 9000, Ghent, Belgium. 8Operation Wallacea Ltd, Wallace House, Old Bolingbroke, Lincolnshire, PE23 4EX, United Kingdom. 9Faculty of Science, University of South Bohemia, 370 05, České Budějovice, Czech Republic. 10School of Environmental and Life Sciences, Biology Building, University of Newcastle, University Drive, Callaghan, NSW, 2308, Australia. 11FAUNA Research Alliance, PO Box 5092, Kahibah, NSW, 2290, Australia. 12Grupo en Conservación y Manejo de Vida Silvestre, Universidad Nacional de Colombia, Bogotá, Colombia. 13Laboratório de Ciência Aplicada à Conservação da Biodiversidade, Department of Zoology, Universidade Federal de Pernambuco (UFPE), Pernambuco, Brazil. 14Department of Biodiversity, Institute of Bioscience, Universidade Estadual Paulista (UNESP), Rio Claro, São Paulo, Brazil. 15Conservation Science Group, Department of Zoology, University of Cambridge, Cambridge, UK. 16 Programa de Pós-Graduação em Ecologia e Conservação, Instituto de Biociências, Universidade Federal de Mato Grosso do Sul, Campo Grande, Brazil. 17Proyecto CUBABAT, Calle América #6503 (Altos) e/ Jáuregui y Santa Isabel, 40100, Matanzas, Cuba. 18Fundación “Antonio Núñez Jiménez” de la Naturaleza y el Hombre, Calle 5ta B, No. 6611 e/ 66 y 70, Miramar, Playa, La Habana, Cuba. 19Sociedad Espeleológica de Cuba (SEC), Calle 9na. #8402 e/ 84 y 84ª. Playa, La Habana, Cuba. 20Department of Forestry and Wildlife Management, Maasai Mara University, Narok, Kenya. SCIENTIFIC DATA | (2022) 9:155 | https://doi.org/10.1038/s41597-022-01234-4 11 www.nature.com/scientificdata/ www.nature.com/scientificdata 21 Sede del Sur, Universidad de Costa Rica, 4000 Alamedas, Golfito, 60701, Costa Rica. 22National Museum of Natural History-Bulgarian Academy of Sciences, Sofia, Bulgaria. 23Laboratoire de Biosurveillance de l’Environnement, Faculté des Sciences de Bizerte, Université de Carthage, 7021, Zarzouna, Tunisia. 24Department of Biology, Southern Luzon State University, Lucban, Quezon, Philippines. 25Centro de Investigación de Recursos Acuáticos, Universidad Autónoma del Beni “José Ballivián” (CIRA-UABJB). Campus “Dr. Hernán Melgar Justiniano”, Santísima Trinidad, Beni, Bolivia. 26Programa para la Conservación de los Murciélagos de Bolivia. Cochabamba y Beni, Beni, Bolivia. 27Centro de Biodiversidad y Genética, Universidad Mayor de San Simón, Casilla 538, Cochabamba, Bolivia. 28 Barbastella Echology, Gentpoortstraat 50, 9800, Deinze, Belgium. 29Museum of Natural History of the University of the Philippines, Los Banos, Laguna, Philippines. 30Asociación para la Sostenibilidad e Investigación Científica en Honduras (ASICH). Barrio La Granja, entre 28 y 29 calle, C. P. 504. Comayagüela M.D.C. Francisco Morazán, Tegucigalpa, Honduras. 31School of Zoology, Faculty of Life sciences, Tel Aviv University, Tel Aviv, Israel. 32National Centre for Biological Sciences (NCBS), Bangalore, India. 33Molecular Epidemiology and Public Health Laboratory, Hopkirk Research Institute, Massey University, Palmerston North, New Zealand. 34Laboratório de Mastozoologia do Departamento de Biologia Animal da Universidade Federal de Viçosa, Minas Gerais, Viçosa, Brasil. 35Division of Biological Sciences, Faculty of Science, Prince of Songkla University, Hat Yai, Songkhla, Thailand. 36School of Biological Sciences, The University of Hong Kong, Hong Kong SAR, China. 37Deceased: Dennis Lizarro. ✉e-mail: tkrizler@gmail.com; danny.haelewaters@gmail.com; ach_conservation2@hotmail.com SCIENTIFIC DATA | (2022) 9:155 | https://doi.org/10.1038/s41597-022-01234-4 12