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The accessibility of global biodiversity information has surged in the past two decades, notably through widespread funding initiatives for museum specimen digitization and emergence of large-scale public participation in community science. Effective use of these data requires the integration of disconnected datasets, but the scientific impacts of consolidated biodiversity data networks have not yet been quantified. To determine whether data integration enables novel research, we carried out a quantitative text analysis and bibliographic synthesis of >4,000 studies published from 2003 to 2019 that use data mediated by the world’s largest biodiversity data network, the Global Biodiversity Information Facility (GBIF). Data available through GBIF increased 12-fold since 2007, a trend matched by global data use with roughly two publications using GBIF-mediated data per day in 2019. Data-use patterns were diverse by authorship, geographic extent, taxonomic group, and dataset type. Despite facilitating global authorship, legacies of colonial science remain. Studies involving species distribution modeling were most prevalent (31% of literature surveyed) but recently shifted in focus from theory to application. Topic prevalence was stable across the 17-y period for some research areas (e.g., macroecology), yet other topics proportionately declined (e.g., taxonomy) or increased (e.g., species interactions, disease). Although centered on biological subfields, GBIF-enabled research extends surprisingly across all major scientific disciplines. Biodiversity data mobilization through global data aggregation has enabled basic and applied research use at temporal, spatial, and taxonomic scales otherwise not possible, launching biodiversity sciences into a new era.

Primary biodiversity data records, available on-line, are essential for conservation planning. Of the mega diversity countries, Brazil have reached a high level of scientific research in describing their biodiversity. However, there still remain significant limitations in recovering, collating and organizing available information on Brazil's biological diversity and its distribution. Since the colonial period, biological material were often collected and transferred to other countries, which were characterized, stored and maintained. As a result, natural history museums worldwide possess large amounts of primary biodiversity data originally from Brazil which are then published on-line in the international Global Biodiversity Information Facility (GBIF) infrastructure. Aiming to recover these data, the Brazilian Biodiversity Information System (SiBBr) developed an automatic repatriation tool capable of retrieving all records registered in Brazil but published outside Brazilian territory in an automated manner. Thus, 2,459,366 records were added to SiBBr’s Repository in one day. Europe and the United States hold about 80% of all records. The data set covers all life kingdoms. Animalia is the most represented group with 3 main phylum's: Chordata, Arthropoda and Mollusca, within more than 40% of all records. Plantae also comprises a large portion of the records with angiosperms having the major number of entries.

To achieve the goals of the Kunming–Montreal Global Biodiversity Framework (KMGBF), agreed by Parties to the Convention on Biological Diversity, there is an urgent need to address the economic drivers of biodiversity loss. The KMGBF includes a target to encourage businesses and financial institutions to disclose their impacts and dependences on biodiversity. While transparent biodiversity disclosures could help shift business operations away from activities that harm biodiversity, the weak target wording implies voluntary and unstandardized disclosures, which tend to be low quality and ineffective. Moreover, examination of scientific and practical insights strongly indicates that the evolving strategy of disclosures led by businesses may prioritize short‐term business and investment interests while neglecting biodiversity outcomes and the wider systemic risks they pose. We argue that there is a risk of limited if not altogether perverse outcomes from the target, where businesses provide ambiguous disclosures that fail to reduce impacts on biodiversity, yet an increase in volume and frequency of disclosures suggests progress toward the target. Consequently, we advocate for a regulatory approach, supported by scientific engagement in the development of disclosure standards and associated policy indicators, to ensure that the emerging response to the KMGBF target on disclosures avoids perverse outcomes and instead results in positive impacts on biodiversity.

Because of a range of constraints, the availability of biodiversity-related information varies considerably over space, time, taxa, and types of data, thereby causing gaps in knowledge. Despite growing awareness of this issue among scientists, it is still poorly known how—and whether— scientific efforts have contributed to overcoming these information gaps. Focusing on spatial gaps in global biodiversity data, we show that the accumulation rates of nonbird species occurrence records stored in the Global Biodiversity Information Facility have not improved—and have even potentially declined—over the past three decades in data-poor, often biodiversity-rich regions. Meanwhile, one citizen-science project, eBird, has been making a considerable contribution to the collection and sharing of bird records, even in the data-poorest countries, and is accelerating the accumulation of bird records globally. We discuss the potentials and limitations of citizen-science projects for tackling gaps in biodiversity information, particularly from the perspective of biodiversity conservation.

Geo‐referenced species occurrences from public databases have become essential to biodiversity research and conservation. However, geographical biases are widely recognized as a factor limiting the usefulness of such data for understanding species diversity and distribution. In particular, differences in sampling intensity across a landscape due to differences in human accessibility are ubiquitous but may differ in strength among taxonomic groups and data sets. Although several factors have been described to influence human access (such as presence of roads, rivers, airports and cities), quantifying their specific and combined effects on recorded occurrence data remains challenging. Here we present sampbias, an algorithm and software for quantifying the effect of accessibility biases in species occurrence data sets. sampbias uses a Bayesian approach to estimate how sampling rates vary as a function of proximity to one or multiple bias factors. The results are comparable among bias factors and data sets. We demonstrate the use of sampbias on a data set of mammal occurrences from the island of Borneo, showing a high biasing effect of cities and a moderate effect of roads and airports. sampbias is implemented as a well‐documented, open‐access and user‐friendly R package that we hope will become a standard tool for anyone working with species occurrences in ecology, evolution, conservation and related fields.

Large‐scale biodiversity databases encompass three main types of data for plants, namely single species point occurrences, co‐occurrences in vegetation plots, and checklists for specific areas. Evidence shows that such data types exhibit specific biases, reporting different species assemblages at local scales. We used the Mediterranean Basin, a global biodiversity hotspot with more than 2200 islands larger than 0.01 km2, to compare island vascular plant diversity patterns emerging from occurrence data (Global Biodiversity Information Facility; GBIF), vegetation plots (European Vegetation Archive; EVA), and species checklists (Global Inventory of Flora and Traits; GIFT). We aggregated plant data at the island level and compared geographic coverage, inventory completeness, and taxonomic coverage among these data sources. The combined databases accounted for 8702 species distributed on 790 islands (35.6% of the target islands). Data availability increased from small (26.8%) over medium (75.7%) to large islands (100.0%). Spatial coverage of databases on a 30 × 30 km grid was high for GBIF (52.8%) and EVA (45.4%), and low for GIFT (21.7%). GIFT provided higher native and alien species richness values for most of the islands, whereas GBIF and EVA consistently missed a considerable fraction of the expected species richness. Taking GIFT as reference, GBIF, and to a lesser extent EVA, showed a positive bias towards perennial species and an underrepresentation of annuals. Despite their lower taxonomic coverage, GBIF and EVA data can complement our knowledge on Mediterranean islands' plant diversity, providing data for islands lacking plant inventories. Moreover, GBIF and EVA's large datasets can be used for investigating other levels of ecological organisation and modelling single species (GBIF) or population (EVA) trends over space and time. Finally, our results advocate for a coordinated effort to fill the knowledge gaps through data collection and digitisation, possibly integrating data collected by experts by means of citizen science initiatives.

Selecting candidates for drug developments using computational design and empirical rules has resulted in a broad discussion about their success. In a previous study, we had shown that a species’ abundance [as expressed by the GBIF (Global Biodiversity Information Facility)] dataset is a core determinant for the development of a natural product into a medicine. Our overarching aim is to understand the unique requirements for natural product-based drug development. Web of Science was queried for research on alkaloids in combination with plant systematics/taxonomy. All alkaloids containing species demonstrated an average increase of 8.66 in GBIF occurrences between 2014 and 2020. Medicinal Species with alkaloids show higher abundance compared to non-medicinal alkaloids, often linked also to cultivation. Alkaloids with high biodiversity are often simple alkaloids found in multiple species with the presence of ’driver species‘ and are more likely to be included in early-stage drug development compared to ‘rare’ alkaloids. Similarly, the success of an alkaloid containing species as a food supplement (‘botanical’) is linked to its abundance. GBIF is a useful tool for assessing the druggability of a compound from a certain source species. The success of any development programme from natural sources must take sustainable sourcing into account right from the start.

Aim Recent unprecedented efforts to digitise and mobilise biodiversity data have resulted in the generation of ‘biodiversity big data’, enabling ecological research at scales previously not possible. However, gaps, biases and uncertainties in these data influence analytical outcomes and the validity of scientific research and conservation actions. Here, we estimated tree species inventory completeness globally and identified where future surveys should focus to maximise regional inventories. Location Global. Methods We analysed spatial patterns in sampling effort of tree species occurrence records from the Global Biodiversity and Information Facility (GBIF) and estimated global tree species inventory completeness for 100 × 100 km grid cells (sampling units) and ecoregions. We also identified forested areas for future botanical exploration, by examining the spatial overlap between inventory completeness, remaining natural habitat and protected areas and degrees of forest modification by anthropogenic pressure (forest integrity). Results Spatial patterns in sampling effort and tree species inventory completeness were unevenly distributed around the world. Only 35% of ecoregions and 18% of sampling units can be considered well surveyed, most of which were concentrated in the Global North, including Europe, North America and Australia. Large areas in species‐rich tropical regions, especially in Southeast Asia, remained poorly documented. Moreover, our results showed that many areas with low inventory completeness overlapped with ecoregions retaining less than 50% of natural habitat and protected land area, as well as sampling units with low forest integrity. Main Conclusions Due to limitations in biodiversity data, simply sampling more will not necessarily lead to increasing knowledge. We illustrated how gaps in these data can be used to improve existing knowledge by identifying priority areas for future surveys. With ongoing anthropogenic impacts and escalating rates of biodiversity loss, limited resources should be allocated to strategically survey regions likely to yield new knowledge and improve biodiversity representativeness.

The analysis of macroecological patterns has necessitated the use of large, composite datasets recording local‐scale species occurrences distributed across the globe. These datasets, however, have various spatial and temporal biases, including taxonomic under sampling, range gaps for many species, and geographic uncertainty. They have rarely been compared to data collected in the field across large spatial gradients. In this paper we use two datasets built from online repositories plus standardised field collections of death assemblages to reconstruct macroecological patterns for marine bivalves along the eastern coastline of Australia – spanning over 20° of latitude and the transition between tropical and temperate regions. We test the strength of the latitudinal diversity gradient using four diversity measures and identify a biogeographical boundary. The field collection demonstrates a strong latitudinal gradient, but results from the composite datasets were varied. Adding observation‐based records to the composite dataset obscured the latitudinal gradient. The location of the biogeographic boundary was the same in all datasets, and the location mirrored two previously published bioregionalisations. Although broad patterns seen in the field can be uncovered from composite macroecological datasets, care both in dataset construction and choice of methods is needed to ensure robust results.

Natural history collections constitute an enormous wealth of information of Life on Earth. It is estimated that over 2 billion specimens are preserved at institutions worldwide, of which less than 10% are accessible via biodiversity data aggregators such as GBIF. Moreover, they are a very important resource for eco-evolutionary research, which greatly depends on knowing the precise location where the specimens were collected in order to characterize the environment in which they lived. Yet, only about 55% of the accessible records are georeferenced and only 31% have coordinate uncertainty information, which is critical for conducting rigorous studies. The awareness of this gap of knowledge which hinders the enormous potential of such data in research led to the organization of a workshop which brought together key players in georeferencing of natural history collections. The discussion and outcomes of this workshop are here presented.