Global Biodiversity Information Facility Research Articles

Mapping the potential distribution of the principal vector of Crimean-Congo haemorrhagic fever virus Hyalomma marginatum in the Old World.

Crimean-Congo haemorrhagic fever (CCHF) is the most widely distributed tick-borne viral disease in humans and is caused by the Crimean-Congo haemorrhagic fever virus (CCHFV). The virus has a broader distribution, expanding from western China and South Asia to the Middle East, southeast Europe, and Africa. The historical known distribution of the CCHFV vector Hyalomma marginatum in Europe includes most of the Mediterranean and the Balkan countries, Ukraine, and southern Russia. Further expansion of its potential distribution may have occurred in and out of the Mediterranean region. This study updated the distributional map of the principal vector of CCHFV, H. marginatum, in the Old World using an ecological niche modeling approach based on occurrence records from the Global Biodiversity Information Facility (GBIF) and a set of covariates. The model predicted higher suitability of H. marginatum occurrences in diverse regions of Africa and Asia. Furthermore, the model estimated the environmental suitability of H. marginatum across Europe. On a continental scale, the model anticipated a widespread potential distribution encompassing the southern, western, central, and eastern parts of Europe, reaching as far north as the southern regions of Scandinavian countries. The distribution of H. marginatum also covered countries across Central Europe where the species is not autochthonous. All models were statistically robust and performed better than random expectations (p < 0.001). Based on the model results, climatic conditions could hamper the successful overwintering of H. marginatum and their survival as adults in many regions of the Old World. Regular updates of the models are still required to continually assess the areas at risk using up-to-date occurrence and climatic data in present-day and future conditions.

Data from Entomological Collections of Aedes (Diptera: Culicidae) in a post-epidemic area of Chikungunya, City of Kinshasa, Democratic Republic of Congo.

Arbovirus epidemics (chikungunya, dengue, West Nile fever, yellow fever and zika) are a growing threat in African areas where Aedes (Stegomyia) aegypti (Linnaeus, 1762) and Aedes albopictus (Skuse, 1895) are present. The lack of comprehensive sampling of these two vectors limits our understanding of their propagation dynamics in areas at risk of arboviruses. Here, we collected 6,943 observations (both larval and human capture) of Ae.aegypti and Ae.albopictus between 2020 and 2022. The study was carried out in the Vallee de la Funa, a post-epidemic zone in the city of Kinshasa, Democratic Republic of Congo. Our results provide important information for future basic and advanced studies on the ecology and phenology of these vectors, as well as on vector dynamics after a post-epidemic period. The data from this study are published in the public domain as the Darwin Core Archive in the Global Biodiversity Information Facility.

Distribution of sika deer (Cervus nippon) and the bioclimatic impact on their habitats in South Korea

Invasive species and climate change are primary factors influencing biodiversity, and examining the behavior of invasive species is essential for effective conservation management. Here, we report the global distribution of the sika deer (Cervus nippon) based on locations reported in published literature (Google Scholar), the Global Biodiversity Information Facility (GBIF) database, and the International Union for Conservation of Nature report. We used the maximum entropy (Maxent) model to examine the impact of climate change on sika deer habitats in South Korea based on GBIF occurrence data and WorldClim bioclimatic variables. Habitat suitability analysis was performed using the Maxent model under Representative Concentration Pathways (RCPs) 4.5 and 8.5 (for predicted climatic conditions in both 2050 and 2070) to project the effects of different climate change scenarios on South Korean sika deer habitats. We identified that the sika deer is distributed in 39 countries worldwide. Due to climate change effects, South Korean sika deer habitats will decline by approximately 24.98% and 20.63% (under RCP 4.5) and by 50.51% and 57.35% (under RCP 8.5) by 2050 and 2070, respectively. Our findings shed light on sika deer ecology and provide reference data for future conservation management strategies and policy design.

Building a community-based taxonomic resource for digitization of parasites and their hosts

Abstract Classification of the biological diversity on Earth is foundational to all areas of research within the natural sciences. Reliable biological nomenclatural and taxonomic systems facilitate efficient access to information about organisms and their names over time. However, broadly sharing, accessing, delivering, and updating these resources remains a persistent problem. This barrier has been acknowledged by the biodiversity data sharing community, yet concrete efforts to standardize and continually update taxonomic names in a sustainable way remain limited. High diversity groups such as arthropods are especially challenging as available specimen data per number of species is substantially lower than vertebrate or plant groups. The Terrestrial Parasite Tracker Thematic Collections Network project developed a workflow for gathering expert-verified taxonomic names across all available sources, aligning those sources, and publishing a single resource that provides a model for future endeavors to standardize digital specimen identification data. The process involved gathering expert-verified nomenclature lists representing the full taxonomic scope of terrestrial arthropod parasites, documenting issues experienced, and finding potential solutions for reconciliation of taxonomic resources against large data publishers. Although discordance between our expert resources and the Global Biodiversity Information Facility are relatively low, the impact across all taxa affects thousands of names that correspond to hundreds of thousands of specimen records. Here, we demonstrate a mechanism for the delivery and continued maintenance of these taxonomic resources, while highlighting the current state of taxon name curation for biodiversity data sharing.

Predicting the distribution of Ixodes ricinus and Dermacentor reticulatus in Europe: a comparison of climate niche modelling approaches

BackgroundThe ticks Ixodes ricinus and Dermacentor reticulatus are two of the most important vectors in Europe. Climate niche modelling has been used in many studies to attempt to explain their distribution and to predict changes under a range of climate change scenarios. The aim of this study was to assess the ability of different climate niche modelling approaches to explain the known distribution of I. ricinus and D. reticulatus in Europe.MethodsA series of climate niche models, using different combinations of input data, were constructed and assessed. Species occurrence records obtained from systematic literature searches and Global Biodiversity Information Facility data were thinned to different degrees to remove sampling spatial bias. Four sources of climate data were used: bioclimatic variables, WorldClim, TerraClimate and MODIS satellite-derived data. Eight different model training extents were examined and three modelling frameworks were used: maximum entropy, generalised additive models and random forest models. The results were validated through internal cross-validation, comparison with an external independent dataset and expert opinion.ResultsThe performance metrics and predictive ability of the different modelling approaches varied significantly within and between each species. Different combinations were better able to define the distribution of each of the two species. However, no single approach was considered fully able to capture the known distribution of the species. When considering the mean of the performance metrics of internal and external validation, 24 models for I. ricinus and 11 models for D. reticulatus of the 96 constructed were considered adequate according to the following criteria: area under the receiver-operating characteristic curve > 0.7; true skill statistic > 0.4; Miller’s calibration slope 0.25 above or below 1; Boyce index > 0.9; omission rate < 0.15.ConclusionsThis comprehensive analysis suggests that there is no single ‘best practice’ climate modelling approach to account for the distribution of these tick species. This has important implications for attempts to predict climate-mediated impacts on future tick distribution. It is suggested here that climate variables alone are not sufficient; habitat type, host availability and anthropogenic impacts, not included in current modelling approaches, could contribute to determining tick presence or absence at the local or regional scale.Graphical abstract

Predicting the potential worldwide distribution of Aedes aegypti under climate change scenarios

Background: Climate change is one of the most important factors associated with medically important insect pests such as mosquitoes (Diptera: Culicidae). Diseases spread by mosquitoes are increasing due to changes in global temperature and weather patterns that are altering vector host ranges allowing spread into new regions. Zika, dengue fever, chikungunya and yellow fever are arboviral infections that are spread by Aedes aegypti (Culicidae). The objective of the current research is to study the potential geographic distribution habitats of Ae. aegypti in the world under current and future climate conditions. Methods: Data of Ae. aegypti was obtained from the global biodiversity information facility and used 19 bioclimatic layers (bio01-bio19) and elevation from the WorldClim database. The scenarios used are the Beijing climate center climate system model (BCC-CSM2-MR) and the institute Pierre-Simon Laplace, coupled model intercomparison project (IPSL-CM6A-LR) with two shared socio-economic pathways (SSPs) for each of the general circulation model (GCMs): SSP126 and SSP585. Results: The results revealed that altitude, temperature, seasonality (standard deviation *100) (bio4), and annual precipitation (bio12) were the most important environmental variables that affect the distribution of Ae. aegypti. Conclusions: The models showed that Africa and South America maintained very high and excellent habitat suitability for Ae. Aegypti under the current potential distribution map.

A Dataset of Non-Indigenous and Native Fish of the Volga and Kama Rivers (European Russia)

Fish in the Volga-Kama River System (the largest river system in Europe) are important as a crucial food source for local populations; fish have the highest trophic level among hydrobionts. The purpose of this research is to describe the diversity of non-indigenous and native fish in the Volga and Kama Rivers, in the European part of Russia. This dataset encompasses data from June 2001 to September 2021 and comprises 1888 records (36,376 individual observations) for littoral and pelagic habitats from 143 sampling sites, representing 52 species from 42 genera in 22 families. The dataset has a Darwin Core standard format and has been fully released in the Global Biodiversity Information Facility (GBIF) under CC-BY 4.0 International license. The data are validated with several international databases such as FishBase, Eschmeyer’s Catalog of Fishes, the Barcode of Life Data System, and the SAS.Planet geoinformations system. Newly established populations have been found for several species belonging to the following Actinopteri families: Alosidae, Anguillidae, Cichlidae, Ehiravidae, Gobiidae, Odontobutidae, Syngnathidae, and Xenocyprididae. Therefore, this dataset can be used in the particular taxon species distribution analysis, which are especially important for non-indigenous species.

Using social media records to inform conservation planning.

Citizen science plays a crucial role in helping monitor biodiversity and inform conservation. With the widespread use of smartphones, many people share biodiversity information on social media, but this information is still not widely used in conservation. Focusing on Bangladesh, a tropical megadiverse and mega-populated country, we examined the importance of social media records in conservation decision-making. We collated species distribution records for birds and butterflies from Facebook and Global Biodiversity Information Facility (GBIF), grouped them into GBIF-only and combined GBIF and Facebook data, and investigated the differences in identifying critical conservation areas. Adding Facebook data to GBIF data improved the accuracy of systematic conservation planning assessments by identifying additional important conservation areas in the northwest, southeast, and central parts of Bangladesh, extending priority conservation areas by 4,000-10,000km2 . Community efforts are needed to drive the implementation of the ambitious Kunming-Montreal Global Biodiversity Framework targets, especially in megadiverse tropical countries with a lack of reliable and up-to-date species distribution data. We highlight that conservation planning can be enhanced by including available data gathered from social media platforms.

The MOBIOS+: A FAIR (Findable, Accessible, Interoperable and Reusable) database for Mindanao's terrestrial biodiversity.

Due to its complex geological history and the emergence of various biogeographic regions, the Philippines boasts an extraordinary array of flora and fauna. This unique combination has contributed to the country's exceptional density of terrestrial species, making it amongst the highest in the world. Mindanao, in the southern part of the Philippines, is the second largest group of islands and supports high levels of endemism and proportion of threatened species. However, consolidated biodiversity records and information remain unavailable for the region. The primary goal of the Mindanao Open Biodiversity Information (MOBIOS+) database is to address these substantial data deficiencies by compiling contemporary biodiversity information from the 21st century. This initiative seeks to enhance our comprehension of biodiversity trends in Mindanao over temporal and spatial dimensions, while also creating an openly-accessible database. The database we present here is the first of its kind and currently the most comprehensive attempt to establish the largest consolidated database for Mindanao biodiversity, based on publicly available literature. With its vast collection of biodiversity data, this database will prove to be a valuable resource for advancing biodiversity research and analysis. It will further facilitate the identification of species and areas that require immediate conservation prioritisation and action, addressing the urgent challenges posed by our rapidly changing planet. The MOBIOS+ database is the first attempt to create a massive FAIR database aiming to collate biodiversity records from published literature in the Mindanao faunal region, south of the Philippines. The database currently includes 12,813 georeferenced specimen occurrences representing 1,907 unique taxa across 10 animal classes inhabiting the terrestrial and freshwater environments of Mindanao faunal region. We made all georeferenced specimen occurrences available in the Global Biodiversity Information Facility (GBIF) platform.

Understanding the users and uses of UK Natural History Collections

UK natural science collections hold over 137 million items, an unrivalled source of data about 4.56 billion years of planetary development and hundreds of years of biological change, including the differences made by humans — but the scientific, commercial, and societal benefits of these collections are constrained by the limits of physical access, and by highly fragmented digitisation efforts with less than 10% digitally available. Following work with Frontier Economics in 2021, which showed potential for £2 billion in benefits to the UK economy from digitising all UK natural science collections, in 2022–23 the Natural History Museum London worked, with analytical support from McKinsey and Company, to understand the impact of what has already been digitised and shared by UK natural science collections — what is the demand for these data, what are they used for, and how does this deliver efficient, effective and impactful research? This study focuses on usage via the Global Biodiversity Information Facility, the largest source of relevant usage data, examining 7.6 million records from twelve UK institutions. While these UK collections data are just 0.3% of total GBIF occurrences, they are cited in 12% of peer reviewed publications citing GBIF data, showing the disproportionate impact of UK collections data and the historical, geographical, and taxonomic richness that they bring. Researchers have already benefited from more than £18 million of efficiency savings from digital UK specimen data. Data from natural science collections held in the UK are uniquely impactful resources, vital to a future in which people and planet thrive, and a step change in the pace of digitisation is needed to unlock their potential for researchers, policymakers, and society.

The observation records from whale and dolphin watching inshore of Hualien, eastern Taiwan.

The eastern waters of Taiwan have been lacking baseline and research data for several years. This study was initiated by Kuroshio Ocean Education Foundation (KOEF) in collaboration with the Turumoan whale-watching company since 1998, collecting long-term ecological data for cetaceans in the inshore of Hualien, eastern Taiwan. This dataset includes 10,675 records of cetacean sightings from June 1998 to December 2021. Collection of cetacean sighting records was paused for one year in 2001 due to budgetary reasons. All of the sighting records were collected by whale-watching boat guides that were trained by KOEF. Following a standardised protocol, guides used a handheld GPS device and cetacean sighting record sheets to document information about the cetacean species identification, location, time, number of individuals, the presence of mother-calf pairs and mixed-species groups and other states of each sighting during a whale-watching tour. The collection of citizen-science data during this period has significantly advanced Taiwan's cetacean baseline data in the study area. Additionally, we make data available to the public in the form of citizen-science, making a substantial contribution to the advancement of ocean scientific research. We have published the dataset on Global Biodiversity Information Facility, allowing users around the world to download the dataset. This is currently the largest dataset of cetacean sighting records in Taiwan (last updated on 2023-09-05). We have also recorded several species on the International Union for Conservation of Nature (IUCN) Red List categorised as "Data Deficient" during our survey inshore of Hualien, eastern Taiwan, including Ginkgo-toothed beaked whales (Mesoplodonginkgodens Nishiwaki & Kamiya, 1958), Omura's whales (Balaenopteraomurai Wada, Oishi & Yamada, 2003) and killer whales (Orcinusorca (Linnaeus, 1758)). There are also sperm whales (Physetermacrocephalus Linnaeus, 1758), categorised as "Vulnerable" in the IUCN Red List and false killer whales (Pseudorcacrassidens (Owen, 1846)), categorised as "Near Threatened". This study is also the first and only long-term study that has documented cetaceans in the study area.

Global access to nomenclatural botanical resources: Evaluating open access availability

Societal Impact StatementPrimary occurrence data (‘what, where, when’) enable study of species distribution and diversity, facilitating reactions to societal challenges from food security to climate change mitigation. Scientific names integrate information and are made concrete through reference to a type specimen. Research and conservation planning requires timely open access to this data. Around 2000 vascular plant species are described each year, and many are narrowly endemic and face conservation threats. Twenty‐four percent of those published between 2012 and 2021 is available openly, and only 12% of taxa is represented by digitised type material served from within their native range. We make several recommendations to increase open access to this vital information to support prompt conservation action and future research.Summary We review access to literature and type specimens, key resources for taxonomic research. Takeup of open access (OA) publishing in plant naming is analysed using the International Plant Names Index (IPNI) data (2012–2021), and online availability of specimens analysed using the Global Biodiversity Information Facility (GBIF). Integration of the World Checklist of Vascular Plants (WCVP) taxonomy and distributional data is used to examine regional variation. We found that 23% of vascular plant names are published OA, and 41% are digitally undiscoverable: contained in bibliographic works without a Digital Object Identifier (DOI) or with an unresolvable DOI. The most common OA publishing model used is ‘gold’. We also found that 30% of taxa are represented by a digitised type specimen mobilised from within the continent of their natural range and only 12% from the (more precise) country. We recommend clear article processing charge (APC) waivers for authors from low and middle income countries to better enable ‘gold’ OA and promotion of deposition repositories to better enable ‘green’ OA. Nomenclators should clearly indicate the OA status of literature and mobilise type citation data as material citations to aggregators like GBIF. Names registration systems should promote the capture of code‐recommended elements such as catalogue numbers for type specimens. Digital mobilisation of specimen metadata and images from collections based in low‐ and middle‐income countries must be accelerated to help increase in country taxonomic capacity to document and conserve plant diversity.

Filling Gaps in Earthworm Digital Diversity in Northern Eurasia from Russian-language Literature

Data availability for certain groups of organisms (ecosystem engineers, invasive or protected species, etc.) is important for monitoring and making predictions in changing environments. One of the most promising directions for research on the impact of changes is species distribution modelling. Such technologies are highly dependent on occurrence data of high quality (Van Eupen et al. 2021). Earthworms (order Crassiclitellata) are a key group of organisms (Lavelle 2014), but their distribution around the globe is underrepresented in digital resources. Dozens of earthworm species, both widespread and endemic, inhabit the territory of Northern Eurasia (Perel 1979), but extremely poor data on them is available through global biodiversity repositories (Cameron 2018). There are two main obstacles to data mobilisation. Firstly, studies of the diversity of earthworms in Northen Eurasia have a long history (since the end of the nineteenth century) and were conducted by several generations of Soviet and Russian researchers. Most of the collected data have been published in "grey literature", now stored only in a few libraries. Until recently, most of these remained largely undigitised, and some are probably irretrievably lost. The second problem is the difference in the taxonomic checklists used by Soviet and European researchers. Not all species and synonyms are included in the GBIF (Global Biodiversity Information Facility) Backbone Taxonomy. As a result, existing earthworm species distribution models (Phillips 2019) potentially miss a significant amount of data and may underestimate biodiversity, and predict distributions inaccurately. To fill this gap, we collected occurrence data from the Russian language literature (published by Soviet and Russian researchers) and digitised species checklists, keeping the original scientific names. To find relevant literature, we conducted a keyword search for "earthworms" and "Lumbricidae" through the Russian national scientific online library eLibrary and screened reference lists from the monographs of leading Soviet and Russian soil zoologist Tamara Perel (Vsevolodova-Perel 1997, Perel 1979). As a result, about 1,000 references were collected, of which 330 papers had titles indicating the potential to contain data on earthworm occurrences. Among these, 219 were found as PDF files or printed papers. For dataset compilation, 159 papers were used; the others had no exact location data or duplicated data contained in other papers. Most of the sources were peer-reviewed articles (Table 1). A reference list is available through Zenodo (Ivanova et al. 2023). The earliest publication we could find dates back to 1899, by Wilhelm Michaelsen. The most recent publication is 2023. About a third of the sources were written by systematists Iosif Malevich and Tamara Perel. Occurrence data were extracted and structured according to the Darwin Core standard (Wieczorek et al. 2012). During the data digitisation process, we tried to include as much primary information as possible. Only one tenth of the literature occurrences contained the geographic coordinates of locations provided by the authors. The remaining occurrences were manually georeferenced using the point-radius method (Wieczorek et al. 2010). The resulting occurrence dataset Earthworm occurrences from Russian-language literature (Shashkov et al. 2023) was published through the Global Biodiversity Information Facility portal. It contains 5304 occurrences of 117 species from 27 countries (Fig. 1). To improve the GBIF Backbone Taxonomy, we digitised two catalogues of earthworm species published for the USSR (Perel 1979) and Russian Federation (Vsevolodova-Perel 1997) by Tamara Perel. Based on these monographs, three checklist datasets were published through GBIF (Shashkov 2023b, 124 records; Shashkov 2023c, 87 records; Shashkov 2023a, 95 records). Now we work towards including these names in the GBIF Backbone so that all species names can be matched and recorded exactly as mentioned in papers published by Soviet and Russian researchers.

Statistics and Analysis of Digital Information on Vascular Plant Specimens and the History of Plant Collecting in Guangzhou, China.

This paper presents a comprehensive analysis of digitized specimen data and relevant literature to investigate the vascular plant diversity in Guangzhou City, China. Specimen data were collected from various sources, including the China Digital Herbarium (CVH), the National Specimen Resource Sharing Platform (NSII), Global Plants on JSTOR, and the Global Biodiversity Information Facility (GBIF). Following data standardization, the study identified 41,890 vascular plant specimens, encompassing 248 families, 1563 genera, and 4536 species, including subspecies and cultivated plants. Among them, the native plants of Guangzhou city accounted for 60.6% of the species. The temporal analysis identified three distinct peaks in specimen collection: 1916-1920, 1928-1936, and 1950-1964. Collection activities were primarily concentrated between the months of April and November. The distribution of collected specimens exhibited significant variation among different species, with families such as Fabaceae, Poaceae, and Myrtaceae having the highest number of specimens. Similarly, genera such as Eucalyptus, Ficus, and Citrus were well-represented. The most frequently collected species included Litchi chinensis, Eucalyptus robusta, and Cycas taiwaniana. Remarkably, 21 species had specimen counts exceeding 100. Unfortunately, approximately three-quarters of the species had fewer than 10 recorded specimens. Alarmingly, 1220 species were represented by only one specimen. Geographically, the majority of specimens originated from the former suburbs of Guangzhou, Conghua Delta Mountain, and Liuxi River areas, while other regions had limited representation. In terms of specimen collections, the Herbarium of South China Botanical Garden of the Chinese Academy of Sciences (IBSC) recorded the highest number of specimens (13,828 specimens), followed by the Tree Herbarium of South China Agricultural University (CANT; 3869 specimens) and the Herbarium of Sun Yat-sen University (SYS; 3654 specimens). The collection history in Guangzhou spans nearly 300 years and can be broadly divided into two distinct periods. The first period extends from the late 13th century to 1949, primarily encompassing the collection efforts of foreign visitors in Guangzhou, and represents the pioneering phase of plant taxonomy research in China. The second period, from 1949 to the present, is characterized by extensive investigations and collection activities conducted by local scholars, with a specific emphasis on native plant resources. By meticulously organizing and verifying information derived from historical documents and specimens, the paper effectively summarizes the plant collection and research history of Guangzhou, providing detailed profiles of the key collectors. These findings furnish reliable historical reference materials for the study of plant taxonomy and diversity in Guangzhou.

Using ChatGPT with Confidence for Biodiversity-Related Information Tasks

Recent advancements in conversational Artificial Intelligence (AI), such as OpenAI's Chat Generative Pre-Trained Transformer (ChatGPT), present the possibility of using large language models (LLMs) as tools for retrieving, analyzing, and transforming scientific information. We have found that ChatGPT (GPT 3.5) can provide accurate biodiversity knowledge in response to questions about species descriptions, occurrences, and taxonomy, as well as structure information according to data sharing standards such as Darwin Core. A rigorous evaluation of ChatGPT's capabilities in biodiversity-related tasks may help to inform viable use cases for today's LLMs in research and information workflows. In this work, we test the extent of ChatGPT's biodiversity knowledge, characterize its mistakes, and suggest how LLM-based systems might be designed to complete knowledge-based tasks with confidence. To test ChatGPT's biodiversity knowledge, we compiled a question-and-answer test set derived from Darwin Core records available in Integrated Digitized Biocollections (iDigBio). Each question focuses on one or more Darwin Core terms to test the model’s ability to recall species occurrence information and its understanding of the standard. The test set covers a range of locations, taxonomic groups, and both common and rare species (defined by the number of records in iDigBio). The results of the tests will be presented. We also tested ChatGPT on generative tasks, such as creating species occurrence maps. A visual comparison of the maps with iDigBio data shows that for some species, ChatGPT can generate fairly accurate representationsof their geographic ranges (Fig. 1). ChatGPT's incorrect responses in our tests show several patterns of mistakes. First, responses can be self-conflicting. For example, when asked "Does Acer saccharum naturally occur in Benton, Oregon?", ChatGPT responded "YES, Acer saccharum DOES NOT naturally occur in Benton, Oregon". ChatGPT can also be misled by semantics in species names. For Rafinesquia neomexicana, the word "neomexicana" leads ChatGPT to believe that the species primarily occurs in New Mexico, USA. ChatGPT may also confuse species, such as when attempting to describe a lesser-known species (e.g., a rare bee) within the same genus as a better-known species. Other causes of mistakes include hallucination (Ji et al. 2023), memorization (Chang and Bergen 2023), and user deception (Li et al. 2023). Some mistakes may be avoided by prompt engineering, e.g., few-shot prompting (Chang and Bergen 2023) and chain-of-thought prompting (Wei et al. 2022). These techniques assist Large Language Models (LLMs) by clarifying expectations or by guiding recollection. However, such methods cannot help when LLMs lack required knowledge. In these cases, alternative approaches are needed. A desired reliability can be theoretically guaranteed if responses that contain mistakes are discarded or corrected. This requires either detecting or predicting mistakes. Sometimes mistakes can be ruled out by verifying responses with a trusted source. For example, a trusted specimen record might be found that corroborates the response. The difficulty, however, is finding such records programmatically; e.g., using iDigBio and Global Biodiversity Information Facility's (GBIF) search Application Programming Interfaces (APIs) requires specifying indexed terms that might not appear in an LLM's response. This presents a secondary problem for which LLMs may be well suited. Note that with presence-only data, it can be difficult to disprove presence claims or prove absence claims. Besides verification, mistakes may be predicted using probabilistic methods. Formulating mistake probabilities often relies on heuristics. For example, variability in a model’s responses to a repeated query can be a sign of hallucination (Manakul et al. 2023). In practice, both probabilistic and verification methods may be needed to reach a desired reliability. LLM outputs that can be verified may be directly accepted (or discarded), while others are judged by estimating mistake probabilities. We will consider a set of heuristics and verification methods, and report empirical assessments of their impact on ChatGPT’s reliability.

Combining Ecological and Socio-Environmental Data and Networks to Achieve Sustainability

Combining Ecological and Socio-Environmental Data and Networks to Achieve Sustainability

Progress with Repository-based Annotation Infrastructure for Biodiversity Applications

Progress with Repository-based Annotation Infrastructure for Biodiversity Applications

A Simple Recipe for Cooking your AI-assisted Dish to Serve it in the International Digital Specimen Architecture

With the rise of Artificial Intelligence (AI), a large set of new tools and services is emerging that supports specimen data mapping, standards alignment, quality enhancement and enrichment of the data. These tools currently operate in isolation, targeted to individual collections, collection management systems and institutional datasets. To address this challenge, DiSSCo, the Distributed System of Scientific Collections, is developing a new infrastructure for digital specimens, transforming them into actionable information objects. This infrastructure incorporates a framework for annotation and curation that allows the objects to be enriched or enhanced by both experts and machines. This creates the unique possibility to plug-in AI-assisted services that can then leverage digital specimens through this infrastructure, which serves as a harmonised Findable, Accessible, Interoperable and Reusable (FAIR) abstraction layer on top of individual institutional systems or datasets. An early example of such services are the ones developed in the Specimen Data Refinery workflow (Hardisty et al. 2022). The new architecture, DS Arch or Digital Specimen Architecture, is built on the concept of FAIR Digital Objects (FDO) (Islam et al. 2020). All digital specimens and related objects are served with persistent identifiers and machine-readable FDO records with information for machines about the object together with a pointer to its machine-readable type description. The type describes the structure of the object, its attributes and describes allowed operations. The digital specimen type and specimen media type are based on existing Biodiversity Information Standards (TDWG) such as Darwin Core, Access to Biological Collection Data (ABCD) Schema and Audiovisual Core Multimedia Resources Metadata Schema, and include support for annotation operations based on the World Wide Web Consortium (W3C) Annotations Data Model. This enables AI-assisted services registered with DS Arch to autonomously discover digital specimen objects and determine the actions they are authorised to perform. AI-assisted services can facilitate various tasks such as digitisation, extract new information from specimen images, create relations with other objects or standardise data. These operations can be done autonomously, upon user request, or in tandem with expert validation. AI-assisted services registered with DS Arch, can interact in the same way with all digital specimens worldwide when served through DS Arch with their uniform FDO representation, even if the content richness, level of standardisation and scope of the specimen is different. DS Arch has been designed to serve digital specimens for living and preserved specimens, and preserved environmental, earth system and astrogeology samples. With the AI-assisted services, data can be annotated with new data, alternative values, corrections, and with new entity relationships. As a result, the digital specimens become Digital Extended Specimens enabling new science and application (Webster et al. 2021). With the implementation of a sophisticated trust model in DS Arch for community acceptance, these annotations will become part of the data itself and can be made available for inclusion in source systems such as collection management systems and aggregators such as Global Biodiversity Information Facility (GBIF), Geoscience Collections Access Service (GeoCASe) and Catalogue of Life. We aim to demonstrate in the session how AI-assisted services can be registered and used to annotate specimen data. Although the DiSSCo DS Arch is still in development and planned to become operational in 2025, we already have a sandbox environment available in which the concept can be tested and AI-assisted services can be piloted to act on digital specimen data. For testing purposes, the operations on specimens are currently limited to individual specimens and open data, however batch operations will also be possible in the future production environment.

I Know Something You Don’t Know: The annotation saga continues…

Over the past 20 years, the biodiversity informatics community has pursued components of the digital annotation landscape with varying degrees of success. We will provide an historical overview of the theory, the advancements made through a few key projects, and will identify some of the ongoing challenges and opportunities. The fundamental principles remain unchanged since annotations were first proposed. Someone (or something): (1) has an enhancement to make elsewhere from the source where original data or information are generated or transcribed; (2) wishes to broadcast these statements to the originator and to others who may benefit; and (3) expects persistence, discoverability, and attribution for their contributions alongside the source. The Filtered Push project (Morris et al. 2013) considered several use cases and pioneered development of services based on the technology of the day. The exchange of data between parties in a universally consistent way necessitated the development of a novel draft standard for data annotations via an extension of the World Wide Web Consortium’s Web Annotation Working Group standard (Sanderson et al. 2013) to be sufficiently informative for a data curator to confidently make a decision. Figure 2 from Morris et al. (2013), reproduced here as Fig. 1, outlines the composition of an annotation data package for a taxonomic identification. The package contains the data object(s) associated with an occurrence, an expression of the motivation(s) for updating, some evidence for an assertion, and a stated expectation for how the receiving entity should take action. The Filtered Push and Annosys (Tschöpe et al. 2013) projects also considered implementation strategies involving collection management systems (e.g., Symbiota) and portals (e.g., European Distributed Institute of Taxonomy, EDIT). However, there remain technological barriers for these systems to operate at scale, the least of which is the absence of globally unique, persistent, resolvable identifiers for shared objects and concepts. Major aggregation infrastructures like the Global Biodiversity Information Facility (GBIF) and the Distributed System of Scientific Collections (DiSSCo) rely on data enhancement to improve the quality of their resources and have annotation services in their work plans. More recently, the Digital Extended Specimen (DES) concept (Hardisty et al. 2022) will rely on annotation services as key components of the proposed infrastructure. Recent work on annotation services more generally has considered various new forms of packaging and delivery such as Frictionless Data (Fowler et al. 2018), Journal Article Tag Suite XML (Agosti et al. 2022), or nanopublications (Kuhn et al. 2018). There is risk in fragmentation of this landscape and disenfranchisement of both biological collections and the wider research community if we fail to align the purpose, content, and structure of these packages or if these fail to remain aligned with FAIR principles. Institutional collection management systems currently represent the canonical data store that provides data to researchers and data aggregators. It is critical that information and/or feedback about the data they release be round-tripped back to them for consideration. However, the sheer volume of annotations that could be generated by both human and machine curation processes will overwhelm local data curators and the systems supporting them. One solution to this is to create a central annotation store with write and discovery services that best support the needs of all stewards of data. This will require an international consortium of parties with a governance and technical model to assure its sustainability.

Amplifying the Power of eDNA by Making it FAIR

Environmental DNA (eDNA) is a fast-growing biomonitoring approach to detect species and map their distributions, with the number of eDNA publications exponentially increasing in the past decade. While millions of DNA sequences are often generated and assigned to taxa in each publication, these records are stored in numerous locations (e.g., supplementary materials at journals’ servers, open data publishing platforms such as Dryad) and in various formats, which makes it difficult to find, access, re-use and integrate datasets. Making eDNA data FAIR (findable, accessible, interoperable, re-usable) has vast potential to improve how the biological environment is measured and how change is detected and understood. For instance, it would allow biomonitoring and species distribution modelling studies across extended space and time scales, which is logistically difficult or impossible for individual projects. It would also shed light on “dark” (unassigned) DNA sequences by facilitating their storage and re-analyses with updated ever-growing DNA reference databases. Several challenges are associated with making eDNA FAIR, including how to standardise data formats and bioinformatics workflows, and simplifying the process of post-publication data archiving so that it is acceptable for eDNA practitioners to adopt. Over the next three years, we plan to work closely with biodiversity data platforms such as the Global Biodiversity Information Facility (GBIF) and Atlas of Living Austrlia (ALA), eDNA science journals, and eDNA practitioners, to solve these challenges and enable eDNA to achieve its revolutionary potential as a unified source of information that supports environmental management.