Biodiversity Data Research Articles

B-Cubed: Leveraging Analysis-Ready Biodiversity Datasets and Cloud Computing for Timely and Actionable Biodiversity Monitoring

Effective biodiversity management and policy decisions require timely access to accurate and reliable information on biodiversity status, trends, and threats. However, the process of data cleaning, aggregation, and analysis is often time-consuming, convoluted, laborious, and irreproducible. Biodiversity monitoring across large areas faces challenges in evaluating data completeness and quantifying sampling effort. Despite these obstacles, unprecedented amounts of biodiversity data are being accumulated from diverse sources, aided by emerging technologies such as automatic sensors, eDNA, and satellite tracking. To address these challenges, the development of tools and infrastructure is crucial for meaningful interpretations and deeper understanding of biodiversity data (Kissling et al. 2017). Furthermore, a significant delay exists in converting biodiversity data into actionable knowledge. Efforts have been made to reduce this lag through rapid mobilisation of biodiversity observations, digitization of collections (Nelson and Ellis 2018), and streamlined workflows for data publication (Reyserhove et al. 2020). However, delays still occur in the analysis, publication, and dissemination of data. The B-Cubed project (Biodiversity Building Blocks for Policy)*1 proposes solutions to overcome these challenges. It implements the concept of Occurrence Cubes (Oldoni et al. 2020), which aggregate occurrence data along spatial, temporal and taxonomic dimensions. Cube generation will be available as a new service provided by the Global Biodiversity Information Facility (GBIF). By leveraging aggregated occupancy cubes as analysis-ready biodiversity datasets, we aim to enhance comprehension and reduce barriers to accessing and interpreting biodiversity data. Automation of workflows will provide regular and reproducible indicators and models that are open and useful to users. Additionally, the use of cloud computing offers scalability, flexibility, and collaborative opportunities for applying advanced data science techniques anywhere. Finally, close collaboration with stakeholders will inform us of the requirements for tools, increase impact, and facilitate the flow of information from primary data to the decision-making processes.

Pre-Publication Data Linking in Taxonomy and Biodiversity: The ARPHA and Metotaxa-Metostem Publishing Systems

The traditional way of publishing in PDF makes it difficult to retrospectively convert the legacy literature into data. This presentation will discuss pre-publication tagging as an alternative solution for publishing FAIR (Findable, Accessible, Interoperable, Resuable) biodiversity data. The Metotaxa-Metostem workflow Тhe MetoTaxa project aims to create a new digital production chain for the European Journal of Taxonomy, which enables the pre-publication semantic structuring of text, automatic tagging and semantic enrichment (annotation). The system is based on a single-source publishing model, where the development of an XML file enables technical editors to automatically enrich text and produce multiple digital outputs. This makes it possible to structure generic or domain-specific sections of articles (e.g., Introduction; Material and methods; Taxon names or Мaterial examined). Thanks to the GoldenGate API developed by Plazi, the Text Encoding Intiative (TEI) XML source file is automatically annotated with JATS TaxPub tags: taxon names are labeled and each authorship can be checked via Catalogue of Life, each element of the material examined is parsed thanks to the preformatting of the text (Chester et al. 2019). Also, each bibliographic reference is parsed into Journal Article Tag Suite (JATS) elements (author names, title, journal, etc.), which automatically links references to their in-text citations. Pre-publication tagging will be carried out by the technical editors and then checked by the authors before publication, and will be sent to databases such as Global Biodiversity Information Facility (GBIF) or Biodiversity Literature Repository (BLR) as soon as the article is published. We will also briefly present MetoStem, which offers a technical solution for the digital transformation of monographs, and particularly floras. The tools and methods developed by this project will enable advanced publication of interoperable structured text and data. ARPHA Publishing Platform Launched in 2010 by Pensoft, ARPHA (Penev et al. 2010) is the first ever scholarly publishing platform to support pre-publication semantic tags and enhancements to entities (e.g., taxon treatments, taxon names, sequences) in the JATS TaxPub XML format developed by Plazi, which are then embedded into the HTML version of the article. Having proved advantageous for biodiversity scientists, Pensoft’s pre-publication tagging workflow has since been adopted by over 30 biodiversity journals hosted on ARPHA. The second development stage of ARPHA was marked by the launch of ARPHA Writing Tool (AWT)*1 and Biodiversity Data Journal in 2013. AWT supports import of Darwin Core structured data from GBIF, Barcode of Life Data Systems (BOLD) and Integrated Digitized Biocollections (iDigBio) directly into manuscripts. These are also exported automatically as published material citations to GBIF. AWT also provides several other unique tools encompassed within the ARPHA-BioDiv toolbox (Penev et al. 2017). Currently, AWT is being redeveloped into a standalone, freely accessible installation (AWT 2.0), based on a micro-service architecture. It enables new semantic enhancements during the authoring process, which can be confirmed by the authors before manuscript submission. Such enhancements include the in-text citations context by CiTO ontology; automated tagging of taxon names and linking to their identifiers in authoritative sources; annotator tool; nanopublication module; automated search and import of references; treatment citation module; export/import to/from JATS TaxPub; and internal communication tool for contributors.

Processing Country Centroids at the Global Biodiversity Information Facility

The Global Biodiversity Information Facility (GBIF) is an international network and data infrastructure that promotes open access to biodiversity data. GBIF serves as a global hub for aggregating and disseminating biodiversity information from diverse sources, including museums, research institutions, and citizen science projects. I will present recent additions to GBIF data quality measures, focusing on the introduction of the country centroid filtering feature. Additionally, I explore the functionality and significance of GBIF data quality flags, which aid in assessing the reliability and usability of the aggregated data. I will also discuss recent changes to the default filtering mechanism employed by GBIF to remove suspicious points. GBIF Data Quality Flags GBIF employs a large set of data quality flags to provide users with an assessment of the reliability and fitness for use of the aggregated data. These flags are assigned based on various quality-related criteria, such as geographic precision, taxonomic accuracy, and completeness of associated metadata. By analyzing these flags, users can make informed decisions regarding the suitability of data for their specific research or conservation needs. GBIF data quality flags serve as valuable indicators that facilitate data filtering and enhance data usability. These flags are assigned to individual occurrence records based on various quality-related criteria and are designed to help data users evaluate the trustworthiness and suitability of the data for their specific needs. Country Centroid filter Retrospective geocoding is a process in which historical occurrence data lacking precise geographic coordinates are assigned approximate mappable locations based on other available information, such as locality descriptions, gazetteers, or administrative boundaries. Retrospective geocoding introduces centroids into occurrence datasets wherever better information than a large named area is unknown. A Country centroid refers to the geographical center point of a country's geographic polygon. Recently, GBIF has implemented country centroid filtering as a feature. By utilizing this filtering technique, GBIF users can identify and exclude occurrences that are country or area centroids. This ensures that data users receive more accurate and reliable information, reducing potential errors resulting from georeferencing issues. The Catalogue of Centroids is a GitHub repository designed to collect and provide a comprehensive list of country centroids for the purpose of occurrence data filtering. The repository serves as a central resource for storing and sharing centroid coordinates associated with each country, area, or province. The main idea is to gather the from sources most often used during retrospective geo-referencing of museum collections. Fig. 1 Default Filtering and Treatment of Suspicious Points GBIF employs default filtering mechanisms to identify suspicious occurrences from its aggregated datasets. For example, the "Country Coordinate Mismatch" data quality flag in GBIF is a quality assessment indicator that highlights occurrences where there is a potential mismatch between the recorded coordinates and the associated country information. Similarly, zero coordinates are often encountered as placeholders or missing data points. GBIF's default filtering process identifies such occurrences from the dataset, as they can introduce inaccuracies when conducting spatial analyses or mapping exercises. This step ensures that only reliable and valid occurrences are available for further analyses and downstream applications. GBIF continues to improve data quality flagging, which significantly enables users to assess the fitness of data for a particular use and increases the utility of the mediated biodiversity information.

OpenBiodiv for Users: Applications and Approaches to Explore a Biodiversity Knowledge Graph

OpenBiodiv is a biodiversity database—knowledge graph based on Resource Description Framework (RDF)—that contains information extracted from the scientific literature. It provides access to an ecosystem of tools and services, including a Linked Open Dataset, an ontology (OpenBiodiv-O) and а website (Dimitrova et al. 2021). Using the available data, OpenBiodiv discovers links between various biodiversity data types (e.g., taxon names, treatments, specimens, sequences, people and institutions), to answer a user’s questions about specific taxa, scientific articles, materials examined and others. The full-text XML content is converted into Linked Open Data from journals on the ARPHA Publishing Platform and treatments extracted by Plazi’s TreatmentBank (stored in the Biodiversity Literature Repository at Zenodo). The database is updated and indexed daily using a workflow based on the Apache Kafka event-streaming platform. The workflow was developed during the European Union-funded Biodiversity Community Integrated Knowledge Library (BiCIKL) project (Penev et al. 2022b). By 1 of August 2023, the graph consisted of 24,939 articles; 167,471 treatments; 130,359 authors; 736,809 taxon names; 129,257 sequences; 1,390 institutions and collections, 117,854 figures; 18,585 tables, and 90,008 materials examined sections. Each semantic statement (e.g., authors, articles, treatments, taxonomic names, localities) has its own globally unique, persistent and resolvable identifier (GUPRI). There are four ways a user can explore the data on OpenBiodiv: General search The search engine is accessible from the OpenBiodiv homepage. The user needs to type in a key term, (e.g., a taxonomic name, authority or an article title), and the system retrieves information about it. Errors caused by misspellings are avoided due to the Elasticsearch index. It can also determine the semantic type of the searched entity. Application Programing Interface (API) OpenBiodiv can be used through a RESTful API for programmatic access. The documentation of the API is described on Swagger. The API construction and functionalities follow the recommendations elaborated by the Technical Research Infrastructures forum of the BiCIKL project (Addink et al. 2023). User applications based on a query algorithm This function can be applied for any data class. The method uses the relationships between an element type (e.g., taxon name) and the type of the section, where it can be found. An application example is Literature exploration, designed to answer the question: Give me information about X mentioned within article section type Y. The results show the number of mentions of the entity (e.g., taxon name) in the section(s) of interest (e.g., Title, Abstract, Treatment). A click navigates the user to the place in the article that mentions the item (Fig. 1). SPARQL queries in a thematic context OpenBiodiv provides a SPARQL endpoint through the Ontotext GraphDB solution*1. Several sample SPARQL queries*2 are also available on the OpenBiodiv website.

Improving Collection-Based Biodiversity Research in Chile: Digitisation and Implementation of Web Portals

Biodiversity portals are emerging as some of the most powerful tools for scientific research and management based on scientific collections. We discuss the importance of web portals in biodiversity research and highlight the commendable progress made by two digitisation projects in Chile: herbariodigital.cl and the Atlas of Chilean Biodiversity. The aim of herbariodigital.cl and ALA-Chile is to take advantage of technological advances to improve the digitisation of biodiversity data. Using cutting-edge methods, herbariodigital.cl has successfully digitised an extensive collection of plant specimens and associated data from Chilean herbaria (>25,000 specimens), promoting a more accessible and inclusive platform for researchers, educators and decision-makers. Recognising that the complex nature of biodiversity information requires a careful approach to data storage and organisation to ensure efficient exchange across different platforms, we discuss our experience of using the Darwin Core standard (Darwin Core Task Group 2009), a widely accepted data standard in the field of biodiversity informatics. In turn, we discuss the incongruence that arises from adopting different global taxonomic checklists, such as the GBIF taxonomic backbone (GBIF Secretariat 2022) and World Flora Online (WFO 2023), in addition to a localised community catalogue. Using these different taxonomic frameworks is a significant challenge, as discrepancies and inconsistencies can arise, leading to potential under- or over-estimation of biodiversity indicators. By addressing these challenges, we believe that both herbariodigital.cl and ALA-Chile will prove to be invaluable assets for scientific research, educational initiatives, and environmental monitoring and assessment. The comprehensive digitisation of biodiversity information will make it easier for researchers to conduct interdisciplinary studies and formulate evidence-based conservation strategies. In addition, educators will be able to use these web portals to develop interactive teaching materials, fostering a deeper connection between students and the natural world. Stakeholders in both the public and private sectors can also make informed decisions about biodiversity offsetting. In conclusion, we highlight the transformative impact of web portals in the field of biodiversity research. The remarkable progress made by herbariodigital.cl and ALA-Chile in digitising Chile's biodiversity information is opening up unprecedented opportunities for scientific research, education and environmental management. By overcoming the challenges of data standardisation and taxonomic harmonisation, these projects are paving the way for a more comprehensive and collaborative approach to understanding and conserving Chile's biodiversity.

Post-Publication Linking

One of the main challenges in biodiversity data reusability is finding ways to transform what is provided in research publications into different and reusable formats, following the FAIR (Findable, Accessible, Interoperable, Reusable) principles (Agosti and Egloff 2009). Most often, data is restricted to text, figures and tables in the so-called “PDF prison” or other flat formats. Plazi's infrastructure and workflow (Guidoti et al. 2021) transform such data into reusable formats that can then be exported and linked across different platforms, such as the Global Biodiversity Information Facility (GBIF), Biodiversity Literature Repository, Zenodo, Synospecies, ChecklistBank, and OpenBiodiv among others. In order to liberate the many relevant pieces of information, such as taxonomic treatments (Catapano 2019), material citations (Darwin Core term MaterialCitation) or bibliographic references from the publication types mentioned above, one has to run a single document or a batch of documents through a series of extraction steps, which can be done manually or automatically, through the use of templates. The latter are a set of parameters that tell the Plazi-dedicated software (GoldenGATE suite) how to read and where to find key pieces of information; these parameters are established by examining publication standards and publisher-specific layouts, followed by a series of iterative tests, to ascertain the quality of the automation. However, even with a high number of tests to ensure a better extraction, human quality control is still needed (Simoes et al. 2021). To that end, Plazi has a quality control process, based on logical rules, which checks the components of the extracted document, flagging errors in four different levels of severity, which can then be checked and corrected (if needed) by a trained user. These errors are also used in a data transit control mechanism, internally dubbed “the gatekeeper”, which blocks certain data transits to create deposits or reuse of data in the presence of specific errors. In this presentation, we will go through the steps of the entire process, from publication to liberated data (and how it is presented in the linked platforms), highlighting the importance of accurate quality control, and explore some of the many challenges along the way.

Assessing the Current State of National Biodiversity Data Integration: An Initiative by the Taiwan Biodiversity Information Alliance (TBIA)

Biodiversity data that adheres to the FAIR principles (Findable, Accessible, Interoperable, and Reusable) is essential for any research and application, particularly in understanding biodiversity, valuing ecosystem services, and guiding environmental monitoring and conservation. However, most biodiversity-related projects are carried out independently, resulting in the dispersion of data across various databases from different organisations. Data users often need to search, collect, and integrate data from multiple sources, which is time consuming and labour intensive. Furthermore, the lack of unified data standards and taxonomy backbones among different databases poses additional challenges for data analysis and application. To address these issues and maximize the value of existing data, we launched a collaborative bottom-up initiative aimed at establishing a national biodiversity data portal in Taiwan. The collaboration among eight units, including research institutes, governmental organisations, and museums, has led to the formation of the Taiwan Biodiversity Information Alliance (TBIA), along with the launching of a 5-year (2021–2025) Taiwan Biodiversity Information Action Campaign. Furthermore, the alliance has developed important documentation, such as the Taiwan Biodiversity Data Standard and the Principles of Opening and Sharing Sensitive Biodiversity Data. These documents have played a crucial role in facilitating data integration across the various units involved by implementing a convergence mechanism that adheres to the Darwin Core data standards (Darwin Core Task Group 2009) and follows a unified taxonomy backbone aligned with the Taiwan Catalogue of Life. This mechanism simplifies and expedites the information-sharing process, enabling users to access relatively comprehensive national biodiversity data. This presentation provides an introduction to TBIA and its development, illustrates recent achievements of TBIA, and provides a brief overview of the Taiwan’s biodiversity data status after integration, accomplished through the efforts of TBIA. The data integration effort has resulted in a cumulative count of 19 million records, while simultaneously enriching the portal with diverse data types owing to the distinctive profession and strength of each participating unit. For instance, integration has resulted in the incorporation of significantly more specimen records of marine and wetland species. Our future task involves promoting data use, establishing new connections with potential databases and publishing records from the integration effort in international data repositories (e.g., Global Biodiversity Information Facility), enhancing the accessibility of the data worldwide.

Setting Sustainability Goals for Biodiversity Informatics Infrastructure

Biodiversity data are of crucial importance in the implementation of sustainable development strategies (UN Sustainable Development Goals, EU Taxonomy for Sustainable Activities), because they contribute to deepening our understanding of marine and terrestrial ecosystems and our assessment of the impact of human activities on the planet. Yet, biodiversity informatics itself is lacking an overall sustainability plan that addresses “the three E’s of environment, equity, and economics” (Brinkmann 2021) in relation to biodiversity data, their lifecycle, and related technologies. This is especially problematic given that data-driven biodiversity research has had a ten-fold increase (GBIF Secretariat 2022). This implies higher costs for data management, storage and transmission, a larger carbon footprint, and open issues about data access and reuse. SMART (Specific, Measurable, Assignable, Realistic, Time-Related) goals can help to set a sustainability strategy for biodiversity informatics because they bring together the ambitions of an organisation with the action plan necessary to realise these ambitions. When G.T. Doran proposed the use of SMART goals (Doran 1981), his aim was not to create a checklist, but a tool to get results and produce deliverables, which is precisely what is required for implementing an effective sustainability strategy. Three examples are provided here to explain the value of a SMART approach to set sustainability goals in biodiversity informatics. Goal 1, Increase energy efficiency of computing hardware: A large part of the energy consumption in data centres is due to hardware operations. Several strategies can be pursued to increase energy efficiency in this area, such as activity optimisation for Graphic Processing Units (GPUs), improvements in the effectiveness of cooling systems, and replacement of Central Processing Units (CPUs) with more efficient GPUs. This sustainability goal is specific because it clearly identifies what needs to be achieved; it is measurable because pre- and post- energy consumption are known and can be easily compared with the expected improvement; it is assignable because data centre managers can be identified as the agents of change; it is realistic because there are multiple technological solutions to achieve the goal; and time-related because a roadmap for change stretching through months or years can be set up. Achieving this goal is evidently beneficial for both the budget and the carbon footprint of biodiversity informatics, as higher energy efficiency is associated to lower running costs and also contributes to reduce greenhouse gases emissions. Goal 2, Reducing data storage costs per terabyte: Data storage is becoming one of the most significant operating expenses in data-driven organisations due to the considerable increase in the amount of data produced. It is therefore imperative to reduce costs by selecting appropriate storage options (on-premises or cloud services), by adopting compressed formats whenever possible, and by avoiding data redundancy. This goal is SMART because it sets a target (the expected cost reduction can be quantified) and establishes measures (cost per terabyte) to assess its achievement. It can be assigned to data managers, who can choose among different technologies and service providers to lower costs, and can be scheduled over time according to budget needs. The achievement of this goal has clear economic benefits, but also increases the social sustainability of biodiversity informatics, because it makes data-driven research affordable even for organisations with limited financial means. Goal 3, Decreasing API (Application Programming Interface) response time and bandwidth for data transmission: Data sharing is a key activity in biodiversity informatics and APIs have become the standard technology for achieving it. Improving API performance is therefore crucial to enhance the user experience for stakeholders interested in biodiversity data. In particular, API response time and bandwidth required for data transmission control whether users will be able to access immediately and download satisfactorily the data they are interested in. Goal 3 is SMART because API response time and bandwidth are known measures and can be improved using strategies such as caching and concurrency to handle data requests more rapidly and delivering data in compressed formats to reduce bandwidth. Software developers and project managers involved in API design and maintenance are the people in charge of this goal. They decide the best strategy and timeline to pursue changes. Improving API performance increases the social sustainability of biodiversity informatics because it promotes uncomplicated data access for all stakeholders interested in biodiversity data. It also lowers energy consumption for downloading data benefitting the environment.

EDNA Metabarcoding Data in the Global Biodiversity Information Facility (GBIF)

The Global Biodiversity Information Facility (GBIF) is an international network and data infrastructure that provides free and open access to biodiversity data from around the world, enabling scientists, policymakers, and the public to explore and analyze information about the Earth's living organisms. Originally developing at a distance from GBIF, metabarcoding of DNA has become a standard tool for detecting species in bulk samples or environmental samples such as soil, water, and air. Raw sequence data (fastq files) are often shared and deposited in dedicated repositories. Seen from a biodiversity documenting perspective, raw sequences have limited value, as several steps of bioinformatic processing and filtering are needed to arrive at a credible set of sequences that can be interpreted by comparing to a sequence reference database. Most often, such interpretated DNA metabarcoding data come in the shape a table with abundances of so-called Amplicon Sequence Variants (ASV) or Operational Taxonomic Units (OTU) across samples—a so-called ASV/OTU table—and some associated files, e.g., spatiotemporal and other sample metadata, and taxonomic inferences of sequences. In this session we present GBIF state-of-work and plans for developing and improving publishing and standardisation of OTU table-like biodiversity data for easier and broader reuse, including a prototype tool using the OTU table as a publishing model, mapping this research-familiar data format to the Darwin Core standard (Darwin Core Task Group 2009).

The ASV Portal Allows for Efficient Handling of DNA Derived Data in the Living Atlas

The Living Atlas platform functions as the national hub for integration of biodiversity data with environmental and contextual information in many countries, but it has had limited functionality for species occurrence data derived from DNA sequences. Meanwhile, molecular methods for species observation, such as metabarcoding of environmental DNA, are becoming increasingly important for documenting the diversity of life. To incorporate this rapidly growing data source into the platform, we developed the Amplicon Sequence Variant (ASV) portal: a web interface for DNA sequence-derived observations in the Living Atlas. We identified three key features that would enhance the utility of the atlas platform for species occurrence data derived from metabarcoding: 1) storage of the processed barcode sequences (ASVs) underlying such occurrences, along with the ability to locate them using sequence alignment search; 2) the option to search for data based on sequencing details, such as target genes and primers; and 3) the possibility to update the taxonomic annotation of observed ASVs as reference databases evolve. In this presentation, we demonstrate how these features have been implemented in the ASV portal, which now operates as a module within the Swedish Biodiversity Data Infrastructure (SBDI). We provide a brief overview of the underlying architecture and its integration with the Living Atlas and Global Biodiversity Information Facility (GBIF) platforms, showcase the online functionality, and conclude by discussing challenges and future development plans.

A Standardized, Large-Scale Ecosystem Assessment for the Southern Ocean and the Underpinning Role of Biodiversity Data

Assessments of change in ecosystems and their drivers are central for meeting the challenge of conserving biodiversity in the long term. Such assessments support national and international agencies to implement management actions that sustain natural systems and maintain the delivery of ecosystem services. Change in marine systems may arise directly from human activities (e.g., fisheries), indirectly from local or global activities (cascading effects through food webs from fisheries or changing environments from climate change and/or ocean acidification), or from naturally varying processes. A particular challenge for managers is to understand the likely impacts of future climate change on ecosystems, and to consider what actions might be needed (climate change mitigation and adaption) to continue to meet conservation requirements into the future. For large regions such as the Southern Ocean, which have the attention of many management or policy-oriented bodies, a standardized process is needed to harmonize the scientific information on the status and trends in ecosystems used by the different bodies. That process also needs to ensure the information is available in a timely manner. The Marine Ecosystem Assessment for the Southern Ocean (MEASO) is the first circumpolar interdisciplinary assessment of Southern Ocean ecosystem status and trends. It is a core activity of the program Integrating Climate and Ecosystem Dynamics in the Southern Ocean (ICED) (a regional program of Integrated Marine Biosphere Research), and co-sponsored by the Scientific Committee on Antarctic Research (SCAR). MEASO is a spatially-structured circumpolar ecosystem assessment that has drawn on a broad range of data, including biodiversity data. It has been a 5-year inclusive international activity, modelled on a working group of the Intergovernmental Panel on Climate Change, providing a forward-looking assessment of status and trends in Southern Ocean ecosystems. To date, it has involved over 200 scientists from across the Antarctic and Southern Ocean scientific community (18 countries, >50% identifying as women, >40% early career), contributing to 25 research articles published in a special research topic in Frontiers journals*1. This presentation will describe the MEASO process and highlight over-arching findings and key messages for Southern Ocean ecosystems. It will highlight the underpinning importance of biodiversity data and standards, and will provide an overview of priorities for improving future assessments and policy-relevant advice, including those that relate to data standards and FAIR principles (the Findability, Accessibility, Interoperability, and Reuse of digital assets).

Identification of herbarium specimen sheet components from high-resolution images using deep learning.

Advanced computer vision techniques hold the potential to mobilise vast quantities of biodiversity data by facilitating the rapid extraction of text- and trait-based data from herbarium specimen digital images, and to increase the efficiency and accuracy of downstream data capture during digitisation. This investigation developed an object detection model using YOLOv5 and digitised collection images from the University of Melbourne Herbarium (MELU). The MELU-trained 'sheet-component' model-trained on 3371 annotated images, validated on 1000 annotated images, run using 'large' model type, at 640 pixels, for 200 epochs-successfully identified most of the 11 component types of the digital specimen images, with an overall model precision measure of 0.983, recall of 0.969 and moving average precision (mAP0.5-0.95) of 0.847. Specifically, 'institutional' and 'annotation' labels were predicted with mAP0.5-0.95 of 0.970 and 0.878 respectively. It was found that annotating at least 2000 images was required to train an adequate model, likely due to the heterogeneity of specimen sheets. The full model was then applied to selected specimens from nine global herbaria (Biodiversity Data Journal, 7, 2019), quantifying its generalisability: for example, the 'institutional label' was identified with mAP0.5-0.95 of between 0.68 and 0.89 across the various herbaria. Further detailed study demonstrated that starting with the MELU-model weights and retraining for as few as 50 epochs on 30 additional annotated images was sufficient to enable the prediction of a previously unseen component. As many herbaria are resource-constrained, the MELU-trained 'sheet-component' model weights are made available and application encouraged.

Global biodiversity data suggest allopolyploid plants do not occupy larger ranges or harsher conditions compared with their progenitors.

Understanding the factors determining species' geographical and environmental range is a central question in evolution and ecology, and key for developing conservation and management practices. Shortly after the discovery of polyploidy, just over 100 years ago, it was suggested that polyploids generally have greater range sizes and occur in more extreme conditions than their diploid congeners. This suggestion is now widely accepted in the literature and is attributed to polyploids having an increased capacity for genetic diversity that increases their potential for adaptation and invasiveness. However, the data supporting this idea are mixed. Here, we compare the niche of allopolyploid plants to their progenitor species to determine whether allopolyploidization is associated with increased geographic range or extreme environmental tolerance. Our analysis includes 123 allopolyploid species that exist as only one known ploidy level, with at least one known progenitor species, and at least 50 records in the Global Biodiversity Information Facility (GBIF) database. We used GBIF occurrence data and range modeling tools to quantify the geographic and environmental distribution of these allopolyploids relative to their progenitors. We find no indication that allopolyploid plants occupy more extreme conditions or larger geographic ranges than their progenitors. Data evaluated here generally indicate no significant difference in range between allopolyploids and progenitors, and where significant differences do occur, the progenitors are more likely to exist in extreme conditions. We concluded that the evidence from these data indicate allopolyploidization does not result in larger or more extreme ranges. Thus, allopolyploidization does not have a consistent effect on species distribution, and we conclude it is more likely the content of an allopolyploid's genome rather than polyploidy per se that determines the potential for invasiveness.

Planning from scratch: A new modelling approach for designing protected areas in remote, data‐poor regions

Abstract Human pressure on ecosystems has strongly increased over the last decades and now impacts even the most remote regions. To help mitigate these impacts, it is crucial to designate protected areas in regions that retain a high level of ecological integrity. However, ecological data remain scarce for many such areas, making the systematic design of new protected zones challenging. Following a request from local managers, we developed an original methodological approach to help design new zoning for a pre‐existing protected area in a remote, data‐poor Sahelian wetland of southern Chad, a vast area rich in biodiversity and exploited by diverse human activities. The method involved first collecting extensive aerial survey data (6252 records) on birds and mammals and then analysing this through a combination of distance sampling and density surface modelling. The biodiversity data, combined with ecological predictors, helped model species distribution layers that were then incorporated with socio‐economic constraints into the systematic conservation planning tool Marxan. This approach produced an array of protected zoning options that met three levels of conservation objectives set by experts, corresponding to proportions of individuals from given species to protect in the proposed protected area. Frequent exchanges with local managers allowed the analyses to be refined, resulting in seven potential scenarios to be considered for conservation purposes. Synthesis and applications. In a context of high data scarcity, lack of access and short‐term conservation objectives, this combined approach that optimizes newly obtained data via a suite of modelling tools can facilitate identifying and protecting natural areas in regions most in need of urgent conservation policy.

Integrating freshwater biodiversity data sources: Key challenges and opportunities

Abstract In order to better quantify spatial and temporal patterns in freshwater biodiversity, and potential underlying drivers of change, we must utilise the increasingly broad range of data available on freshwater ecosystems. Statistical advances in the field of integrated modelling provide new opportunities to further our understanding through the combined and simultaneous analysis of these diverse datasets. We briefly introduce integrated modelling in the context of freshwater biodiversity and outline the key steps involved in its implementation, from data collection to analysis. We highlight both opportunities and challenges for the application of integrated approaches. To illustrate the potential for integrated models to improve our understanding of freshwater biodiversity compared to standard approaches, we combine two datasets collected using different methods to model the distribution of Agabus water beetles in England. The integrated model had greater power to detect covariate effects on Agabus distribution, and reduced parameter uncertainty compared with analysis using only a single dataset. We show that integrated methods have the potential to increase our understanding of freshwater systems and enable us to make full use of the diversity of freshwater data available.

Unified andpluralistic ideals fordata sharing andreuse inbiodiversity.

How should billions of species observations worldwide be shared and made reusable? Many biodiversity scientists assume the ideal solution is to standardize all datasets according to a single, universal classification and aggregate them into a centralized, global repository. This ideal has known practical and theoretical limitations, however, which justifies investigating alternatives. To support better community deliberation and normative evaluation, we develop a novel conceptual framework showing how different organizational models, regulative ideals and heuristic strategies are combined to form shared infrastructures supporting data reuse. The framework is anchored in a general definition of data pooling as an activity of making a taxonomically standardized body of information available for community reuse via digital infrastructure. We describe and illustrate unified and pluralistic ideals for biodiversity data pooling and show how communities may advance toward these ideals using different heuristic strategies. We present evidence for the strengths and limitations of the unification and pluralistic ideals based on systemic relationships of power, responsibility and benefit they establish among stakeholders, and we conclude the pluralistic ideal is better suited for biodiversity data.

Good coral cover and high diversity of corals in non-MPA reefs of the Verde Island Passage as a basis for increased protection and conservation

The Verde Island Passage (VIP) is a region in the Philippines with very high marine biodiversity. Baseline coral assessments were conducted in selected sites in the four provinces of the VIP (Batangas, Marinduque, Occidental Mindoro, and Oriental Mindoro) to compare coral abundance and biodiversity in Marine Protected Area (MPA) and non-MPA sites that were subjected to anthropogenic stressors. A total of eight sites were surveyed to measure average hard coral cover (HCC) relative to other benthic components. Generic richness and the dominant coral genera were also obtained for the eight sites. The average HCC and generic richness of Batangas MPA and Batangas non-MPA were (39.82 ± 7.59%, 22 coral TAUs) and (38.80 ± 5.56%, 27 coral TAUs), respectively. The average HCC and generic richness of Marinduque MPA and Marinduque non-MPA were (25.58 ± 2.37%, 13 coral TAUs) and (35.73 ± 4.05%, 17 coral TAUs), respectively. The average HCC and generic richness of Occidental Mindoro MPA and Occidental Mindoro non-MPA were (49.49 ± 4.50%, 26 coral TAUs) and (32.32 ± 5.60%, 19 coral TAUs), respectively. The average HCC and generic richness of Oriental Mindoro MPA and Oriental Mindoro non-MPA were (25.02 ± 4.72%, 24 coral TAUs) and (5.93 ± 2.10%, 10 coral TAUs), respectively. Batangas non-MPA and Marinduque non-MPA showed potential to become MPAs due to their higher HCC and generic richness which were attributed to natural disturbances and duration of local reef management in their MPA counterparts. Continued monitoring and assessment should be done to build on the growing database of biodiversity data that is being compiled for the VIP.

Regional overprint of the GOBE: Dendroid graptolites reveal palaeoecological bias

Dendroids have palaeoecological potential but are underrated in general. The present study is focused on the interval from the Floian to the earliest Sandbian (Klabava, Šárka and Dobrotivá formations) in the Ordovician of the Prague Basin (Czech Republic). It is based especially on the new datasets of all dendroid graptolites from the Šárka and Dobrotivá formations at our disposal combined with the data of the overall biodiversity in the studied interval. The analysis of diversity illustrates that there is a remarkable contrast in the studied period: dendroids declined when other groups flourished and the diversity in the Prague Basin reached the first of the two Ordovician maxima coeval with the GOBE. The patterns of those shifts seem to be primarily influenced by local environmental changes, i.e. ecologically controlled in the Prague Basin. The decline of dendroid graptolites was apparently caused by the disappearance of habitats, represented by proper substrate in the oxygenated environment, due to a gradual but continuing sea level rise. Our case study resulted in the distribution model providing a general example of local factors masking those global and exemplifying a significance of differentiation between global and local causes of the GOBE.

Building use-inspired species distribution models: Using multiple data types to examine and improve model performance.

Species distribution models (SDMs) are becoming an important tool for marine conservation and management. Yet while there is an increasing diversity and volume of marine biodiversity data for training SDMs, little practical guidance is available on how to leverage distinct data types to build robust models. We explored the effect of different data types on the fit, performance and predictive ability of SDMs by comparing models trained with four data types for a heavily exploited pelagic fish, the blue shark (Prionace glauca), in the Northwest Atlantic: two fishery dependent (conventional mark-recapture tags, fisheries observer records) and two fishery independent (satellite-linked electronic tags, pop-up archival tags). We found that all four data types can result in robust models, but differences among spatial predictions highlighted the need to consider ecological realism in model selection and interpretation regardless of data type. Differences among models were primarily attributed to biases in how each data type, and the associated representation of absences, sampled the environment and summarized the resulting species distributions. Outputs from model ensembles and a model trained on all pooled data both proved effective for combining inferences across data types and provided more ecologically realistic predictions than individual models. Our results provide valuable guidance for practitioners developing SDMs. With increasing access to diverse data sources, future work should further develop truly integrative modeling approaches that can explicitly leverage the strengths of individual data types while statistically accounting for limitations, such as sampling biases.

Multiple facets of biodiversity are threatened by mining‐induced land‐use change in the Brazilian Amazon

AbstractAimMining is increasingly pressuring areas of critical importance for biodiversity conservation, such as the Brazilian Amazon. Biodiversity data are limited in the tropics, restricting the scope for risks to be appropriately estimated before mineral licensing decisions are made. As the distributions and range sizes of other taxa differ markedly from those of vertebrates—the common proxy for analysis of risk to biodiversity from mining—whether mining threatens lesser‐studied taxonomic groups differentially at a regional scale is unclear.LocationBrazilian Amazon.MethodsWe assess risks to several facets of biodiversity from industrial mining by comparing mining areas (within 70 km of an active mining lease) and areas unaffected by mining, employing species richness, species endemism, phylogenetic diversity and phylogenetic endemism metrics calculated for angiosperms, arthropods and vertebrates.ResultsMining areas contained higher densities of species occurrence records than the unaffected landscape, and we accounted for this sampling bias in our analyses. None of the four biodiversity metrics differed between mining and nonmining areas for vertebrates. For arthropods, species endemism was greater in mined areas. Mined areas also had greater angiosperm species richness, phylogenetic diversity and phylogenetic endemism, although less species endemism than unmined areas.Main ConclusionsUnlike for vertebrates, facets of angiosperm and arthropod diversity are relatively higher in areas of mining activity, underscoring the need to consider multiple taxonomic groups and biodiversity facets when assessing risk and evaluating management options for mining threats. Particularly concerning is the proximity of mining to areas supporting deep evolutionary history, which may be impossible to recover or replace. As pressures to expand mining in the Amazon grow, impact assessments with broader taxonomic reach and metric focus will be vital to conserving biodiversity in mining regions.