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1. A review of the heterogeneous landscape of biodiversity databases: Opportunities and challenges for a synthesized biodiversity knowledge base

   https://doi.org/10.1111/geb.13497  

   Feng, Xiao; Enquist, Brian J.; Park, Daniel S.; Boyle, Brad; Breshears, David D.; Gallagher, Rachael V.; Lien, Aaron; Newman, Erica A.; Burger, Joseph R.; Maitner, Brian S.; et al (April 2022, Global Ecology and Biogeography)

   Abstract AimAddressing global environmental challenges requires access to biodiversity data across wide spatial, temporal and taxonomic scales. Availability of such data has increased exponentially recently with the proliferation of biodiversity databases. However, heterogeneous coverage, protocols, and standards have hampered integration among these databases. To stimulate the next stage of data integration, here we present a synthesis of major databases, and investigate (a) how the coverage of databases varies across taxonomy, space, and record type; (b) what degree of integration is present among databases; (c) how integration of databases can increase biodiversity knowledge; and (d) the barriers to database integration. LocationGlobal. Time periodContemporary. Major taxa studiedPlants and vertebrates. MethodsWe reviewed 12 established biodiversity databases that mainly focus on geographic distributions and functional traits at global scale. We synthesized information from these databases to assess the status of their integration and major knowledge gaps and barriers to full integration. We estimated how improved integration can increase the data coverage for terrestrial plants and vertebrates. ResultsEvery database reviewed had a unique focus of data coverage. Exchanges of biodiversity information were common among databases, although not always clearly documented. Functional trait databases were more isolated than those pertaining to species distributions. Variation and potential incompatibility of taxonomic systems used by different databases posed a major barrier to data integration. We found that integration of distribution databases could lead to increased taxonomic coverage that corresponds to 23 years’ advancement in data accumulation, and improvement in taxonomic coverage could be as high as 22.4% for trait databases. Main conclusionsRapid increases in biodiversity knowledge can be achieved through the integration of databases, providing the data necessary to address critical environmental challenges. Full integration across databases will require tackling the major impediments to data integration: taxonomic incompatibility, lags in data exchange, barriers to effective data synchronization, and isolation of individual initiatives. 

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2. Frugivoria: A trait database for birds and mammals exhibiting frugivory across contiguous Neotropical moist forests

   https://doi.org/10.1111/geb.13716  

   Gerstner, Beth E.; Bills, Patrick; Zarnetske, Phoebe L. (June 2023, Global Ecology and Biogeography)

   Abstract MotivationBiodiversity in many areas is rapidly declining because of global change. As such, there is an urgent need for new tools and strategies to help identify, monitor and conserve biodiversity hotspots. This is especially true for frugivores, species consuming fruit, because of their important role in seed dispersal and maintenance of forest structure and health. One way to identify these areas is by quantifying functional diversity, which measures the unique roles of species within a community and is valuable for conservation because of its relationship with ecosystem functioning. Unfortunately, the functional trait information required for these studies can be sparse for certain taxa and specific traits and difficult to harmonize across disparate data sources, especially in biodiversity hotspots. To help fill this need, we compiled Frugivoria, a trait database containing ecological, life‐history, morphological and geographical traits for mammals and birds exhibiting frugivory. Frugivoria encompasses species in contiguous moist montane forests and adjacent moist lowland forests of Central and South America—the latter specifically focusing on the Andean states. Compared with existing trait databases, Frugivoria harmonizes existing trait databases, adds new traits, extends traits originally only available for mammals to birds also and fills gaps in trait categories from other databases. Furthermore, we create a cross‐taxa subset of shared traits to aid in analysis of mammals and birds. In total, Frugivoria adds 8662 new trait values for mammals and 14,999 for birds and includes a total of 45,216 trait entries with only 11.37% being imputed. Frugivoria also contains an open workflow that harmonizes trait and taxonomic data from disparate sources and enables users to analyse traits in space. As such, this open‐access database, which aligns with FAIR data principles, fills a major knowledge gap, enabling more comprehensive trait‐based studies of species in this ecologically important region. Main Types of Variable ContainedEcological, life‐history, morphological and geographical traits. Spatial Location and GrainNeotropical countries (Mexico, Guatemala, Costa Rica, Panama, El Salvador, Belize, Nicaragua, Ecuador, Colombia, Peru, Bolivia, Argentina, Venezuela and Chile) with contiguous montane regions. Time Period and GrainIUCN spatial data: obtained February 2023, spanning range maps collated from 1998 to 2022. IUCN species data: obtained June 2019–September 2022. Newly included traits: span 1924 to 2023. Major Taxa and Level of MeasurementClasses Mammalia and Aves; 40,074 species‐level traits; 5142 imputed traits for 1733 species (mammals: 582; birds: 1147) and 16 sub‐species (mammals). Software Format.csv; R. 

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3. ZooTraits: An R shiny app for exploring animal trait data for ecological and evolutionary research

   https://doi.org/10.1002/ece3.11334  

   Gonçalves‐Souza, Thiago; Milz, Beatriz; Sanders, Nathan J; Reich, Peter B; Maitner, Brian; Chaves, Leonardo S; Boldorini, Gabriel X; Ferreira, Natália; Gusmão, Reginaldo_A F; Perônico, Phamela Bernardes; et al (May 2024, Ecology and Evolution)

   Abstract Animal trait data are scattered across several datasets, making it challenging to compile and compare trait information across different groups. For plants, the TRY database has been an unwavering success for those ecologists interested in addressing how plant traits influence a wide variety of processes and patterns, but the same is not true for most animal taxonomic groups. Here, we introduce ZooTraits, a Shiny app designed to help users explore and obtain animal trait data for research in ecology and evolution. ZooTraits was developed to tackle the challenge of finding in a single site information of multiple trait datasets and facilitating access to traits by providing an easy‐to‐use, open‐source platform. This app combines datasets centralized in the Open Trait Network, raw data from the AnimalTraits database, and trait information for animals compiled by Gonçalves‐Souza et al. (2023,Ecology and Evolution13, e10016). Importantly, the ZooTraits app can be accessed freely and provides a user‐friendly interface through three functionalities that will allow users to easily visualize, compare, download, and upload trait data across the animal tree of life—ExploreTrait,FeedTrait, andGetTrait. By usingExploreTraitandGetTrait, users can explore, compare, and extract 3954 trait records from 23,394 species centralized in the Open Traits Network, and trait data for ~2000 species from the AnimalTraits database. The app summarizes trait information for numerous taxonomic groups within the Animal Kingdom, encompassing data from diverse aquatic and terrestrial ecosystems and various geographic regions worldwide. Moreover, ZooTraits enables researchers to upload trait information, serving as a hub for a continually expanding global trait database. By promoting the centralization of trait datasets and offering a platform for data sharing, ZooTraits is facilitating advancements in trait‐based ecological and evolutionary studies. We hope that other trait databases will evolve to mirror the approach we have outlined here. 

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4. Data sharing and ontology use among agricultural genetics, genomics, and breeding databases and resources of the Agbiodata Consortium

   https://doi.org/10.1093/database/baad076  

   Clarke, Jennifer L; Cooper, Laurel D; Poelchau, Monica F; Berardini, Tanya Z; Elser, Justin; Farmer, Andrew D; Ficklin, Stephen; Kumari, Sunita; Laporte, Marie-Angélique; Nelson, Rex T; et al (January 2023, Database)

   Abstract Over the last couple of decades, there has been a rapid growth in the number and scope of agricultural genetics, genomics and breeding databases and resources. The AgBioData Consortium (https://www.agbiodata.org/) currently represents 44 databases and resources (https://www.agbiodata.org/databases) covering model or crop plant and animal GGB data, ontologies, pathways, genetic variation and breeding platforms (referred to as ‘databases’ throughout). One of the goals of the Consortium is to facilitate FAIR (Findable, Accessible, Interoperable, and Reusable) data management and the integration of datasets which requires data sharing, along with structured vocabularies and/or ontologies. Two AgBioData working groups, focused on Data Sharing and Ontologies, respectively, conducted a Consortium-wide survey to assess the current status and future needs of the members in those areas. A total of 33 researchers responded to the survey, representing 37 databases. Results suggest that data-sharing practices by AgBioData databases are in a fairly healthy state, but it is not clear whether this is true for all metadata and data types across all databases; and that, ontology use has not substantially changed since a similar survey was conducted in 2017. Based on our evaluation of the survey results, we recommend (i) providing training for database personnel in a specific data-sharing techniques, as well as in ontology use; (ii) further study on what metadata is shared, and how well it is shared among databases; (iii) promoting an understanding of data sharing and ontologies in the stakeholder community; (iv) improving data sharing and ontologies for specific phenotypic data types and formats; and (v) lowering specific barriers to data sharing and ontology use, by identifying sustainability solutions, and the identification, promotion, or development of data standards. Combined, these improvements are likely to help AgBioData databases increase development efforts towards improved ontology use, and data sharing via programmatic means. Database URL https://www.agbiodata.org/databases 

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5. ‘rtry’: An R package to support plant trait data preprocessing

   https://doi.org/10.1002/ece3.11292  

   Lam, Olee_Hoi Ying; Kattge, Jens; Tautenhahn, Susanne; Boenisch, Gerhard; Kovach, Kyle R; Townsend, Philip A (May 2024, Ecology and Evolution)

   Abstract Plant trait data are used to quantify how plants respond to environmental factors and can act as indicators of ecosystem function. Measured trait values are influenced by genetics, trade‐offs, competition, environmental conditions, and phenology. These interacting effects on traits are poorly characterized across taxa, and for many traits, measurement protocols are not standardized. As a result, ancillary information about growth and measurement conditions can be highly variable, requiring a flexible data structure. In 2007, the TRY initiative was founded as an integrated database of plant trait data, including ancillary attributes relevant to understanding and interpreting the trait values. The TRY database now integrates around 700 original and collective datasets and has become a central resource of plant trait data. These data are provided in a generic long‐table format, where a unique identifier links different trait records and ancillary data measured on the same entity. Due to the high number of trait records, plant taxa, and types of traits and ancillary data released from the TRY database, data preprocessing is necessary but not straightforward. Here, we present the ‘rtry’ R package, specifically designed to support plant trait data exploration and filtering. By integrating a subset of existing R functions essential for preprocessing, ‘rtry’ avoids the need for users to navigate the extensive R ecosystem and provides the functions under a consistent syntax. ‘rtry’ is therefore easy to use even for beginners in R. Notably, ‘rtry’ does not support data retrieval or analysis; rather, it focuses on the preprocessing tasks to optimize data quality. While ‘rtry’ primarily targets TRY data, its utility extends to data from other sources, such as the National Ecological Observatory Network (NEON). The ‘rtry’ package is available on the Comprehensive R Archive Network (CRAN;https://cran.r‐project.org/package=rtry) and the GitHub Wiki (https://github.com/MPI‐BGC‐Functional‐Biogeography/rtry/wiki) along with comprehensive documentation and vignettes describing detailed data preprocessing workflows. 

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