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Genetic diversity within species represents a fundamental yet underappreciated level of biodiversity. Because genetic diversity can indicate species resilience to changing climate, its measurement is relevant to many national and global conservation policy targets. Many studies produce large amounts of genome‐scale genetic diversity data for wild populations, but most (87%) do not include the associated spatial and temporal metadata necessary for them to be reused in monitoring programs or for acknowledging the sovereignty of nations or Indigenous peoples. We undertook a distributed datathon to quantify the availability of these missing metadata and to test the hypothesis that their availability decays with time. We also worked to remediate missing metadata by extracting them from associated published papers, online repositories, and direct communication with authors. Starting with 848 candidate genomic data sets (reduced representation and whole genome) from the International Nucleotide Sequence Database Collaboration, we determined that 561 contained mostly samples from wild populations. We successfully restored spatiotemporal metadata for 78% of these 561 data sets (n = 440 data sets with data on 45,105 individuals from 762 species in 17 phyla). Examining papers and online repositories was much more fruitful than contacting 351 authors, who replied to our email requests 45% of the time. Overall, 23% of our email queries to authors unearthed useful metadata. The probability of retrieving spatiotemporal metadata declined significantly as age of the data set increased. There was a 13.5% yearly decrease in metadata associated with published papers or online repositories and up to a 22% yearly decrease in metadata that were only available from authors. This rapid decay in metadata availability, mirrored in studies of other types of biological data, should motivate swift updates to data‐sharing policies and researcher practices to ensure that the valuable context provided by metadata is not lost to conservation science forever. [ABSTRACT FROM AUTHOR]

Aim: Species occurrence records are essential to understanding Earth's biodiversity and addressing global environmental issues, but do not always reflect actual locations of occurrence. Certain geographical coordinates are assigned repeatedly to thousands of observation/collection records. This may result from imperfect data management and georeferencing practices, and can greatly bias the inferred distribution of biodiversity and associated environmental conditions. Nonetheless, these 'biodiverse' coordinates are often overlooked in taxon‐centric studies, as they are identifiable only in aggregate across taxa and datasets, and it is difficult to determine their true circumstance without in‐depth, focused investigation. Here we assess highly recurring coordinates in biodiversity data to determine artificial hotspots of occurrences. Location: Global. Taxon: Land plants, birds, mammals, insects. Methods: We identified highly recurring coordinates across plant, bird, insect and mammal records in the Global Biodiversity Information Facility, the largest aggregator of biodiversity data. We determined which are likely artificial hotspots by examining metadata from over 40 million records, assessing spatial distributions of associated datasets, contacting data managers and reviewing literature. These results were compiled into the Artificial Hotspot Occurrence Inventory (AHOI). Results: Artificial biodiversity hotspots generally comprised geopolitical and grid centroids. The associated uncertainty ranged from several square kilometres to millions. Such artificial biodiversity hotspots were most prevalent in plant records. For instance, over 100,000 plant occurrence records were assigned the centroid coordinates of Brazil, and points that have at least 1000 associated occurrences comprised over 9 million records. In contrast, highly recurring coordinates in animal data more often reflected actual sites of observation. Main conclusions AHOI can be used to (i) improve accuracy of biodiversity assessments; (ii) estimate uncertainty associated with records from artificial hotspots and make informed decisions on whether to include them in scientific studies; and (iii) identify problems in biodiversity informatics workflows and priorities for improvement. [ABSTRACT FROM AUTHOR]

Biodiversity underlies ecosystem resilience, ecosystem function, sustainable economies, and human well‐being. Understanding how biodiversity sustains ecosystems under anthropogenic stressors and global environmental change will require new ways of deriving and applying biodiversity data. A major challenge is that biodiversity data and knowledge are scattered, biased, collected with numerous methods, and stored in inconsistent ways. The Group on Earth Observations Biodiversity Observation Network (GEO BON) has developed the Essential Biodiversity Variables (EBVs) as fundamental metrics to help aggregate, harmonize, and interpret biodiversity observation data from diverse sources. Mapping and analyzing EBVs can help to evaluate how aspects of biodiversity are distributed geographically and how they change over time. EBVs are also intended to serve as inputs and validation to forecast the status and trends of biodiversity, and to support policy and decision making. Here, we assess the feasibility of implementing Genetic Composition EBVs (Genetic EBVs), which are metrics of within‐species genetic variation. We review and bring together numerous areas of the field of genetics and evaluate how each contributes to global and regional genetic biodiversity monitoring with respect to theory, sampling logistics, metadata, archiving, data aggregation, modeling, and technological advances. We propose four Genetic EBVs: (i) Genetic Diversity; (ii) Genetic Differentiation; (iii) Inbreeding; and (iv) Effective Population Size (Ne). We rank Genetic EBVs according to their relevance, sensitivity to change, generalizability, scalability, feasibility and data availability. We outline the workflow for generating genetic data underlying the Genetic EBVs, and review advances and needs in archiving genetic composition data and metadata. We discuss how Genetic EBVs can be operationalized by visualizing EBVs in space and time across species and by forecasting Genetic EBVs beyond current observations using various modeling approaches. Our review then explores challenges of aggregation, standardization, and costs of operationalizing the Genetic EBVs, as well as future directions and opportunities to maximize their uptake globally in research and policy. The collection, annotation, and availability of genetic data has made major advances in the past decade, each of which contributes to the practical and standardized framework for large‐scale genetic observation reporting. Rapid advances in DNA sequencing technology present new opportunities, but also challenges for operationalizing Genetic EBVs for biodiversity monitoring regionally and globally. With these advances, genetic composition monitoring is starting to be integrated into global conservation policy, which can help support the foundation of all biodiversity and species' long‐term persistence in the face of environmental change. We conclude with a summary of concrete steps for researchers and policy makers for advancing operationalization of Genetic EBVs. The technical and analytical foundations of Genetic EBVs are well developed, and conservation practitioners should anticipate their increasing application as efforts emerge to scale up genetic biodiversity monitoring regionally and globally. [ABSTRACT FROM AUTHOR]

Several recent papers have highlighted best practices, defined metadata standards and proposed checklists for reproducibility of species distribution models (SDMs). A new paper by Zurell et al. (2020) formalizes, extends and implements many of these ideas in a standardized protocol for reporting SDMs called ODMAP (Overview, Data, Model, Assessment, Prediction). ODMAP has the potential to revolutionize species distribution modeling, not through the development of new methods, but rather by providing a long overdue system to document, communicate and reproduce methods. ODMAP could lead to improved basic and applied science outcomes by increasing comprehension and transparency, and by guiding implementation of best practices. However, the impact that ODMAP has on SDMs depends in large part on the extent to which journals, reviewers and funding agencies encourage its use and whether mentors introduce it to the next generation of biodiversity scientists. Important next steps include formalizing mechanisms for the broader research community to participate in future development of ODMAP and related standards for best practices in biodiversity modeling. [ABSTRACT FROM AUTHOR]

Genetic data represent a relatively new frontier for our understanding of global biodiversity. Ideally, such data should include both organismal DNA‐based genotypes and the ecological context where the organisms were sampled. Yet most tools and standards for data deposition focus exclusively either on genetic or ecological attributes. The Genomic Observatories Metadatabase (GEOME: geome‐db.org) provides an intuitive solution for maintaining links between genetic data sets stored by the International Nucleotide Sequence Database Collaboration (INSDC) and their associated ecological metadata. GEOME facilitates the deposition of raw genetic data to INSDCs sequence read archive (SRA) while maintaining persistent links to standards‐compliant ecological metadata held in the GEOME database. This approach facilitates findable, accessible, interoperable and reusable data archival practices. Moreover, GEOME enables data management solutions for large collaborative groups and expedites batch retrieval of genetic data from the SRA. The article that follows describes how GEOME can enable genuinely open data workflows for researchers in the field of molecular ecology. [ABSTRACT FROM AUTHOR]

Systematic reviews (SRs) and systematic mapping aim to maximize transparency and comprehensiveness while minimizing subjectivity and bias. These are time‐consuming and complex tasks, so SRs are considered resource intensive, but published estimates of systematic‐review resource requirements are largely anecdotal. We analyzed all Collaboration for Environmental Evidence (CEE) SRs (n = 66) and maps (n = 20) published from 2012 to 2017 to estimate the average number of articles retained at each review stage. We also surveyed 33 experienced systematic reviewers to collate information on the rate at which those stages could be completed. In combination, these data showed that the average CEE SR takes an estimated 164 d (full‐time equivalent) (SD 23), and the average CEE systematic map (SM) (excluding critical appraisal) takes 211 d (SD 53). While screening titles and abstracts is widely considered time‐consuming, metadata extraction and critical appraisal took as long or longer to complete, especially for SMs. Given information about the planned methods and evidence base, we created a software tool that predicts time requirements of a SR or map with evidence‐based defaults as a starting point. Our results shed light on the most time‐consuming stages of the SR and mapping processes, will inform review planning, and can direct innovation to streamline processes. Future predictions of effort required to complete SRs and maps could be improved if authors provide more details on methods and results. Article impact statement: We provide data on the effort needed to complete systematic literature reviews and maps and new software to help users apply our findings. [ABSTRACT FROM AUTHOR]

Improving technology and increasing affordability mean that camera trapping—the use of remotely triggered cameras to photograph wildlife—is becoming an increasingly common tool in the monitoring and conservation of wild populations. Each camera trap study generates a vast amount of data, which need to be processed and labeled before analysis. Traditionally, processing camera trap data has been performed manually by entering data into a spreadsheet. This is time‐consuming, prone to human error, and data management may be inconsistent between projects, hindering collaboration. Recently, several programs have become available to facilitate and quicken data processing. Here, we review available software and assess their ability to better standardize camera trap data management and facilitate data sharing and collaboration. To identify available software for camera trap data management, we used internet searches and contacted researchers and practitioners working on large camera trap projects, as well as software developers. We tested all available programs against a range of software characteristics in addition to their ability to record a suite of important data variables extracted from images. We identified and reviewed 12 available programs for the management of camera trap data. These ranged from simple software assisting with the extraction of metadata from an image, through to comprehensive programs that facilitate data entry and analysis. Many of the programs tested were developed for use on specific studies and so do not cover all possible software or data collection requirements that different projects may have. We highlight the importance of a standardized software solution for camera trap data management. This approach would allow all possible data to be collected, enabling researchers to share data and contribute to other studies, as well as facilitating multi‐project comparisons. By standardizing camera trap data collection and management in this way, future studies would be better placed to guide conservation policy on a global level. The use of camera traps in ecology and conservation is increasingly common, and studies using camera traps can generate masses of data. These data are often handled and stored differently, hindering comparison and collaboration. We reviewed 12 camera trap data management programs and advise that that a universal approach to managing, storing, and analzsing camera trap data will facilitate collaborations and the sharing of data and will be beneficial to global conservation. [ABSTRACT FROM AUTHOR]

Abstract: DNA‐based techniques are increasingly used for measuring the biodiversity (species presence, identity, abundance and community composition) of terrestrial and aquatic ecosystems. While there are numerous reviews of molecular methods and bioinformatic steps, there has been little consideration of the methods used to collect samples upon which these later steps are based. This represents a critical knowledge gap, as methodologically sound field sampling is the foundation for subsequent analyses. We reviewed field sampling methods used for metabarcoding studies of both terrestrial and freshwater ecosystem biodiversity over a nearly three‐year period (n = 75). We found that 95% (n = 71) of these studies used subjective sampling methods and inappropriate field methods and/or failed to provide critical methodological information. It would be possible for researchers to replicate only 5% of the metabarcoding studies in our sample, a poorer level of reproducibility than for ecological studies in general. Our findings suggest greater attention to field sampling methods, and reporting is necessary in eDNA‐based studies of biodiversity to ensure robust outcomes and future reproducibility. Methods must be fully and accurately reported, and protocols developed that minimize subjectivity. Standardization of sampling protocols would be one way to help to improve reproducibility and have additional benefits in allowing compilation and comparison of data from across studies. [ABSTRACT FROM AUTHOR]

Spatial and/or temporal biases in biodiversity data can directly influence the utility, comparability, and reliability of ecological and evolutionary studies. While the effects of biased spatial coverage of biodiversity data are relatively well known, temporal variation in data quality (i.e., the congruence between recorded and actual information) has received much less attention. Here, we develop a conceptual framework for understanding the influence of time on biodiversity data quality based on three main processes: (1) the natural dynamics of ecological systems-such as species turnover or local extinction; (2) periodic taxonomic revisions, and; (3) the loss of physical and metadata due to inefficient curation, accidents, or funding shortfalls. Temporal decay in data quality driven by these three processes has fundamental consequences for the usage and comparability of data collected in different time periods. Data decay can be partly ameliorated by adopting standard protocols for generation, storage, and sharing data and metadata. However, some data degradation is unavoidable due to natural variations in ecological systems. Consequently, changes in biodiversity data quality over time need be carefully assessed and, if possible, taken into account when analyzing aging datasets. [ABSTRACT FROM AUTHOR]

Aim Comparative phylogeography across a large number of species allows investigating community-level processes at regional and continental scales. An effective approach to such studies would involve automatic retrieval of georeferenced sequence data from nucleotide databases (a first step towards an 'automated phylogeography'). It remains unclear if, despite repeated calls, georeferencing of nucleotide databases has increased in frequency, and if accumulated data allow for broad applications based on automated retrieval of sequence data and associated geographical information. Here, we investigated geographical information available in NCBI GenBank accessions for tetrapods, exploring temporal and geographical patterns in georeferencing, and quantifying data available for automated phylogeography. Location Global. Methods We developed Python and R scripts to (1) download metadata from GenBank (1,125,514 accessions, > 20,000 species); (2) geocode accessions from associated metadata; (3) map originally georeferenced and geocoded accessions and plot their frequency against time; (4) assess the size of intraspecific sets of homologous sequences and compare their geographical extent with species ranges, thus evaluating their potential for phylogeographical analyses. Results Only 6.2% of surveyed tetrapod GenBank submissions reported geographical coordinates, without increase in recent years. Our geocoding raised georeferenced accessions to 15.1%. The geographical distribution of georeferenced accessions is patchy, and especially sparse in economically underdeveloped areas. Automatically retrievable informative data sets covering most of the range are available for very few species of wide-ranging tetrapods. Main conclusions Although geocoding offers a partial solution to the scarcity of direct georeferencing, the amount of data potentially useful for automated phylogeography is still limited. Strong underrepresentation of hard-to-access areas suggests that sampling logistics represent a main hindrance to global data availability. We propose that, besides enhancing georeferencing of genetic data, future research agendas should focus on collaborative efforts to sample genetic diversity in biodiversity-rich tropical areas. [ABSTRACT FROM AUTHOR]

We are witnessing a growing gap separating primary research data from derived data products presented as knowledge in publications. Although journals today more often require the underlying data products used to derive the results as a prerequisite for a publication, the important link to the primary data is lost. However, documenting the postprocessing steps of data linking, the primary data with derived data products has the potential to increase the accuracy and the reproducibility of scientific findings significantly. Here, we introduce the rBEFdata R package as companion to the collaborative data management platform BEFdata. The R package provides programmatic access to features of the platform. It allows to search for data and integrates the search with external thesauri to improve the data discovery. It allows to download and import data and metadata into R for analysis. A batched download is available as well which works along a paper proposal mechanism implemented by BEFdata. This feature of BEFdata allows to group primary data and metadata and streamlines discussions and collaborations revolving around a certain research idea. The upload functionality of the R package in combination with the paper proposal mechanism of the portal allows to attach derived data products and scripts directly from R, thus addressing major aspects of documenting data postprocessing. We present the core features of the rBEFdata R package along an ecological analysis example and further discuss the potential of postprocessing documentation for data, linking primary data with derived data products and knowledge. [ABSTRACT FROM AUTHOR]

Premise: Heterogeneity of biodiversity data from the collections, research, and management communities presents challenges for data findability, accessibility, interoperability, and reusability. Workflows designed with data collection, standards, dissemination, and reuse in mind will generate better information across geopolitical, administrative, and institutional boundaries. Here, we present our data workflow as a case study of how we collected, shared, and used data from multiple sources. Methods: In 2012, we initiated the collection of biodiversity data relating to Michigan prairie fens, including data on plant communities and the federally endangered Poweshiek skipperling (Oarisma poweshiek). Results: Over 23,000 occurrence records were compiled in a database following Darwin Core standards. The records were linked with media and biological, chemical, and geometric measurements. We published the data as Global Biodiversity Information Facility data sets and in Symbiota SEINet portals. Discussion: We highlight data collection techniques that optimized transcription time, including the use of predetermined and controlled vocabulary, Darwin Core terms, and data dictionaries. The validity and longevity of our data were supported by voucher specimens, metadata with measurement records, and published manuscripts detailing methods and data sets. Key to our data dissemination was cooperation among partners and the utilization of dynamic tools. To increase data interoperability, we need flexible and customizable data collection templates, coding, and enhanced communication among communities using biodiversity data. [ABSTRACT FROM AUTHOR]