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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]

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]

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]

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]

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]

Healthy ecosystems include many species (high richness) with similar abundances (high evenness). Thus, both aspects of biodiversity are worthy of conservation. Simultaneously conserving richness and evenness might be difficult, however, if, for example, the restoration of previously absent species to low densities brings a cost in reduced evenness. Using meta-analysis, we searched for benefits to biodiversity following adoption of two common land-management schemes: the implementation of organic practices by farmers and of controlled burning by natural-land managers. We used rarefaction to eliminate sampling bias in all of our estimates of richness and evenness. Both conservation practices significantly increased evenness and overall abundance across taxonomic classifications (arthropods, birds, non-bird vertebrates, plants, soil organisms). Evenness and richness varied independently, leading to no richness-evenness correlation and no significant overall change in richness. Demonstrating the importance of rarefaction, analyses of raw data that did not receive rarefaction indicated misleadingly strong benefits of organic agriculture and burning for richness while underestimating true gains in evenness. Both organic farming and burning favored species that were not numerically dominant, re-balancing communities as uncommon species gained individuals. Our results support the assertion that richness and evenness capture separate facets of biodiversity, each needing individual attention during conservation. [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]