31
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      The history and impact of digitization and digital data mobilization on biodiversity research.

      Read this article at

      ScienceOpenPublisherPMC
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The first two decades of the twenty-first century have seen a rapid rise in the mobilization of digital biodiversity data. This has thrust natural history museums into the forefront of biodiversity research, underscoring their central role in the modern scientific enterprise. The advent of mobilization initiatives such as the United States National Science Foundation's Advancing Digitization of Biodiversity Collections (ADBC), Australia's Atlas of Living Australia (ALA), Mexico's National Commission for the Knowledge and Use of Biodiversity (CONABIO), Brazil's Centro de Referência em Informação (CRIA) and China's National Specimen Information Infrastructure (NSII) has led to a rapid rise in data aggregators and an exponential increase in digital data for scientific research and arguably provide the best evidence of where species live. The international Global Biodiversity Information Facility (GBIF) now serves about 131 million museum specimen records, and Integrated Digitized Biocollections (iDigBio) in the USA has amassed more than 115 million. These resources expose collections to a wider audience of researchers, provide the best biodiversity data in the modern era outside of nature itself and ensure the primacy of specimen-based research. Here, we provide a brief history of worldwide data mobilization, their impact on biodiversity research, challenges for ensuring data quality, their contribution to scientific publications and evidence of the rising profiles of natural history collections.This article is part of the theme issue 'Biological collections for understanding biodiversity in the Anthropocene'.

          Related collections

          Most cited references55

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Using Deep Learning for Image-Based Plant Disease Detection

          Crop diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to the lack of the necessary infrastructure. The combination of increasing global smartphone penetration and recent advances in computer vision made possible by deep learning has paved the way for smartphone-assisted disease diagnosis. Using a public dataset of 54,306 images of diseased and healthy plant leaves collected under controlled conditions, we train a deep convolutional neural network to identify 14 crop species and 26 diseases (or absence thereof). The trained model achieves an accuracy of 99.35% on a held-out test set, demonstrating the feasibility of this approach. Overall, the approach of training deep learning models on increasingly large and publicly available image datasets presents a clear path toward smartphone-assisted crop disease diagnosis on a massive global scale.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Phylogenetic measures of biodiversity and neo- and paleo-endemism in Australian Acacia.

            Understanding spatial patterns of biodiversity is critical for conservation planning, particularly given rapid habitat loss and human-induced climatic change. Diversity and endemism are typically assessed by comparing species ranges across regions. However, investigation of patterns of species diversity alone misses out on the full richness of patterns that can be inferred using a phylogenetic approach. Here, using Australian Acacia as an example, we show that the application of phylogenetic methods, particularly two new measures, relative phylogenetic diversity and relative phylogenetic endemism, greatly enhances our knowledge of biodiversity across both space and time. We found that areas of high species richness and species endemism are not necessarily areas of high phylogenetic diversity or phylogenetic endemism. We propose a new method called categorical analysis of neo- and paleo-endemism (CANAPE) that allows, for the first time, a clear, quantitative distinction between centres of neo- and paleo-endemism, useful to the conservation decision-making process.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Amazon plant diversity revealed by a taxonomically verified species list

              Large floristic datasets that purportedly represent the diversity and composition of the Amazon tree flora are being widely used to draw conclusions about the patterns and evolution of Amazon plant diversity, but these datasets are fundamentally flawed in both their methodology and the resulting content. We have assembled a comprehensive dataset of Amazonian seed plant species from published sources that includes falsifiable data based on voucher specimens identified by taxonomic specialists. This growing list should serve as a basis for addressing the long-standing debate on the number of plant species in the Amazon, as well as for downstream ecological and evolutionary analyses aimed at understanding the origin and function of the exceptional biodiversity of the vast Amazonian forests. Recent debates on the number of plant species in the vast lowland rain forests of the Amazon have been based largely on model estimates, neglecting published checklists based on verified voucher data. Here we collate taxonomically verified checklists to present a list of seed plant species from lowland Amazon rain forests. Our list comprises 14,003 species, of which 6,727 are trees. These figures are similar to estimates derived from nonparametric ecological models, but they contrast strongly with predictions of much higher tree diversity derived from parametric models. Based on the known proportion of tree species in neotropical lowland rain forest communities as measured in complete plot censuses, and on overall estimates of seed plant diversity in Brazil and in the neotropics in general, it is more likely that tree diversity in the Amazon is closer to the lower estimates derived from nonparametric models. Much remains unknown about Amazonian plant diversity, but this taxonomically verified dataset provides a valid starting point for macroecological and evolutionary studies aimed at understanding the origin, evolution, and ecology of the exceptional biodiversity of Amazonian forests.
                Bookmark

                Author and article information

                Journal
                Philos Trans R Soc Lond B Biol Sci
                Philosophical transactions of the Royal Society of London. Series B, Biological sciences
                The Royal Society
                1471-2970
                0962-8436
                November 19 2018
                : 374
                : 1763
                Affiliations
                [1 ] iDigBio, Florida State University, 142 Collegiate Loop, Tallahassee, FL 32306, USA gnelson@bio.fsu.edu.
                [2 ] Florida Museum of Natural History, University of Florida, 1659 Museum Road, Gainesville, FL 32611, USA.
                Article
                rstb.2017.0391
                10.1098/rstb.2017.0391
                6282090
                30455209
                88c4f239-5272-400f-8561-5619177f6b28
                History

                Anthropocene,biodiversity,data mobilization,digital data,digitization,iDigBio

                Comments

                Comment on this article