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Cited by 380Research Data Management PracticesSpecies Distribution and Climate ChangeEcology and Vegetation Dynamics Studies

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Data integration enables global biodiversity synthesis

Open access
Heberling, J. Mason, Miller, Joseph T., Noesgaard, Daniel, Weingart, Scott B., Schigel, Dmitry·2021·Proceedings of the National Academy of Sciences
DOI 10.1073/pnas.2018093118PMC PMC8017944
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Abstract

Significance: As anthropogenic impacts to Earth systems accelerate, biodiversity knowledge integration is urgently required to support responses to underpin a sustainable future. Consolidating information from disparate sources (e.g., community science programs, museums) and data types (e.g., environmental, biological) can connect the biological sciences across taxonomic, disciplinary, geographical, and socioeconomic boundaries. In an analysis of the research uses of the world’s largest cross-taxon biodiversity data network, we report the emerging roles of open-access data aggregation in the development of increasingly diverse, global research. These results indicate a new biodiversity science landscape centered on big data integration, informing ongoing initiatives and the strategic prioritization of biodiversity data aggregation across diverse knowledge domains, including environmental sciences and policy, evolutionary biology, conservation, and human health.

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Crossref · 10.1073/pnas.2018093118

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Figures

Fig. 1. Growth over time of the biodiversity occurrence data accessible via the Global Biodiversity Information Facility (GBIF) (A) and peer-reviewed articles using these data (B). Occurrence data (solid line in A) is further broken into observation-based records (dashed) and museum specimen-based records (dotted). Pie charts illustrate proportional taxonomic representation in GBIF datasets as of July 2020 (A) and corresponding representation of data use in recently published articles (2016 to 2019; solid black line) (B). “Other” refers to organismal groups not included in other categories (A and B). “>1 category” refers to data use of multiple organismal groups (B). Citable digital object identifiers (DOIs) were provided with each GBIF data download since 2016 (dash line in B).
Fig. 1. Growth over time of the biodiversity occurrence data accessible via the Global Biodiversity Information Facility (GBIF) (A) and peer-reviewed articles using these data (B). Occurrence data (solid line in A) is further broken into observation-based records (dashed) and museum specimen-based records (dotted). Pie charts illustrate proportional taxonomic representation in GBIF datasets as of July 2020 (A) and corresponding representation of data use in recently published articles (2016 to 2019; solid black line) (B). “Other” refers to organismal groups not included in other categories (A and B). “>1 category” refers to data use of multiple organismal groups (B). Citable digital object identifiers (DOIs) were provided with each GBIF data download since 2016 (dash line in B).CC BY · PubMed Central
Fig. 2. Geography of GBIF data use and authorship. World map (A) highlights disparities between country-level biodiversity data use and author affiliation. The map overlays two normalized datasets: orange circles indicate country-level biodiversity data use, and teal circles indicate country-level author affiliations. Circle sizes are proportional to the maximum value in each dataset. Researcher affiliation (teal) is overlaid atop research coverage (orange), mixing to form brown where they overlap. Wider teal rings indicate disproportionately higher number of researchers than research specific to that country (e.g., United Kingdom), whereas wider orange rings (e.g., Mexico) indicate the opposite. Brown circles with no external rings indicate a proportionally similar number of studies about a given country to authors from a given country (e.g., United States). Bar charts show the corresponding frequency of studies published in 2016 to 2019 about a specific region, excluding global studies (B) and the frequency of authorship from each region (C; unique country-level affiliation by study counts). GBIF regions follow ref. 63.
Fig. 2. Geography of GBIF data use and authorship. World map (A) highlights disparities between country-level biodiversity data use and author affiliation. The map overlays two normalized datasets: orange circles indicate country-level biodiversity data use, and teal circles indicate country-level author affiliations. Circle sizes are proportional to the maximum value in each dataset. Researcher affiliation (teal) is overlaid atop research coverage (orange), mixing to form brown where they overlap. Wider teal rings indicate disproportionately higher number of researchers than research specific to that country (e.g., United Kingdom), whereas wider orange rings (e.g., Mexico) indicate the opposite. Brown circles with no external rings indicate a proportionally similar number of studies about a given country to authors from a given country (e.g., United States). Bar charts show the corresponding frequency of studies published in 2016 to 2019 about a specific region, excluding global studies (B) and the frequency of authorship from each region (C; unique country-level affiliation by study counts). GBIF regions follow ref. 63.CC BY · PubMed Central
Fig. 3. Structural topic model results from 4,035 studies that used GBIF-mediated data published from 2003 to 2019. Topic correlations network visualizes quantitative associations between topics (nodes), with topics near each other and connected by a gray line more likely to appear together in a given study. Node color denotes the relative change in prevalence over time within each topic, comparing topic prevalence in earlier studies (2003 to 2015) to those recently published (2016 to 2019). Node sizes are proportional to overall topic proportions. Network graphed using the Fruchterman–Reingold algorithm. (Inset) Bar chart of topic proportions across all years, indicating the percentage of the total corpus that belongs to each topic, with topic numbers corresponding to topic names in network graph and bar color corresponding to temporal change. The top six words by probability associated with each topic are given in italics (SI Appendix, Table S1).
Fig. 3. Structural topic model results from 4,035 studies that used GBIF-mediated data published from 2003 to 2019. Topic correlations network visualizes quantitative associations between topics (nodes), with topics near each other and connected by a gray line more likely to appear together in a given study. Node color denotes the relative change in prevalence over time within each topic, comparing topic prevalence in earlier studies (2003 to 2015) to those recently published (2016 to 2019). Node sizes are proportional to overall topic proportions. Network graphed using the Fruchterman–Reingold algorithm. (Inset) Bar chart of topic proportions across all years, indicating the percentage of the total corpus that belongs to each topic, with topic numbers corresponding to topic names in network graph and bar color corresponding to temporal change. The top six words by probability associated with each topic are given in italics (SI Appendix, Table S1).CC BY · PubMed Central
Fig. 4. The GBIF map of science, visualizing the network of interdisciplinary knowledge facilitated through GBIF-mediated data in the context of a broader research landscape. The reference base map (gray lines), the UCSD map of science (36), displays a network of >25,000 journals classified across 554 subdisciplines (nodes), grouped into 13 primary disciplines (colors). Circles illustrate GBIF-mediated studies (2003–2019) centered on subdiscipline node assignments with circle size proportion to number of studies. Note that only GBIF-mediated studies published in journals in UCSD map of science are included (2,810 articles, 548 journals). Map is a 2D projection of a spherical 3D layout (i.e., the right and left of map connect) and produced using the Sci2 Tool (61).
Fig. 4. The GBIF map of science, visualizing the network of interdisciplinary knowledge facilitated through GBIF-mediated data in the context of a broader research landscape. The reference base map (gray lines), the UCSD map of science (36), displays a network of >25,000 journals classified across 554 subdisciplines (nodes), grouped into 13 primary disciplines (colors). Circles illustrate GBIF-mediated studies (2003–2019) centered on subdiscipline node assignments with circle size proportion to number of studies. Note that only GBIF-mediated studies published in journals in UCSD map of science are included (2,810 articles, 548 journals). Map is a 2D projection of a spherical 3D layout (i.e., the right and left of map connect) and produced using the Sci2 Tool (61).CC BY · PubMed Central

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Metadata

Authors
Heberling, J. M., Miller, J. T., Noesgaard, D., Weingart, S. B., & Schigel, D.
Affiliations
  • Carnegie Museum of Natural History (US)
  • Global Biodiversity Information Facility (DK)
  • Carnegie Mellon University (US)
Corresponding author
Heberling, J. Mason
ORCID
Venue
Proceedings of the National Academy of Sciences
Publisher
National Academy of Sciences
Published
2021-02
Volume
118
Issue
6
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
DOI
10.1073/pnas.2018093118
PMC
PMC8017944
OpenAlex
W3127515314
Cited by
380
References
65
Citations by year
2026: 81 · 2025: 76 · 2024: 72 · 2023: 66 · 2022: 64 · 2021: 21
FWCI
50.42
Type
journal article
Language
en
Open access
Licence
Locations
  • Proceedings of the National Academy of Sciences (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Information Systems › Research Data Management Practices
  • Physical Sciences › Environmental Science › Ecological Modeling › Species Distribution and Climate Change
  • Physical Sciences › Environmental Science › Nature and Landscape Conservation › Ecology and Vegetation Dynamics Studies
Keywords
biodiversity data integration, GBIF, citizen science, species distribution modeling, macroecology
MeSH terms
Animals, Classification, Humans, Museums, Databases, Factual, Biodiversity

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