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Biodiversity data integration—the significance of data resolution and domain

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  • Christian König
  • Patrick Weigelt
  • Julian Schrader
  • Amanda Taylor
  • Jens Kattge
  • Holger Kreft

Abstract

Recent years have seen an explosion in the availability of biodiversity data describing the distribution, function, and evolutionary history of life on earth. Integrating these heterogeneous data remains a challenge due to large variations in observational scales, collection purposes, and terminologies. Here, we conceptualize widely used biodiversity data types according to their domain (what aspect of biodiversity is described?) and informational resolution (how specific is the description?). Applying this framework to major data providers in biodiversity research reveals a strong focus on the disaggregated end of the data spectrum, whereas aggregated data types remain largely underutilized. We discuss the implications of this imbalance for the scope and representativeness of current macroecological research and highlight the synergies arising from a tighter integration of biodiversity data across domains and resolutions. We lay out effective strategies for data collection, mobilization, imputation, and sharing and summarize existing frameworks for scalable and integrative biodiversity research. Finally, we use two case studies to demonstrate how the explicit consideration of data domain and resolution helps to identify biases and gaps in global data sets and achieve unprecedented taxonomic and geographical data coverage in macroecological analyses.This Essay highlights data resolution as central property of biodiversity data that affects the precision and representativeness of macroecological inferences. It also discusses ways to maximize synergies among data types and showcases the potential of cross-resolution, cross-domain data integration.

Suggested Citation

  • Christian König & Patrick Weigelt & Julian Schrader & Amanda Taylor & Jens Kattge & Holger Kreft, 2019. "Biodiversity data integration—the significance of data resolution and domain," PLOS Biology, Public Library of Science, vol. 17(3), pages 1-16, March.
    • Handle: RePEc:plo:pbio00:3000183
    DOI: 10.1371/journal.pbio.3000183
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    References listed on IDEAS

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    2. VanDerWal, Jeremy & Shoo, Luke P. & Graham, Catherine & Williams, Stephen E., 2009. "Selecting pseudo-absence data for presence-only distribution modeling: How far should you stray from what you know?," Ecological Modelling, Elsevier, vol. 220(4), pages 589-594.
    3. Virginia Gewin, 2016. "Data sharing: An open mind on open data," Nature, Nature, vol. 529(7584), pages 117-119, January.
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    Cited by:

    1. Anne E Thessen & Paul Bogdan & David J Patterson & Theresa M Casey & César Hinojo-Hinojo & Orlando de Lange & Melissa A Haendel, 2021. "From Reductionism to Reintegration: Solving society’s most pressing problems requires building bridges between data types across the life sciences," PLOS Biology, Public Library of Science, vol. 19(3), pages 1-12, March.
    2. Zaenal Akbar & Hani Febri Mustika & Dwi Setyo Rini & Lindung Parningotan Manik & Ariani Indrawati & Agusdin Dharma Fefirenta & Tutie Djarwaningsih, 2021. "An Ontology-Driven Personalized Faceted Search for Exploring Knowledge Bases of Capsicum," Future Internet, MDPI, vol. 13(7), pages 1-17, June.

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