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Data-intensive science applied to broad-scale citizen science.

Wesley M Hochachka1, Daniel Fink, Rebecca A Hutchinson

  • 1Cornell Lab of Ornithology, Ithaca, NY 14850, USA.

Trends in Ecology & Evolution
|December 24, 2011
PubMed
Summary
This summary is machine-generated.

Citizen scientists help gather species occurrence data for ecological pattern identification. Data-intensive science methods reveal complex environmental associations for accurate continental-scale species distribution estimates.

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Area of Science:

  • Ecology
  • Data Science
  • Conservation Biology

Background:

  • Ecological pattern identification across large scales necessitates advanced data handling methods.
  • Citizen science initiatives generate vast amounts of species occurrence data, presenting unique challenges.
  • Integrating and modeling diverse, large-scale datasets is crucial for ecological research.

Purpose of the Study:

  • To describe how data-intensive science can improve species distribution estimation.
  • To address challenges in collecting and analyzing citizen science data for ecological studies.
  • To reveal complex environmental associations for accurate continental-scale species distribution modeling.

Main Methods:

  • Utilizing data-intensive science approaches for ecological data analysis.
  • Developing and implementing data collection tools for citizen science projects.
  • Applying novel analytical and visualization techniques to species occurrence data.
  • Identifying complex environmental associations to model species distributions.

Main Results:

  • Accurate species distribution estimates at continental scales were achieved.
  • Complex environmental associations were identified using data-intensive methods.
  • Demonstrated the value of citizen science data in ecological research.

Conclusions:

  • Data-intensive science offers powerful tools for ecological research at broad scales.
  • Citizen science data, when properly managed and analyzed, can yield significant ecological insights.
  • Understanding environmental associations is key to predicting species distributions accurately.