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Bringing Modeling to the Masses: A Web Based System to Predict Potential Species Distributions

Author

Listed:
  • Jim Graham

    (Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523, USA)

  • Greg Newman

    (Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523, USA)

  • Sunil Kumar

    (Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523, USA)

  • Catherine Jarnevich

    (Fort Collins Science Center, U.S. Geological Survey, 2150 Centre Ave. Building C, Fort Collins, CO 80526, USA)

  • Nick Young

    (Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523, USA)

  • Alycia Crall

    (The Nelson Institute of Environmental Studies, University of Wisconsin, Madison, WI 53706, USA)

  • Thomas J. Stohlgren

    (Fort Collins Science Center, U.S. Geological Survey, 2150 Centre Ave. Building C, Fort Collins, CO 80526, USA)

  • Paul Evangelista

    (Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523, USA)

Abstract

Predicting current and potential species distributions and abundance is critical for managing invasive species, preserving threatened and endangered species, and conserving native species and habitats. Accurate predictive models are needed at local, regional, and national scales to guide field surveys, improve monitoring, and set priorities for conservation and restoration. Modeling capabilities, however, are often limited by access to software and environmental data required for predictions. To address these needs, we built a comprehensive web-based system that: (1) maintains a large database of field data; (2) provides access to field data and a wealth of environmental data; (3) accesses values in rasters representing environmental characteristics; (4) runs statistical spatial models; and (5) creates maps that predict the potential species distribution. The system is available online at www.niiss.org, and provides web-based tools for stakeholders to create potential species distribution models and maps under current and future climate scenarios.

Suggested Citation

  • Jim Graham & Greg Newman & Sunil Kumar & Catherine Jarnevich & Nick Young & Alycia Crall & Thomas J. Stohlgren & Paul Evangelista, 2010. "Bringing Modeling to the Masses: A Web Based System to Predict Potential Species Distributions," Future Internet, MDPI, vol. 2(4), pages 1-11, November.
    • Handle: RePEc:gam:jftint:v:2:y:2010:i:4:p:624-634:d:10186
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    References listed on IDEAS

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    1. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 4.
    2. John Harte & Annette Ostling & Jessica L. Green & Ann Kinzig, 2004. "Climate change and extinction risk," Nature, Nature, vol. 430(6995), pages 34-34, July.
    3. ,, 1999. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 15(5), pages 777-788, October.
    4. ,, 1999. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 15(1), pages 151-160, February.
    5. Chris D. Thomas & Alison Cameron & Rhys E. Green & Michel Bakkenes & Linda J. Beaumont & Yvonne C. Collingham & Barend F. N. Erasmus & Marinez Ferreira de Siqueira & Alan Grainger & Lee Hannah & Lesle, 2004. "Extinction risk from climate change," Nature, Nature, vol. 427(6970), pages 145-148, January.
    6. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 3.
    7. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 2.
    8. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 2.
    9. ,, 1999. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 15(3), pages 427-432, June.
    10. ,, 1999. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 15(4), pages 629-637, August.
    11. Thomas J. Stohlgren & John L. Schnase, 2006. "Risk Analysis for Biological Hazards: What We Need to Know about Invasive Species," Risk Analysis, John Wiley & Sons, vol. 26(1), pages 163-173, February.
    12. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 4.
    13. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 3.
    14. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 1.
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