Cuddington2013

Référence

Cuddington, K., Fortin, M.-J., Gerber, L.R., Hastings, A., Liebhold, A., O'connor, M. and Ray, C. (2013) Process-based models are required to manage ecological systems in a changing world. Ecosphere, 4(2). (Scopus )

Résumé

Several modeling approaches can be used to guide management decisions. However, some approaches are better fitted than others to address the problem of prediction under global change. Processbased models, which are based on a theoretical understanding of relevant ecological processes, provide a useful framework to incorporate specific responses to altered environmental conditions. As a result, these models can offer significant advantages in predicting the effects of global change as compared to purely statistical or rule-based models based on previously collected data. Process-based models also offer more explicitly stated assumptions and easier interpretation than detailed simulation models. We provide guidelines for identifying the appropriate type of model and level of complexity for management decisions. Finally we outline some of those factors that make modeling for local and regional management under global change a particular challenge: changes to relevant scales and processes, additional sources of uncertainty, legacy effects, threshold dynamics, and socio-economic impacts. © 2013 Cuddington et al.

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@ARTICLE { Cuddington2013,
    AUTHOR = { Cuddington, K. and Fortin, M.-J. and Gerber, L.R. and Hastings, A. and Liebhold, A. and O'connor, M. and Ray, C. },
    TITLE = { Process-based models are required to manage ecological systems in a changing world },
    JOURNAL = { Ecosphere },
    YEAR = { 2013 },
    VOLUME = { 4 },
    NUMBER = { 2 },
    NOTE = { cited By 37 },
    ABSTRACT = { Several modeling approaches can be used to guide management decisions. However, some approaches are better fitted than others to address the problem of prediction under global change. Processbased models, which are based on a theoretical understanding of relevant ecological processes, provide a useful framework to incorporate specific responses to altered environmental conditions. As a result, these models can offer significant advantages in predicting the effects of global change as compared to purely statistical or rule-based models based on previously collected data. Process-based models also offer more explicitly stated assumptions and easier interpretation than detailed simulation models. We provide guidelines for identifying the appropriate type of model and level of complexity for management decisions. Finally we outline some of those factors that make modeling for local and regional management under global change a particular challenge: changes to relevant scales and processes, additional sources of uncertainty, legacy effects, threshold dynamics, and socio-economic impacts. © 2013 Cuddington et al. },
    AFFILIATION = { Department of Biology, University of Waterloo, Waterloo, ON N2L 3G1, Canada; Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, ON M5S3G5, Canada; School of Life Sciences, Arizona State University, Tempe, AZ 85287, United States; Department of Environmental Science and Policy, University of California, Davis, CA 95616, United States; Northern Research Station, United States Department of Agriculture Forest Service, Morgantown, WV 26505, United States; Department of Zoology, University of British Columbia, Vancouver, BC V6T 1Z4 CDN, Canada; Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, CO 80309, United States },
    ART_NUMBER = { 20 },
    AUTHOR_KEYWORDS = { Climate change; Expert opinion; Extrapolation; Simulation model },
    DOCUMENT_TYPE = { Article },
    DOI = { 10.1890/ES12-00178.1 },
    SOURCE = { Scopus },
    URL = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84886417783&doi=10.1890%2fES12-00178.1&partnerID=40&md5=8a4830c898f577c99cbd5df64272d94d },
}

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