Green2005501

Référence

Green, J.L., Hastings, A., Arzberger, P., Ayala, F.J., Cottingham, K.L., Cuddington, K., Davis, F., Dunne, J.A., Fortin, M.-J., Gerber, L. and Neubert, M. (2005) Complexity in ecology and conservation: Mathematical, statistical, and computational challenges. BioScience, 55(6):501-510. (Scopus )

Résumé

Creative approaches at the interface of ecology, statistics, mathematics, informatics, and computational science are essential for improving our understanding of complex ecological systems. For example, new information technologies, including powerful computers, spatially embedded sensor networks, and Semantic Web tools, are emerging as potentially revolutionary tools for studying ecological phenomena. These technologies can play an important role in developing and testing detailed models that describe real-world systems at multiple scales. Key challenges include choosing the appropriate level of model complexity necessary for understanding biological patterns across space and time, and applying this understanding to solve problems in conservation biology and resource management. Meeting these challenges requires novel statistical and mathematical techniques for distinguishing among alternative ecological theories and hypotheses. Examples from a wide array of research areas in population biology and community ecology highlight the importance of fostering synergistic ties across disciplines for current and future research and application. © 2005 American Institute of Biological Sciences.

Format EndNote

Vous pouvez importer cette référence dans EndNote.

Format BibTeX-CSV

Vous pouvez importer cette référence en format BibTeX-CSV.

Format BibTeX

Vous pouvez copier l'entrée BibTeX de cette référence ci-bas, ou l'importer directement dans un logiciel tel que JabRef .

@ARTICLE { Green2005501,
    AUTHOR = { Green, J.L. and Hastings, A. and Arzberger, P. and Ayala, F.J. and Cottingham, K.L. and Cuddington, K. and Davis, F. and Dunne, J.A. and Fortin, M.-J. and Gerber, L. and Neubert, M. },
    TITLE = { Complexity in ecology and conservation: Mathematical, statistical, and computational challenges },
    JOURNAL = { BioScience },
    YEAR = { 2005 },
    VOLUME = { 55 },
    NUMBER = { 6 },
    PAGES = { 501-510 },
    NOTE = { cited By 72 },
    ABSTRACT = { Creative approaches at the interface of ecology, statistics, mathematics, informatics, and computational science are essential for improving our understanding of complex ecological systems. For example, new information technologies, including powerful computers, spatially embedded sensor networks, and Semantic Web tools, are emerging as potentially revolutionary tools for studying ecological phenomena. These technologies can play an important role in developing and testing detailed models that describe real-world systems at multiple scales. Key challenges include choosing the appropriate level of model complexity necessary for understanding biological patterns across space and time, and applying this understanding to solve problems in conservation biology and resource management. Meeting these challenges requires novel statistical and mathematical techniques for distinguishing among alternative ecological theories and hypotheses. Examples from a wide array of research areas in population biology and community ecology highlight the importance of fostering synergistic ties across disciplines for current and future research and application. © 2005 American Institute of Biological Sciences. },
    AFFILIATION = { School of Natural Sciences, University of California, PO Box 2039, Merced, CA 95344, United States; Department of Environmental Science and Policy, University of California, Davis, CA 95616, United States; National Biomedical Computation Resource, University of California, San Diego, San Diego, CA 92093, United States; Department of Ecology and Evolutionary Biology, University of California, Irvine, CA 92697, United States; Department of Biological Sciences, Dartmouth College, Hanover, NH 03755, United States; Department of Biological Sciences, Quantitative Biology Institute, Ohio University, Athens, OH 45701, United States; Donald Bren School of Environmental Science and Management, University of California, Santa Barbara, CA 93106, United States; Pacific Ecoinformatics and Computation Ecology Lab., 1604 McGee Avenue, Berkeley, CA 94703, United States; Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, United States; Department of Zoology, University of Toronto, Toronto, Ont. M5S 3G5, Canada; School of Life Sciences, Arizona State University, PO Box 871501, Tempe, AZ 85287, United States; Biology Department, Woods Hole Oceanographic Institution, Woods Hole, MA 02543, United States },
    AUTHOR_KEYWORDS = { Cyberinfrastructure; Ecological complexity; Metadata; Quantitative conservation biology; Semantic Web },
    DOCUMENT_TYPE = { Review },
    SOURCE = { Scopus },
    URL = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-17444419892&partnerID=40&md5=68c42428ed7c98c2f96fb0e6ad8c8a37 },
}

********************************************************** ***************** Facebook Twitter *********************** **********************************************************

Abonnez-vous à
l'Infolettre du CEF!

********************************************************** ************* Écoles d'été et formation **************************** **********************************************************

Écoles d'été et formations

********************************************************** ***************** Pub - Symphonies_Boreales ****************** **********************************************************

********************************************************** ***************** Boîte à trucs *************** **********************************************************

CEF-Référence
La référence vedette !

Jérémie Alluard (2016) Les statistiques au moments de la rédaction 

  • Ce document a pour but de guider les étudiants à intégrer de manière appropriée une analyse statistique dans leur rapport de recherche.

Voir les autres...