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dc.contributor.authorGreen, Jessica L.
dc.contributor.authorHastings, Alan
dc.contributor.authorArzberger, Peter
dc.contributor.authorAyala, Francisco J.
dc.contributor.authorCottingham, Kathryn L.
dc.contributor.authorCuddingham, Kim
dc.contributor.authorDavis, Frank
dc.contributor.authorDunne, Jennifer A.
dc.contributor.authorFortin, Marie-Josee
dc.contributor.authorGerber, Leah
dc.contributor.authorNeubert, Michael G.
dc.date.accessioned2006-12-06T16:56:00Z
dc.date.available2006-12-06T16:56:00Z
dc.date.issued2005-06
dc.identifier.citationBioScience 55 (2005): 501–510en
dc.identifier.urihttp://hdl.handle.net/1912/1367
dc.descriptionAuthor Posting. © American Institute of Biological Sciences, 2005. This article is posted here by permission of American Institute of Biological Sciences for personal use, not for redistribution. The definitive version was published in BioScience 55 (2005): 501–510, doi:10.1641/0006-3568(2005)055[0501:CIEACM]2.0.CO;2.en
dc.description.abstractCreative 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.en
dc.description.sponsorshipThis paper is the result of a National Science Foundation (NSF) workshop on quantitative environmental and integrative biology (DEB-0092081). J. L. G. would like to acknowledge financial support from the NSF (DEB-0107555).en
dc.format.extent577104 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen
dc.publisherAmerican Institute of Biological Sciencesen
dc.relation.urihttp://dx.doi.org/10.1641/0006-3568(2005)055[0501:CIEACM]2.0.CO;2
dc.subjectEcological complexityen
dc.subjectQuantitative conservation biologyen
dc.subjectCyberinfrastructureen
dc.subjectMetadataen
dc.subjectSemantic Weben
dc.titleComplexity in ecology and conservation : mathematical, statistical, and computational challengesen
dc.typeArticleen
dc.identifier.doi10.1641/0006-3568(2005)055[0501:CIEACM]2.0.CO;2


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