Complexity in ecology and conservation : mathematical, statistical, and computational challenges


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dc.contributor.author Green, Jessica L.
dc.contributor.author Hastings, Alan
dc.contributor.author Arzberger, Peter
dc.contributor.author Ayala, Francisco J.
dc.contributor.author Cottingham, Kathryn L.
dc.contributor.author Cuddingham, Kim
dc.contributor.author Davis, Frank
dc.contributor.author Dunne, Jennifer A.
dc.contributor.author Fortin, Marie-Josee
dc.contributor.author Gerber, Leah
dc.contributor.author Neubert, Michael G.
dc.date.accessioned 2006-12-06T16:56:00Z
dc.date.available 2006-12-06T16:56:00Z
dc.date.issued 2005-06
dc.identifier.citation BioScience 55 (2005): 501–510 en
dc.identifier.uri http://hdl.handle.net/1912/1367
dc.description Author 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.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. en
dc.description.sponsorship This 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.extent 577104 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US en
dc.publisher American Institute of Biological Sciences en
dc.relation.uri http://dx.doi.org/10.1641/0006-3568(2005)055[0501:CIEACM]2.0.CO;2
dc.subject Ecological complexity en
dc.subject Quantitative conservation biology en
dc.subject Cyberinfrastructure en
dc.subject Metadata en
dc.subject Semantic Web en
dc.title Complexity in ecology and conservation : mathematical, statistical, and computational challenges en
dc.type Article en
dc.identifier.doi 10.1641/0006-3568(2005)055[0501:CIEACM]2.0.CO;2

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