Using Bayesian Mixture Models That Combine Expert Knowledge and GIS Data to Define Ecoregions

Williams, Kristen J., Low-Choy, Samantha, Rochester, Wayne, & Alston, Clair (2012) Using Bayesian Mixture Models That Combine Expert Knowledge and GIS Data to Define Ecoregions. In Perera, Ajith H., Drew, C. Ashton, & Johnston, Chris J. (Eds.) Expert Knowledge and Its Application in Landscape Ecology. Springer Science+Business Media, United States of America, pp. 229-251.

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Abstract

Conservation planning and management programs typically assume relatively homogeneous ecological landscapes. Such “ecoregions” serve multiple purposes: they support assessments of competing environmental values, reveal priorities for allocating scarce resources, and guide effective on-ground actions such as the acquisition of a protected area and habitat restoration. Ecoregions have evolved from a history of organism–environment interactions, and are delineated at the scale or level of detail required to support planning. Depending on the delineation method, scale, or purpose, they have been described as provinces, zones, systems, land units, classes, facets, domains, subregions, and ecological, biological, biogeographical, or environmental regions. In each case, they are essential to the development of conservation strategies and are embedded in government policies at multiple scales.

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ID Code: 80503
Item Type: Book Chapter
Keywords: bioregionalisation, finite mixture models, expert elicitation, Bayesian hierarchical modelling, expert knowledge, ecology
DOI: 10.1007/978-1-4614-1034-8_12
ISBN: 9781461410331
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400) > Applied Statistics (010401)
Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400) > Statistical Theory (010405)
Divisions: Current > Schools > School of Mathematical Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Funding:
Copyright Owner: Copyright 2012 Springer Science+Business Media, LLC
Copyright Statement: The final publication is available at Springer via http://dx.doi.org/10.1007/978-1-4614-1034-8_12
Deposited On: 21 Jan 2015 22:48
Last Modified: 25 Jul 2015 06:44

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