RHINOMITE

A test of Bayesian decision analysis and the implications for conservation

Coordinatore	IMPERIAL COLLEGE OF SCIENCE, TECHNOLOGY AND MEDICINE    more  Organization address address: SOUTH KENSINGTON CAMPUS EXHIBITION ROAD city: LONDON postcode: SW7 2AZ contact info Titolo: Ms. Nome: Brooke Cognome: Alasya Email: send email Telefono: +44 207 594 1181 Fax: +44 207 594 1418
Nazionalità Coordinatore	United Kingdom [UK]
Totale costo	174˙240 €
EC contributo	174˙240 €
Programma	FP7-PEOPLE Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
Code Call	FP7-PEOPLE-2009-IIF
Funding Scheme	MC-IIF
Anno di inizio	2011
Periodo (anno-mese-giorno)	2011-02-21   -   2013-02-20

 Partecipanti

#	participant	country	role	EC contrib. [€]
1	IMPERIAL COLLEGE OF SCIENCE, TECHNOLOGY AND MEDICINE  Organization address address: SOUTH KENSINGTON CAMPUS EXHIBITION ROAD city: LONDON postcode: SW7 2AZ contact info Titolo: Ms. Nome: Brooke Cognome: Alasya Email: send email Telefono: +44 207 594 1181 Fax: +44 207 594 1418	UK (LONDON)	coordinator	174˙240.80

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 Obiettivo del progetto (Objective)

'Conservation biologists work hard to prevent species extinction and rely on robust quantitative methods to achieve these ends. Decision frameworks allow assessment of alternate management strategies for threatened species, and may rely on simulated population models where data are unavailable or unreliable. Bayesian inference can be used to inform decision frameworks, and has come to the attention of many biologists because the Baye’s approach allows expert opinion or informed priors to update models where data are otherwise unavailable. These require further testing, and this is often difficult where data for long-lived species are difficult to collect. Here I will use short-lived soil mite (Sancassania berlesei) populations in a laboratory environment to replicate subpopulations critically endangered black rhino (Diceros bicornis). By confronting Bayesian models with real data, I hope to test the robustness of priors in Bayesian models, compare this approach to more traditional frequentist approaches and gain insight into the usefulness of Bayesian in decision-making. I propose to take this further and use the knowledge gained to develop and configure a decision making framework for the management of black rhino populations across southern Africa.'