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A test of Bayesian decision analysis and the implications for conservation

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.

Call for proposal

FP7-PEOPLE-2009-IIF
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Coordinator

IMPERIAL COLLEGE OF SCIENCE TECHNOLOGY AND MEDICINE
EU contribution
€ 174 240,80
Address
SOUTH KENSINGTON CAMPUS EXHIBITION ROAD
SW7 2AZ LONDON
United Kingdom

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Region
London Inner London — West Westminster
Activity type
Higher or Secondary Education Establishments
Administrative Contact
Brooke Alasya (Ms.)
Links
Total cost
No data