Obiettivo
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.
Campo scientifico
Invito a presentare proposte
FP7-PEOPLE-2009-IIF
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Meccanismo di finanziamento
MC-IIF - International Incoming Fellowships (IIF)Coordinatore
SW7 2AZ LONDON
Regno Unito