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A decision theory framework can be a powerful technique to derive optimal management decisions for endangered species. We built a spatially realistic stochastic metapopulation model for the Mount Lofty Ranges Southern Emu-wren (Stipiturus malachurus intermedius), a critically endangered Australian bird. Using diserete-time Markov,chains to describe the dynamics of a metapopulation and stochastic dynamic programming (SDP) to find optimal solutions, we evaluated the following different management decisions: enlarging existing patches, linking patches via corridors, and creating a new patch. This is the first application of SDP to optimal landscape reconstruction and one of the few times that landscape reconstruction dynamics have been integrated with population dynamics. SDP is a powerful tool that has advantages over standard Monte Carlo simulation methods because it can give the exact optimal strategy for every landscape configuration (combination of patch areas and presence of corridors) and pattern of metapopulation occupancy, as well as a trajectory of strategies. It is useful when a sequence of management actions can be performed over a given time horizon, as is the case for many endangered species recovery programs, where only fixed amounts of resources are available in each time step. However, it is generally limited by computational constraints to rather small networks of patches. The model shows that optimal metapopulation, management decisions depend greatly on the current state of the metapopulation,. and there is no strategy that is universally the best. The extinction probability over 30 yr for the optimal state-dependent management actions is 50-80% better than no management, whereas the best fixed state-independent sets of strategies are only 30% better than no management. This highlights the advantages of using a decision theory tool to investigate conservation strategies for metapopulations. It is clear from these results that the sequence of management actions is critical, and this can only be effectively derived from stochastic dynamic programming. The model illustrates the underlying difficulty in determining simple rules of thumb for the sequence of management actions for a metapopulation. This use of a decision theory framework extends the capacity of population viability analysis (PVA) to manage threatened species.

Lorenzo Pradelli,1 Massimiliano Povero,1 Hartmut Bürkle,2 Tim-Gerald Kampmeier,3 Giorgio Della-Rocca,4 Astrid Feuersenger,5 Jean-Francois Baron,6 Martin Westphal5 1AdRes HE&OR, Torino, Italy; 2Department of Anaesthesiology and Critical Care Medicine, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, 3Clinic for Anesthesiology and Surgical Intensive Care Medicine, University Hospital Münster, Münster, Germany; 4Department of Anaesthesia and Intensive Care Medicine, Medical School of the University of Udine, Udine, Italy; 5Fresenius Kabi Deutschland GmbH, Bad Homburg, Germany; 6Fresenius Kabi ELAMA, Paris Cedex, France Purpose: This evaluation compares propofol and benzodiazepine sedation for mechanically ventilated patients in intensive care units (ICUs) in order to identify the potential economic benefits from different payers’ perspectives. Methods: The patient-level simulation model incorporated efficacy estimates from a structured meta-analysis and ICU-related costs from Italy, Germany, France, UK, and the USA. Efficacy outcomes were ICU length of stay (LOS), mechanical ventilation duration, and weaning time. We calculated ICU costs from mechanical ventilation duration and ICU LOS based on national average ICU costs with and without mechanical ventilation. Three scenarios were investigated: 1) long-term sedation >24 hours based on results from randomized controlled trials (RCTs); 2) long-term sedation based on RCT plus non-RCT results; and 3) short-term sedation