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Preservation of biodiversity is a central goal of conservation management, yet the conditions that promote persistence may differ for the species in the community. For systems subject to stochastic disturbances such as fire, understanding which management practices promote persistence for all species in a community is complex. Before deciding on the best course of action, an objective must be specified. Yet an overarching goal of species persistence can be specified into a measureable objective many different ways. We investigated four alternative management objectives for maximizing species persistence that use common biodiversity indices: (1) attaining the minimally acceptable mix of successional vegetation states to support species' relative abundances, (2) maximizing the arithmetic mean abundance of species, (3) maximizing the geometric mean abundance of species, and (4) minimizing the average extinction risk of species. We used stochastic dynamic programming to model successional changes in vegetation in the presence of both planned and unplanned fires, and utilize an extensive data set on the occurrence of birds, reptiles, and small mammals in different successional states in semiarid Australia. We investigated the influence the choice of objective function and taxonomic focus has on the optimal fire management recommendations. We also evaluated a recent hazard reduction policy to annually burn a fixed amount of the landscape and compare results to the optimal solution. The optimal management strategy to maximize species persistence over a 100-year period is predominantly to minimize wildfires. This is because the majority of species are more likely to occur in intermediate and late successional vegetation. However the optimal solution showed sensitivity to the objective and the species included in the analysis. These results highlight the need for careful consideration when specifying an objective to represent overarching conservation goals. Using the extinction risk objective, we show that a policy to annually burn 5% of the landscape could increase the average probability of extinction for the modelled species by 7% over the next 100 years compared to the optimal management scenario. [ABSTRACT FROM AUTHOR]

The article presents a study regarding the active adaptive management of the Tasmanian devil which is an endangered species in Australia. It focuses on the management action for a facial tumor disease affecting the endangered species. It states that a stochastic dynamic programming with Bayesian model has been adopted to determine the best management strategy to maximize the population of the Tasmanian devil.

Australian urban water utilities face a significant challenge in designing appropriate demand management and supply augmentation policies in the presence of significant water scarcity and climate variability. This article considers the design of optimal demand management and supply augmentation policies for urban water. In particular, scarcity pricing is considered as a potential alternative to the predominant demand management policy of water restrictions. A stochastic dynamic programming model of an urban water market is developed based on data from the ACT region. Given a specification of the demand and supply for urban water state dependent optimal price and investment policies are estimated. The results illustrate how the optimal urban water price varies inversely with the prevailing storage level and how the optimal timing of investment differs significantly between rain dependent and rain independent augmentation options. [ABSTRACT FROM AUTHOR]

Translocation is a useful management option for conservation of threatened animal species. It can be used to increase the range of a species, augment the numbers in a critical population, or establish new populations and hence spread the risk of extinction through local catastrophes. As it is an important and expensive conservation tool, translocation management decisions must be carefully considered, with the objective of the translocation project in mind. By analysing the translocation problem within a decision-theory framework, we find optimal management decisions that are rational and transparent. We illustrate our approach using a case study of the bridled nailtail wallaby (Onychogalea fraenata). Our particular translocation question is: if we have a set number of wallabies to translocate in each time period and two translocation sites, how many wallabies should we put at each site given the state of each population to maximise the benefit to the species? We model the translocated populations with first-order Markov chain stochastic population models, and use stochastic dynamic programming to determine the optimal management decisions. We look at two sites with different growth rates – one increasing and one decreasing – and compare the optimal strategies for two different objective functions. The first is a long-term persistence objective function, which maximises the persistence of translocated populations a large number of time steps after the end of the translocation program. The second maximises total population size at the end of the translocation program. Although these objective functions are similar, they generate surprisingly different optimal translocation strategies. When maximising the long-term persistence of the translocated populations, translocation decisions are not important as long as an increasing population is established. This indicates that site quality – rather than the number and timing of translocations – primarily determines the long-term persistence of populations. When maximising total population size, the optimal strategy is to add to the increasing population unless it is above a size where it is likely to reach its carrying capacity over the planning timeframe. As translocation decisions are important in fulfilling the objective, this objective function is more useful in creating practical advice for translocation managers. The discrepancy between the optimal strategies given by the two objectives demonstrates the importance of careful consideration when specifying the goals of a project. This observation applies not only to translocation programs, but any project where clear decision-making is needed. [Copyright &y& Elsevier]

Seasonal variability is an important source of risk faced by farmers and, regardless of an individual's attitude to risk, there are options to tactically adjust production strategies as the outcomes of risk become known. The objective of this article is to measure the economic benefits of alternative approaches to managing weeds, one of the most serious production problems in Australian cropping systems. A bioeconomic model that combines weed biology, crop growth and economics is developed to value the effects of seasonal variability and the role of tactical responses and sequential decision making in determining an optimal integrated weed management strategy. This shows that there are substantial differences in the measured long-term benefits from deterministic and stochastic simulations. It is concluded that, for research evaluation of technologies that involve complex biological and dynamic systems, ignoring the impacts of seasonal variability, responses to risk and sequential decision making can lead to an incorrect estimate of the economic benefits of a technology. In this case study of optimal weed management strategies in Australia, the size of the error is high. [ABSTRACT FROM AUTHOR]

The article discusses problem of fire management in Ngarkat Conservation Park (CP) in South Australia, Australia and present a mathematical model to maintain to maintain community diversity from fire disturbance event in the park. It discusses optimal management strategy to manage wildfires. It also mentions development of methodology for conservation of plant biodiversity.