Detection experiments are relatively new to weed management and provide an opportunity to assess the capability of weed surveys. Long-held statistical principles such as stratification, randomisation and replication should guide detection experiment design, but these can conflict with the need to create a realistic search environment, and the logistics of conducting the experiment. In this paper we outline some of these principles and trade-offs, with particular reference to a recent experiment on the detectability of hawkweeds in the Victorian Alps.
The quality of environmental decisions are gauged according to the management objectives of a conservation project. Management objectives are generally about maximising some quantifiable measure of system benefit, for instance population growth rate. They can also be defined in terms of learning about the system in question, in such a case actions would be chosen that maximise knowledge gain, for instance in experimental management sites. Learning about a system can also take place when managing practically. The adaptive management framework (Walters 1986) formally acknowledges this fact by evaluating learning in terms of how it will improve management of the system and therefore future system benefit. This is taken into account when ranking actions using stochastic dynamic programming (SDP). However, the benefits of any management action lie on a spectrum from pure system benefit, when there is nothing to be learned about the system, to pure knowledge gain. The current adaptive management framework does not permit management objectives to evaluate actions over the full range of this spectrum. By evaluating knowledge gain in units distinct to future system benefit this whole spectrum of management objectives can be unlocked. This paper outlines six decision making policies that differ across the spectrum of pure system benefit through to pure learning. The extensions to adaptive management presented allow specification of the relative importance of learning compared to system benefit in management objectives. Such an extension means practitioners can be more specific in the construction of conservation project objectives and be able to create policies for experimental management sites in the same framework as practical management sites.
Adaptive management has a long history in the natural resource management literature, but despite this, few practitioners have developed adaptive strategies to conserve threatened species. Active adaptive management provides a framework for valuing learning by measuring the degree to which it improves long-run management outcomes. The challenge of an active adaptive approach is to find the correct balance between gaining knowledge to improve management in the future and achieving the best short-term outcome based on current knowledge. We develop and analyze a framework for active adaptive management of a threatened species. Our case study concerns a novel facial tumor disease affecting the Australian threatened species Sarcophilus harrisii: the Tasmanian devil. We use stochastic dynamic programming with Bayesian updating to identify the management strategy that maximizes the Tasmanian devil population growth rate, taking into account improvements to management through learning to better understand disease latency and the relative effectiveness of three competing management options. Exactly which management action we choose each year is driven by the credibility of competing hypotheses about disease latency and by the population growth rate predicted by each hypothesis under the competing management actions. We discover that the optimal combination of management actions depends on the number of sites available and the time remaining to implement management. Our approach to active adaptive management provides a framework to identify the optimal amount of effort to invest in learning to achieve long-run conservation objectives.
Translocation-the deliberate, human-mediated movement of organisms-is a useful conservation tool most often employed in attempts to increase persistence of threatened or depleted species. Translocation projects involve difficult management decisions regarding the allocation of animals between sites. This research provides a rational scientific basis for these decisions. We use a stochastic population model and Stochastic Dynamic Programming to determine optimal translocation strategies for theoretical populations, and apply this framework to a case study on the Bridled Nailtail Wallaby (Onychogalea fraenata).
Summary Ecosystem‐based management requires predictive models of ecosystem dynamics. There are typically insufficient empirical data available to parameterise these complex models, and so decision‐makers commonly rely on beliefs elicited from experts. However, such expert beliefs are necessarily limited because (i) only a small proportion of ecosystem components and dynamics have been observed; (ii) uncertainty about ecosystem dynamics can result in contradictory expert judgements and (iii) elicitation time and resources are limited. We use an ensemble of dynamic ecosystem models to extrapolate a limited set of stated expert beliefs into a wider range of revealed beliefs about how the ecosystem will respond to perturbations and management. Importantly, the method captures the expert uncertainty and propagates it through to predictions. We demonstrate this process and its potential value by applying it to the conservation of the threatened malleefowl ( Leipoa ocellata ) in the Murray mallee ecosystems of southern Australia. In two workshops, we asked experts to construct a qualitative ecosystem interaction network and to describe their beliefs about how the ecosystem will respond to particular perturbations. We used this information to constrain an ensemble of 10 9 community models, leaving a subset that could reproduce stated expert beliefs. We then interrogated this ensemble of models to reveal experts’ implicit beliefs about management scenarios that were not a part of the initial elicitation exercises. Our method uses straightforward questions to efficiently elicit expert beliefs, and then applies a flexible modelling approach to reveal those experts’ beliefs about the dynamics of the entire ecosystem. It allows rapid planning of ecosystem‐based management informed by expert judgement, and provides a basis for value‐of‐information analyses and adaptive management.
Abstract Aim Decision‐making in weed management involves consideration of limited budgets, long time horizons, conflicting priorities, and as a result, trade‐offs. Economics provides tools that allow these issues to be addressed and is thus integral to management of the risks posed by weeds. One of the critical issues in weed risk management during the early stages of an invasion concerns feasibility of eradication. We briefly review how economics may be used in weed risk management, concentrating on this management strategy. Location Australia. Methods A range of innovative studies that investigate aspects of weed risk management are reviewed. We show how these could be applied to newly invading weeds, focussing on methods for investigating eradication feasibility. In particular, eradication feasibility is analysed in terms of cost and duration of an eradication programme, using a simulation model based on field‐derived parameter values for chromolaena, Chromolaena odorata . Results The duration of an eradication programme can be reduced by investing in progressively higher amounts of search effort per hectare, but increasing search area will become relatively more expensive as search effort increases. When variation in survey and control success is taken into account, increasing search effort also reduces uncertainty around the required duration of the eradication programme. Main conclusions Economics is integral to the management of the risks posed by weeds. Decision analysis, based on economic principles, is now commonly used to tackle key issues that confront weed managers. For eradication feasibility, duration and cost of a weed eradication programme are critical components; the dimensions of both factors can usefully be estimated through simulation.
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