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1. Making effective management decisions is challenging in multi-species, multi-threat systems because of uncertainty about the effects of different threats on different species. To inform management decisions, we often monitor species to detect spatial or temporal trends that can help us learn about threatening processes. However, which species to monitor and how to monitor to inform the management of threats can be difficult to determine.2. Value of information (VOI) analysis is an approach for quantifying the value of monitoring to inform management decisions. We developed a novel method that applies VOI analysis to quantify the benefits of different species monitoring strategies in multi-threat, multi-species systems. We applied the approach to compare the effectiveness of surveillance monitoring (monitoring species without experimentation) to targeted monitoring (monitoring species with experimentation to learn about a specific threat), and how prior information drives the benefits of these two different strategies and the species to monitor. We also illustrate the approach by applying it to two contrasting case studies for monitoring and managing declining mammals in Western Australia.3. Our approach shows that surveillance monitoring generally provides far lower benefits than targeted monitoring for managing threats in multi-species, multi-threat systems under economic constraints.4. Our approach also informs the choice of species to monitor and which threats to manage experimentally to most improve threat management outcomes. We show that the key parameters driving these choices include: the budget available for management, prior understanding of which threats cause declines in which species, the relative cost of managing these threats, and the background probability of decline.5. Our new VOI approach allows the evaluation of monitoring decisions in multi-species, multi-threat systems in the face of uncertainty, while explicitly accounting for the improvement in management outcomes. We recommend that managers need to explicitly consider a range of decision parameters when selecting which species to monitor to inform management. Our framework provides an objective way to do this.

1.Species management requires decision-making under uncertainty. Given a management objective and limited budget, managers need to decide what to do, and where and when to do it. A schedule of management actions that achieves the best performance is an optimal policy. A popular optimisation technique used to find optimal policies in ecology and conservation is stochastic dynamic programming (SDP). Most SDP approaches can only accommodate actions of equal durations. However, in many situations, actions take time to implement or cannot change rapidly. Calculating the optimal policy of such problems is computationally demanding and becomes intractable for large problems. Here, we address the problem of implementing several actions of different durations simultaneously. 2.We demonstrate analytically that synchronising actions and their durations provide upper and lower bounds of the optimal performance. These bounds provide a simple way to evaluate the performance of any policy, including rules of thumb. We apply this approach to the management of a dynamic ecological network of Aedes albopictus, an invasive mosquito that vectors human diseases. The objective is to prevent mosquitoes from colonising mainland Australia from the nearby Torres Straits Islands where managers must decide between management actions that differ in duration and effectiveness. 3.We were unable to compute an optimal policy for more than eight islands out of 17, but obtained upper and lower bounds for up to 13 islands. These bounds are within 16% of an optimal policy. We used the bounds to recommend managing highly populated islands as a priority. 4.Our approach calculates upper and lower bounds for the optimal policy by solving simpler problems that are guaranteed to perform better and worse than the optimal policy, respectively. By providing bounds on the optimal solution, the performance of policies can be evaluated even if the optimal policy cannot be calculated. Our general approach can be replicated for problems where simultaneous actions of different durations need to be implemented.

Summary Applied ecologists continually advocate further research, under the assumption that obtaining more information will lead to better decisions. Value of information (VoI) analysis can be used to quantify how additional information may improve management outcomes: despite its potential, this method is still underused in environmental decision-making. We provide a primer on how to calculate the VoI and assess whether reducing uncertainty will change a decision. Our aim is to facilitate the application of VoI by managers who are not familiar with decision-analytic principles and notation, by increasing the technical accessibility of the tool. Calculating the VoI requires explicit formulation of management objectives and actions. Uncertainty must be clearly structured and its effects on management outcomes evaluated. We present two measures of the VoI. The expected value of perfect information is a calculation of the expected improvement in management outcomes that would result from access to perfect knowledge. The expected value of sample information calculates the improvement in outcomes expected by collecting a given sample of new data. We guide readers through the calculation of VoI using two case studies: (i) testing for disease when managing a frog species and (ii) learning about demographic rates for the reintroduction of an endangered turtle. We illustrate the use of Bayesian updating to incorporate new information. The VoI depends on our current knowledge, the quality of the information collected and the expected outcomes of the available management actions. Collecting information can require significant investments of resources; VoI analysis assists managers in deciding whether these investments are justified.

When managing endangered species the consequences of making a poor decision can be extinction. To make a good decision, we must account for the stochastic dynamic of the population over time. To this end stochastic dynamic programming (SDP) has become the most widely used tool to calculate the optimal policy to manage a population over time and under uncertainty. However, as a result of its prohibitive computational complexity, SDP has been limited to solving small dimension problems, which results in SDP models that are either oversimplified or approximated using greedy heuristics that only consider the immediate rewards of an action. We present a heuristic sampling (HS) method that approximates the optimal policy for any starting state. The method is attractive for problems with large state spaces as the running time is independent of the size of the problem state space and improves with time. We demonstrate that the HS method out-performs a commonly used greedy heuristic and can quickly solve a problem with 33million states. This is roughly 3 orders of magnitude larger than the largest problems that can currently be solved with SDP methods. 5.We found that HS out-performs greedy heuristics and can give near-optimal policies in shorter timeframes than SDP. HS can solve problems with state spaces that are too large to optimize with SDP. Where the state space size precludes SDP, we argue that HS is the best technique. © 2010 The Authors. Methods in Ecology and Evolution