Helen M. Regan, Yakov Ben-Haim, Bill Langford, Will G. Wilson, Per Lundberg, Sandy J. Andelman, Mark A. Burgman, 2005, Robust decision making under severe uncertainty for conservation management, Ecological Applications, vol.15(4): 1471-1477.
Mark A. Burgman and Helen M. Regan, 2012, Information-gap decision theory fills a gap in ecological applications, Letter to the Editors, Ecological Applications, 24(1), pp. 227-228.
Mark A. Burgman, 2008, Shakespeare, Wald and decision making under uncertainty, Decision Point #23, p.10. On-line version (see p.10).
S. Arnold, S. Attinger, K. Frank, P. Baxter, H. Possingham and A. Hildebrandt, 2014, Ecosystem management along ephemeral rivers: trading off socioeconomic water supply and vegetation conservation under flood regime uncertainty, River Research and Applications, Article first published online: 22 Oct 2014, DOI: 10.1002/rra.2853.
Hiroyuki Yokomizo, Shaun R. Coutts and Hugh P. Possingham, 2014, Decision science for effective management of populations subject to stochasticity and imperfect knowledge, Population Ecology, 56:41-53.
Many species are threatened by human activity through processes such as habitat modification, water management, hunting, and introduction of invasive species. These anthropogenic threats must be mitigated as efficiently as possible because both time and money available for mitigation are limited. For example, it is essential to address the type and degree of uncertainties present to derive effective management strategies for managed populations. Decision science provides the tools required to produce effective management strategies that can maximize or minimize the desired objective(s) based on imperfect knowledge, taking into account stochasticity. Of particular importance are questions such as how much of available budgets should be invested in reducing uncertainty and which uncertainties should be reduced. In such instances, decision science can help select efficient environmental management actions that may be subject to stochasticity and imperfect knowledge. Here, we review the use of decision science in environmental management to demonstrate the utility of the decision science framework. Our points are illustrated using examples from the literature.We conclude that collaboration between theoreticians and practitioners is crucial to maximize the benefits of decision science’s rational approach to dealing with uncertainty.
Adaptive management, Information-gap decision theory, Monitoring, Stochastic dynamic programming, Uncertainty, Value of information analysis
Adam W. Schapaugh and Andrew J. Tyre, 2013, Accounting for parametric uncertainty in Markov decision processes, Ecological Modelling, 254: 15-21.
Markov decision processes have become the standard tool for modeling sequential decision-making problems in conservation. In many real-world applications, however, it is practically infeasible to accurately parameterize the state transition function. In this study, we introduce a new way of dealing with ambiguity in the state transition function. In contrast to existing methods, we explore the effects of uncertainty at the level of the policy, rather than at the level of decisions within states. We use information-gap decision theory to ask the question of how much uncertainty in the state transition function can be tolerated while still delivering a specified expected value given by the objective function. Accordingly, the goal of the optimization problem is no longer to maximize expected value, but to maximize local robustness to uncertainty (while still meeting the desired level of performance). We analyze a simple land acquisition problem, using info-gap decision theory to propagate uncertainties and rank alternative policies. Rather than requiring information about the extent of parameter uncertainty at the outset, info-gap addresses the question of how much uncertainty is permissible in the state transition function before the optimal policy would change.
Post van der Burg, M., Bly, B.B., Vercauteren, T., Grand, J.B.d, Tyre, A.J., 2014, On the role of budget sufficiency, cost efficiency, and uncertainty in species management, Journal of Wildlife Management, 78 (1) pp. 153-163.
Many conservation planning frameworks rely on the assumption that one should prioritize locations for management actions based on the highest predicted conservation value (i.e., abundance, occupancy). This strategy may underperform relative to the expected outcome if one is working with a limited budget or the predicted responses are uncertain. Yet, cost and tolerance to uncertainty rarely become part of species management plans. We used field data and predictive models to simulate a decision problem involving western burrowing owls (Athene cunicularia hypugaea) using prairie dog colonies (Cynomys ludovicianus) in western Nebraska. We considered 2 species management strategies: one maximized abundance and the other maximized abundance in a cost-efficient way. We then used heuristic decision algorithms to compare the 2 strategies in terms of how well they met a hypothetical conservation objective. Finally, we performed an info-gap decision analysis to determine how these strategies performed under different budget constraints and uncertainty about owl response. Our results suggested that when budgets were sufficient to manage all sites, the maximizing strategy was optimal and suggested investing more in expensive actions. This pattern persisted for restricted budgets up to approximately 50% of the sufficient budget. Below this budget, the cost-efficient strategy was optimal and suggested investing in cheaper actions. When uncertainty in the expected responses was introduced, the strategy that maximized abundance remained robust under a sufficient budget. Reducing the budget induced a slight trade-off between expected performance and robustness, which suggested that the most robust strategy depended both on one’s budget and tolerance to uncertainty. Our results suggest that wildlife managers should explicitly account for budget limitations and be realistic about their expected levels of performance.
Walshe, Terry and Tilo Massenbauer, 2008, Decision-making under climatic uncertainty: A case study involving an Australian Ramsar-listed wetland, Ecological Management and Restoration , Volume 9, Issue 3, December 2008, Pages 202-208.
van der Burg, M.P. and Tyre, A.J., 2011, Integrating info-gap decision theory with robust population management: a case study using the Mountain Plover, Ecological Applications, 21(1): 303-12.
Wildlife managers often make decisions under considerable uncertainty. In the most extreme case, a complete lack of data leads to uncertainty that is unquantifiable. Information-gap decision theory deals with assessing management decisions under extreme uncertainty, but it is not widely used in wildlife management. So too, robust population management methods were developed to deal with uncertainties in multiple-model parameters. However, the two methods have not, as yet, been used in tandem to assess population management decisions. We provide a novel combination of the robust population management approach for matrix models with the information-gap decision theory framework for making conservation decisions under extreme uncertainty. We applied our model to the problem of nest survival management in an endangered bird species, the Mountain Plover (Charadrius montanus). Our results showed that matrix sensitivities suggest that nest management is unlikely to have a strong effect on population growth rate, confirming previous analyses. However, given the amount of uncertainty about adult and juvenile survival, our analysis suggested that maximizing nest marking effort was a more robust decision to maintain a stable population. Focusing on the twin concepts of opportunity and robustness in an information-gap model provides a useful method of assessing conservation decisions under extreme uncertainty.
Eve McDonald-Madden, Peter W. J. Baxter and Hugh P. Possingham, 2008, Making robust decisions for conservation with restricted money and knowledge, Journal of Applied Ecology, 45, pp.1630-1638.
In conservation decision-making, we operate within the confines of limited funding. Furthermore, we often assume particular relationships between management impact and our investment in management. The structure of these relationships, however, is rarely known with certainty – there is model uncertainty. We investigate how these two fundamentally limiting factors in conservation management, money and knowledge, impact optimal decision-making.
We use information-gap decision theory to find strategies for maximizing the number of extant subpopulations of a threatened species that are most immune to failure due to model uncertainty. We thus find a robust framework for exploring optimal decision-making.
The performance of every strategy decreases as model uncertainty increases.
The strategy most robust to model uncertainty depends not only on what performance is perceived to be acceptable but also on available funding and the time horizon over which extinction is considered.
Synthesis and applications . We investigate the impact of model uncertainty on robust decisionmaking in conservation and how this is affected by available conservation funding. We show that subpopulation triage can be a natural consequence of robust decision-making. We highlight the need for managers to consider triage not as merely giving up, but as a tool for ensuring species persistence in light of the urgency of most conservation requirements, uncertainty and the poor state of conservation funding. We illustrate this theory by a specific application to allocation of funding to reduce poaching impact on the Sumatran tiger Panthera tigris sumatrae in Kerinci Seblat National Park.
McDonald-Madden, E., Baxter, P.W.J., Possingham, H.P., 2011, Robust conservation decision-making, International Conference on Vulnerability and Risk Analysis and Management, ICVRAM 2011Hyattsville, MD; 11-13 April 2011, pp.945-952.
Decision-making for conservation is conducted within the margins of limited funding. Furthermore, to allocate these scarce resources we make assumptions about the relationship between management impact and expenditure. The structure of these relationships, however, is rarely known with certainty. We present a summary of work investigating the impact of model uncertainty on robust decision-making in conservation and how this is affected by available conservation funding. We show that achieving robustness in conservation decisions can require a triage approach, and emphasize the need for managers to consider triage not as surrendering but as rational decision making to ensure species persistence in light of the urgency of the conservation problems, uncertainty, and the poor state of conservation funding. We illustrate this theory by a specific application to allocation of funding to reduce poaching impact on the Sumatran tiger Panthera tigris sumatrae in Kerinci Seblat National Park, Indonesia. To conserve our environment, conservation managers must make decisions in the face of substantial uncertainty. Further, they must deal with the fact that limitations in budgets and temporal constraints have led to a lack of knowledge on the systems we are trying to preserve and on the benefits of the actions we have available (Balmford & Cowling 2006). Given this paucity of decision-informing data there is a considerable need to assess the impact of uncertainty on the benefit of management options (Regan et al. 2005). Although models of management impact can improve decision making (e.g.Tenhumberg et al. 2004), they typically rely on assumptions around which there is substantial uncertainty. Ignoring this ‘model uncertainty’, can lead to inferior decision-making (Regan et al. 2005), and potentially, the loss of the species we are trying to protect. Current methods used in ecology allow model uncertainty to be incorporated into the model selection process (Burnham & Anderson 2002; Link & Barker 2006), but do not enable decision-makers to assess how this uncertainty would change a decision. This is the basis of information-gap decision theory (info-gap); finding strategies most robust to model uncertainty (Ben-Haim 2006). Info-gap has permitted conservation biology to make the leap from recognizing uncertainty to explicitly incorporating severe uncertainty into decision-making. In this paper we present a summary of McDonald-Madden et al (2008a) who use an info-gap framework to address the impact of uncertainty in the functional representations of biological systems on conservation decision-making. Furthermore, we highlight the importance of two key elements limiting conservation decision-making – funding and knowledge – and how they interact to influence the best management strategy for a threatened species.
Crone, Elizabeth E., Debbie Pickering and Cheryl B. Schultz, 2007, Can captive rearing promote recovery of endangered butterflies? An assessment in the face of uncertainty, Biological Conservation, vol.139, #1-2,pp.103-112.
Federico Montesino Pouzols, Mark A. Burgman and Atte Moilanen, 2012, Methods for allocation of habitat management, maintenance, restoration and offsetting, when conservation actions have uncertain consequences, Biological Conservation, 153 (2012) 41-50.
We develop methods for conservation resource allocation, to help with decisions about targeting of protection, habitat management, maintenance and restoration or biodiversity offsetting. We construct a framework, where conservation actions have different responses for different biodiversity features in different environments, and in which uncertainty in responses and the time perspective are explicitly considered. Costs of actions in different environments are also accounted for. Costs can be defined as constants, functions of time or as functions of the total area in which an action is performed. We optimize the combination of actions to maximize conservation value given uncertain responses, limited resources, different robustness requirements and limits to the area in which different actions can be undertaken. Accounting for the uncertainty in responses to actions or accounting for time can change the optimal combination of actions. We can account for both negative consequences of uncertainty (robustness analysis) and positive aspects of uncertainty (opportunity analysis). To allow for the complexity of the analysis above and to significantly reduce data demands, we have omitted an explicit spatial structure from these analyses. Nevertheless, we describe approaches that account for spatial considerations, for example, by using the present methods in combination with software that is intended for the spatial analysis of static biodiversity pattern. The proposed analyses have been implemented in a software package called RobOff, which will be made freely, publicly available. Thereby it is possible for the first time to effectively find solutions to a significant set of conservation resource allocation problems. These analyses can assist conservation scientists and managers in decision making based on quantitative analysis.
Benefit function, Complementarity, Computational sustainability, Decision support tool, Scoring, Robustness
Atte Moilanen, Michael C. Runge, Jane Elith, Andrew Tyre, Yohay Carmel, Eric Fegraus, Brendan Wintle, Mark Burgman and Yakov Ben-Haim, 2006, Planning for robust reserve networks using uncertainty analysis,Ecological Modelling, vol. 199, issue 1, pp.115-124.
M. A. Burgman, D.B. Lindenmayer, and J. Elith, Managing landscapes for conservation under uncertainty,Ecology, 86(8), 2005, pp. 2007–-2017.
Burgman, Mark, 2005, Risks and Decisions for Conservation and Environmental Management, Cambridge University Press, Cambridge.
An article, commentary, and rejoinder:
Benjamin S. Halpern, Helen M. Regan, Hugh P. Possingham and Michael A. McCarthy, 2006, Accounting for uncertainty in marine reserve design, Ecology Letters, 9: 2-11.
Commentary on this article by Marc Mangel: Marc Mangel, 2006, Accounting for uncertainty in marine reserve design, Ecology Letters, 9: 11-12.
Benjamin S. Halpern, Helen M. Regan, Hugh P. Possingham and Michael A. McCarthy, 2006, Rejoinder: Uncertainty and decision making, Ecology Letters, 9: 13-14.
Moilanen, A. and B.A. Wintle, 2006, Uncertainty analysis favours selection of spatially aggregated reserve structures. Biological Conservation, Volume 129, Issue 3, May 2006, Pages 427-434.
Moilanen, A., B.A. Wintle., J. Elith and M. Burgman, 2006, Uncertainty analysis for regional-scale reserve selection. Conservation Biology, Vol.20, No. 6, 1688–1697.
Nicholson, Emily and Hugh P. Possingham, 2007, Conservation planning for the persistence of multiple species under uncertainty: an application of info-gap decision theory, Ecological Applications, 17: 251-265