WS22 Adaptive management for managing biodiversity in a changing world
New Zealand 4 Thursday, December 8th 2011
Organizer(s): E. McDonald-Madden, T.G. Martin
12:30 From prediction to action - the science of saving species under climate change. Wintle, B.A., University of Melbourne
; Possingham, H.P.*, University of Queensland
Substantial investment in climate change research has led to dire predictions of the impacts and risks to biodiversity; the IPCC Fourth Assessment Report1 cites 28,586 studies demonstrating significant biological changes in terrestrial systems. Yet there is little advice or precedent in the literature to guide climate adaptation investment for conserving biodiversity. Given that there is an impending extinction crisis, we need to move urgently from predictive science to decision science in order to support difficult choices between climate adaptation options under severe uncertainty. Here we present the first systematic ecological and economic analysis of a climate adaptation problem in one of the world's most species rich and threatened ecosystems; the South African Fynbos. We discover a counterâintuitive optimal investment strategy that switches twice between options as the available adaptation budgets increases. We demonstrate that optimal investment is nonâlinearly dependent on available resources, making the choice of how much to invest as important as determining what to invest in and where. Our study emphasises the importance of a sound analytical framework for adaptation investment that integrates information and tools from ecology, economics, social science and decision science. Our method for prioritising investment can be applied at any scale to minimise the loss of species under climate change. We anticipate that the approach illustrated here will form the basis of future climate adaptation investments.
12:45 Using the expected value of information to identify critical uncertainties for adaptive management in the face of climate change. Runge, M.C.*, United States Geological Survey
Uncertainty clouds discussions about climate change, not simply by making it difficult to predict future outcomes, but also by distracting dialogue away from the substantive decisions at hand. In political discussions about how to respond to climate change, all sides use uncertainty to their advantage (by suggesting different risk tolerances and insisting on different burdens of proof), with the common outcome that true action is delayed while more information is gathered, and vague notions of adaptive management are advanced. The field of decision analysis does provide explicit and useful tools for analysing and understanding uncertainty, notably a technique known as the expected value of information. The value of information is the expected increase in the outcome of a decision if uncertainty is resolved; uncertainty that has a high value of information is uncertainty that impedes a decision-makerâs ability to choose the best course of action. The calculation of value of information, however, requires a decision-maker to clearly frame the decision and explicitly articulate uncertainty. For management of natural resources in the face of climate change, these are constructive and healthy challenges because they ground the discussion of uncertainty in the practical context of how it affects decisions, and they lead to development of adaptive management that focuses on relevant learning. The use of value of information to understand uncertainty and design an adaptive management approach is illustrated in the context of managed relocation, a climate adaptation strategy for moving species threatened with habitat loss.
13:00 When to move a species in the face of climate change. McDonald-Madden, E.*, University of Queensland and CSIRO Ecosystem Sciences
; Runge, M.C., United Stages Geological Survey; Possingham, H.P., University of Queensland ; Martin, T.G., CSIRO Ecosystem Sciences
A highly controversial biodiversity adaptation strategy to combat negative climate change impacts is managed relocation. While the scientific community debates the merits of managed relocation, species are already being moved to new areas predicted to be more suitable under climate change. Hence, guidance on when to implement managed relocation is urgently required. Using decision science thinking, we construct a framework to guide the timing of relocation given climate change. Counter-intuitively, we show that in some circumstances it may be optimal to wait and allow small populations to grow before moving them. Where there is uncertainty about the impact of climate change, it can be advantageous to wait and learn. These counterintuitive results show the importance of our framework for aiding decision-making on the timing of manage relocation. Our framework advances decision-making in the face of uncertainty about climate change.
13:15 Rethinking barriers and bridges to AM: risk, uncertainty, and indeterminism. Tyre, A.*, University of Nebraska
; Michaels, S., University of Nebraska
Adaptive Management doesnât provide what people need in all circumstances. Why? One reason is the failure to acknowledge that there are social origins of indeterminism with different consequences than natural sources of indeterminism. Consequently, we introduce the idea of social indeterminism as a form of not knowing what will happen that arises from the unpredictability of human interactions. We argue that Adaptive Management is most suited for situations where social indeterminism is low. We develop a matrix of natural and social indeterminism for explaining where Adaptive Management is most useful, and where it is most likely to be dissatisfying. We go on to discuss strategies that may be applicable when social indeterminism is high.
13:30 POMDPs: a solution for modelling adaptive management problems in conservation biology Chades, I.*, CSIRO Ecosystem Sciences
; Jalladeau, L., CSIRO Ecosystem Sciences; Carwardine, J., CSIRO Ecosystem Sciences; Martin, T.G., CSIRO Ecosystem Sciences; Nicol, S., University of Alaska; Buffet, O., INRIA, France
Adaptive management is the principle tool for conserving endangered species under global change, yet adaptive management problems suffer from a poor suite of solution methods. The common approach used to solve an adaptive management problem is to assume the system state is known and the system dynamics can be one of a set of pre-defined models. The solution method used is unsatisfactory, employing value iteration on a discretized belief MDP which restricts the study to very small problems. We show how to overcome this limitation by modelling an adaptive management problem as a special case of Partially Observable Markov Decision Process (POMDP) called a Mixed Observability MDP (MOMDP). We illustrate the use of our adaptive MOMDP to manage a population of the threatened Gouldian finch, a bird species endemic to Northern Australia.