The value of information for woodland management: updating a state–transition model

Abstract

Citation: Morris, W. K., M. C. Runge, and P. A. Vesk. 2017. The value of information for woodland management:updating a state-transition model. Ecosphere 8(11):e01998. 10. 1002/ecs2.1998 Abstract. Value of information (VOI) analyses reveal the expected benefit of reducing uncertainty to a decision maker. Most ecological VOI analyses have focused on population models rarely addressing more complex community models. We performed a VOI analysis for a complex state-transition model of Box-Ironbark Forest and Woodland management. With three management alternatives (limited harvest/ firewood removal (HF), ecological thinning (ET), and no management), managing the system optimally (for 150 yr) with the original information would, on average, increase the amount of forest in a desirable state from 19% to 35% (a 16-percentage point increase). Resolving all uncertainty would, on average, increase the final percentage to 42% (a 19-percentage point increase). However, only resolving the uncertainty for a single parameter was worth almost two-thirds the value of resolving all uncertainty. We found the VOI to depend on the number of management options, increasing as the management flexibility increased. Our analyses show it is more cost-effective to monitor low-density regrowth forest than other states and more cost-effective to experiment with the no-management alternative than the other management alternatives. Importantly, the most cost-effective strategies did not include either the most desired forest states or the least understood management strategy, ET. This implies that managers cannot just rely on intuition to tell them where the most VOI will lie, as critical uncertainties in a complex system are sometimes cryptic.

Combining Species Distribution Models and Value of Information Analysis for Spatial Allocation of Conservation Resources

“…How much this new information is expected to improve a decision, is known as the expected value of sample information (EVSI) and can be used to evaluate different information gathering scenarios (Raiffa & Schlaifer, 1961). VOI theory has been explored in many disciplines such as environmental health risk (Yokota & Thompson, 2004), disease control (Shea, Tildesley, Runge, Fonnesbeck, & Ferrari 2004), environmental remediation (Dakins, Toll, & Small 1994;Dakins, Toll, Small & Brand 1996;Dakins, 1999), and resource management (Williams, Eaton & Breininger 2011;Williams & Johnson, 2015;Morris, Runge & Vesk 2017). However, application of VOI to conservation has been limited, and has yet to be applied at realistic scales Sequential decisions involving the allocation of conservation resources over time have been assessed previously using stochastic dynamic programing (SDP) to solve Completely (MDP) or Partially (POMDP) Observable Markov Decision Process (Marescot et al, 2013) for situations involving fewer than three populations (Chadès et al, 2008, McDonald-Madden et al, 2011).…”