title: Two-step adaptive management for choosing between two management actions.
abstract: Adaptive management is widely advocated to improve environmental management. Derivations of optimal strategies for adaptive management, however, tend to be case specific and time consuming. In contrast, managers might seek relatively simple guidance, such as insight into when a new potential management action should be considered, and how much effort should be expended on trialing such an action. We constructed a two-time-step scenario where a manager is choosing between two possible management actions. The manager has a total budget that can be split between a learning phase and an implementation phase. We use this scenario to investigate when and how much a manager should invest in learning about the management actions available. The optimal investment in learning can be understood intuitively by accounting for the expected value of sample information, the benefits that accrue during learning, the direct costs of learning, and the opportunity costs of learning. We find that the optimal proportion of the budget to spend on learning is characterized by several critical thresholds that mark a jump from spending a large proportion of the budget on learning to spending nothing. For example, as sampling variance increases, it is optimal to spend a larger proportion of the budget on learning, up to a point: if the sampling variance passes a critical threshold, it is no longer beneficial to invest in learning. Similar thresholds are observed as a function of the total budget and the difference in the expected performance of the two actions. We illustrate how this model can be applied using a case study of choosing between alternative rearing diets for hihi, an endangered New Zealand passerine. Although the model presented is a simplified scenario, we believe it is relevant to many management situations. Managers often have relatively short time horizons for management, and might be reluctant to consider further investment in learning and monitoring beyond collecting data from a single time period.
authors.affiliation: School of Biosciences, The University of Melbourne, Parkville, Victoria, 3010, Australia.|Unité de Mathématiques et Informatique Appliquées (MIAT), Toulouse INRA, Auzeville, BP 52627 31326 Cedex, France.
authors.forename: Alana L
authors.initials: AL
authors.lastname: Moore
authors.affiliation: RSPB Centre for Conservation Science, RSPB, The Lodge, Sandy, Bedfordshire, SG19 2DL, United Kingdom.
authors.forename: Leila
authors.initials: L
authors.lastname: Walker
authors.affiliation: United States Geological Survey, Patuxent Wildlife Research Centre, 12100 Beech Forest Road, Laurel, Maryland, 20708, USA.
authors.forename: Michael C
authors.initials: MC
authors.lastname: Runge
authors.affiliation: Centre for Biodiversity and Conservation Science, School of Geography, Planning and Environmental Management, University of Queensland, St Lucia, Queensland, 4069, Australia.
authors.forename: Eve
authors.initials: E
authors.lastname: McDonald-Madden
authors.affiliation: School of Biosciences, The University of Melbourne, Parkville, Victoria, 3010, Australia.
authors.forename: Michael A
authors.initials: MA
authors.lastname: McCarthy
mesh_terms: D000818:Animals; D003247:Conservation of Natural Resources / Q000191:economics / Q000379:methods*; D003362:Cost-Benefit Analysis; D003657:Decision Making; D004032:Diet; D056727:Endangered Species; D008954:Models, Biological; D009520:New Zealand; D020308:Songbirds
keywords: Bayesian experimental design; adaptive management; decision analysis; expected value of perfect and sample information; monitoring costs; optimal sample size