Williams Byron K, Johnson Fred A
The Wildlife Society, Bethesda, Maryland, United States of America.
Southeast Ecological Science Center, U.S. Geological Survey, Gainesville, Florida, United States of America.
PLoS One. 2017 Aug 11;12(8):e0182934. doi: 10.1371/journal.pone.0182934. eCollection 2017.
Adaptive management involves learning-oriented decision making in the presence of uncertainty about the responses of a resource system to management. It is implemented through an iterative sequence of decision making, monitoring and assessment of system responses, and incorporating what is learned into future decision making. Decision making at each point is informed by a value or objective function, for example total harvest anticipated over some time frame. The value function expresses the value associated with decisions, and it is influenced by system status as updated through monitoring. Often, decision making follows shortly after a monitoring event. However, it is certainly possible for the cadence of decision making to differ from that of monitoring. In this paper we consider different combinations of annual and biennial decision making, along with annual and biennial monitoring. With biennial decision making decisions are changed only every other year; with biennial monitoring field data are collected only every other year. Different cadences of decision making combine with annual and biennial monitoring to define 4 scenarios. Under each scenario we describe optimal valuations for active and passive adaptive decision making. We highlight patterns in valuation among scenarios, depending on the occurrence of monitoring and decision making events. Differences between years are tied to the fact that every other year a new decision can be made no matter what the scenario, and state information is available to inform that decision. In the subsequent year, however, in 3 of the 4 scenarios either a decision is repeated or monitoring does not occur (or both). There are substantive differences in optimal values among the scenarios, as well as the optimal policies producing those values. Especially noteworthy is the influence of monitoring cadence on valuation in some years. We highlight patterns in policy and valuation among the scenarios, and discuss management implications and extensions.
适应性管理涉及在资源系统对管理的响应存在不确定性的情况下进行以学习为导向的决策。它通过决策、监测和评估系统响应的迭代序列来实施，并将所学内容纳入未来的决策中。每个阶段的决策都基于一个价值或目标函数，例如在某个时间段内预期的总收获量。价值函数表达了与决策相关的价值，并且它会受到通过监测更新的系统状态的影响。通常，决策在监测事件后不久就会做出。然而，决策的节奏与监测的节奏肯定有可能不同。在本文中，我们考虑了年度决策和两年期决策的不同组合，以及年度监测和两年期监测。采用两年期决策时，决策每隔一年才会改变；采用两年期监测时，实地数据每隔一年才收集一次。不同的决策节奏与年度和两年期监测相结合，定义了4种情景。在每种情景下，我们描述了主动和被动适应性决策的最优估值。我们强调了情景之间估值的模式，这取决于监测和决策事件的发生情况。年份之间的差异与这样一个事实有关，即无论情景如何，每隔一年都可以做出新的决策，并且有状态信息可用于为该决策提供依据。然而，在随后的一年中，在4种情景中的3种情景下，要么重复决策，要么不进行监测（或两者都有）。情景之间的最优值以及产生这些值的最优策略存在实质性差异。特别值得注意的是监测节奏在某些年份对估值的影响。我们强调了情景之间政策和估值的模式，并讨论了管理意义和扩展内容。
