Department of Zoology, Mathematical Ecology Research Group, South Parks Road, Oxford, OX1 3PS, United Kingdom.
St. Peter's College, New Inn Hall Street, Oxford, OX1 2DL, United Kingdom.
Ecol Appl. 2019 Mar;29(2):e01851. doi: 10.1002/eap.1851. Epub 2019 Feb 12.
Ecological decision problems, such as those encountered in agriculture, often require managing conflicts between short-term costs and long-term benefits. Dynamic programming is an ideal method for optimally solving such problems but agricultural problems are often subject to additional complexities that produce state spaces intractable to exact solutions. In contrast, look-ahead policies, a class of approximate dynamic programming (ADP) algorithm, may attempt to solve problems of arbitrary magnitude. However, these algorithms focus on a temporally truncated caricature of the full decision problem over a defined planning horizon and as such are not guaranteed to suggest optimal actions. Thus, look-ahead policies may offer promising means of addressing detail-rich ecological decision problems but may not be capable of fully utilizing the information available to them, especially in scenarios where the best short- and long-term solutions may differ. We constructed and applied look-ahead policies to the management of a hypothetical, stage-structured, continually reproducing, agricultural insect pest. The management objective was to minimize the combined costs of management actions and crop damage over a 16-week growing season. The manager could elect to utilize insecticidal sprays or one of six release ratios of male-selecting transgenic insects where the release ratio determines the number of transgenic insects to be released for each wild-type male insect in the population. Complicating matters was the expression of insecticide resistance at non-trivial frequencies in the pest population. We assessed the extent to which look-ahead policies were able to recognize the potential threat of insecticide resistance and successfully integrate insecticides and transgenic releases to capitalize upon their respective benefits. Look-ahead policies were competent at anticipating and responding to ecological and economic information. Policies with longer planning horizons made fewer, better-timed insecticidal sprays and made more frequent transgenic releases, which consequently facilitated lower resistance allele frequencies. However, look-ahead policies were ultimately inefficient resistance managers, and directly responded to resistance only when it was dominant and prevalent. Effective long-term agricultural management requires the capacity to anticipate and respond to the evolution of resistance. Look-ahead policies can accommodate all the information pertinent to making the best long-term decision but may lack the perspective to actually do so.
生态决策问题，如农业中遇到的问题，通常需要在短期成本和长期利益之间进行权衡。动态规划是解决此类问题的理想方法，但农业问题通常还存在其他复杂性，导致状态空间难以精确求解。相比之下，前瞻策略是一类近似动态规划（ADP）算法，可以尝试解决任意规模的问题。然而，这些算法专注于定义规划期内完整决策问题的时间截断模拟，因此不能保证建议最优行动。因此，前瞻策略可能是解决细节丰富的生态决策问题的一种有前途的方法，但可能无法充分利用可用信息，特别是在最佳短期和长期解决方案可能不同的情况下。我们构建并应用了前瞻策略来管理一个假设的、具有阶段结构的、不断繁殖的农业昆虫害虫。管理目标是在 16 周的生长季节内，将管理措施和作物损失的综合成本降到最低。管理者可以选择使用杀虫剂喷雾或六次释放比例的雄性选择转基因昆虫，释放比例决定了在种群中的每只野生型雄性昆虫中释放的转基因昆虫数量。使问题复杂化的是，害虫种群中存在相当高频率的杀虫剂抗性表达。我们评估了前瞻策略识别杀虫剂抗性潜在威胁并成功整合杀虫剂和转基因释放以利用各自优势的程度。前瞻策略能够很好地预测和应对生态和经济信息。规划期较长的策略会减少、更及时地进行杀虫剂喷雾，并更频繁地进行转基因释放，从而降低抗性等位基因频率。然而，前瞻策略最终并不是有效的抗性管理者，只有在抗性占主导地位且普遍存在时，才会直接应对抗性。有效的长期农业管理需要有能力预测和应对抗性的演变。前瞻策略可以容纳与做出最佳长期决策相关的所有信息，但可能缺乏实际做出决策的视角。
