Quantifying the Adaptive Value of Learning in Foraging Behavior
Quantifying the Adaptive Value of Learning in Foraging Behavior
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DOI：
10.1086/605370
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发表时间：
2009-10-01
影响因子：
2.9
通讯作者：
Giske, Jarl
中科院分区：
文献类型：
--
作者：
Eliassen, Sigrunn;Jorgensen, Christian;Giske, Jarl
获取环境信息的价值取决于收集环境信息的成本及其效用。寻找斑块分布资源的觅食者可以利用之前斑块的经验来了解栖息地的质量，并调整自己的行为。我们绘制了一系列条件下学习进化的生态景观图，包括空间和时间异质性。我们将学习策略与遗传固定的斑块离开规则进行了比较，并将其与具有自由且完美的环境信息的觅食策略进行了比较。该模型表明，当低相遇随机性导致可靠的斑块质量估计时，当没有或几乎没有时间变化时，以及当空间变异性很小时，学习效率最高。这与学习的价值形成了部分对比，当存在时间变化时，学习的价值最高，因为灵活的策略可能会跟踪环境趋势，当存在空间差异时，因为需要区分好的和不好的斑块。当补丁信息准确且存在时间变化时，具有短期记忆的学习规则是有益的，而更新缓慢的学习规则通常对空间变化更健壮。
The value of acquiring environmental information depends on the costs of collecting it and its utility. Foragers that search for patchily distributed resources may use experiences in previous patches to learn the habitat quality and adjust their behavior. We map the ecological landscape for the evolution of learning under a range of conditions, including both spatial and temporal heterogeneity. We compare the learning strategy with genetically fixed patch-leaving rules and with strategies of foragers that have free and perfect information about their environment. The model reveals that the efficiency of learning is highest when low encounter stochasticity results in reliable estimates of patch quality, when there is no or little temporal change, and when there is little spatial variability. This partially contrasts with the value of learning, which is highest when there is temporal change, because flexible strategies may track the environmental trend, and when there is spatial variability, because there is a need to distinguish between good and bad patches. Learning rules with short-term memory are beneficial when patch information is accurate and when there is temporal change, whereas learning rules that update slowly are generally more robust to spatial variability.