Klaassen Raymond H G, Nolet Bart A, van Gils Jan A, Bauer Silke
Department of Plant-Animal Interactions, Netherlands Institute of Ecology, Rijksstraatweg 6, Nieuwersluis, The Netherlands.
Theor Popul Biol. 2006 Dec;70(4):452-63. doi: 10.1016/j.tpb.2006.04.002. Epub 2006 Apr 25.
If the food distribution contains spatial pattern, the food density in a particular patch provides a forager with information about nearby patches. Foragers might use this information to exploit patchily distributed resources profitably. We model the decision on how far to move to the next patch in linear environments with different spatial patterns in the food distribution (clumped, random, and regular) for foragers that differ in their degree of information. An ignorant forager is uninformed and therefore always moves to the nearest patch (be it empty or filled). In contrast, a prescient forager is fully informed and only exploits filled patches, skipping all empty patches. A Bayesian assessor has prior knowledge about the content of patches (i.e. it knows the characteristics of the spatial pattern) and may skip neighbouring patches accordingly by moving to the patch where the highest gain rate is expected. In most clumped and regular distributions there is a benefit of assessment, i.e. Bayesian assessors achieve substantially higher long-term gain rates than ignorant foragers. However, this is not the case in distributions with less strong spatial pattern, despite the fact that there is a large potential benefit from a sophisticated movement rule (i.e. a large penalty of ignorance). Bayesian assessors do also not achieve substantially higher gain rates in environments that are relatively rich or poor in food. These results underline that an incompletely informed forager that is sensitive to spatial pattern should not always respond to existing pattern. Furthermore, we show that an assessing forager can enhance its long-term gain rate in highly clumped and some specific near-regular food distributions, by sampling the environment in slightly larger spatial units.
如果食物分布具有空间格局，那么特定斑块中的食物密度会为觅食者提供有关附近斑块的信息。觅食者可能会利用这些信息来有效地利用斑块状分布的资源。我们针对食物分布具有不同空间格局（聚集、随机和规则）的线性环境，为信息程度不同的觅食者建立模型，以确定移动到下一个斑块的距离。一个无知的觅食者没有信息，因此总是移动到最近的斑块（无论其为空还是有食物）。相比之下，一个有先见之明的觅食者信息完全，只利用有食物的斑块，跳过所有空斑块。一个贝叶斯评估者对斑块的内容有先验知识（即它知道空间格局的特征），并且可能会相应地跳过相邻斑块，移动到预期增益率最高的斑块。在大多数聚集和规则分布中，评估是有好处的，即贝叶斯评估者比无知的觅食者实现更高的长期增益率。然而，在空间格局不太明显的分布中情况并非如此，尽管复杂的移动规则有很大的潜在好处（即无知有很大的代价）。在食物相对丰富或匮乏的环境中，贝叶斯评估者也没有实现显著更高的增益率。这些结果强调，一个对空间格局敏感但信息不完全的觅食者不应总是对现有的格局做出反应。此外，我们表明，通过以稍大的空间单位对环境进行采样，一个进行评估的觅食者可以在高度聚集和一些特定的近规则食物分布中提高其长期增益率。
