一个消费者,两种资源模式下的成功偏见社会学习

IF 1.2 4区 生物学 Q4 ECOLOGY Theoretical Population Biology Pub Date : 2022-08-01 DOI:10.1016/j.tpb.2022.05.004
Talia Borofsky, Marcus W. Feldman
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引用次数: 0

摘要

先前的分析预测,社会学习不应该在捕食者-猎物系统中进化。在这里,我们研究了成功偏向的社会学习,即社会学习者复制成功的示范,是否允许觅食者的社会学习进化。我们构建了一个一个捕食者,两个猎物的系统,在这个系统中,觅食者必须学会如何在一个觅食信息很难单独获得的环境中,以耗尽的猎物种群为食。我们分析了两种社会学习是成功偏向的模型:在第一种模型中,个人学习不依赖于资源动态,在第二种模型中,它依赖于资源的相对频率。与之前的结果不同,我们发现社会学习不会导致捕食者过度捕获一种类型的猎物。此外,增加社会学习的概率会增加学习成功觅食行为的概率,特别是当个人学习的信息往往是不准确的。在第一个模型中,社会学习不会在个体学习者中进化,而在第二个模型中,资源依赖学习的假设允许具有更高社会学习概率的突变体在觅食群体中传播。
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Success-biased social learning in a one-consumer, two-resource model

Previous analyses have predicted that social learning should not evolve in a predator–prey system. Here we examine whether success-biased social learning, by which social learners copy successful demonstrators, allows social learning by foragers to evolve. We construct a one-predator, two-prey system in which foragers must learn how to feed on depletable prey populations in an environment where foraging information can be difficult to obtain individually. We analyze two models in which social learning is success-biased: in the first, individual learning does not depend on the resource dynamics, and in the second model it depends on the relative frequency of the resource. Unlike previous results, we find that social learning does not cause predators to over-harvest one type of prey over the other. Furthermore, increasing the probability of social learning increases the probability of learning a successful foraging behavior, especially when individually learned information tends to be inaccurate. Whereas social learning does not evolve among individual learners in the first model, the assumption of resource-dependent learning in the second model allows a mutant with an increased probability of social learning to spread through the forager population.

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来源期刊
Theoretical Population Biology
Theoretical Population Biology 生物-进化生物学
CiteScore
2.50
自引率
14.30%
发文量
43
审稿时长
6-12 weeks
期刊介绍: An interdisciplinary journal, Theoretical Population Biology presents articles on theoretical aspects of the biology of populations, particularly in the areas of demography, ecology, epidemiology, evolution, and genetics. Emphasis is on the development of mathematical theory and models that enhance the understanding of biological phenomena. Articles highlight the motivation and significance of the work for advancing progress in biology, relying on a substantial mathematical effort to obtain biological insight. The journal also presents empirical results and computational and statistical methods directly impinging on theoretical problems in population biology.
期刊最新文献
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