Modeling vigilance in mixed‐species groups

IF 1.8 4区 环境科学与生态学 Q3 ENVIRONMENTAL SCIENCES Natural Resource Modeling Pub Date : 2022-01-24 DOI:10.1111/nrm.12340
F. Langevelde, L. Suselbeek, Joel s. Brown
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引用次数: 2

Abstract

Mixed‐species groups are usually explained by foraging advantages and reduced predation risk for at least one of the participating species. Given that animals trade‐off foraging and vigilance, the optimal level of vigilance of individuals in mixed‐species groups depends partly on the vigilance levels of both conspecifics and heterospecifics. However, the benefits and costs of being part of a mixed‐species group do not need to be evenly distributed between the species in a group. In this paper, we modeled the evolutionary stable strategy (ESS) for the optimal level of vigilance of an individual in a mixed‐species group influenced by the effects of “many eyes,” “dilution” and “attraction,” and unequal costs and benefits between the species. Our model illustrates under what conditions associations with other species may facilitate reduced predation risk for at least one of the participating species. We show that vigilance of individuals in mixed‐species groups becomes a social game, and that the ESS of these vigilance games may predict the individual's adaptive level of vigilance. This paper provides the first step in the development of a predictive theory for the numerous empirical studies on mixed‐species groups.
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混合物种群体警觉性建模
混合物种群体通常可以用至少一种参与物种的觅食优势和捕食风险降低来解释。考虑到动物在觅食和警惕之间进行权衡,混合物种群体中个体的最佳警惕水平部分取决于同种和异种物种的警惕水平。然而,作为混合物种群体一部分的收益和成本并不需要在群体中的物种之间均匀分布。在本文中,我们为混合物种群体中个体的最佳警惕性水平建模了进化稳定策略(ESS),该策略受“多眼”、“稀释”和“吸引”效应以及物种之间不平等的成本和收益的影响。我们的模型说明了在什么条件下,与其他物种的关联可能有助于降低至少一个参与物种的捕食风险。我们发现,在混合物种群体中,个体的警惕性成为一种社会游戏,这些警惕性游戏的ESS可以预测个体的适应警惕性水平。本文为混合物种群体的大量实证研究提供了预测理论发展的第一步。
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来源期刊
Natural Resource Modeling
Natural Resource Modeling 环境科学-环境科学
CiteScore
3.50
自引率
6.20%
发文量
28
审稿时长
>36 weeks
期刊介绍: Natural Resource Modeling is an international journal devoted to mathematical modeling of natural resource systems. It reflects the conceptual and methodological core that is common to model building throughout disciplines including such fields as forestry, fisheries, economics and ecology. This core draws upon the analytical and methodological apparatus of mathematics, statistics, and scientific computing.
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