Using Machine Learning to Identify Associations between the Environment, Occurrence, and Outcomes of Songbird Displacements at Supplemental Feeders

Q4 Agricultural and Biological Sciences Western Birds Pub Date : 2022-09-15 DOI:10.3390/birds3030021
C. Philson, Tara A. Pelletier, Sarah L. Foltz, Jason E. Davis
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引用次数: 2

Abstract

The context and outcome of aggressive interactions between individuals has important fitness consequences. Displacements—an aggressive interaction wherein one individual is chased from a location by another—also have implications for social hierarchy formation and geographic distribution in songbirds. Morphological correlates, like body size, and social correlates, such as dominance rank, have been shown to mediate displacements in songbirds. However, the role of the physical environment, namely temperature, humidity, and time of day, which may influence an individual’s energy needs and thus displacement motivation, has remained understudied. We monitored songbird feeding and displacement behaviors using computerized automated feeders. We observed asymmetric differences across species in displacement involvement. To identify the conditions of the social and physical environment that are associated with the occurrence and outcome of songbird displacements at supplemental feeders, we use the machine learning approach, random forest, which is a novel method to the fields of ornithology and animal behavior. From our random forest models, we found that the attributes of the physical environment (i.e., humidity and the time of day) are associated with the occurrence of a displacement event, whereas the attributes of the social environment (i.e., species of the displacer and displaced individuals) are associated with which species are involved. These results provide context to develop further observational and experimental hypotheses to tease apart the inner workings of these multifactorial behaviors on a larger scale and provide a proof of concept for our analytical methods in the study of avian behavior.
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使用机器学习来识别补充喂食器中鸣禽位移的环境,发生和结果之间的关联
个体间攻击性互动的环境和结果具有重要的适应度影响。迁徙——一种攻击性的相互作用,其中一个个体被另一个个体从一个位置追赶——也对鸣禽的社会等级形成和地理分布有影响。形态相关因素,如体型,社会相关因素,如统治地位,已被证明可以调节鸣禽的迁移。然而,物理环境的作用,即温度、湿度和一天中的时间,可能会影响个人的能量需求,从而影响位移动机,仍然没有得到充分的研究。我们使用电脑自动喂食器监测鸣禽的摄食和位移行为。我们观察到不同物种在位移参与方面的不对称差异。为了确定与鸣禽迁移的发生和结果相关的社会和物理环境条件,我们使用了机器学习方法,随机森林,这是鸟类学和动物行为学领域的一种新方法。从我们的随机森林模型中,我们发现物理环境的属性(即湿度和一天中的时间)与流离失所事件的发生有关,而社会环境的属性(即流离失所者和流离失所者的物种)与所涉及的物种有关。这些结果为进一步发展观察和实验假设提供了背景,以在更大范围内梳理这些多因素行为的内部运作,并为我们在鸟类行为研究中的分析方法提供了概念证明。
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Western Birds
Western Birds Agricultural and Biological Sciences-Animal Science and Zoology
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0.60
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