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  • 标题:Data-Driven Inference Reveals Distinct and Conserved Dynamic Pathways of Tool Use Emergence across Animal Taxa
  • 本地全文:下载
  • 作者:Iain G. Johnston ; Ellen C. Røyrvik
  • 期刊名称:iScience
  • 印刷版ISSN:2589-0042
  • 出版年度:2020
  • 卷号:23
  • 期号:6
  • 页码:1-25
  • DOI:10.1016/j.isci.2020.101245
  • 语种:English
  • 出版社:Elsevier
  • 摘要:SummaryTool use is a striking aspect of animal behavior, but it is hard to infer how the capacity for different types of tool use emerged across animal taxa. Here we address this question with HyperTraPS, a statistical approach that uses contemporary observations to infer the likely orderings in which different types of tool use (digging, reaching, and more) were historically acquired. Strikingly, despite differences linked to environment and family, many similarities in these appear across animal taxa, suggesting some universality in the process of tool use acquisition across different animals and environments. Four broad classes of tool use are supported, progressing from simple object manipulations (acquired relatively early) to more complex interactions and abstractions (acquired relatively late or not at all). This data-driven, comparative approach supports existing and suggests new mechanistic hypotheses, predicts future and possible unobserved behaviors, and sheds light on patterns of tool use emergence across animals.Graphical AbstractDisplay OmittedHighlights•Historical pathways of tool use acquisition inferred from large catalog of data•Striking similarities in acquisition pathways across environments and lineages•Acquisitions of different modes of tool use broadly follow conceptual complexity•Wild/domestic differences and predictions of future/unobserved behaviors quantifiedBiocomputational Method; Bioinformatics; Ethology; Evolutionary Biology
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