Odom Karan J, Araya-Salas Marcelo, Morano Janelle L, Ligon Russell A, Leighton Gavin M, Taff Conor C, Dalziell Anastasia H, Billings Alexis C, Germain Ryan R, Pardo Michael, de Andrade Luciana Guimarães, Hedwig Daniela, Keen Sara C, Shiu Yu, Charif Russell A, Webster Michael S, Rice Aaron N
Cornell Lab of Ornithology, Cornell University, Ithaca, NY, 14850, U.S.A.
Department of Neurobiology and Behavior, Cornell University, Ithaca, NY, 14853, U.S.A.
Biol Rev Camb Philos Soc. 2021 Aug;96(4):1135-1159. doi: 10.1111/brv.12695. Epub 2021 Mar 2.
Animals produce a wide array of sounds with highly variable acoustic structures. It is possible to understand the causes and consequences of this variation across taxa with phylogenetic comparative analyses. Acoustic and evolutionary analyses are rapidly increasing in sophistication such that choosing appropriate acoustic and evolutionary approaches is increasingly difficult. However, the correct choice of analysis can have profound effects on output and evolutionary inferences. Here, we identify and address some of the challenges for this growing field by providing a roadmap for quantifying and comparing sound in a phylogenetic context for researchers with a broad range of scientific backgrounds. Sound, as a continuous, multidimensional trait can be particularly challenging to measure because it can be hard to identify variables that can be compared across taxa and it is also no small feat to process and analyse the resulting high-dimensional acoustic data using approaches that are appropriate for subsequent evolutionary analysis. Additionally, terminological inconsistencies and the role of learning in the development of acoustic traits need to be considered. Phylogenetic comparative analyses also have their own sets of caveats to consider. We provide a set of recommendations for delimiting acoustic signals into discrete, comparable acoustic units. We also present a three-stage workflow for extracting relevant acoustic data, including options for multivariate analyses and dimensionality reduction that is compatible with phylogenetic comparative analysis. We then summarize available phylogenetic comparative approaches and how they have been used in comparative bioacoustics, and address the limitations of comparative analyses with behavioural data. Lastly, we recommend how to apply these methods to acoustic data across a range of study systems. In this way, we provide an integrated framework to aid in quantitative analysis of cross-taxa variation in animal sounds for comparative phylogenetic analysis. In addition, we advocate the standardization of acoustic terminology across disciplines and taxa, adoption of automated methods for acoustic feature extraction, and establishment of strong data archival practices for acoustic recordings and data analyses. Combining such practices with our proposed workflow will greatly advance the reproducibility, biological interpretation, and longevity of comparative bioacoustic studies.
动物会发出一系列具有高度可变声学结构的声音。通过系统发育比较分析，有可能了解不同分类群中这种变异的原因和后果。声学和进化分析的复杂性正在迅速增加，以至于选择合适的声学和进化方法变得越来越困难。然而，正确的分析选择可能会对结果和进化推断产生深远影响。在这里，我们通过为具有广泛科学背景的研究人员提供一个在系统发育背景下量化和比较声音的路线图，来识别并应对这一不断发展的领域所面临的一些挑战。声音作为一种连续的、多维度的特征，测量起来可能特别具有挑战性，因为很难识别可以在不同分类群之间进行比较的变量，而且使用适合后续进化分析的方法来处理和分析由此产生的高维声学数据也并非易事。此外，还需要考虑术语不一致以及学习在声学特征发展中的作用。系统发育比较分析也有其自身需要考虑的一系列注意事项。我们提供了一组将声学信号划分为离散的、可比较的声学单元的建议。我们还提出了一个三阶段工作流程，用于提取相关声学数据，包括多变量分析和降维的选项，这些选项与系统发育比较分析兼容。然后，我们总结了现有的系统发育比较方法以及它们在比较生物声学中的应用方式，并探讨了行为数据比较分析的局限性。最后，我们建议如何将这些方法应用于一系列研究系统中的声学数据。通过这种方式，我们提供了一个综合框架，以帮助对动物声音的跨分类群变异进行定量分析，用于比较系统发育分析。此外，我们提倡跨学科和分类群对声学术语进行标准化，采用自动声学特征提取方法，并建立强大的声学记录和数据分析数据存档实践。将这些实践与我们提出的工作流程相结合，将极大地提高比较生物声学研究的可重复性、生物学解释能力和持久性。
