School of Biology, University of St Andrews, Bute Building, Westburn Lane, St Andrews, Fife KY16 9TS, United Kingdom.
J Theor Biol. 2010 Apr 21;263(4):544-55. doi: 10.1016/j.jtbi.2010.01.004. Epub 2010 Jan 11.
In recent years researchers have drawn attention to a need for new methods with which to identify the spread of behavioural innovations through social transmission in animal populations. Network-based analyses seek to recognise diffusions mediated by social learning by detecting a correspondence between patterns of association and the flow of information through groups. Here we introduce a new order of acquisition diffusion analysis (OADA) and develop established time of acquisition diffusion analysis (TADA) methods further. Through simulation we compare the merits of these and other approaches, demonstrating that OADA and TADA have greater power and lower Type I error rates than available alternatives, and specifying when each approach should be deployed. We illustrate the new methods by applying them to reanalyse an established dataset corresponding to the diffusion of foraging innovations in starlings, where OADA and TADA detect social transmission that hitherto had been missed. The methods are potentially widely applicable by researchers wishing to detect social learning in natural and captive populations of animals, and to facilitate this we provide code to implement OADA and TADA in the statistical package R.
近年来，研究人员已经开始关注需要新的方法来识别动物种群中通过社会传播的行为创新的传播。基于网络的分析旨在通过检测群体中信息流动与关联模式之间的对应关系，识别由社会学习介导的扩散。在这里，我们引入了一种新的获得扩散分析（OADA）的顺序，并进一步开发了已建立的获得时间扩散分析（TADA）方法。通过模拟，我们比较了这些方法和其他方法的优点，证明 OADA 和 TADA 比可用的替代方法具有更高的功效和更低的Ⅰ类错误率，并指定了每种方法应该部署的情况。我们通过将这些方法应用于重新分析一个与星鸦觅食创新扩散相对应的已有数据集来说明新方法，其中 OADA 和 TADA 检测到了迄今为止被忽略的社会传播。希望在自然和圈养动物群体中检测社会学习的研究人员可以广泛地应用这些方法，为此，我们提供了在统计软件包 R 中实现 OADA 和 TADA 的代码。
