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引用次数: 0
摘要
性状数据集是大量生态学和进化论研究的基础，可用于推断祖先形态、量化物种灭绝风险或评估生物群落的功能多样性。然而，这些数据集常常受到数据缺失的困扰，例如，由于取样不完整、数据有限和资源可用性等原因。目前有几种预测缺失值的估算方法，最近的研究探讨了这些方法在生物数据集连续性特征方面的性能。然而，人们对这些方法用于分类性状的准确性知之甚少。在这里，我们结合系统发育比较方法、机器学习和深度学习模型，探讨了不同估算方法在分类生物性状上的性能。为此，我们开发了一个开源 R 软件包，用于对性状数据进行估算，同时整合了一个模拟框架，以评估它们在合成数据集上的性能。我们在不同的缺失率、机制、偏差和进化模型下运行了一系列模拟。我们提出了系统发育比较方法和机器学习估算之间的整合方法，以及一种组合方法，其中将选定的估算方法结合在一起。我们的模拟结果表明，这种方法能提供最稳健、最准确的预测。我们将我们的估算管道应用于 1015 种鳞鳃类物种（即鲨鱼、鳐鱼和鳐）的不完整性状数据集，发现基于专家对缺失性状的评估，估算预测的准确率很高。总之，我们的 R 软件包有助于比较多种估算方法，并对缺失性状值进行稳健预测。我们的研究凸显了将系统进化模型与机器学习推断结合起来以扩充不完整生物数据集的好处。
期刊介绍：
A British Ecological Society journal, Methods in Ecology and Evolution (MEE) promotes the development of new methods in ecology and evolution, and facilitates their dissemination and uptake by the research community. MEE brings together papers from previously disparate sub-disciplines to provide a single forum for tracking methodological developments in all areas.
MEE publishes methodological papers in any area of ecology and evolution, including:
-Phylogenetic analysis
-Statistical methods
-Conservation & management
-Theoretical methods
-Practical methods, including lab and field
-This list is not exhaustive, and we welcome enquiries about possible submissions. Methods are defined in the widest terms and may be analytical, practical or conceptual.
A primary aim of the journal is to maximise the uptake of techniques by the community. We recognise that a major stumbling block in the uptake and application of new methods is the accessibility of methods. For example, users may need computer code, example applications or demonstrations of methods.
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