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为分支系统分析对行为数据进行编码:使用无简约法的动态同源性

Coding behavioural data for cladistic analysis: using dynamic homology without parsimony.

作者信息

Japyassú Hilton F, Machado Fabio de A

机构信息

Universidade Federal da Bahia, Instituto de Biologia, Instituto de Biologia, Rua Barão de Geremoabo s/n, Salvador BA, CEP 40170-115, Brazil.

Universidade de São Paulo, NeC, Instituto de Psicologia, São Paulo, Brazil.

出版信息

Cladistics. 2010 Dec;26(6):625-642. doi: 10.1111/j.1096-0031.2010.00309.x.

DOI:10.1111/j.1096-0031.2010.00309.x
PMID:34879599
Abstract

Many of the controversies around the concept of homology rest on the subjectivity inherent to primary homology propositions. Dynamic homology partially solves this problem, but there has been up to now scant application of it outside of the molecular domain. This is probably because morphological and behavioural characters are rich in properties, connections and qualities, so that there is less space for conflicting character delimitations. Here we present a new method for the direct optimization of behavioural data, a method that relies on the richness of this database to delimit the characters, and on dynamic procedures to establish character state identity. We use between-species congruence in the data matrix and topological stability to choose the best cladogram. We test the methodology using sequences of predatory behaviour in a group of spiders that evolved the highly modified predatory technique of spitting glue onto prey. The cladogram recovered is fully compatible with previous analyses in the literature, and thus the method seems consistent. Besides the advantage of enhanced objectivity in character proposition, the new procedure allows the use of complex, context-dependent behavioural characters in an evolutionary framework, an important step towards the practical integration of the evolutionary and ecological perspectives on diversity. © The Willi Hennig Society 2010.

摘要

围绕同源性概念的许多争议都基于初级同源性命题所固有的主观性。动态同源性部分解决了这个问题,但到目前为止,它在分子领域之外的应用还很少。这可能是因为形态和行为特征在属性、联系和性质方面非常丰富,所以相互冲突的特征界定空间较小。在这里,我们提出了一种直接优化行为数据的新方法,该方法依赖于这个丰富的数据库来界定特征,并依靠动态程序来确定特征状态的一致性。我们利用数据矩阵中的种间一致性和拓扑稳定性来选择最佳的分支图。我们用一组蜘蛛的捕食行为序列来测试该方法,这些蜘蛛进化出了向猎物喷射黏液这种高度特化的捕食技巧。恢复的分支图与文献中先前的分析完全一致,因此该方法似乎是可靠的。除了在特征命题方面提高客观性的优点外,新方法还允许在进化框架中使用复杂的、依赖上下文的行为特征,这是朝着将进化和生态视角实际整合到多样性研究中迈出的重要一步。© 威利·亨尼希学会 2010 年。

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