Martin Stephen J, Drijfhout Falko P
Department of Animal and Plant Sciences, University of Sheffield, Sheffield S102TN, UK.
J Chem Ecol. 2009 Mar;35(3):375-82. doi: 10.1007/s10886-009-9610-z. Epub 2009 Mar 5.
Numerous recent studies have correlated cuticular hydrocarbon profiles with a wide range of behaviors, particularly in social insects. These findings are wholly or partly based on multivariate statistical methods such as discriminate analysis (DA) or principal component analysis (PCA). However, these methods often provide limited insight into the biological processes that generate the small differences usually detected. This may be a consequence of variability in the system due to inadequate sample sizes and the assumption that all compounds are independent. A fundamental problem is that these methods combine rather than separate the effects of signal components. By using cuticular hydrocarbon data from previous social insect studies, we showed that: (1) in 13 species of Formica ants and seven species of Vespa hornets, at least one group of hydrocarbons in each species was highly (r(2) > 0.8) correlated, indicating that all compounds are not independent; (2) DA was better at group separation that PCA; (3) the relationships between colonies (chemical distance) were unstable and sensitive to variability in the system; and (4) minor compounds had a disproportionately large effect on the analysis. All these factors, along with sample size, need to be considered in the future analysis of complex chemical profiles.
最近大量研究已将表皮碳氢化合物谱与广泛的行为关联起来，尤其是在社会性昆虫中。这些发现全部或部分基于多元统计方法，如判别分析（DA）或主成分分析（PCA）。然而，这些方法往往对产生通常所检测到的微小差异的生物学过程洞察有限。这可能是由于样本量不足以及所有化合物相互独立这一假设导致系统存在变异性的结果。一个根本问题是这些方法是将信号成分的效应合并而非分离。通过使用先前社会性昆虫研究中的表皮碳氢化合物数据，我们表明：（1）在13种蚁属蚂蚁和7种胡蜂中，每个物种至少有一组碳氢化合物高度相关（r²>0.8），这表明并非所有化合物都是独立的；（2）判别分析在群体分离方面比主成分分析更好；（3）群体之间的关系（化学距离）不稳定且对系统中的变异性敏感；（4）次要化合物对分析有不成比例的重大影响。在未来对复杂化学谱的分析中，所有这些因素以及样本量都需要加以考虑。
