Klingenberg Christian Peter
Faculty of Life Sciences, University of Manchester, Michael Smith Building, Oxford Road, Manchester M13 9PT, UK
Philos Trans R Soc Lond B Biol Sci. 2014 Aug 19;369(1649):20130249. doi: 10.1098/rstb.2013.0249.
Although most studies on integration and modularity have focused on variation among individuals within populations or species, this is not the only level of variation for which integration and modularity exist. Multiple levels of biological variation originate from distinct sources: genetic variation, phenotypic plasticity resulting from environmental heterogeneity, fluctuating asymmetry from random developmental variation and, at the interpopulation or interspecific levels, evolutionary change. The processes that produce variation at all these levels can impart integration or modularity on the covariance structure among morphological traits. In turn, studies of the patterns of integration and modularity can inform about the underlying processes. In particular, the methods of geometric morphometrics offer many advantages for such studies because they can characterize the patterns of morphological variation in great detail and maintain the anatomical context of the structures under study. This paper reviews biological concepts and analytical methods for characterizing patterns of variation and for comparing across levels. Because research comparing patterns across level has only just begun, there are relatively few results, generalizations are difficult and many biological and statistical questions remain unanswered. Nevertheless, it is clear that research using this approach can take advantage of an abundance of new possibilities that are so far largely unexplored.
尽管大多数关于整合与模块性的研究都聚焦于种群或物种内个体间的变异,但这并非存在整合与模块性的唯一变异层次。多个生物变异层次源自不同的来源:遗传变异、环境异质性导致的表型可塑性、随机发育变异产生的波动不对称性,以及在种群间或物种间层次上的进化变化。在所有这些层次上产生变异的过程能够赋予形态性状间协方差结构以整合性或模块性。反过来,对整合与模块性模式的研究能够为潜在过程提供信息。特别是,几何形态测量学方法为这类研究提供了诸多优势,因为它们能够极其详细地描述形态变异模式,并保持所研究结构的解剖学背景。本文综述了用于描述变异模式以及跨层次比较的生物学概念和分析方法。由于跨层次比较模式的研究才刚刚起步,相关结果相对较少,难以进行概括,许多生物学和统计学问题仍未得到解答。然而,很明显,采用这种方法的研究能够利用大量目前基本上尚未探索的新可能性。