Polly P David
Earth & Atmospheric Sciences, Biology, and Anthropology, Indiana University, Bloomington, IN 47405, USA.
Camb Prism Extinct. 2023 Jun 26;1:e17. doi: 10.1017/ext.2023.16. eCollection 2023.
Processes of extinction, especially selectivity, can be studied using the distribution of species in morphospace. Random extinction reduces the number of species but has little effect on the range of morphologies or ecological roles in a fauna or flora. In contrast, selective extinction culls species based on their functional relationship to the altered environment and, therefore, to their position within a morphospace. Analysis of the distribution of extinctions within morphospaces can thus help understand whether the drivers of the extinction are linked to functional traits. Current approaches include measuring changes in disparity, mean morphology, or evenness between pre- and post-extinction morphologies. Not all measurements are straightforward, however, because morphospaces may be non-metric or non-linear in ways that can mislead interpretation. Dimension-reduction techniques like principal component analysis - commonly used with highly multivariate geometric morphometric data sets - have properties that can make the center of morphospace falsely appear to be densely populated, can make selective extinctions appear randomly distributed, or can make a group of non-specialized morphologies appear to be extreme outliers. Applying fully multivariate metrics and statistical tests will prevent most misinterpretations, as will making explicit functional connections between morphology and the underlying extinction processes.
灭绝过程,尤其是选择性,可以通过物种在形态空间中的分布来研究。随机灭绝会减少物种数量,但对动植物群中形态或生态角色的范围影响很小。相比之下,选择性灭绝会根据物种与变化环境的功能关系,进而根据它们在形态空间中的位置来淘汰物种。因此,分析形态空间内灭绝的分布有助于了解灭绝的驱动因素是否与功能特征有关。目前的方法包括测量灭绝前后形态之间的差异、平均形态或均匀度。然而,并非所有测量都很简单,因为形态空间可能是非度量的或非线性的,这可能会误导解释。像主成分分析这样的降维技术——通常用于高度多变量的几何形态测量数据集——具有一些特性,这些特性可能会使形态空间的中心错误地显得人口密集,使选择性灭绝显得随机分布,或者使一组非特化形态显得是极端异常值。应用完全多变量指标和统计测试将防止大多数误解,明确形态与潜在灭绝过程之间的功能联系也能防止误解。