Extinction and Morphospace Occupation: A Critical Review

P. Polly
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引用次数: 1

Abstract

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.
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灭绝与形态空间占据:一个批判性的回顾
灭绝的过程,特别是选择性,可以用物种在形态空间中的分布来研究。随机灭绝减少了物种的数量,但对动植物的形态范围或生态作用几乎没有影响。相比之下,选择性灭绝是根据物种与改变的环境的功能关系,从而根据它们在形态空间中的位置来剔除物种。因此,对形态空间内灭绝分布的分析可以帮助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和灭绝后形态的差异、平均形态或均匀性的变化。然而,并非所有的测量都是直接的,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——通常用于高度多元的几何形态测量数据集——具有使形态空间的中心错误地看起来是密集的特性,可以使选择性灭绝看起来是随机分布的,或者可以使一组非专业形态看起来是极端的异常值。充分应用多元度量和统计测试将防止大多数误解,在形态学和潜在灭绝过程之间建立明确的功能联系也是如此。
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