Incomplete specimens in geometric morphometric analyses
Incomplete specimens in geometric morphometric analyses
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DOI：
10.1111/2041-210x.12128
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发表时间：
2014-01-01
影响因子：
6.6
通讯作者：
Brown, Caleb M.
中科院分区：
文献类型：
--
作者：
Arbour, Jessica H.;Brown, Caleb M.
形态多样性的分析经常依赖于使用多变量方法来表征生物形状。然而，许多这些方法是不能容忍的缺失数据，这可能会限制稀有类群的使用，并阻碍生态多样性和形态进化的广泛模式的研究。本研究采用多数据集方法比较缺失数据估计的变化及其对分类学变量组、地标位置和样本量的几何形态测量分析的影响。缺失的形态标志数据是从五个真实的完整的数据集模拟的，包括现代鱼类，灵长类动物和灭绝的兽脚亚目恐龙。然后使用几种标准方法和一种几何形态学特定方法估计缺失的标志。确定每种估计方法、标志位置和形态学数据集的缺失数据估计准确度。使用Procrustes叠加比较原始标志数据的几何形态测量分析的特征向量和主成分评分，以及A）估计缺失值或B）排除模拟不完整标本的数据集，以获得不同程度的标本不完整性和样本量。标准的估计技术是更可靠的估计，并有较低的影响，形态分析相比，几何形态的具体估计。对于大多数数据集和估计技术，估计缺失数据产生了更好的适合原始数据的结构比排除不完整的标本，这是保持即使在相当大的样本量减少。缺失数据对几何形态测量分析的影响不成比例地受到最零碎的标本的影响。缺失数据估计受特定解剖特征变异性的影响，并且可以通过更好地理解数据集中存在的形状变异来改善。我们的研究结果表明，包括不完整的样本，通过使用有效的缺失数据估计更好地反映了数据集内的形状变化的模式，而不是只使用完整的样本，但是，缺失数据估计的有效性可以最大限度地排除最不完整的样本。建议对每个数据集和地标独立评估缺失数据估计量，因为估计量的有效性在不同分类群和结构之间可能会有很大的差异且不可预测。
The analysis of morphological diversity frequently relies on the use of multivariate methods for characterizing biological shape. However, many of these methods are intolerant of missing data, which can limit the use of rare taxa and hinder the study of broad patterns of ecological diversity and morphological evolution. This study applied a mutli-data set approach to compare variation in missing data estimation and its effect on geometric morphometric analyses across taxonomically variable groups, landmark position and sample sizes. Missing morphometric landmark data were simulated from five real, complete data sets, including modern fish, primates and extinct theropod dinosaurs. Missing landmarks were then estimated using several standard approaches and a geometric-morphometric-specific method. The accuracy of missing data estimation was determined for each estimation method, landmark position and morphological data set. Procrustes superimposition was used to compare the eigenvectors and principal component scores of a geometric morphometric analysis of the original landmark data, to data sets with A) missing values estimated, or B) simulated incomplete specimens excluded, for varying levels of specimens incompleteness and sample sizes. Standard estimation techniques were more reliable estimators and had lower impacts on morphometric analysis compared with a geometric-morphometric-specific estimator. For most data sets and estimation techniques, estimating missing data produced a better fit to the structure of the original data than exclusion of incomplete specimens, and this was maintained even at considerably reduced sample sizes. The impact of missing data on geometric morphometric analysis was disproportionately affected by the most fragmentary specimens. Missing data estimation was influenced by variability of specific anatomical features and may be improved by a better understanding of shape variation present in a data set. Our results suggest that the inclusion of incomplete specimens through the use of effective missing data estimators better reflects the patterns of shape variation within a data set than using only complete specimens; however, the effectiveness of missing data estimation can be maximized by excluding only the most incomplete specimens. It is advised that missing data estimators be evaluated for each data set and landmark independently, as the effectiveness of estimators can vary strongly and unpredictably between different taxa and structures.