Department of Anatomy, New York Institute of Technology, Old Westbury, New York, United States of America.
Division of Paleontology, American Museum of Natural History, New York, New York, United States of America.
PLoS One. 2018 Jun 4;13(6):e0198341. doi: 10.1371/journal.pone.0198341. eCollection 2018.
Accurate characterization of morphological variation is crucial for generating reliable results and conclusions concerning changes and differences in form. Despite the prevalence of landmark-based geometric morphometric (GM) data in the scientific literature, a formal treatment of whether sampled landmarks adequately capture shape variation has remained elusive. Here, I introduce LaSEC (Landmark Sampling Evaluation Curve), a computational tool to assess the fidelity of morphological characterization by landmarks. This task is achieved by calculating how subsampled data converge to the pattern of shape variation in the full dataset as landmark sampling is increased incrementally. While the number of landmarks needed for adequate shape variation is dependent on individual datasets, LaSEC helps the user (1) identify under- and oversampling of landmarks; (2) assess robustness of morphological characterization; and (3) determine the number of landmarks that can be removed without compromising shape information. In practice, this knowledge could reduce time and cost associated with data collection, maintain statistical power in certain analyses, and enable the incorporation of incomplete, but important, specimens to the dataset. Results based on simulated shape data also reveal general properties of landmark data, including statistical consistency where sampling additional landmarks has the tendency to asymptotically improve the accuracy of morphological characterization. As landmark-based GM data become more widely adopted, LaSEC provides a systematic approach to evaluate and refine the collection of shape data--a goal paramount for accumulation and analysis of accurate morphological information.
准确描述形态变化对于生成有关形态变化和差异的可靠结果和结论至关重要。尽管基于地标几何形态测量学（GM）数据在科学文献中很常见，但对于采样地标是否充分捕捉形状变化的正式处理仍然难以捉摸。在这里，我引入了 LaSEC（地标采样评估曲线），这是一种评估地标形态特征保真度的计算工具。这项任务是通过计算随着地标采样的逐步增加，子采样数据如何收敛到全数据集的形状变化模式来实现的。虽然充分描述形状变化所需的地标数量取决于个别数据集，但 LaSEC 可以帮助用户：1. 识别地标采样不足和过度采样；2. 评估形态特征描述的稳健性；3. 确定可以在不影响形状信息的情况下删除的地标数量。在实践中，这种知识可以减少与数据收集相关的时间和成本，在某些分析中保持统计能力，并使包含不完整但重要的标本成为数据集的一部分。基于模拟形状数据的结果还揭示了地标数据的一般特性，包括统计一致性，即采样更多地标有趋向于渐近地提高形态特征描述准确性的趋势。随着基于地标 GM 数据的广泛采用，LaSEC 提供了一种系统的方法来评估和改进形状数据的收集，这是积累和分析准确形态信息的首要目标。
