School of Archaeology and Anthropology, The Australian National University, Canberra, Australia.
School of Biology and Environmental Science, Queensland University of Technology, Brisbane, QLD, Australia.
BMC Ecol Evol. 2024 Oct 18;24(1):127. doi: 10.1186/s12862-024-02313-3.
Isolating phylogenetic signal from morphological data is crucial for accurately merging fossils into the tree of life and for calibrating molecular dating. However, subjective character definition is a major limitation which can introduce biases that mislead phylogenetic inferences and divergence time estimation. The use of quantitative data, e.g., geometric morphometric (GMM; shape) data can allow for more objective integration of morphological data into phylogenetic inference. This systematic review describes the current state of the field in using continuous morphometric data (e.g., GMM data) for phylogenetic reconstruction and assesses the efficacy of these data compared to discrete characters using the PRISMA-EcoEvo v1.0. reporting guideline, and offers some pathways for approaching this task with GMM data. A comprehensive search string yielded 11,123 phylogenetic studies published in English up to Oct 2023 in the Web of Science database. Title and abstract screening removed 10,975 articles, and full-text screening was performed for 132 articles. Of these, a total of twelve articles met final inclusion criteria and were used for downstream analyses.
Phylogenetic performance was compared between approaches that employed continuous morphometric and discrete morphological data. Overall, the reconstructed phylogenies did not show increased resolution or accuracy (i.e., benchmarked against molecular phylogenies) as continuous data alone or combined with discrete morphological datasets.
An exhaustive search of the literature for existing empirical continuous data resulted in a total of twelve articles for final inclusion following title/abstract, and full-text screening. Our study was performed under a rigorous framework for systematic reviews, which showed that the lack of available comparisons between discrete and continuous data hinders our understanding of the performance of continuous data. Our study demonstrates the problem surrounding the efficacy of continuous data as remaining relatively intractable despite an exhaustive search, due in part to the difficulty in obtaining relevant comparisons from the literature. Thus, we implore researchers to address this issue with studies that collect discrete and continuous data sets with directly comparable properties (i.e., describing shape, or size).
从形态数据中分离出系统发育信号对于准确地将化石合并到生命之树中以及校准分子定年至关重要。然而，主观的特征定义是一个主要的限制因素，它会引入偏差，从而误导系统发育推断和分歧时间估计。使用定量数据，例如几何形态测量（GMM；形状）数据，可以更客观地将形态数据整合到系统发育推断中。本系统评价描述了目前使用连续形态计量数据（例如 GMM 数据）进行系统发育重建的领域现状，并使用 PRISMA-EcoEvo v1.0 报告准则评估了这些数据与离散特征相比的效果，并为使用 GMM 数据处理此任务提供了一些途径。全面的搜索字符串在 Web of Science 数据库中产生了截至 2023 年 10 月发表的 11123 篇英文系统发育研究。标题和摘要筛选去除了 10975 篇文章，并对 132 篇文章进行了全文筛选。其中，共有 12 篇文章符合最终纳入标准，并用于下游分析。
比较了使用连续形态计量和离散形态数据的方法的系统发育性能。总体而言，仅连续数据或与离散形态数据集结合使用时，重建的系统发育树并没有显示出更高的分辨率或准确性（即，与分子系统发育相比）。
通过对文献中现有经验性连续数据的详尽搜索，在标题/摘要和全文筛选后，共有 12 篇文章最终被纳入。我们的研究是在系统评价的严格框架下进行的，该框架表明，离散数据和连续数据之间缺乏可用的比较阻碍了我们对连续数据性能的理解。我们的研究表明，尽管进行了详尽的搜索，但由于难以从文献中获得相关比较，连续数据的有效性仍然是一个相对棘手的问题。因此，我们恳请研究人员通过收集具有直接可比属性（即描述形状或大小）的离散和连续数据集的研究来解决这个问题。
