系统发育分析通过形态特征或分子性状帮助理解不同生物类群的演化历史。化石作为许多已灭绝生物的唯一遗存, 为研究地球生命多样化提供了宝贵的机会。这使得基于离散型形态特征的系统发育分析在探讨生物的起源与早期演化方面发挥着不可或缺的作用。系统发育分析方法多样, 早期常常依赖最大简约法对化石形态特征进行分析。近年来, 基于模型的贝叶斯系统发育分析在古生物学中获得越来越多的重视。贝叶斯系统发育分析可以提供准确且易解释的系统发育关系, 并为不确定性提供评估指标。本文旨在讨论系统发育分析过程中的不确定性影响因素, 如数据集、替换模型以及马尔科夫链, 以此提高对系统发育分析结果的可信度。在本文的案例中, 利用已发表的苔藓虫数据集探讨不同版本的Mk模型和马尔可夫链的稳定性对系统发育树的影响。研究结果表明这些因素对系统发育树的后验概率和拓扑结构都有一定影响, 这意味着在系统发育分析中需要仔细分析这些因素, 选择合适的模型。

Phylogenetic analysis reconstructs the evolutionary history of different groups of organisms based on morphological and/or molecular data. Fossils, as the only remaining evidence for most extinct species, offer a direct glimpse into the evolutionary events that have shaped the diversification of life on Earth. Consequently, phylogenetic analysis based on discrete morphological data is essential for understanding the early origins and evolution of various organisms. Over the past decade, phylogenetic analysis has primarily relied on the maximum parsimony method. Re cently, model-based Bayesian inference has gained prominence in paleobiology. The Bayesian method provides robust estimates of phylogeny along with clear measures of phylogenetic uncertainty, making the results both accurate and easily interpretable. In this study, we discuss the likely factors influencing uncertainty in the phylogenetic analysis, such as data set composition, substitution models, and Markov chain parameters, to improve confidence in phyloge netic inference. In a simulation study, we investigated the effects of different versions of the Markov model and the stability of the Markov chain on the phylogenetic tree, using a pubulished bryozoa data set. Results indicate that these factors influence both topology and posterior probabilities, underscoring the need to carefully consider them in phylo genetic analysis.

国家自然科学基金青年科学项目(41902115)和国家自然科学基金项目(42372128)资助