Stability of Numerical Classifications

在此基于成就的续约建议书中， 调查员索卡尔计划继续研究各种数字 祖先-后代关系重建的途径 关系）及其相关的生物分类。 索卡尔过去的工作一直试图建立准确性， 三种不同定序方法的稳定性 分类群之间（瓦格纳简约树，最大似然 估计，和他自己的创造，UPGMA，聚类技术）。 在本项目期间，计算机模拟将 为不同的进化特征假设而构建 变化 将比较6到8个真实的数据集， 模拟数据，并对两组数据进行稳定性分析 和准确性使用所列的三种方法。 系统学见证了方法的扩散， 过去十年的数据分析，以及索卡尔过去在 这些方法在模拟数据上的相对优点， 广泛的观众。 然而，实践系统主义者还不能 先验地预测哪种方法最适合于真实的 数据在手。 基于当前模型的拟议模拟 的字符变化，然后与真实的数据集进行比较， 将解决这个问题。 如果成功，它将填补差距 在真实的和模拟数据之间，从而将打开一个区域， 卓有成效的研究。

In this Accomplishment Based Renewal proposal, Principal Investigator Sokal plans continued study of the various numerical approaches to reconstructing phylogenies (ancestor-descendant relationships) and their associated classifications of organisms. Sokal's past work has sought to establish the accuracy and stability of three different methods for ordering relationships among taxa (Wagner parsimony trees, maximum likelihood estimation, and his own creation, UPGMA, a clustering technique). During the period of this project, computer simulations will be constructed for different hypotheses of evolutionary character change. Between six and eight real datasets will be compared to the simulated data, and both sets will be analyzed for stability and accuracy using the three methods listed. Systematics has witnessed the proliferation of approaches to data analysis in the past decade, and Sokal's past work on the relative merits of these approaches on simulated data has had a wide audience. Yet the practicing systematist is not yet able to predict a priori which approach is the most suitable for the real data at hand. The proposed simulations based on current models of character change, followed by comparisons with real data sets, will address this problem. If successful, it will fill the gap between real and simulated data and will thereby open an area of fruitful research.