Phylogenetic Analyses of the Evolution of Continuous Characters

密切相关的物种往往彼此相似， 特色 跨物种的统计分析 （比较）数据集不能假设物种数据点是 统计上独立的或从共同的基础上得出的 分布 前一种情况会导致对 假设检验的自由度;后者可能导致以下问题： 估计。 一些作者提出了分析方法， 试图通过绘制生态学特征图来解决这些问题， 或对“已知”遗传学的生理兴趣，估计从 独立信息（例如，DNA序列）。 计算机模拟 技术将被用来调查的统计性质 几种这样的方法。 首先，I型错误率、功效和 两种新的相关分析方法的估计能力 连续性状的进化，基于自相关模型 （Cheverud等人，1985年; Gittleman和Kot，1990年），另一个在 谱系模型从数量遗传学（林奇，1991年），将是 与以前研究的（非系统发育，独立 对比，最小进化）。 第二，模拟方法， 将开发协方差的系统发育分析。 第三、 将开发计算机程序，（1）模拟数据 在不同的进化模式下， (2)对所述模拟数据执行协方差分析以获得 F-统计量的空分布，以及（3）允许基于图形的 数据输入和树型结构及字符数据的转换。 %%% 对遗传关系的比较分析是一个重要的 跨学科的方法，将机构研究与 生态学、行为学和生理学。 该提案的重点是 比较分析的统计技术并将验证 并进一步发展对这一方法至关重要的比较方法。

Closely related species often resemble each other for many characteristics. Statistical analyses of cross-species (comparative) data sets cannot assume that species data points are statistically independent or drawn from a common underlying distribution. The former leads to uncertainties about degrees of freedom for hypothesis testing; the latter may lead to problems of estimation. Several authors have proposed analytical methods that attempt to solve these problems by mapping characters of ecological or physiological interest on a "known" phylogeny, estimated from independent information (e.g., DNA sequences). Computer simulation techniques will be used to investigate the statistical properties of several such methods. First, the Type I error rates, power, and estimation abilities of two new methods for analyzing correlated evolution of continuous traits, one based on autocorrelation models (Cheverud et al., 1985; Gittleman and Kot, 1990) and the other on pedigree models from quantitative genetics (Lynch, 1991), will be compared to those previously studied (non-phylogenetic, independent contrasts, minimum evolution). Second, a simulation approach to phylogenetic analysis of covariance will be developed. Third, computer programs will be developed that (1) simulate data phylogenetically under alternative models of evolutionary change, (2) perform analysis of covariance on the simulated data to obtain null distributions of F-statistics, and (3) allow graphically-based data entry and transformation of tree structure and character data. %%% Comparative analyses of phylogentic relationships is an important interdisciplinary approach linking studies in phylogency with ecology, behavior and physiology. This proposal focuses on statistical techniques of comparative analysis and will validate and further develop comparative methods essential to this approach.