Modern comparative biology owes much to phylogenetic regression. At its conception, this technique sparked a revolution that armed biologists with phylogenetic comparative methods (PCMs) for disentangling evolutionary correlations from those arising from hierarchical phylogenetic relationships. Over the past few decades, the phylogenetic regression framework has become a paradigm of modern comparative biology that has been widely embraced as a remedy for shared ancestry. However, recent evidence has sown doubt over the efficacy of phylogenetic regression, and PCMs more generally, with the suggestion that many of these methods fail to provide an adequate defense against unreplicated evolution—the primary justification for using them in the first place. Importantly, some of the most compelling examples of biological innovation in nature result from abrupt lineage-specific evolutionary shifts, which current regression models are largely ill-equipped to deal with. Here we explore a solution to this problem by applying robust linear regression to comparative trait data. We formally introduce robust phylogenetic regression to the PCM toolkit with linear estimators that are less sensitive to model violations than the standard least-squares estimator, while still retaining high power to detect true trait associations. Our analyses also highlight an ingenuity of the original algorithm for phylogenetic regression based on independent contrasts, whereby robust estimators are particularly effective. Collectively, we find that robust estimators hold promise for improving tests of trait associations and offer a path forward in scenarios where classical approaches may fail. Our study joins recent arguments for increased vigilance against unreplicated evolution and a better understanding of evolutionary model performance in challenging–yet biologically important–settings.

中文翻译：

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稳健的系统发育回归


现代比较生物学很大程度上归功于系统发育回归。这项技术一经提出，就引发了一场革命，为生物学家提供了系统发育比较方法（PCM），以将进化相关性与等级系统发育关系所产生的相关性分开。在过去的几十年里，系统发育回归框架已成为现代比较生物学的范式，并被广泛接受为共同祖先的补救措施。然而，最近的证据对系统发育回归和更普遍的 PCM 的有效性产生了怀疑，认为许多这些方法无法提供针对非复制进化的充分防御——这正是使用它们的首要理由。重要的是，自然界中一些最引人注目的生物创新例子是由突然的谱系特异性进化转变造成的，而当前的回归模型在很大程度上无法应对这种转变。在这里，我们通过将稳健线性回归应用于比较性状数据来探索该问题的解决方案。我们正式将稳健的系统发育回归引入 PCM 工具包，其中线性估计器对模型违规的敏感度低于标准最小二乘估计器，同时仍保留检测真实性状关联的高功效。我们的分析还强调了基于独立对比的系统发育回归原始算法的独创性，由此稳健的估计器特别有效。总的来说，我们发现稳健的估计量有望改进特质关联测试，并在经典方法可能失败的情况下提供一条前进的道路。 我们的研究加入了最近的论点，即提高对不可复制进化的警惕性，并更好地理解进化模型在具有挑战性但具有生物学重要性的环境中的表现。