Data from: An assessment of phylogenetic tools for analyzing the interplay between interspecific interactions and phenotypic evolution

Much ecological and evolutionary theory predicts that interspecific interactions often drive phenotypic diversification and that species phenotypes in turn influence species interactions. Several phylogenetic comparative methods have been developed to assess the importance of such processes in nature; however, the statistical properties of these methods have gone largely untested. Focusing mainly on scenarios of competition between closely-related species, we assess the performance of available comparative approaches for analyzing the interplay between interspecific interactions and species phenotypes. We find that many currently used statistical methods often fail to detect the impact of interspecific interactions on trait evolution, that sister-taxa analyses are particularly unreliable in general, and that recently developed process-based models have more satisfactory statistical properties. Methods for detecting predictors of species interactions are generally more reliable than methods for detecting character displacement. In weighing the strengths and weaknesses of different approaches, we hope to provide a clear guide for empiricists testing hypotheses about the reciprocal effect of interspecific interactions and species phenotypes and to inspire further development of process-based models.诸多生态学与进化理论均表明，种间相互作用常推动表型分化，而物种的表型反过来也会影响种间相互作用。目前已有多种系统发育比较方法（phylogenetic comparative methods）被开发出来，用于评估这类自然过程的重要性；然而，这些方法的统计特性大多未得到充分检验。本研究主要以近缘物种间的竞争场景为对象，评估现有比较方法在分析种间相互作用与物种表型间交互影响时的表现。研究发现，诸多当前常用的统计方法往往无法检测到种间相互作用对性状演化的影响；姊妹类群分析（sister-taxa analyses）整体上尤其不可靠，而新近开发的基于过程的模型（process-based models）则具备更优异的统计特性。用于检测物种相互作用预测因子的方法，整体上比检测性状替换（character displacement）的方法更为可靠。通过权衡不同方法的优劣，我们希望为检验种间相互作用与物种表型间双向影响假说的实验研究者提供清晰的指导，并推动基于过程的模型的进一步开发。