Anderson Sean A S, Kaushik Sachin, Matute Daniel R
School of Biological Sciences, Georgia Institute of Technology, USA.
Department of Biology, University of North Carolina at Chapel Hill, USA.
Syst Biol. 2025 Sep 5. doi: 10.1093/sysbio/syaf061.
For many questions in ecology and evolution, the most relevant data to consider are attributes of lineage pairs. Comparative tests for causal relationships among traits like 'diet niche overlap', 'divergence time', and 'strength of reproductive isolation (RI)' - measured for pairwise combinations of related species or populations - have led to several groundbreaking insights, but the correct statistical approach for these analyses has never been clear. Lineage-pair traits are non-independent, but unlike the expected covariance among species' traits, which is captured by a phylogenetic covariance matrix arising from a given model, the expected covariance among lineage-pair traits has not been explicitly formulated. Analyses of pairwise-defined data have thus employed untested workarounds for non-independence rather than direct models of lineage-pair covariance, with consequences that are unexplored. Here, we consider how evolutionary relatedness among taxa translates into non-independence among taxonomic pairs. We develop models by which phylogenetic signal in an underlying character generates covariance among pairs in a lineage-pair trait. We incorporate the resulting lineage-pair covariance matrices into modified versions of phylogenetic generalized least squares and a new phylogenetic beta regression for bounded response variables. Both outperform previous approaches in simulation tests. We find that a common heuristic method, node averaging, imparts a greater cost to model performance than does the non-independence it was designed to correct. We re-analyze two empirical datasets to find dramatic improvements in model fit and, in the case of avian hybridization data, an even stronger relationship between pair age and RI than is revealed from uncorrected analysis. We finally present a new tool, the R package phylopairs, that allows empiricists to test relationships among pairwise-defined variables in a way that is statistically robust and more straightforward to implement.
对于生态学和进化领域的许多问题而言，最相关的数据是谱系对的属性。对“饮食生态位重叠”“分化时间”和“生殖隔离强度（RI）”等性状之间的因果关系进行比较测试（针对相关物种或种群的成对组合进行测量），已经带来了一些开创性的见解，但这些分析的正确统计方法一直不明确。谱系对性状是非独立的，但与物种性状之间预期的协方差不同（后者由给定模型产生的系统发育协方差矩阵捕获），谱系对性状之间预期的协方差尚未明确表述。因此，对成对定义数据的分析采用了未经检验的非独立性解决方法，而非谱系对协方差的直接模型，其后果尚待探索。在这里，我们考虑分类单元之间的进化相关性如何转化为分类对之间的非独立性。我们开发了一些模型，通过这些模型，潜在性状中的系统发育信号会在谱系对性状的成对之间产生协方差。我们将由此产生的谱系对协方差矩阵纳入系统发育广义最小二乘法的修改版本以及一种针对有界响应变量的新的系统发育β回归中。在模拟测试中，这两种方法都优于以前的方法。我们发现，一种常见的启发式方法——节点平均法，给模型性能带来的代价比它旨在纠正的非独立性带来的代价更大。我们重新分析了两个实证数据集，发现模型拟合有显著改善，而且在鸟类杂交数据的情况下，成对年龄与RI之间的关系比未经校正的分析所揭示的更强。我们最终展示了一个新工具——R包phylopairs，它使实证研究人员能够以一种统计上稳健且更易于实施的方式测试成对定义变量之间的关系。
