Premise Recent advances in generating large‐scale phylogenies enable broad‐scale estimation of species diversification. These now common approaches typically are characterized by (1) incomplete species coverage without explicit sampling methodologies and/or (2) sparse backbone representation, and usually rely on presumed phylogenetic placements to account for species without molecular data. We used empirical examples to examine the effects of incomplete sampling on diversification estimation and provide constructive suggestions to ecologists and evolutionary biologists based on those results. Methods We used a supermatrix for rosids and one well‐sampled subclade (Cucurbitaceae) as empirical case studies. We compared results using these large phylogenies with those based on a previously inferred, smaller supermatrix and on a synthetic tree resource with complete taxonomic coverage. Finally, we simulated random and representative taxon sampling and explored the impact of sampling on three commonly used methods, both parametric (RPANDA and BAMM) and semiparametric (DR). Results We found that the impact of sampling on diversification estimates was idiosyncratic and often strong. Compared to full empirical sampling, representative and random sampling schemes either depressed or inflated speciation rates, depending on methods and sampling schemes. No method was entirely robust to poor sampling, but BAMM was least sensitive to moderate levels of missing taxa. Conclusions We suggest caution against uncritical modeling of missing taxa using taxonomic data for poorly sampled trees and in the use of summary backbone trees and other data sets with high representative bias, and we stress the importance of explicit sampling methodologies in macroevolutionary studies.

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用不完全抽样估计多样化的速率和模式：玫瑰属的案例研究

前提 产生大规模系统发育的最新进展使对物种多样化的大规模估计成为可能。这些现在常见的方法的典型特征是 (1) 没有明确采样方法的不完整物种覆盖和/或 (2) 稀疏的骨架表示，并且通常依赖于假定的系统发育位置来解释没有分子数据的物种。我们使用实证例子来检验不完整抽样对多样化估计的影响，并根据这些结果为生态学家和进化生物学家提供建设性的建议。方法我们使用了蔷薇属的超矩阵和一个采样良好的亚分支（葫芦科）作为实证案例研究。我们将使用这些大型系统发育的结果与基于先前推断的结果进行了比较，较小的超级矩阵和具有完整分类学覆盖的合成树资源。最后，我们模拟了随机和有代表性的分类单元抽样，并探讨了抽样对三种常用方法的影响，参数（RPANDA 和 BAMM）和半参数（DR）。结果 我们发现抽样对多元化估计的影响是特殊的，而且通常很强烈。与完全经验抽样相比，代表性和随机抽样方案要么降低要么夸大物种形成率，具体取决于方法和抽样方案。没有一种方法对不良抽样完全稳健，但 BAMM 对中等水平的缺失分类群最不敏感。