Data from: Inferring state-dependent diversification rates using approximate Bayesian computation (ABC)

State-dependent speciation and extinction (SSE) models provide a framework for quantifying whether species traits have an impact on evolutionary rates and how this shapes the variation in species richness among clades in a phylogeny. However, SSE models are becoming increasingly complex, limiting the application of likelihood-based inference methods. Approximate Bayesian computation (ABC), a likelihood-free approach, is a potentially powerful alternative for estimating parameters. One of the key challenges in using ABC is the selection of efficient summary statistics, which can greatly affect the accuracy and precision of the parameter estimates. In state-dependent diversification models, summary statistics need to capture the complex relationships between rates of diversification and species traits. Here, we develop an ABC framework to estimate state-dependent speciation, extinction and transition rates in the BiSSE (binary state dependent speciation and extinction) model. Using different sets of candidate summary statistics, we then compare the inference ability of ABC with that of using likelihood-based maximum likelihood (ML) and Markov chain Monte Carlo (MCMC) methods. Our results show the ABC algorithm can accurately estimate state-dependent diversification rates for most of the model parameter sets we explored. The inference error of the parameters associated with the species-poor state is larger with ABC than in the likelihood estimations only when the speciation rate is highly asymmetric between the two states (λ1 / λ0 = 5). Furthermore, we find that the combination of normalized lineage-through-time (nLTT) statistics and phylogenetic signal in binary traits (Fitz and Purvis’s D) constitute efficient summary statistics for the ABC method. By providing insights into the selection of suitable summary statistics, our work aims to contribute to the use of the ABC approach in the development of complex state-dependent diversification models, for which a likelihood is not available.状态依赖物种形成与灭绝（State-dependent speciation and extinction, SSE）模型为量化物种性状是否对演化速率产生影响，以及该影响如何塑造系统发育中各支系的物种丰富度差异提供了研究框架。然而，SSE模型正日趋复杂，这限制了基于似然的推断方法的应用。近似贝叶斯计算（Approximate Bayesian computation, ABC）作为一种无需似然的方法，是估计模型参数的极具潜力的替代方案。使用ABC的核心挑战之一在于高效摘要统计量的选取，这会极大影响参数估计的准确性与精度。在状态依赖多样化模型中，摘要统计量需要捕捉多样化速率与物种性状间的复杂关联。在此，我们构建了一套ABC框架，用于估计二元状态依赖物种形成与灭绝（BiSSE）模型中的状态依赖物种形成、灭绝以及状态转换速率。随后，我们通过多组候选摘要统计量，对比了ABC与基于似然的最大似然（Maximum Likelihood, ML）及马尔可夫链蒙特卡洛（Markov chain Monte Carlo, MCMC）方法的推断性能。研究结果表明，在我们探索的绝大多数模型参数组合下，ABC算法可准确估计状态依赖多样化速率。仅当两个状态间的物种形成速率高度不对称（λ₁/λ₀=5）时，ABC对与低物种丰富度状态相关的参数的推断误差，要高于似然估计方法。此外，我们发现归一化类群随时间累积（normalized lineage-through-time, nLTT）统计量与二元性状的系统发育信号（Fitz和Purvis的D统计量）的组合，可作为ABC方法的高效摘要统计量。本研究通过为合适的摘要统计量选取提供思路，旨在助力ABC方法在无法获取似然函数的复杂状态依赖多样化模型中的应用。