Accurate characterization of sexual dimorphism is crucial in evolutionary biology because of its significance in understanding present and past adaptations involving reproductive and resource use strategies of species. However, inferring dimorphism in fossil assemblages is difficult, particularly with relatively low dimorphism. Commonly used methods of estimating dimorphism levels in fossils include the mean method, the binomial dimorphism index, and the coefficient of variation method. These methods have been reported to overestimate low levels of dimorphism, which is problematic when investigating issues such as canine size dimorphism in primates and its relation to reproductive strategies. Here, we introduce the posterior density peak (pdPeak) method that utilizes the Bayesian inference to provide posterior probability densities of dimorphism levels and within-sex variance. The highest posterior density point is termed the pdPeak. We investigated performance of the pdPeak method and made comparisons with the above-mentioned conventional methods via 1) computer-generated samples simulating a range of conditions and 2) application to canine crown-diameter datasets of extant known-sex anthropoids. Results showed that the pdPeak method is capable of unbiased estimates in a broader range of dimorphism levels than the other methods and uniquely provides reliable interval estimates. Although attention is required to its underestimation tendency when some of the distributional assumptions are violated, we demonstrate that the pdPeak method enables a more accurate dimorphism estimate at lower dimorphism levels than previously possible, which is important to illuminating human evolution.

性二态性的准确表征在进化生物学中至关重要，因为它在理解涉及物种生殖和资源利用策略的当前和过去适应方面具有重要意义。然而，推断化石组合中的二态性是困难的，尤其是在二态性相对较低的情况下。估计化石中二态性水平的常用方法包括均值法、二项式二态性指数和变异系数法。据报道，这些方法高估了低水平的二态性，这在研究灵长类动物的犬类大小二态性及其与生殖策略的关系等问题时存在问题。这里，我们引入了后验密度峰值 (pdPeak) 方法，该方法利用贝叶斯推理来提供二态性水平和性别内方差的后验概率密度。最高的后验密度点称为 pdPeak。我们研究了 pdPeak 方法的性能，并通过 1) 模拟一系列条件的计算机生成样本和 2) 应用于现存的已知性别类人猿的犬冠直径数据集，与上述传统方法进行了比较。结果表明，与其他方法相比，pdPeak 方法能够在更广泛的二态水平范围内进行无偏估计，并且独特地提供了可靠的区间估计。尽管在违反某些分布假设时需要注意其低估趋势，