Size and shape variations of shells can be used to identify natural phenotypic clusters and thus delimit snail species. Here, we apply both supervised and unsupervised machine learning algorithms to a geometric morphometric dataset to investigate size and shape variations of the shells of the endemic land snail Placostylus from New Caledonia. We sampled eight populations of Placostylus from the Isle of Pines, where two species of this genus reportedly coexist. We used neural network analysis as a supervised learning algorithm and Gaussian mixture models as an unsupervised learning algorithm. Using a training dataset of individuals assigned to species using nuclear markers, we found that supervised learning algorithms could not unambiguously classify all individuals of our expanded dataset using shell size and shape. Unsupervised learning showed that the optimal division of our data consisted of three phenotypic clusters. Two of these clusters correspond to the established species Placostylus fibratus and P. porphyrostomus, while the third cluster was intermediate in both shape and size. Most of the individuals that were not clearly classified using supervised learning were classified to this intermediate phenotype by unsupervised learning, and most of these individuals came from previously unsampled populations. These results may indicate the presence of persistent putative-hybrid populations of Placostylus in the Isle of Pines.

中文翻译：

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几何形态测量学和机器学习挑战目前接受的新喀里多尼亚松树岛上陆蜗牛 Placostylus（Pulmonata：Bothriembryontidae）的物种限制

贝壳的大小和形状变化可用于识别天然表型簇，从而界定蜗牛物种。在这里，我们将有监督和无监督的机器学习算法应用于几何形态测量数据集，以研究来自新喀里多尼亚的地方性蜗牛 Placostylus 壳的大小和形状变化。我们从松树岛采集了 8 个 Placostylus 种群，据报道该属的两个物种共存。我们使用神经网络分析作为监督学习算法，使用高斯混合模型作为无监督学习算法。使用使用核标记分配给物种的个体的训练数据集，我们发现监督学习算法无法使用壳大小和形状对我们扩展数据集中的所有个体进行明确分类。无监督学习表明，我们数据的最佳划分由三个表型簇组成。其中两个簇对应于已建立的物种 Placostylus fibratus 和 P. porphyrostomus，而第三个簇在形状和大小上都处于中等水平。使用监督学习没有明确分类的大多数个体通过无监督学习被归类为这种中间表型，并且这些个体中的大多数来自先前未采样的群体。这些结果可能表明在松树岛上存在持续的推定杂种 Placostylus 种群。而第三个集群在形状和大小上都处于中等水平。使用监督学习没有明确分类的大多数个体通过无监督学习被归类为这种中间表型，并且这些个体中的大多数来自先前未采样的群体。这些结果可能表明在松树岛上存在持续的推定杂种 Placostylus 种群。而第三个集群在形状和大小上都处于中等水平。使用监督学习没有明确分类的大多数个体通过无监督学习被归类为这种中间表型，并且这些个体中的大多数来自先前未采样的群体。这些结果可能表明在松树岛上存在持续的推定杂种 Placostylus 种群。