Multidimensional trait morphology predicts ecology across ant lineages

1. Understanding the link between ecology and morphology is a fundamental goal in biology. Ants are diverse terrestrial organisms, known to exhibit ecologically-driven morphological variation. While relationships between individual traits and ecologies have been identified, multidimensional interactions among traits and their cumulative predictive power remain unknown. Because selective pressures may generate convergent syndromes spanning multiple traits, we applied multivariate analyses across a wide sampling of taxa to assess ecomorphological variation in an integrative context. 2. How well does morphology predict ecology? Moreover, are there quantitatively-supported ant ecomorphs? We investigated the links between trait morphology and ecology by assembling a morphometric dataset spanning over 160 species within 110 genera. Because ants occupy a wide range of ecologies, we compiled natural history data on nesting microhabitat, foraging stratum, and functional role into 35 defined niche combinations. This tripartite ecological classification and our morphological dataset were optimized under dimension reduction techniques including Principal Component Analysis, Principal Coordinate Analysis, Linear Discriminant Analysis, and Random Forest supervised machine learning. 3. Our results describe ant ecomorphospace as comprising regions of shared, generalized morphology as well as unique phenotypic space associated with specialized ecologies. Dimension reduction and model-based approaches predict ecology with 77-85% accuracy and Random Forest analysis consistently outperforms LDA. While accounting for shared ancestry, we found eye, antennal scape, and leg morphology to be most informative in differentiating among ecologies. We also note some heterogeneity between trait significance in each ecological aspect (nesting niche, foraging niche, functional role). To increase the utility of ecomorphological classification we simplified our 35 observed niche combinations into 10 ecomorph syndromes, which were also predicted by morphology. The predictive power of these machine learning methods underscores the strong role that ecology has in convergently shaping overall body plan morphology across ant lineages. We include a pipeline for predictive ecomorphological modeling using morphometric data, which may be expanded with additional specimen-based and natural history data.

1. 探究生态学与形态学之间的关联是生物学的核心研究目标之一。蚂蚁是种类繁多的陆生生物，已知其存在生态驱动的形态变异。尽管学界已阐明单一性状与生态位间的关联，但性状间的多维互作及其累积预测能力仍有待揭示。由于选择压力可能催生跨多个性状的趋同性状组合，我们基于广泛的类群采样开展多变量分析，在整合框架下评估蚂蚁的生态形态学（ecomorphological）变异。
2. 形态学对生态学的预测能力究竟如何？此外，是否存在经定量验证的蚂蚁生态形态群（ecomorphs）？我们通过构建涵盖110个属、160余种蚂蚁的形态测量（morphometric）数据集，探究了性状形态与生态学之间的关联。鉴于蚂蚁占据的生态位范围广泛，我们将关于筑巢微生境、觅食层位与功能角色的自然历史数据整合为35种明确的生态位组合。我们采用主成分分析（Principal Component Analysis）、主坐标分析（Principal Coordinate Analysis）、线性判别分析（Linear Discriminant Analysis）以及随机森林（Random Forest）监督机器学习等降维技术，对上述三分法生态分类体系与形态测量数据集进行了优化处理。
3. 我们的研究结果表明，蚂蚁生态形态空间（ecomorphospace）既包含共享泛化形态的区域，也存在与特化生态位相关的独特表型空间。降维与基于模型的方法对生态学的预测准确率可达77%至85%，且随机森林分析的表现始终优于线性判别分析（LDA）。在考量共同祖先效应的前提下，我们发现眼部、触角柄节（antennal scape）以及腿部的形态是区分不同生态位的最具区分效力的性状。我们还注意到，不同生态维度（筑巢生态位、觅食生态位与功能角色）间的性状显著性存在一定异质性。为提升生态形态学分类的实用性，我们将观测到的35种生态位组合简化为10种生态形态综合征（ecomorph syndromes），该分类体系同样可通过形态学数据进行预测。上述机器学习方法的预测能力凸显了生态学在趋同塑造各蚂蚁演化支系整体躯体构型形态中的关键作用。本研究附带了一套基于形态测量数据的预测性生态形态学建模流程，该流程可通过新增基于标本的自然历史数据进一步拓展完善。