Multidimensional trait morphology predicts ecology across ant lineages

• Published in: Functional ecology 2021-01, Vol.35 (1), p.139-152
• Main Authors: Sosiak, Christine E., Barden, Phillip, Higham, Timothy
• Format: Article
• Language: English
• Subjects: Ants; Classification; Convergence; Datasets; Discriminant analysis; Disorders; Ecology; ecomorphology; Genera; Heterogeneity; Learning algorithms; Machine learning; Microenvironments; Microhabitats; Morphology; morphospace; Nesting; Niches; Principal components analysis; Reduction; Terrestrial environments; trait morphology
• ISSN: 0269-8463; 1365-2435
• DOI: 10.1111/1365-2435.13697

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. As 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. 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. As 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. 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 modelling using morphometric data, which may be expanded with additional specimen‐based and natural history data. A free Plain Language Summary can be found within the Supporting Information of this article. A free Plain Language Su