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.