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Dryad

Feeding ecology has a stronger evolutionary influence on functional morphology than on body mass in mammals

Cite this dataset

Grossnickle, David (2020). Feeding ecology has a stronger evolutionary influence on functional morphology than on body mass in mammals [Dataset]. Dryad. https://doi.org/10.5061/dryad.573n5tb40

Abstract

Ecological specialization is a central driver of adaptive evolution. However, selective pressures may uniquely affect different ecomorphological traits (e.g., size and shape), complicating efforts to investigate the role of ecology in generating phenotypic diversity. Comparative studies can help remedy this issue by identifying specific relationships between ecologies and morphologies, thus elucidating functionally-relevant traits. Jaw shape is a dietary correlate that offers considerable insight on mammalian evolution, but few studies have examined the influence of diet on jaw morphology across mammals. To this end, I apply phylogenetic comparative methods to mandibular measurements and dietary data for a diverse sample of mammals. Especially powerful predictors of diet are metrics that capture either the size of the angular process, which increases with greater herbivory, or the length of the posterior portion of the jaw, which decreases with greater herbivory. The size of the angular process likely reflects sizes of attached muscles that produce jaw movements needed to grind plant material. Further, I examine the impact of feeding ecology on body mass, an oft-used ecological surrogate in macroevolutionary studies. Although body mass commonly increases with evolutionary shifts to herbivory, it is outperformed by functional jaw morphology as a predictor of diet. Body mass is influenced by numerous factors beyond diet, and it may be evolutionarily labile relative to functional morphologies. This suggests that ecological diversification events may initially facilitate body mass diversification at smaller taxonomic and temporal scales, but sustained selective pressures will subsequently drive greater trait partitioning in functional morphologies.

Funding

National Science Foundation, Award: DBI-1812126