Trait-Based Paleontological Niche Prediction Recovers Extinct Ecological Breadth of the Earliest Specialized Ant Predators

Christine Sosiak, Tyler Janovitz, Vincent Perrichot, John Paul Timonera, Phillip Barden

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Paleoecological estimation is fundamental to the reconstruction of evolutionary and environmental histories. The ant fossil record preserves a range of species in three-dimensional fidelity and chronicles faunal turnover across the Cretaceous and Cenozoic; taxonomically rich and ecologically diverse, ants are an exemplar system to test new methods of paleoecological estimation in evaluating hypotheses. We apply a broad extant ecomorphological dataset to evaluate random forest machine learning classification in predicting the total ecological breadth of extinct and enigmatic hell ants. In contrast to previous hypotheses of extinction-prone arboreality, we find that hell ants were primarily leaf litter or ground-nesting and foraging predators, and by comparing ecospace occupations of hell ants and their extant analogs, we recover a signature of ecomorphological turnover across temporally and phylogenetically distinct lineages on opposing sides of the Cretaceous-Paleogene boundary. This paleoecological predictive framework is applicable across lineages and may provide new avenues for testing hypotheses over deep time.

Original languageEnglish (US)
Pages (from-to)E147-E162
JournalAmerican Naturalist
Volume202
Issue number6
DOIs
StatePublished - Dec 2023

All Science Journal Classification (ASJC) codes

  • Ecology, Evolution, Behavior and Systematics

Keywords

  • ants
  • machine learning
  • morphology
  • paleoecology

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