Convergent Evolution of the Army Ant Syndrome and Congruence in Big-Data Phylogenetics

• Published in: Systematic biology Vol. 68; no. 4; pp. 642 - 656
• Main Author: Borowiec, Marek L.
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01.07.2019
• Subjects: algorithms; convergent evolution; data collection; foraging; Formicidae; geographical distribution; loci; phylogeny; REGULAR ARTICLES; k-means; partitioning; Formicidae; ultraconserved elements; phylogenomics; Dorylinae
• ISSN: 1063-5157; 1076-836X; 1076-836X
• DOI: 10.1093/sysbio/syy088

Army ants are a charismatic group of organisms characterized by a suite of morphological and behavioral adaptations that includes obligate collective foraging, frequent colony relocation, and highly specialized wingless queens. This army ant syndrome underlies the ecological success of army ants and its evolution has been the subject of considerable debate. It has been argued to have arisen once or multiple times within the ant subfamily Dorylinae. To address this question in a phylogenetic framework I generated data from 2166 loci and a comprehensive taxon sampling representing all 27 genera and 155 or approximately 22% of doryline species. Most analyses show strong support for convergent evolution of the army ant syndrome in the Old and New World but certain relationships are sensitive to analytics. I examine the signal present in this data set and find that conflict is diminished when only loci less likely to violate common phylogenetic model assumptions are considered. I also provide a temporal and spatial context for doryline evolution with time-calibrated, biogeographic, and diversification rate shift analyses. This study shows that the army ant syndrome is both an example of remarkable convergence of a complex set of traits and a case of long-term evolutionary stasis. The sensitivity of some of the phylogenetic results underscores the need for cautious analysis of phylogenomic data and calls for more efficient algorithms employing better-fitting models of molecular evolution. Congruence among results obtained using different analytics may be used to assess robustness in phylogenomics.