Abstract

Intraspecies aggression has profound ecological and evolutionary consequences, as recipients can suffer injuries, decreases in fitness, and become outcasts from social groups. Although animals implement diverse strategies to avoid hostile confrontations, the extent to which social influences affect escape tactics is unclear. Here, we used computational and machine-learning approaches to analyze complex behavioral interactions as mixed-sex groups of mice, Mus musculus, freely interacted. Mice displayed a rich repertoire of behaviors marked by changes in behavioral state, aggressive encounters, and mixed-sex interactions. A prominent behavioral sequence consistently occurred after aggressive encounters, where males in submissive states quickly approached and transiently interacted with females immediately before the aggressor engaged with the same female. The behavioral sequences were also associated with substantially fewer physical altercations. Furthermore, the male's behavioral state and the interacting partners could be predicted by distinct features of the behavioral sequence, such as kinematics and the latency to and duration of male-female interactions. More broadly, our work revealed an ethologically relevant escape strategy influenced by the presence of females that may serve as a mechanism for de-escalating social conflict and preventing consequential reductions in fitness.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

* Updated manuscript includes the supporting information.

Details

Title
Automated behavioral analysis reveals that mice employ a bait-and-switch escape mechanism to de-escalate social conflict.
Author
Clein, Rachel; Warren, Megan; Neunuebel, Joshua P
University/institution
Cold Spring Harbor Laboratory Press
Section
New Results
Publication year
2024
Publication date
Jan 13, 2024
Publisher
Cold Spring Harbor Laboratory Press
ISSN
2692-8205
Source type
Working Paper
Language of publication
English
ProQuest document ID
2913577819
Copyright
© 2024. This article is published under http://creativecommons.org/licenses/by/4.0/ (“the License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.