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Mice employ a bait-and-switch escape mechanism to de-escalate social conflict [1]

['Rachel S. Clein', 'Department Of Psychological', 'Brain Sciences', 'University Of Delaware', 'Newark', 'Delaware', 'United States Of America', 'Megan R. Warren', 'Joshua P. Neunuebel', 'Interdisciplinary Neuroscience Program']

Date: 2024-10

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 distinctive 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 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.

Funding: This research was funded by National Institutes of Mental Health (R01MH122752 to JPN), National Institutes of Health (P20GM103653), the University of Delaware Research Foundation (awarded to JPN), and Delaware’s General University Research Program (awarded to JPN). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright: © 2024 Clein et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Quantifying and evaluating the effectiveness of naturalistic escape behaviors elicited by hostile interactions is a formidable task. It requires unbiasedly extracting and assessing discrete events within the diverse behavioral repertoires of individual animals. By addressing these challenges, we can gain a comprehensive understanding of the dynamics of escape behavior and the role of behavioral state in this evolutionarily conserved process. In this study, we monitored the behavior of multiple freely interacting mice in a large arena and employed multiple computational approaches to analyze individual behaviors. Using behavioral state as a centralized framework, we discovered a robust phenomenon where males subjected to agonistic encounters appear to escape and avoid conflict by exploiting nearby females to divert the attention of the aggressor. These findings highlight sophisticated social dynamics elucidated through systematic observation of naturalistic behavior, demonstrate the influence of prior social experience and behavioral state on subsequent behavior, and reveal a novel mechanism animals use to escape hostile encounters with aggressive males.

Animals respond to threats with both learned and innate escape behaviors [ 5 , 6 ] and environmental features significantly influence their choice of escape strategies [ 7 ]. For example, associating a neutral context with a noxious stimulus leads to learned freezing behaviors [ 8 ], while predator cues or hostile interactions with conspecifics trigger natural, non-conditioned responses [ 9 ]. Rodents, for example, adopt a defensive posture and scan their surroundings when they detect predator odors or freeze in response to looming shadows [ 10 , 11 ]. Aggressive conspecifics prompt various escape behaviors depending on the threat’s proximity and the likelihood of evasion [ 12 , 13 ]. Additionally, prior experiences shape escape strategies; animals frequently exposed to conflicts may avoid social encounters to minimize future attacks [ 14 , 15 ]. Whether triggered by pain, predators, or aggression, effectively executing escape strategies is crucial for survival. Failure to do so can lead to significant fitness reductions [ 6 ]. Thus, understanding the behavioral strategies animals use to evade danger is essential.

Social animals navigate complex environments by evaluating sensory cues, assessing risks, integrating new information with existing knowledge, and executing appropriate behaviors [ 1 , 2 ]. This behavioral flexibility is crucial for their physiological fitness, driving the development of cognitive mechanisms to respond to social cues and environmental changes [ 3 ]. For example, animals employ transitive inference to deduce social ranks by observing others’ behaviors, enabling them to adapt their actions based on hierarchical status and perceived threats [ 4 ]. This adaptability is key to their survival and success.

Results

Computational controls To ensure that sample size did not bias the finding that aggressed males were more likely to engage with females after a hostile interaction (S7A Fig), we used a permutation test [21]. We randomly selected 50 sequences from each recording and calculated a difference index between subsequent aggressed and aggressor interactions with a female (Methods). For all permutations, the difference index was below zero, suggesting that after hostile interactions between males, the aggressed male consistently interacts with the female first, and the effects are not due to sample size. We employed a similar approach for nonsocial, nonaggressive (S7B Fig) and social, nonaggressive (S7C Fig) triggered sequences, finding that the distributions of indices were not significantly skewed towards either male. These analyses suggest that neither sample size nor a subset of examples underlies the results. To further address the importance of behavioral state, we performed another permutation analysis. First, we randomized the identity of the males during aggressive encounters that preceded social interactions. We then calculated a difference index between subsequent aggressed and aggressor interactions with the shuffled data. This procedure was performed 1,000 times to generate a distribution of index values. The observed difference index (−0.25) was significantly lower than the mean of the distribution of shuffled index values (S7D Fig). Additionally, decision tree classifiers were used to predict the type of behavioral sequences (aggressive or control behavior—combined walking and investigating) at rates higher than chance levels (S7E Fig). The model’s accuracy did not depend on the number of examples, maintaining high accuracy with equal numbers of aggressive and control sequences (S7E Fig). Accuracy was low when we attempted to decode sequence type on data where the timing of the behavior preceding the social interaction was randomized (S7E Fig). Together, these analyses substantiate the finding that a male’s behavioral state affects subsequent interactions with females after a hostile interaction.

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[1] Url: https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3002496

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