Experimental evidence that female rhesus macaques (Macaca mulatta) perceive variation in male facial masculinity, bioRxiv, 2017-11-25

AbstractAmong many primate species, face shape is sexually dimorphic, and male facial masculinity has been proposed to influence female mate choice and male-male competition by signalling competitive ability. However, whether conspecifics pay attention to facial masculinity has only been assessed in humans. In a study of free-ranging rhesus macaques, Macaca mulatta, we used a two-alternative look-time experiment to test whether females perceive male facial masculinity. We presented 107 females with pairs of images of male faces – one with a more masculine shape and one more feminine – and recorded their looking behaviour. Females looked at the masculine face longer than at the feminine face in more trials than predicted by chance. Although there was no overall difference in average look-time between masculine and feminine faces across all trials, females looked significantly longer at masculine faces in a subset of trials for which the within-pair difference in masculinity was most pronounced. Additionally, the proportion of time subjects looked toward the masculine face increased as the within-pair difference in masculinity increased. This study provides evidence that female macaques perceive variation in male facial shape, a necessary condition for intersexual selection to operate on such a trait. It also highlights the potential impact of perceptual thresholds on look-time experiments.

biorxiv animal-behavior-and-cognition 0-100-users 2017

A practical guide for inferring reliable dominance hierarchies and estimating their uncertainty, bioRxiv, 2017-02-24

AbstractMany animal social structures are organized hierarchically, with dominant individuals monopolizing resources. Dominance hierarchies have received great attention from behavioural and evolutionary ecologists. As a result, there are many methods for inferring hierarchies from social interactions. Yet, there are no clear guidelines about how many observed dominance interactions (i.e. sampling effort) are necessary for inferring reliable dominance hierarchies, nor are there any established tools for quantifying their uncertainty. In this study, we simulated interactions (winners and losers) in scenarios of varying steepness (the probability that a dominant defeats a subordinate based on their difference in rank). Using these data, we (1) quantify how the number of interactions recorded and hierarchy steepness affect the performance of three methods, (2) propose an amendment that improves the performance of a popular method, and (3) suggest two easy procedures to measure uncertainty in the inferred hierarchy. First, we found that the ratio of interactions to individuals required to infer reliable hierarchies is surprisingly low, but depends on the hierarchy steepness and method used. We then show that David’s score and our novel randomized Elo-rating are the two best methods, whereas the original Elo-rating and the recently described ADAGIO perform less well. Finally, we propose two simple methods to estimate uncertainty at the individual and group level. These uncertainty measures further allow to differentiate non-existent, very flat and highly uncertain hierarchies from intermediate, steep and certain hierarchies. Overall, we find that the methods for inferring dominance hierarchies are relatively robust, even when the ratio of observed interactions to individuals is as low as 10 to 20. However, we suggest that implementing simple procedures for estimating uncertainty will benefit researchers, and quantifying the shape of the dominance hierarchies will provide new insights into the study organisms.Highlights<jatslist list-type=bullet><jatslist-item>David’s score and the randomized Elo-rating perform best.<jatslist-item><jatslist-item>Method performance depends on hierarchy steepness and sampling effort.<jatslist-item><jatslist-item>Generally, inferring dominance hierarchies requires relatively few observations.<jatslist-item><jatslist-item>The R package “aniDom” allows easy estimation of hierarchy uncertainty.<jatslist-item><jatslist-item>Hierarchy uncertainty provides insights into the shape of the dominance hierarchy.<jatslist-item>

biorxiv animal-behavior-and-cognition 0-100-users 2017

 

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