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Mandrill mothers associate with infants who look like their own offspring using phenotype matching

Cite this dataset

Charpentier, Marie et al. (2022). Mandrill mothers associate with infants who look like their own offspring using phenotype matching [Dataset]. Dryad.


Behavioral discrimination of kin is a key process structuring social relationships in animals. In this study, we provide a first example of discrimination towards non-kin by third-parties through a mechanism of phenotype matching. In mandrills, we recently demonstrated increased facial resemblance among paternally-related juvenile and adult females indicating adaptive opportunities for paternal kin recognition. Here, we hypothesize that mothers use offspring’s facial resemblance with other infants to guide offspring’s social opportunities towards similarly-looking ones. Using deep learning for face recognition in 80 wild mandrill infants, we first show that infants born to the same father or conceived during the tenure of the same alpha male resemble each other the most, independently of their age, sex or maternal origin, extending previous results to the youngest age class. Using long-term behavioral observations on association patterns and controlling for matrilineal origin, maternal relatedness and infant age and sex, we then demonstrate that, as hypothesized, mothers are spatially closer to infants that resemble their own offspring more, thereby facilitating associations among similar-looking infants. Using theoretical modeling, we describe a plausible evolutionary process whereby mothers gain fitness benefits by promoting nepotism among paternally related infants. This mechanism, that we call “second order kin selection”, may extend beyond mother-infant interactions and has the potential to explain cooperative behaviors among non-kin in social species, including humans.


Field data (behavior and pictures) and AI methods.


Agence Nationale de la Recherche, Award: 17-CE02-0002

Agence Nationale de la Recherche, Award: 20-CE02-0005-01

Deutsche Forschungsgemeinschaft, Award: KA 1082-20-1