Antagonistic parent-offspring co-adaptation.
BACKGROUND
In species across taxa, offspring have means to influence parental investment (PI). PI thus evolves as an interacting phenotype and indirect genetic effects may strongly affect the co-evolutionary dynamics of offspring and parental behaviors. Evolutionary theory focused on explaining how exaggerated offspring solicitation can be understood as resolution of parent-offspring conflict, but the evolutionary origin and diversification of different forms of family interactions remains unclear.


RESULTS
In contrast to previous theory that largely uses a static approach to predict how "offspring individuals" and "parental individuals" should interact given conflict over PI, we present a dynamic theoretical framework of antagonistic selection on the PI individuals obtain/take as offspring and the PI they provide as parents to maximize individual lifetime reproductive success; we analyze a deterministic and a stochastic version of this dynamic framework. We show that a zone for equivalent co-adaptation outcomes exists in which stable levels of PI can evolve and be maintained despite fast strategy transitions and ongoing co-evolutionary dynamics. Under antagonistic co-adaptation, cost-free solicitation can evolve as an adaptation to emerging preferences in parents.


CONCLUSIONS
We show that antagonistic selection across the offspring and parental life-stage of individuals favors co-adapted offspring and parental behavior within a zone of equivalent outcomes. This antagonistic parent-offspring co-adaptation does not require solicitation to be costly, allows for rapid divergence and evolutionary novelty and potentially explains the origin and diversification of the observed provisioning forms in family life.
Kölliker
Mathias
M
Zoological Institute, University of Basel, Basel, Switzerland. mathias.koelliker@unibas.ch
Ridenhour
Benjamin J
BJ
Gaba
Sabrina
S
D000222:Adaptation, Physiological; D005075:Biological Evolution; D003220:Conflict, Psychological; D008962:Models, Theoretical; D013269:Stochastic Processes