Toward greater realism in inclusive fitness models: the case of caste fate conflict in insect societies

Evol Lett. 2024 Jan 11;8(3):387-396. doi: 10.1093/evlett/qrad068. eCollection 2024 Jun.

Abstract

In the field of social evolution, inclusive fitness theory has been successful in making a wide range of qualitative predictions on expected patterns of cooperation and conflict. Nevertheless, outside of sex ratio theory, inclusive fitness models that make accurate quantitative predictions remain relatively rare. Past models dealing with caste fate conflict in insect societies, for example, successfully predicted that if female larvae can control their own caste fate, an excess should opt to selfishly develop as queens. Available models, however, were unable to accurately predict levels of queen production observed in Melipona bees-a genus of stingless bees where caste is self-determined-as empirically observed levels of queen production are approximately two times lower than the theoretically predicted ones. Here, we show that this discrepancy can be resolved by explicitly deriving the colony-level cost of queen overproduction from a dynamic model of colony growth, requiring the incorporation of parameters of colony growth and demography, such as the per-capita rate at which new brood cells are built and provisioned, the percentage of the queen's eggs that are female, costs linked with worker reproduction and worker mortality. Our revised model predicts queen overproduction to more severely impact colony productivity, resulting in an evolutionarily stable strategy that is approximately half that of the original model, and is shown to accurately predict actual levels of queen overproduction observed in different Melipona species. Altogether, this shows how inclusive fitness models can provide accurate quantitative predictions, provided that costs and benefits are modeled in sufficient detail and are measured precisely.

Keywords: caste fate conflict; inclusive fitness theory; social evolution; stingless bees.

Associated data

  • Dryad/10.5061/dryad.1g1jwsv3d