Agent-based models (ABMs) combined with the framework of nutritional geometry can be a powerful tool to help us understand how animals should meet their simultaneous requirements for multiple nutrients in a complex environment. Previously, geometric ABMs have been used demonstrate how an optimal level of social cohesion can increase the foraging efficiency of individuals in groups. Such models, however, make the assumption that there is little, or no, variance in the nutritional requirements of individuals in the group. Recent meta-analysis exemplifies that this assumption may be incorrect.

We expanded previously published ABMs to incorporate different distributions of nutritional requirements. We then used an evolutionary algorithm to explore how group cohesion increases foraging efficiency under different assumptions about within-group variance in nutritional requirements, and in different nutritional environments.

Our results suggest that for many distributions of nutrient needs, group cohesion remains an effective and simple strategy to reduce the costs of foraging in a complex environment. However, if the group contains a distinct bimodal distribution of nutrient requirements (for example, where different sexes have very different needs) the benefits of group cohesion may be negated. These results clearly demonstrate the benefits of social foraging in complex environments.