Foraging Theory

Every Ant Tells a Story - And Scientists Explain Their Stories Here
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  • Csata, E. and A. Dussutour. 2019. Nutrient regulation in ants (Hymenoptera: Formicidae): a review. Myrmecological News. 29:111-124. doi:10.25849/myrmecol.news_029:111

This paper considers how ant colonies, and their foraging and their nutrient needs, may be understood through the use of “geometric framework” modeling.

As for all living organisms, the nutritional needs of ants change over time in response to varying environmental conditions and demands for growth, health, and reproduction. Solitary individuals regulate their nutritional intake to maintain an appropriate balance of nutrients by selectively choosing the quality and quantity of food that meet their nutritional requirements. For social organisms, such as ants, food collection for the entire colony relies on a few individuals whose nutritional requirements may be very different from those of other members of their colony. Recent studies have used an integrative framework, the “geometric framework”, to better understand how living organisms regulate macronutrient intake to defend a specific nutrient “intake target”. In this review, we first reveal how the geometric framework has been used to deepen our understanding of ant communal nutrition. Second, we describe how this framework might be used to also understand the nutritional strategies used by ants facing infection challenges. Lastly, we conclude with a brief discussion of the promising techniques that could be used in the future to improve our understanding of communal nutrition in ants.

  • Waldner, F. and T. Merkle. 2018. A simple mathematical model using centred loops and random perturbations accurately reconstructs search patterns observed in desert ants. Journal of Comparative Physiology a-Neuroethology Sensory Neural and Behavioral Physiology. 204:985-998. doi:10.1007/s00359-018-1297-6

This paper describes a new mathematical model that is based on centred loops to reconstruct the “Systematic Search” behaviour of Cataglyphis desert ants. The notable advantage of this model is the combination of simplicity, efficiency and performance. All model input is kept to a minimum, using only parameters that previous research has shown to be available to the animals at all times: distance from the origin, direction of the last step and home vector. Outbound and inbound search paths are being combined into loops that return to the origin, sampling this area more intensely. A stochastic element is added by random perturbations during the next step, mimicking unsystematic errors during the process of path integration and yielding the typical search patterns observed in Cataglyphis desert ants. The model output is compared to runs observed in the field.