Social hierarchy shapes foraging decisions
Blum Moyse, L.; El Hady, A.
Show abstract
Social foraging is a widespread form of animal foraging in which groups of individuals coordinate their decisions to exploit resources in the environment. Animals show a variety of social structures from egalitarian to hierarchical. In this study, we examine how different forms of social hierarchy shape foraging decisions. We developed a mechanistic analytically tractable model to study the underlying processes of social foraging, tying the microscopic individual to the macroscopic group levels. Based on a stochastic evidence accumulation framework, we developed a model of patch-leaving decisions in a large hierarchical group with leading and following individuals. Across a variety of information sharing mechanisms, we were able to analytically quantify emergent collective dynamics. We found that follower-leader dynamics through observations of leader movements or through counting the number of individuals in a patch confers, for most conditions, a benefit for the following individuals by increasing their accuracy in inferring patch richness. On the other hand, misinformation, through the communication of false beliefs about food rewards or patch quality, shows to be detrimental to following individuals, but paradoxically leads to increased group cohesion. In an era where there is a huge amount of animal foraging data collected, our model provides a systematic way to conceptualize and understand those data by uncovering hidden mechanisms underlying social foraging decisions.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Socially driven negative feedback regulates activity and energy use in ant colonies 98%
- Levy movements and a slowly decaying memory allow efficient collective learning in groups of interacting foragers 98%
- Spatial exclusion leads to tug-of-war ecological dynamics between competing species within microchannels 97%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.