Cristin, J. | bioRxiv Science

bioRxiv2026

Dynamic Workforce Modulation and Foraging Efficiency in Eusocial Insect Colonies

Understanding the fitness advantages conferred by eusociality remains a central challenge in behavioral ecology. One promising approach is to identify collective strategies that shift efficiency within social groups. Here, we test the hypothesis that reserve workforces in eusocial insect colonies represent an adaptive mechanism that enhances flexibility and foraging efficiency under fluctuating environmental conditions. We examine how such reserve workers modulate the departure and return rates of foragers and how these time-dependent dynamics shape the colonys overall energetic balance. By integrating an energetic-balance framework with stochastic search simulations inspired by empirical results from Aphaenogaster senilis, we quantify the energetic requirements for colony viability, incorporating energy intake, search costs, and basal metabolic demands. Our results show that as colonies grow, maintaining a positive energy balance requires a disproportionately larger relative workforce. By modulating departure and return rates over time, colonies control the synchrony of their collective search and efficiently activate or suppress their reserve workforce to scale foraging effort as needed. These findings suggest that the "lazy" or weakly engaged workers commonly observed in large colonies function as an essential reserve that stabilizes colony energetics and enhances responsiveness. Together, our results provide a functional explanation for sublinear metabolic scaling in eusocial groups and highlight workforce modulation as a key factor underlying their energetic stability and evolutionary success.

ecology↗

bioRxiv2020

Random searchers cope with cognitive errors and uncertainty better than path planners

There is a widespread belief in ecology that the capacity of animals to orchestrate systematic and planned paths should represent a significant benefit for efficient search and exploration. Within this view, stochasticity observed in real animal trajectories is mostly understood as undesirable noise caused by internal or external effects. Far less is known, however, about the case when cognitive errors and limitations inherent to living systems are explicitly put into play. Here we compare within this context the search efficiency of (i) walkers driven by Bayesian rules generating deterministic paths, (ii) standard random walkers, and (iii) human trajectories obtained from search experiments in a soccer field and on the computer screen. Our results clearly challenge the view that deterministic paths are generally better for exploration than random strategies, as the latter are more resilient to cognitive errors. Instead, we provide numerical and experimental evidence that stochasticity would provide living organisms with a sufficient and cognitively simple exploration solution to the problem of uncertainty.

ecology↗