More Like this

1. The Power of Population Effect in Temnothorax Ants House-Hunting: A Computational Modeling Approach.

   Zhao, J. ; Lynch, N. ; Pratt, S.C. ( January 2022 , Journal of computational biology)

   The decentralized cognition of animal groups is both a challenging biological problem and a potential basis for bio-inspired design. In this study, we investigated the house-hunting algorithm used by emigrating colonies of Temnothorax ants to reach consensus on a new nest. We developed a tractable model that encodes accurate individual behavior rules, and estimated our parameter values by matching simulated behaviors with observed ones on both the individual and group levels. We then used our model to explore a potential, but yet untested, component of the ants’ decision algorithm. Specifically, we examined the hypothesis that incorporating site population (the number of adult ants at each potential nest site) into individual perceptions of nest quality can improve emigration performance. Our results showed that attending to site population accelerates emigration and reduces the incidence of split decisions. This result suggests the value of testing empirically whether nest site scouts use site population in this way, in addition to the well demonstrated quorum rule. We also used our model to make other predictions with varying degrees of empirical support, including the high cognitive capacity of colonies and their rational time investment during decision-making. Additionally, we provide a versatile and easy-to-use Python simulator that can be used to explore other hypotheses or make testable predictions. It is our hope that the insights and the modeling tools can inspire further research from both the biology and computer science community. 

   more » « less
2. Social inhibition maintains adaptivity and consensus of honeybees foraging in dynamic environments

   https://doi.org/10.1098/rsos.191681  

   Bidari, Subekshya ; Peleg, Orit ; Kilpatrick, Zachary P. ( December 2019 , Royal Society Open Science)

   To effectively forage in natural environments, organisms must adapt to changes in the quality and yield of food sources across multiple timescales. Individuals foraging in groups act based on both their private observations and the opinions of their neighbours. How do these information sources interact in changing environments? We address this problem in the context of honeybee colonies whose inhibitory social interactions promote adaptivity and consensus needed for effective foraging. Individual and social interactions within a mathematical model of collective decisions shape the nutrition yield of a group foraging from feeders with temporally switching quality. Social interactions improve foraging from a single feeder if temporal switching is fast or feeder quality is low. When the colony chooses from multiple feeders, the most beneficial form of social interaction is direct switching, whereby bees flip the opinion of nest-mates foraging at lower-yielding feeders. Model linearization shows that effective social interactions increase the fraction of the colony at the correct feeder (consensus) and the rate at which bees reach that feeder (adaptivity). Our mathematical framework allows us to compare a suite of social inhibition mechanisms, suggesting experimental protocols for revealing effective colony foraging strategies in dynamic environments. 

   more » « less
3. Multi-queen breeding is associated with the origin of inquiline social parasitism in ants

   https://doi.org/10.1038/s41598-022-17595-0  

   Dahan, Romain A. ; Rabeling, Christian ( August 2022 , Scientific Reports)

   Social parasites exploit the brood care behavior of their hosts to raise their own offspring. Social parasites are common among eusocial Hymenoptera and exhibit a wide range of distinct life history traits in ants, bees, and wasps. In ants, obligate inquiline social parasites are workerless (or nearly-so) species that engage in lifelong interactions with their hosts, taking advantage of the existing host worker forces to reproduce and exploit host colonies’ resources. Inquiline social parasites are phylogenetically diverse with approximately 100 known species that evolved at least 40 times independently in ants. Importantly, ant inquilines tend to be closely related to their hosts, an observation referred to as ‘Emery’s Rule’. Polygyny, the presence of multiple egg-laying queens, was repeatedly suggested to be associated with the origin of inquiline social parasitism, either by providing the opportunity for reproductive cheating, thereby facilitating the origin of social parasite species, and/or by making polygynous species more vulnerable to social parasitism via the acceptance of additional egg-laying queens in their colonies. Although the association between host polygyny and the evolution of social parasitism has been repeatedly discussed in the literature, it has not been statistically tested in a phylogenetic framework across the ants. Here, we conduct a meta-analysis of ant social structure and social parasitism, testing for an association between polygyny and inquiline social parasitism with a phylogenetic correction for independent evolutionary events. We find an imperfect but significant over-representation of polygynous species among hosts of inquiline social parasites, suggesting that while polygyny is not required for the maintenance of inquiline social parasitism, it (or factors associated with it) may favor the origin of socially parasitic behavior. Our results are consistent with an intra-specific origin model for the evolution of inquiline social parasites by sympatric speciation but cannot exclude the alternative, inter-specific allopatric speciation model. The diversity of social parasite behaviors and host colony structures further supports the notion that inquiline social parasites evolved in parallel across unrelated ant genera in the formicoid clade via independent evolutionary pathways.

    

   more » « less
4. Decoding alarm signal propagation of seed-harvester ants using automated movement tracking and supervised machine learning

   https://doi.org/10.1098/rspb.2021.2176  

   Guo, Xiaohui ; Lin, Michael R. ; Azizi, Asma ; Saldyt, Lucas P. ; Kang, Yun ; Pavlic, Theodore P. ; Fewell, Jennifer H. ( January 2022 , Proceedings of the Royal Society B: Biological Sciences)

   Alarm signal propagation through ant colonies provides an empirically tractable context for analysing information flow through a natural system, with useful insights for network dynamics in other social animals. Here, we develop a methodological approach to track alarm spread within a group of harvester ants, Pogonomyrmex californicus . We initially alarmed three ants and tracked subsequent signal transmission through the colony. Because there was no actual standing threat, the false alarm allowed us to assess amplification and adaptive damping of the collective alarm response. We trained a random forest regression model to quantify alarm behaviour of individual workers from multiple movement features. Our approach translates subjective categorical alarm scores into a reliable, continuous variable. We combined these assessments with automatically tracked proximity data to construct an alarm propagation network. This method enables analyses of spatio-temporal patterns in alarm signal propagation in a group of ants and provides an opportunity to integrate individual and collective alarm response. Using this system, alarm propagation can be manipulated and assessed to ask and answer a wide range of questions related to information and misinformation flow in social networks. 

   more » « less
5. An Upper and Lower Bound for the Convergence Time of House-Hunting in Temnothorax Ant Colonies.

   Zhang, E. ; Zhao, J. ; Lynch, N. ( January 2022 , Journal of computational biology)

   We study the problem of house-hunting in ant colonies, where ants reach consensus on a new nest and relocate their colony to that nest, from a distributed computing perspective. We propose a house-hunting algorithm that is biologically inspired by Temnothorax ants. Each ant is modeled as a probabilistic agent with limited power, and there is no central control governing the ants. We show an O( log n) lower bound on the running time of our proposed house-hunting algorithm, where n is the number of ants. Furthermore, we show a matching upper bound of expected O( log n) rounds for environments with only one candidate nest for the ants to move to. Our work provides insights into the house-hunting process, giving a perspective on how environmental factors such as nest quality or a quorum rule can affect the emigration process. 

   more » « less