The complex adaptive dynamics of honeybee societies

Monday, June 14 at 11:30am (PDT)
Monday, June 14 at 07:30pm (BST)
Tuesday, June 15 03:30am (KST)

SMB2021 SMB2021 Follow Monday (Tuesday) during the "MS02" time block.
Note: this minisymposia has multiple sessions. The second session is MS03-ECOP (click here).

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Jun Chen (Arizona State University, USA), Yun Kang (Arizona State University, USA), Gabriela Zuloaga (Arizona State University, USA)


Social insect colonies are complex adaptive systems where collective behavior emerging from local interactions determines group survival in dynamic environments, which include parasites, epidemics, seasonality and human behavior. Honeybees are ideal model organisms to study how social systems adapt to complex environmental changes since both group and individual features can be experimentally measured and manipulated. They also play an irreplaceable role in ecology, agriculture and economy through, for example, pollination and honey production. Our session will discuss how honeybee colonies maintain their health and social organization while adapting to the dynamic environmental factors. This mini-symposia will bring together a group of distinguished applied mathematicians and biologists who have great expertise in applying experimental approaches, mathematical models and theory to focus on complex adaptive systems in dynamic environments. It will provide an effective platform for presenting and discussing current research as well as generating connections and promoting collaboration in an interdisciplinary group of researchers across different universities and career stages.

Chelsea Cook

(Marquette University, Biological Sciences, Milwaukee Wisconsin, United States)
"Individual Learning Phenotypes Drive Collective Foraging Behavior in Honey Bees"
Variation in cognition can influence how individuals respond to and communicate about their environment, which may scale to shape how a collective solves a cognitive task. However, few empirical examples of variation in collective cognition emerges from variation in individual cognition exist. Here, I show that interactions among individuals that differ in the performance of a cognitive task drives collective foraging behavior in honey bee colonies by utilizing a naturally variable and heritable learning behavior called latent inhibition (LI). I artificially selected two distinct phenotypes: high LI bees that are better at ignoring previously unrewarding familiar stimuli, and low LI bees that can learn previously unrewarding and novel stimuli equally well. I then provided colonies composed of these distinct phenotypes with a choice between a familiar feeder or a novel feeder. Colonies of high LI individuals preferred to visit familiar food locations, while low LI colonies visited novel and familiar food locations equally. However, in colonies of mixed learning phenotypes, the low LI bees showed a preference to visiting familiar feeders, which contrasts with their behavior when in a uniform low LI group. I show that the shift in feeder preference of low LI bees is driven by foragers of the high LI phenotype dancing more intensely and attracting more followers. I also present potential mechanisms that may be mediating the individual variation. These results reveal that cognitive abilities of individuals and their interactions drive emergent collective outcomes in social insects.

Hermann Eberl

( University of Guelph, Canada)
"Between hive transmission of nosemosis by drifitng"
The vast majority of mathematical models of honeybee diseases is for single colonies that have no interaction with other colonies. This misses an important aspect of the ecoepidemiology in an apiary. For an earlier model of nosemosis with direct and indirect transmission routes we formulate a metapopulation model that accounts for the transmission of the disease between colonies by drifting. Since even the underlying single hive model is too complex for a thorough rigorous analysis, we explore the model in extensive numerical simulations. Our results suggest that for the model at hand the spread of the disease in the apiary is primarily controlled by seasonal effects, whereas the actual drifting rate has little quantitative effect.

Natalie J. Lemanski

(Rutgers University New Brunswick (current), University of California Los Angeles (where work was performed), United States)
"Individual learning affects the accuracy of collective decisions for honey bee colonies foraging on different quality resources"
To survive, animals need to find resources and make decisions about which resource patches to invest time in exploiting. Balancing these tasks can be a complex decision-making challenge, particularly when patches are rapidly changing, heterogeneously distributed, and variable in quality. Social insects, such as honeybees, navigate this challenge in the absence of centralized control by allocating different individuals to exploration or exploitation based on differences in individual behavior. To investigate how differences in individual learning affect a colony’s collective ability to locate and choose among different quality food resources, we develop an agent-based model and test its predictions empirically using two genetic lines of honey bees (Apis mellifera), selected for differences in their learning behavior. We show that colonies containing individuals that are better at learning to ignore unrewarding stimuli are worse at collectively choosing the highest quality resource. This work highlights how differences in individual behavior may have unexpected consequences for the emergence of collective behavior.

Gloria DeGrandi-Hoffman

(USDA-ARS, United States)
"Simulating how combinations of stress factors can affect honey bee colony growth and survival"
Biotic and abiotic factors can exert stress on honey bee colonies and limit their growth ultimately causing colony death. A colony population dynamics model was used to predict effects on colony growth of pesticide stress exerted during different times of year. Poor queen quality and infestation by parasitic Varroa mites were added into the simulations to determine the impact of multiple stress factors on colony growth and survival. The model predicts that colony survival after pesticide exposure depends on the time of year when exposure occurred. Poor queen quality makes colonies more vulnerable to loss from pesticide exposure as do high infestations of Varroa mites. Predictions highlight the difficulties is assigning causation of colony loss to a single factor.

Hosted by SMB2021 Follow
Virtual conference of the Society for Mathematical Biology, 2021.