Age structure is critical to the population dynamics and survival of honeybee colonies

Abstract

Age structure is an important feature of the division of labour within honeybee colonies, but its effects on colony dynamics have rarely been explored. We present a model of a honeybee colony that incorporates this key feature, and use this model to explore the effects of both winter and disease on the fate of the colony. The model offers a novel explanation for the frequently observed phenomenon of ‘spring dwindle’, which emerges as a natural consequence of the age-structured dynamics. Furthermore, the results indicate that a model taking age structure into account markedly affects the predicted timing and severity of disease within a bee colony. The timing of the onset of disease with respect to the changing seasons may also have a substantial impact on the fate of a honeybee colony. Finally, simulations predict that an infection may persist in a honeybee colony over several years, with effects that compound over time. Thus, the ultimate collapse of the colony may be the result of events several years past.

Bee++: An Object-Oriented, Agent-Based Simulator for Honey Bee Colonies

“…In addition, note that Bee++ is able to capture the decreasing spring population as seen in [25]. The results suggest that this drop is caused by a combination of the increased average age of the population within the colony at the end of winter, and the temperature oscillations around the threshold temperature.…”

“…Others have further broadened the scope to account for the combined effects of pesticides and parasites, as well as a lack of biodiversity in the local environment [24]. Our previous studies have focused on the combined effects of temperature, seasonality and disease [13,25]. …”

Markerless tracking of an entire insect colony

“…Additionally, the quantitation of behavior and aggregate colony dynamics over short time scales could be used in combination with the long term sociometric observation of the same colonies. The accuracy of CNNs in detection of visual detail together with the large numbers of trajectories of unmarked bees offered by our methods present vast new opportunities for behavior analysis of bees and open avenues for more quantitative approaches to modeling colony dynamics [62][63][64][65][66] . We also see no obvious obstacles in generalizing our approach to other dense insect collectives such as ants.…”

Industrial bees: The impact of apicultural intensification on local disease prevalence

“…Two clear candidates for future development of this model include seasonality and demography, which are closely linked. Honeybee demography within a colony influences epidemiology (Betti, Wahl, & Zamir, ) due in part to the temporal polyethism of task allocation influencing exposure and immunity (Calderone & Page, ), as well as the flexible ability of honeybees to regain immune function when they revert roles (Amdam et al, ; Robinson, Page, Strambi, & Strambi, ). However, patterns in how age and immunosenescence in honeybees relates to survival and infectiousness remain complicated (Roberts & Hughes, ).…”