How territoriality reduces disease transmission among social insect colonies.

ABSTRACT: Social behavior can have a major impact on the dynamics of infectious disease outbreaks. For animals that live in dense social groups, such as the eusocial insects, pathogens pose an especially large risk because frequent contacts among individuals can allow rapid spread within colonies. While there has been a large body of work examining adaptations to mitigate the spread of infectious disease within social insect colonies, there has been less work on strategies to prevent the introduction of pathogens into colonies in the first place. We develop an agent-based model to examine the effect of territorial behavior on the transmission of infectious diseases between social insect colonies. We find that by preventing the introduction of infected foreign workers into a colony, territoriality can flatten the curve of an epidemic, delaying the introduction of an infectious disease and reducing its maximum prevalence, but only for diseases with moderate to low transmissibility. Our results have implications for understanding how pathogen risk influences the evolution of territorial behavior in social insects and other highly social animals. SIGNIFICANCE STATEMENT: Infectious disease outbreaks can impose a large fitness cost to animals that live in social groups. The frequency and pattern of contacts both within and among groups can have a large impact on the speed and extent of an epidemic. Using an individual-based model, we examined how the exclusion of foreign workers from a territory around the nest influences disease transmission between social insect colonies. We find that territoriality can protect colonies from outbreaks of low to moderately contagious pathogens by delaying the spillover from other colonies and reducing the maximum number of workers who are infected. These results suggest that the relative threat posed by infectious diseases may have played an important role in shaping the diversity of territorial behaviors seen in different social insect species. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00265-021-03095-0.

How disease constrains the evolution of social systems.

Animal populations are occasionally shocked by epidemics of contagious diseases. The ability of social systems to withstand epidemic shocks and mitigate disruptions could shape the evolution of complex animal societies. We present a mathematical model to explore the potential impact of disease on the evolutionary fitness of different organizational strategies for populations of social species whose survival depends on collaborative efficiency. We show that infectious diseases select for a specific feature in the organization of collaborative roles-cohort stability-and that this feature is costly, and therefore unlikely to be maintained in environments where infection risks are absent. Our study provides evidence for an often-stated (but rarely supported) claim that pathogens have been the dominant force shaping the complexity of division of labour in eusocial societies of honeybees and termites and establishes a general theoretical approach for assessing evolutionary constraints on social organization from disease risk in other collaborative taxa.

A generic arboviral model framework for exploring trade-offs between vector control and environmental concerns.

Effective public health measures must balance potentially conflicting demands from populations they serve. In the case of infectious disease risks from mosquito-borne infections, such as Zika virus, public concern about the pathogen may be counterbalanced by public concern about environmental contamination from chemical agents used for vector control. Here we introduce a generic framework for modeling how the spread of an infectious pathogen might lead to varying public perceptions, and therefore tolerance, of both disease risk and pesticide use. We consider how these dynamics might impact the spread of a vector-borne disease. We tailor and parameterize our model for direct application to Zika virus as spread by Aedes aegypti mosquitoes, though the framework itself has broad applicability to any arboviral infection. We demonstrate how public risk perception of both disease and pesticides may drastically impact the spread of a mosquito-borne disease in a susceptible population. We conclude that models hoping to inform public health decision making about how best to mitigate arboviral disease risks should explicitly consider the potential public demand for, or rejection of, chemical control of mosquito populations.

Identifying robustness in the regulation of collective foraging of ant colonies using an interaction-based model with backward bifurcation.

Collective behaviors in social insect societies often emerge from simple local rules. However, little is known about how these behaviors are dynamically regulated in response to environmental changes. Here, we use a compartmental modeling approach to identify factors that allow harvester ant colonies to regulate collective foraging activity in response to their environment. We propose a set of differential equations describing the dynamics of: (1) available foragers inside the nest, (2) active foragers outside the nest, and (3) successful returning foragers, to understand how colony-specific parameters, such as baseline number of foragers, interactions among foragers, food discovery rates, successful forager return rates, and foraging duration might influence collective foraging dynamics, while maintaining functional robustness to perturbations. Our analysis indicates that the model can undergo a forward (transcritical) bifurcation or a backward bifurcation depending on colony-specific parameters. In the former case, foraging activity persists when the average number of recruits per successful returning forager is larger than one. In the latter case, the backward bifurcation creates a region of bistability in which the size and fate of foraging activity depends on the distribution of the foraging workforce among the model's compartments. We validate the model with experimental data from harvester ants (Pogonomyrmex barbatus) and perform sensitivity analysis. Our model provides insights on how simple, local interactions can achieve an emergent and robust regulatory system of collective foraging activity in ant colonies.