Periodic Reporting for period 5 - DISEASE (Disease Risk And Immune Strategies In Social Insects)
Période du rapport: 2023-09-01 au 2025-03-31
Group-living provides many benefits but also increases the risk of infectious disease transmission. In dense, highly connected groups, pathogens can spread rapidly unless countered by effective defence mechanisms. Social insects, such as ants, have evolved an exceptional range of collective sanitary behaviours and organisational strategies that limit disease spread—known as “social immunity”—yet it remains unclear how these interact with, or replace, individual immune defences.
The DISEASE project aimed to experimentally determine how the socio-spatial organisation of ant colonies influences epidemic risk, how these effects depend on colony size, and how colonies adjust their disease defences under sustained pathogen pressure. By combining automated behavioural tracking, social network analysis, and controlled pathogen exposure, we tested predictions from network epidemiology in real animal societies. We also developed novel experimental and analytical tools for manipulating and quantifying network structure and transmission. The results provide the first integrated empirical assessment of how social organisation contributes to disease management in insect societies, with broader implications for understanding epidemic control in other social species, including humans.
The DISEASE project aimed to experimentally determine how the socio-spatial organisation of ant colonies influences epidemic risk, how these effects depend on colony size, and how colonies adjust their disease defences under sustained pathogen pressure. By combining automated behavioural tracking, social network analysis, and controlled pathogen exposure, we tested predictions from network epidemiology in real animal societies. We also developed novel experimental and analytical tools for manipulating and quantifying network structure and transmission. The results provide the first integrated empirical assessment of how social organisation contributes to disease management in insect societies, with broader implications for understanding epidemic control in other social species, including humans.
Over the course of the project, we established 10 automated ant tracking systems, developed a suite of new methodologies—including dual-colour fluorescent microbead flow cytometry for simultaneous transmission quantification, a general framework for independently manipulating key social network properties, calcofluor staining for in vivo fungal germination timing, and the DAVI pipeline for identity correction in deep-learning pose estimation within small groups of ants.
Using these tools, we carried out a series of large-scale experiments:
• Aim 1: Demonstrated that experimentally altering nest architecture can selectively change network properties, and revealed “architectural immunity”—the first evidence of animals modifying their built environment to reduce future epidemic risk (Science, 2025).
• Aim 2: Showed that organisational immunity is more effective in large colonies than small ones, but only when disease originates from foragers; gene expression analyses confirmed risk-based immune investment.
• Aim 3: Found that ants carrying high pathogen loads avoid high-contact with heterologously contaminated individuals, reducing co-infection risk, and documented pathogen-specific complementarity between individual and social immunity (Nature Communications, 2024).
Additional research uncovered two simple movement rules that explain spatial segregation in social insect colonies (Nature Communications, 2022), identified a pathogen-related vibratory signal (“body shakes”), documented pesticide–pathogen synergies, explored the diffusion of queen pheromones in honeybee colonies (BMC Biology, 2024), and the effect of modular organisation on spatio-temporal collective rhythms.
Results have been widely disseminated via peer-reviewed publications, conference talks, media coverage (including national radio and television), and outreach activities.
Using these tools, we carried out a series of large-scale experiments:
• Aim 1: Demonstrated that experimentally altering nest architecture can selectively change network properties, and revealed “architectural immunity”—the first evidence of animals modifying their built environment to reduce future epidemic risk (Science, 2025).
• Aim 2: Showed that organisational immunity is more effective in large colonies than small ones, but only when disease originates from foragers; gene expression analyses confirmed risk-based immune investment.
• Aim 3: Found that ants carrying high pathogen loads avoid high-contact with heterologously contaminated individuals, reducing co-infection risk, and documented pathogen-specific complementarity between individual and social immunity (Nature Communications, 2024).
Additional research uncovered two simple movement rules that explain spatial segregation in social insect colonies (Nature Communications, 2022), identified a pathogen-related vibratory signal (“body shakes”), documented pesticide–pathogen synergies, explored the diffusion of queen pheromones in honeybee colonies (BMC Biology, 2024), and the effect of modular organisation on spatio-temporal collective rhythms.
Results have been widely disseminated via peer-reviewed publications, conference talks, media coverage (including national radio and television), and outreach activities.
This project delivered several conceptual and methodological advances beyond the state of the art:
• Architectural immunity: First demonstration that animals can proactively shape their built environment to reduce disease spread, integrating spatial structure with behavioural defences.
• Pathogen-specific immune complementarity: Empirical evidence that effective social immunity can drive erosion of individual immune defences against fungi, while bacterial-targeted immunity is maintained—resolving a long-standing debate.
• Network property manipulation: Developed a general framework for independent manipulation of correlated network properties, enabling empirical testing of theoretical predictions from network epidemiology.
• Bipartite network analysis: Introduced a spatial–social mapping approach that integrates location and individual interaction data, revealing simple universal movement rules underlying organisational immunity.
• Advanced tracking methods: Combined deep-learning pose estimation with novel identity correction to extract full contact networks from unmarked individuals, and developed machine learning methods to automatically identify target behaviours (grooming, trophallaxis) from lists of coordinates.
These advances establish a new foundation for testing how socio-spatial organisation can be leveraged to manage epidemic risk and for applying bio-inspired strategies to human and animal health management.
• Architectural immunity: First demonstration that animals can proactively shape their built environment to reduce disease spread, integrating spatial structure with behavioural defences.
• Pathogen-specific immune complementarity: Empirical evidence that effective social immunity can drive erosion of individual immune defences against fungi, while bacterial-targeted immunity is maintained—resolving a long-standing debate.
• Network property manipulation: Developed a general framework for independent manipulation of correlated network properties, enabling empirical testing of theoretical predictions from network epidemiology.
• Bipartite network analysis: Introduced a spatial–social mapping approach that integrates location and individual interaction data, revealing simple universal movement rules underlying organisational immunity.
• Advanced tracking methods: Combined deep-learning pose estimation with novel identity correction to extract full contact networks from unmarked individuals, and developed machine learning methods to automatically identify target behaviours (grooming, trophallaxis) from lists of coordinates.
These advances establish a new foundation for testing how socio-spatial organisation can be leveraged to manage epidemic risk and for applying bio-inspired strategies to human and animal health management.