Animal social networks
Fig. 1. From: Dugatkin & Hasenjager (2015)
Many animal populations are characterized by complex social networks that shape diverse processes, including social learning, disease transmission, and cooperation [1,2]. Such processes have significant ecological and evolutionary consequences, making it important to understand the factors influencing how animal social networks are structured and how networks in turn impact individual and collective behaviour. Through my research, I investigate: the ecological factors and social processes shaping animal social networks, the bidirectional relationships between network structure and transmission dynamics, and the ways in which information flow and network properties impact individual and collective decision-making within dynamic and heterogeneous environments.
1. Hasenjager MJ, Dugatkin LA. 2015. Social network analysis in behavioral ecology. Advances in the Study of Behavior 47, 39-114. doi: 10.1016/bs.asb.2015.02.003
2. Dugatkin LA, Hasenjager M. 2015. The Networked Animal. Scientific American, June 2015, pp. 50-55. doi: 10.1038/scientificamerican0615-50
1. Hasenjager MJ, Dugatkin LA. 2015. Social network analysis in behavioral ecology. Advances in the Study of Behavior 47, 39-114. doi: 10.1016/bs.asb.2015.02.003
2. Dugatkin LA, Hasenjager M. 2015. The Networked Animal. Scientific American, June 2015, pp. 50-55. doi: 10.1038/scientificamerican0615-50
Environmental drivers of network structure and function
Fig. 2. El Cedro River, Arima, Trinidad
Social network structure emerges from the patterning of interactions among individuals. For my doctoral work with Lee Dugatkin at the University of Louisville, I investigated how environmental factors, such as predation risk and group composition, modulated the social network structure of shoals of Trinidadian guppies (Poecilia reticulata) and influenced the spread of foraging information. For example, I found that high perceived predation risk generated guppy networks that were more strongly assorted by body size and drove individuals to explore their environment in the company of preferred social partners [1]. In addition, I found that how individuals learned was contingent not only on their own risk-taking tendency, but on that of their group mates [2]. In other words, group personality composition shaped how information spread among individuals.
1. Hasenjager MJ, Dugatkin LA. 2017. Fear of predation shapes social network structure and the acquisition of foraging information in guppy shoals. Proceedings of the Royal Society B 284, 20172020. doi: 10.1098/rspb.2017.2020
2. Hasenjager MJ, Hoppitt W, Dugatkin LA. 2020. Personality composition determines social learning pathways within shoaling fish. Proceedings of the Royal Society B 287, 20201871. doi: 10.1098/rspb.2020.1871
1. Hasenjager MJ, Dugatkin LA. 2017. Fear of predation shapes social network structure and the acquisition of foraging information in guppy shoals. Proceedings of the Royal Society B 284, 20172020. doi: 10.1098/rspb.2017.2020
2. Hasenjager MJ, Hoppitt W, Dugatkin LA. 2020. Personality composition determines social learning pathways within shoaling fish. Proceedings of the Royal Society B 287, 20201871. doi: 10.1098/rspb.2020.1871
Honeybee communication networks
Fig. 3. Nectar exchange during trophallaxis
The honeybee (Apis mellifera) waggle dance allows successful foragers to transmit the location of profitable resources to their nestmates. However, disrupting these dances rarely reduces colony foraging success and bees can use alternative social information sources to locate food [1]. As such, identifying the conditions under which honeybees rely on dance information has been challenging. As a postdoctoral researcher with Elli Leadbeater at Royal Holloway, University of London, I investigated how different communication networks (e.g. dance-following, olfactory communication) were integrated under different foraging contexts by combining social network analyses [2] with experimental manipulations of feeder arrays. In particular, I quantified honeybees' relative reliance on waggle dances versus olfactory communication (e.g., trophallaxis, antennation) in two key contexts: recruitment to novel foraging sites and reactivation to known sites. I found that dances were key for recruitment, but that reactivation (which is far more common during daily foraging) was elicited by a combination of dance and olfactory communication [3,4]. These findings demonstrate why disrupting dances rarely impacts foraging, as most dance-following occurs in contexts for which other information-sharing pathways are similarly useful. This work further illustrates how the multi-network nature of honeybee communication enables flexible information-use strategies, conferring robustness to collective foraging efforts across dynamic floral landscapes.
1. Leadbeater E, Hasenjager MJ. 2019. Honeybee communication: There's more on the dancefloor. Current Biology 29, R285-R287. doi: 10.1016/j.cub.2019.03.009
2. Hasenjager MJ, Leadbeater E, Hoppitt W. 2021. Detecting and quantifying social transmission using network-based diffusion analysis. Journal of Animal Ecology, 90, 8-26. doi: 10.1111/1365-2656.13307
3. Hasenjager MJ, Hoppitt W, Leadbeater E. 2020. Network-based diffusion analysis reveals context-specific dominance of dance communication in foraging honeybees. Nature Communications 11, 625. doi: 10.1038/s41467-020-14410-0
4. Hasenjager MJ, Hoppitt W, Cunningham-Eurich I, Franks V, Leadbeater E. 2024. Coupled information networks drive honeybee (Apis mellifera) collective foraging. Journal of Animal Ecology 93, 71-82. doi: 10.1111/1365-2656.14029
1. Leadbeater E, Hasenjager MJ. 2019. Honeybee communication: There's more on the dancefloor. Current Biology 29, R285-R287. doi: 10.1016/j.cub.2019.03.009
2. Hasenjager MJ, Leadbeater E, Hoppitt W. 2021. Detecting and quantifying social transmission using network-based diffusion analysis. Journal of Animal Ecology, 90, 8-26. doi: 10.1111/1365-2656.13307
3. Hasenjager MJ, Hoppitt W, Leadbeater E. 2020. Network-based diffusion analysis reveals context-specific dominance of dance communication in foraging honeybees. Nature Communications 11, 625. doi: 10.1038/s41467-020-14410-0
4. Hasenjager MJ, Hoppitt W, Cunningham-Eurich I, Franks V, Leadbeater E. 2024. Coupled information networks drive honeybee (Apis mellifera) collective foraging. Journal of Animal Ecology 93, 71-82. doi: 10.1111/1365-2656.14029
Ant-inspired design of supply networks
Fig. 4. Ant workers (C. fragilis) individually marked with BEEtags. Image credit: Noa Pinter-wollman.
Social insects produce coordinated collective responses with a robustness and flexibility that often exceeds human-designed systems [1]. For example, ant colonies enact efficient, demand-driven food distribution under dynamic and uncertain foraging conditions based solely on local decision-making by workers. As a postdoctoral researcher with Nina Fefferman at the University of Tennessee, Knoxville, I investigated how insights from ant behavior could be applied to buffer the stability of human supply chains. With collaborators at UCLA (https://pinterwollmanlab.eeb.ucla.edu/), we used automated tracking to observe how carpenter ants (Camponotus fragilis) modified their food distribution networks in response to experimental food-supply manipulations [2]. Guided by the ants' behavior, I designed decision-making algorithms and evaluated their impact on simulated supply chains, finding that algorithms that emulated the function of ants' responses to disruption often enhanced supply chain resilience [3].
1. Hasenjager MJ, Guo X, Pinter-Wollman N, Fefferman N. 2023. Designing sustainable systems using nature's toolbox. Sustainability Science 18, 2787-2793. doi: 10.1007/s11625-023-01417-x.
2. Guo X, Hasenjager MJ, Fefferman N, Pinter-Wollman N. 2024. Social interactions among ants are impacted by food availability and group size. Biology Open 13, bio060422. doi: 10.1242/bio.060422.
3. Hasenjager MJ, Derryberry G, Guo X, Pinter-Wollman N, Fefferman N. 2024. Nature-inspired design principles promote supply network resilience. Physica A: Statistical Mechanics and its Applications 654, 130133. doi: 10.1016/j.physa.2024.130133.
1. Hasenjager MJ, Guo X, Pinter-Wollman N, Fefferman N. 2023. Designing sustainable systems using nature's toolbox. Sustainability Science 18, 2787-2793. doi: 10.1007/s11625-023-01417-x.
2. Guo X, Hasenjager MJ, Fefferman N, Pinter-Wollman N. 2024. Social interactions among ants are impacted by food availability and group size. Biology Open 13, bio060422. doi: 10.1242/bio.060422.
3. Hasenjager MJ, Derryberry G, Guo X, Pinter-Wollman N, Fefferman N. 2024. Nature-inspired design principles promote supply network resilience. Physica A: Statistical Mechanics and its Applications 654, 130133. doi: 10.1016/j.physa.2024.130133.
Social transmission in hypernetworks
Fig. 5. A hypernetwork and its corresponding dyadic network. The former allows for multi-way interactions and retains information on group size and composition.
In a conventional network, connections are strictly dyadic (i.e., they link two individuals), yet many social processes involve simultaneous multi-way interactions among three or more individuals. For example, audience effects refer to signal-receiver interactions modulated by third-party observers, but such multi-way interactions cannot be explicitly represented and studied using dyadic networks (Fig. 5). Hypernetworks permit such interactions, but the tools to analyze them are limited. As an Intelligence Community Postdoctoral Fellow, I developed hypernetwork-based centrality metrics and applied them to model how multi-way interactions impact social learning processes. In an agent-based model investigating impacts of social aging on cultural transmission, I found that when behaviour spreads as a complex contagion (such that transmission is reinforced by simultaneous exposure to multiple demonstrators), hypernetwork centrality can better reflect individuals’ social influence than dyadic centrality [1]. In addition, when social learning opportunities and outcomes are highly sensitive to variation in group size and composition, utilizing hypernetworks that can explicitly incorporate such information can be especially important [2,3].
1. Hasenjager MJ, Fefferman NH. 2024. Social ageing and higher-order interactions: social selectiveness can enhance older individuals' capacity to transmit knowledge. Philosophical Transactions of the Royal Society B 379, 20220461. doi: 10.1098/rstb.2022.0461.
2. Hasenjager MJ, Bailey MM, Fefferman NH. 2026. Group composition influences diffusion dynamics via impacts on behavioural production. Animal Behaviour, 233, 123482. doi: 10.1016/j.anbehav.2026.123482.
3. Hasenjager MJ, Bailey MM, Fefferman NH. 2026. Social learning within groups: hypernetworks capture effects of within-group dynamics on social contagions. Under review.
4. Hasenjager MJ, Bailey MM, Fefferman NH. (Eds.) 2026. Higher-order network methods for intelligence analysis: A compendium for practitioners. National Intelligence University Press. In press.
1. Hasenjager MJ, Fefferman NH. 2024. Social ageing and higher-order interactions: social selectiveness can enhance older individuals' capacity to transmit knowledge. Philosophical Transactions of the Royal Society B 379, 20220461. doi: 10.1098/rstb.2022.0461.
2. Hasenjager MJ, Bailey MM, Fefferman NH. 2026. Group composition influences diffusion dynamics via impacts on behavioural production. Animal Behaviour, 233, 123482. doi: 10.1016/j.anbehav.2026.123482.
3. Hasenjager MJ, Bailey MM, Fefferman NH. 2026. Social learning within groups: hypernetworks capture effects of within-group dynamics on social contagions. Under review.
4. Hasenjager MJ, Bailey MM, Fefferman NH. (Eds.) 2026. Higher-order network methods for intelligence analysis: A compendium for practitioners. National Intelligence University Press. In press.
