Natural disasters can cause rapid demographic changes that disturb the social structure of a population as individuals may lose connections. These changes also have indirect effects as survivors alter their within-group connections or move between groups. As group membership and network position may influence individual fitness, indirect effects may affect how individuals and populations recover from catastrophic events. Here we study changes in the social structure after a large predation event in a population of wild house mice ( Mus musculus domesticus ), when a third of adults were lost. Using social network analysis, we examine how heterogeneity in sociality results in varied responses to losing connections. We then investigate how these differences influence the overall network structure. An individual's reaction to losing associates depended on its sociality prior to the event. Those that were less social before formed more weak connections afterwards, while more social individuals reduced the number of survivors they associated with. Otherwise, the number and size of social groups were highly robust. This indicates that social preferences can drive how individuals adjust their social behaviour after catastrophic turnover events, despite the population's resilience in social structure.

Rhythmical activity patterns are ubiquitous in nature. We study an oscillatory biological system: collective activity cycles in ant colonies. Ant colonies have become model systems for research on biological networks because the interactions between the component parts are visible to the naked eye, and because the time-ordered contact network formed by these interactions serves as the substrate for the distribution of information and other resources throughout the colony. To understand how the collective activity cycles influence the contact network transport properties, we used an automated tracking system to record the movement of all the individuals within nine different ant colonies. From these trajectories we extracted over two million ant-to-ant interactions. Time-series analysis of the temporal fluctuations of the overall colony interaction and movement rates revealed that both the period and amplitude of the activity cycles exhibit a diurnal cycle, in which daytime cycles are faster and of greater amplitude than night cycles. Using epidemiology-derived models of transmission over networks, we compared the transmission properties of the observed periodic contact networks with those of synthetic aperiodic networks. These simulations revealed that contrary to some predictions, regularly-oscillating contact networks should impede information transmission. Further, we provide a mechanistic explanation for this effect, and present evidence in support of it. Author summaryMany complex biological systems, from cardiac tissues to entire animal populations, exhibit rhythmical oscillations. Here we studied a textbook example of a complex living system-colonies of Leptothorax ants, which exhibit short (15 minute) collective activity cycles. In ant colonies, information, food, and chemical signals are transported throughout the group via worker-to-worker physical contacts, and it has therefore been suggested that the activity cycles might serve to increase the rapidity of information transmission. To test this, we used an automatic ant tracking system to identify physical contacts between workers, from which we reconstructed the dynamical network of physical contacts. We used models of information transmission derived from the study of contagious PLOS Computational Biology | https://doi.org/10.1371/journal.pcbi

Living in groups is a widely adopted strategy in gregarious species. For group-living individuals it is crucial to be capable to integrate into a social structure. While there is an intuitive understanding that the concept of a group arises through some form of cohesion between its members, the exact definition of what constitutes a group and thus tasks like the detection of the dynamics of a group over time is a challenge. One way of measuring cohesion is through direct interactions between individuals. However, there is increasing evidence that associations between individuals can be mediated by others, and thus, that the drivers for group cohesion extend beyond direct individual interactions. We use dynamic community detection, allowing to relate individuals beyond direct contacts, both structurally and temporally, to study the social structure in a long-term study of a population of free-ranging house mice in a barn in Switzerland. During the 2-year study period, mice had unlimited access to food, and population density increased by 50%. Despite strong fluctuations in individual contact behaviour, population demography and structure embed into long-lived dynamic communities that are characterised by spatial fidelity, persist over several seasons and reproduction cycles, and considerably extend the life-span of single individuals. Within these multi-male and multi-female communities, seasonal changes strongly affect their structure, leading to fission-fusion like dynamics. We identify femalefemale interactions as the main driver for the longevity of these communities, a finding that contrasts with prior reports of the importance of a dominant male for the stability of a group. Moreover, males have a drastically shorter presence time in the study population and more often move between communities than females. Nevertheless, interacting with other breeding males in stable communities increases the duration of male presence and thus, potentially, reproductive success. Our analysis of contact patterns in a rodent that uses shelters to rest, hide and rear offspring emphasises the importance of female-bonded communities in the structuring of the population.

Intense use of antibiotics for the treatment of diseases such as tuberculosis, malaria, Staphylococcus aureus or gonorrhea has led to rapidly increasing population levels of drug resistance. This has generally necessitated a switch to new drugs and the discontinuation of older ones, after which resistance often only declines slowly or even persists indefinitely. These long-term effects are usually ascribed to low fitness costs of resistance in absence of the drug. Here we show that structure in the host population, in particular heterogeneity in number of contacts, also plays an important role in the reversion dynamics. Host contact structure acts both during the phase of intense treatment, leading to non-random distributions of the resistant strain among the infected population, and after the discontinuation of the drug, by affecting the competition dynamics resulting in a mitigation of fitness advantages. As a consequence, we observe both a lower rate of reversion and a lower probability that reversion to sensitivity on the population level occurs after treatment is stopped. Our simulations show that the impact of heterogeneity in the host structure is maximal in the biologically most plausible parameter range, namely when fitness costs of resistance are small.

When studying social behaviour, it can be important to determine whether the behaviour being recorded is actually driven by the social preferences of individuals. Many studies of animal social networks therefore attempt to disentangle social preferences from spatial preferences or restrictions. As such, there are a large number of techniques with which to test whether results from network analysis can be explained by random interactions, or interactions driven by similarities in space use. Selecting which of these methods to use will require determining to what extent space might influence social structure. Here we present a simple method (Social Spatial Community Assignment Test) to quantify the similarity between social and spatial group structure. We then apply this method to both simulated and empirical data of social interactions to demonstrate that it can successfully tease apart social and spatial explanations for groups. We first show that it can resolve the relative importance of space and social preferences in three simulated datasets in which interaction patterns are driven purely by space use, purely by social preferences or a mixture of the two. We then apply it to empirical data from a long-term study of free-ranging house mice. We find that while social structure is similar to spatial structure, there is still evidence for individuals possessing social preferences, with the importance of these preferences fluctuating between seasons. Our method provides a robust way of assessing the overlap between spatial and social structure, which will be invaluable to researchers when investigating the underlying drivers of social structure in wild populations.