Biology subjects

Webber, Q.

Publications and source records attributed to Webber, Q..

2 recordsLinked to original sources

bioRxiv2024

Territorial behavior as a route of social microbial transmission in an asocial mammal

Microbial transmission is a major benefit of sociality, facilitated by affiliative behaviors such as grooming and communal nesting in group-living animals. The spread of microbial symbionts through these pathways, and their incorporation into host microbiomes, can enhance host health and fitness by contributing to pathogen protection and metabolic flexibility. Are pathways that facilitate microbial transfer across hosts also present in animals that do not form social groups because territoriality limits social interactions and prevents group formation? Here, we addressed this question by combining longitudinal sampling of individual gut microbial communities, demographic data, and dynamic behavioral and spatial measures of territoriality from a non-social, highly territorial small mammal: wild North American red squirrels (Tamiasciurus hudsonicus). As squirrel densities increased, individual gut microbial communities became richer and more phylogenetically diverse, while among-individual differences in composition decreased. This pattern was characterized primarily by increases in obligately anaerobic and non-sporulating taxa with little to no tolerance for oxygen-rich environments, suggesting social rather than environmental routes of transmission. Moreover, territorial intrusions--in which conspecifics were found on within an individuals territorial space--increased gut microbial diversity among individuals defending larger territorial spaces. Using an intrusion-based social network analysis, we found that that pairs with stronger social association (via intrusions) exhibited higher gut microbial similarity. Taken together, our findings provide some of the first evidence for social microbial transmission in a non-social species, and suggest that increased density and territorial behavior can diversify and homogenize host gut microbial communities despite social isolation.

ecology↗

bioRxiv2024

Density-dependent network structuring within and across wild animal systems

High population density should drive individuals to more frequently share space and interact, producing better-connected spatial and social networks [1-4]. Although this theory is fundamental to our understanding of disease dynamics [2,5-8], it remains unconfirmed how local density generally drives individuals positions within their networks, which reduces our ability to understand and predict density-dependent processes [4,9,10]. Here we provide the first general evidence that density drives greater network connectedness at fine spatiotemporal scales, at the scale of individuals within wild animal populations. We analysed 36 datasets of simultaneous spatial and social behaviour in >58,000 individual animals, spanning 30 species of fish, reptiles, birds, mammals, and insects. 80% of systems exhibited strong positive relationships between local density and network centrality. However, >80% of relationships were nonlinear and 75% became shallower at higher values, signifying that demographic and behavioural processes counteract densitys effects, thereby producing saturating trends [11-15]. Densitys effect was much stronger and less saturating for spatial than social networks, such that individuals become disproportionately spatially connected rather than socially at higher densities. Consequently, ecological processes that depend on spatial connections (e.g. indirect pathogen transmission, resource competition, and territory formation) are likely more density-dependent than those involving social interactions (e.g. direct pathogen transmission, aggression, and social learning). These findings reveal fundamental ecological rules governing societal structuring, with widespread implications. Identifying scaling rules based on processes that generalise across systems, such as these patterns of density dependence, might provide the ability to predict network structures in novel systems.

ecology↗