Biology subjects

Beisner, B. A.

Publications and source records attributed to Beisner, B. A..

2 recordsLinked to original sources

bioRxiv2022

Differential effects of multiplex and uniplex affiliative relationships on biomarkers of inflammation

Social relationships profoundly impact health in social species. Much of what we know regarding the impact of affiliative social relationships on health in nonhuman primates (NHPs) has focused on the structure of connections or the quality of relationships. These relationships are often quantified by comparing different types of affiliative behaviors (e.g., contact sitting, grooming, alliances, proximity) or pooling affiliative behaviors into an overall measure of affiliation. The influence of the breadth of affiliative behaviors (e.g., how many different types or which ones) a dyad engages in on health and fitness outcomes remains unknown. Here we employed a social network approach to explicitly explore whether the integration of different affiliative behaviors within a relationship can point to the potential function of those relationships and their impact on health-related biomarkers (i.e., pro-inflammatory cytokines) in a commonly studied non-human primate model system, the rhesus macaque (Macaca mulatta). Being well connected in multiplex grooming networks (networks where individuals both contact sat and groomed), which were more modular and kin biased, was associated with lower inflammation (IL-6, TNF-alpha). In contrast, being well connected in uniplex grooming networks (dyad engaged only in grooming and not in contact sitting), which were more strongly linked with social status, was associated with greater inflammation. Results suggest that multiplex relationships may function as supportive relationships that promote health. In contrast, the function of uniplex grooming relationships may be more transactional and may incur physiological costs. This complexity is important to consider for understanding the mechanisms underlying the association of social relationships on human and animal health.

animal behavior and cognition↗

bioRxiv2021

A comparative network approach to assess the social-ecological underpinnings of zoonotic outbreaks at human-wildlife interfaces

O_LIPandemics caused by wildlife-origin pathogens, like COVID-19, highlight the importance of understanding the ecology of zoonotic transmission and outbreaks among wildlife populations at human-wildlife interfaces. To-date, the relative effects of human-wildlife and wildlife-wildlife interactions on the likelihood of such outbreaks remain unclear. C_LIO_LIIn this study, we used social network analysis and epidemiological Susceptible Infected Recovered (SIR) models, to track zoonotic outbreaks through wild animals joint propensities to engage in social-ecological co-interactions with humans, and their social grooming interactions with conspecifics. C_LIO_LIWe collected behavioral and demographic data on 10 groups of macaques (Macaca spp.) living in (peri)urban environments across Asia. Outbreak sizes predicted by the SIR models were related to structural features of the social networks, and particular properties of individual animals connectivity within those networks. C_LIO_LIOutbreak sizes were larger when the first-infected animal was highly central, in both types of networks. Across host-species, particularly for rhesus and bonnet macaques, the effects of network centrality on outbreak sizes were stronger through macaques human co-interaction networks compared to grooming networks. C_LIO_LIOur findings, independent of pathogen-transmissibility, suggest that wildlife populations in the Anthropocene are vulnerable to zoonosis more so due to their propensities to aggregate around anthropogenic factors than their gregariousness with conspecifics. Thus, the costs of zoonotic outbreaks may outweigh the potential/perceived benefits of jointly interacting with humans to procure anthropogenic food. From One Health perspectives, animals that consistently interact with both humans and conspecifics across time and space are useful targets for disease spillover assessments and control. C_LI

ecology↗