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 m..., Data collection:
Data were collected on a four groups of Rhesus macaques. Behavioural observations were conducted all adult individuals (3+ years) in the group and serum samples were collected. Affiliative and agonistic interactions were recorded.  Serum samples were assay for inflammatory cytokines.
Data processing:
Behavioural observations were used to construct a weighted, directed behavioural network for each of the following behaviours: (1) all grooming, (2) all contact sitting, (3) multiplex grooming (dyads that both groomed and contact sat, edge-wieghts reflect grooming frequency), (4) uniplex grooming (dyads that only groomed and never contact sat, edge-wieghts reflect grooming frequency), (5) multiplex contact sitting (dyads that both groomed and contact sat, edge-wieghts reflect contact sitting frequency), and (6) uniplex grooming (dyads that only contact sat and never groomed, edge-wieghts reflect contact sitting frequency).  Whole network metrics including density, modularit..., , # Data from: Differential effects of multiplex and uniplex affiliative relationships on biomarkers of inflammation

[https://doi.org/10.5061/dryad.866t1g1xq](https://doi.org/10.5061/dryad.866t1g1xq)

We have included ***2 data files and 3 R code files***. The R code describes the processing of data that generated the two data files which form the basis for analyses presented in the manuscript.

## Description of the data and file structure

**Dataset_IndividualLevel**: Produced using the function contained in the file “*CalculateIndividualCentralityFunction.txt*” and R code file “*IndividualLevelAnalysis.txt*” and used for running individual level analyses presented.

ID = Unique animal identifier      

Cage = Social group identifier

Sex = Subject sex

Age = subject age in years

Mat = Identifier for subject matriline

IL60 = Concentration of serum IL-6 in pg/mL

TNFa0 = Concentration of serum Tumor necrosis factor alpha in pg/mL

DominanceCertainty = Subject dominance certainty as c...