Differential effects of multiplex and uniplex affiliative relationships on biomarkers of inflammation
Data files
Mar 14, 2024 version files 96.43 KB
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Dataset_IndividualLevel_Rev1_FINAL.csv
87.26 KB
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Dataset_NetworkLevelMetrics_FINAL.csv
3.08 KB
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README.md
6.09 KB
Abstract
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.
README: Data from: Differential effects of multiplex and uniplex affiliative relationships on biomarkers of inflammation
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 calculated using the R package Perc. Ranges from 0.5 (ambiguous relationship) to 1.0 (certain relationship).
PercentileDominanceRank = Subject dominance rank as calculated using the R package Perc. Expressed as the proportion of animals outranked and ranging from 0 (lowest ranked individual) to 1 (highest ranked individual)
OrderGrp0 = Order in which samples were processed on collection days
AllGRdegree = individual degree in the all grooming network
AllGRdegreeWeight = individual strength centrality (i.e., weighted degree) in the all grooming network
AllGRbetweenness = individual betweenness centrality in the all grooming network
AllGReigenvector = individual eigenvector centrality in the all grooming network
AllGRcloseness = individual closeness centrality in the all grooming network
AllGRclustering = individual clustering coefficient in the all grooming network
AllCSdegree = individual degree centrality in the all contact sitting network
AllCSdegreeWeight = individual strength centrality (i.e., weighted degree) in the all contact sitting network
AllCSbetweenness = individual betweenness centrality in the all contact sitting network
AllCSeigenvector = individual eigenvector centrality in the all contact sitting network
AllCScloseness = individual closeness centrality in the all contact sitting network
AllCSclustering = individual clustering coefficient in the all contact sitting network
MultiGRdegree = individual degree centrality in the multiplex grooming network
MultiGRdegreeWeight = individual strength centrality (i.e., weighted degree) in the multiplex grooming network
MultiGRbetweenness = individual betweenness centrality in the multiplex grooming network
MultiGReigenvector = individual eigenvector centrality in the multiplex grooming network
MultiGRcloseness = individual closeness centrality in the multiplex grooming network
MultiGRclustering = individual clustering coefficient in the multiplex grooming network
UniGRdegree = individual degree centrality in the uniplex grooming network
UniGRdegreeWeight= individual strength centrality (i.e., weighted degree) in the uniplex grooming network
UniGRbetweenness = individual betweenness centrality in the uniplex grooming network
UniGReigenvector = individual eigenvector centrality in the uniplex grooming network
UniGRcloseness = individual closeness centrality in the uniplex grooming network
UniGRclustering = individual clustering coefficient in the uniplex grooming network
MultiCSdegree = individual degree centrality in the multiplex contact sitting network
MultiCSdegreeWeight = individual strength centrality (i.e., weighted degree) in the multiplex contact sitting network
MultiCSbetweenness = individual betweenness centrality in the multiplex contact sitting network
MultiCSeigenvector = individual eigenvector centrality in the multiplex contact sitting network
MultiCScloseness = individual closeness centrality in the multiplex contact sitting network
MultiCSclustering = individual clustering coefficient in the multiplex contact sitting network
UniCSdegree = individual degree centrality in the uniplex contact sitting network
UniCSdegreeWeight = individual strength centrality (i.e., weighted degree) in the uniplex contact sitting network
UniCSbetweenness = individual betweenness centrality in the uniplex contact sitting network
UniCSeigenvector = individual eigenvector centrality in the uniplex contact sitting network
UniCScloseness = individual closeness centrality in the uniplex contact sitting network
UniCSclustering = individual clustering coefficient in the uniplex contact sitting network
useIL6 = indicator on which rows to use in the analysis of IL-6. Filters out an outlier.
Dataset_NetworkLevelMetrics: Produced using the code file “WholeNetworkMetrics.txt” and used as the basis for network level analyses.
Network = Indicator for what type of network was analyzed for which social group, CS indicates a contact sitting network, GR indicates a grooming network.
density = density of the network
modularity = modularity of the network
eigen_centr = eigenvector centralization of the network
avg.edgeW = average edge weight (total weight/number of unique edges)
clustering = global clustering coefficient
reciprocity = reciprocity in a directed network (only valid for directed networks). NA values indicate not applicable for undirected networks.
propKin = Proportion of edges that connect animals from the same matriline
UpRank = proportion of directed grooming edges that source at a lower ranked individual and target a higher ranked individual (only valid for directed networks). NA values indicate not applicable for undirected networks.
RankDisp = average disparity in rank of connected dyads
RankEVC = correlation between individual rank and individual eigenvector centrality within each network
Sharing/Access information
These data are to supplement the associated publication from PeerJ and a bioRxiv pre-print which was reviewed and recommended by PCI Network Science.
Methods
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, modularity, eigenvector centralization, average edge weight, clustering coefficient were calcualted. For calculation of individual level network metrics all networks were treated as undirected, these metrics included degree, strength, eigenvector, betweenness, closeness centralities and local clustering coefficient. Dominance ranks were calculated from agonistic interactions for all adult individuals using the Perc package.
Files include the code used to execute these processing steps as well as the processed datasets.