DOI: 10.5061/dryad.866t1g1xq
title: Differential effects of multiplex and uniplex affiliative relationships on biomarkers of inflammation

description: 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, proximity) or pooling affiliative behaviors
 into an overall measure of affiliation. However, it is unclear how the
 breadth of affiliative behaviors (e.g., how many different types or which
 ones) a dyad engages in impact health and fitness outcomes. We used a
 novel social network approach to quantify the breadth of affiliative
 relationships based on two behaviors: grooming and sitting in contact.
 Dyadic relationships were filtered into separate networks depending on
 whether the pair engaged in multiple affiliative behaviors (multiplex
 networks) or just one (uniplex networks). Typically, in social network
 analysis, the edges in the network represent the presence of a single
 behavior (e.g., grooming) regardless of the presence or absence of other
 behaviors (e.g., contact sitting, proximity). Therefore, to validate this
 method, we first compared the overall structure of the standard network
 for each affiliative behavior: all grooming interactions regardless of
 contact sitting, and all contact sitting interactions regardless of
 grooming. We then similarly compared the structure of our filtered
 multiplex vs. uniplex networks. Results indicated that multiplex networks
 were more modular, reciprocal, and kin-based while connections in uniplex
 networks were more strongly associated with social status. These
 differences were not replicated when comparing networks based on a single
 behavior alone (i.e., all grooming networks vs. all contact sitting
 networks). Next, we evaluated whether individual network position in
 multiplex vs. uniplex (novel approach) or grooming vs. contact sitting
 (traditional approach) networks differentially impact inflammatory
 biomarkers in a commonly studied non-human primate model system, the
 rhesus macaque (Macaca mulatta). Being well connected in multiplex
 networks (networks where individuals both contact sat and groomed) 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) was associated with greater inflammation.
 Altogether, these results suggest that multiplex relationships may
 function as supportive relationships (e.g., those between kin or strong
 bonds) that promote health. In contrast, the function of uniplex grooming
 relationships may be more transactional (e.g., based on social tolerance
 or social status) 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.
description: <strong>Data collection:</strong>
 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. <strong>Data
 processing:</strong> 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.
description: # 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 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 and reflects the frequency at which an individual interacts 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 and reflects the frequency at which an individual interacts 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 and reflects the frequency at which an individual interacts 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 and reflects the frequency at which an individual interacts 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 and reflects the frequency at which an individual interacts 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 and reflects the frequency at which an individual interacts 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 or contSit indicates a contact sitting network, GR or groom 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 (interaction rate/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. ## Version Changes 31-oct-2024:  Variables in the dataset "DatasetIndividualLevel" that are related to degree weight (i.e., AllGRdegreeWeight, AllCSdegreeWeight, MultiGRdegreeWeight, UniGRdegreeWeight, MultiCSdegreeWeight, and UniCSdegreeWeight) were previously raw counts.  The current version replaces the raw counts with a scaled version that reflects interaction rates (i.e., raw counts of interactions/shared observation time).  Similarly, in the dataset "Dataset_NetworkLevelMetrics" the variable avg.edgeW now reflects an average interaction frequency rather than an average of raw counts.