Group and individual social network metrics are robust to changes in resource distribution in experimental populations of forked fungus beetles

Social interactions drive many important ecological and evolutionary processes. It is therefore essential to understand the intrinsic and extrinsic factors that underlie social patterns. A central tenet of the field of behavioral ecology is the expectation that the distribution of resources shapes patterns of social interactions. We combined experimental manipulations with social network analyses to ask how patterns of resource distribution influence complex social interactions. We experimentally manipulated the distribution of an essential food and reproductive resource in semi-natural populations of forked fungus beetles (Bolitotherus cornutus). We aggregated resources into discrete clumps in half of the populations and evenly dispersed resources in the other half. We then observed social interactions between individually marked beetles. Half-way through the experiment, we reversed the resource distribution in each population, allowing us to control any demographic or behavioral differences between our experimental populations. At the end of the experiment, we compared individual and group social network characteristics between the two resource distribution treatments. We found a statistically significant but quantitatively small effect of resource distribution on individual social network position and detected no effect on group social network structure. Individual connectivity (individual strength) and individual cliquishness (local clustering coefficient) increased in environments with clumped resources, but this difference explained very little of the variance in individual social network position. Individual centrality (individual betweenness) and measures of overall social structure (network density, average shortest path length, and global clustering coefficient) did not differ between environments with dramatically different distributions of resources. Our results illustrate that the resource environment, despite being fundamental to our understanding of social systems, does not always play a central role in shaping social interactions. Instead, our results suggests that sex differences and temporally fluctuating environmental conditions may be more important in determining patterns of social interactions.These data are organized into two different datasets: "IndividSN.csv" and "groupSN.csv". Variable names for each dataset are described in the attached readme file "README_Costello.txt". Briefly, "IndividSN.csv" lists weighted individual social network metrics for each individual forked fungus beetle during each observation period. Beetles with one or no social partners have undefined local clustering coefficients (variable "am"). "groupSN.csv" lists weighted group social network metrics for each experimental population of forked fungus beetles during each observation period.Funding provided by: National Science FoundationCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000001Award Number: IOS-1355029Funding provided by: National Science FoundationCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000001Award Number: DEB-1911485Funding provided by: National Science FoundationCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000001Award Number: REU grant 1461169These data describe patterns of social interaction in 12 experimental populations of Bolitotherus cornutus. Each population experienced both a clumped and an even distribution of bracket fungi resources over two different three-week observation periods (i.e. the resource distribution was switched between observation periods). Social interactions were defined as individuals in close proximity (within 5cm) of one another and were collected through scan sampling three times a day. Behavioral datasets were error-checked to remove all unidentifiable beetles and all impossible or partially recorded interactions. Weigthed individual and group social network metrics were calcualted from observed social interactions using the simple ratio index. Network density was calculated in the R package sna. All other weighted social network metrics were calculated in the R package tnet