2.7 | Data analysis
We used logistic regression to determine the effects of spatial distance, genetic differentiation (using pairwiseFST values), social form, within-nest relatedness coefficients between workers, and site on whether or not untreated nests shared with the treated nest. To do this, we constructed generalized linear models with a binomial distribution using the glm function in base R statistical software v3.6.1 (R Core Team, 2019). Distance from the treated nest, pairwise F ST values (compared between the treated and untreated nests), social form of both treated and untreated nests (i.e., monogyne or polygyne), within-nest relatedness coefficients between workers in both treated and untreated nests, and site were treated as independent variables. The sharing status of the untreated nests (i.e., “shared with the treated nest” or “did not share with the treated nest”) was the dependent, binary variable. Nests that were identified as having shared with the treated nest had \(\delta\)15N values greater than 20‰, as these values were far higher than any natural abundance isotope values observed at our field sites (mean natural abundance\(\delta\)15N values before tracer treatment: 5.00‰ ± 0.15‰). Untreated nests could only have attained\(\delta\)15N values greater than 20‰ by freely exchanging workers and/or resources with the treated nest. All other nests were designated as “did not share with the treated nest.” All plots were generated using ggplot2 (Wickham, 2016).
Data from the laboratory experiment were analyzed in R statistical software using paired t-tests. Percentage data were arcsine-square-root transformed prior to analysis. All graphs were produced with untransformed data. A more detailed description of the methods can be found in Appendix S4.