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.