Data and R code for "Challenges in assessing the roles of nepotism and reciprocity in cooperation networks"

See "_READ_ME.txt".

Nepotism and reciprocity are not mutually exclusive explanations for cooperation, because helping decisions can depend on both kinship cues and past reciprocal help. The importance of these two factors can therefore be difficult to disentangle using observational data. We developed a resampling procedure for inferring the statistical power to detect observational evidence of nepotism and reciprocity. We first applied this procedure to simulated datasets resulting from perfect reciprocity, where the probability and duration of helping events from individual A to B equaled that from B to A. We then assessed how the probability of detecting correlational evidence of reciprocity was influenced by (1) the number of helping observations and (2) varying degrees of simultaneous nepotism. Last, we applied the same analysis to empirical data on food sharing in vampire bats and allogrooming in mandrills and Japanese macaques. We show that at smaller sample sizes, the effect of kinship was easier to detect and the relative role of kinship was overestimated compared to the effect of reciprocal help in both simulated and empirical data, even with data simulating perfect reciprocity and imperfect nepotism. We explain the causes and consequences of this difference in power for detecting the roles of kinship versus reciprocal help. To compare the relative importance of genetic and social relationships, we therefore suggest that researchers measure the relative reliability of both coefficients in the model by plotting these coefficients and their detection probability as a function of sampling effort. We provide R scripts to allow others to do this power analysis with their own datasets.