Challenges in assessing the roles of nepotism and reciprocity in cooperation networks

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 data sets resulting from perfect reciprocity, where the probability and duration of helping events from individual A to B equalled that from B to A. We then assessed how the probability of detecting correlational evidence of reciprocity was influenced by (1) an increasing number of helping observations and (2) an increasing degree of simultaneous nepotism. Last, we applied the same analyses to empirical data on food sharing in common vampire bats, Desmodus rotundus, and allogrooming in mandrills, Mandrillus sphinx, and Japanese macaques, Macaca fuscata. We show that at smaller sample sizes, the effect of kinship was easier to detect and overestimated relative to the effect of reciprocal help. This bias in power was true in both empirical and simulated data, including when 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. When comparing the relative evidence for kin-biased help and reciprocal help, we suggest that researchers measure the relative reliability of both kinship bias and symmetry in the model by plotting the 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 data sets.