Cultural history, not ecological environment, is the main determinant of human behaviour.

Towner et al. [1] question the methods and the theoretical framework of our study of behavioural variation among Native American tribes of Western North America [2]. Here we show that their concerns are unfounded and that our results are robust. We also clarify the theoretical issues that motivated our paper, and explain why it is critical to disentangle the role of ecology and cultural inheritance in a cultural species like humans. Towner et al. contend that the higher summed absolute value of the cultural–historical betas (i.e. C ) relative to that of ecology (i.e. E) is due to the fact that cultural history has 10 potential predictors and ecology has seven. Their argument is based on the fact that the expectation of the absolute beta of a predictor representing stochastic noise is not zero, but ð2s2=pÞ (assuming the coefficients are drawn from a Gaussian distribution with mean zero and standard deviation s). This is because the probability density on the negative side of the distribution of beta is shifted to the positive side in the distribution of the absolute beta. Thus, any predictor in a model will contribute to C or E, even when they represent stochastic noise. Towner et al. suggest that the correct measure of the effect size of cultural history and ecology is C MCð2s2=pÞ and E MEð2s2=pÞ, where MC and ME are the number of cultural historical and ecological predictors, respectively. We show here that this correction is not necessary, and neither does it change th results. The model selection approach shields our results from the effect of predictor class size. Predictors that represent stochastic noise will have betas with large standard error relative to their effect size. Such predictors are unlikely to be included in the best model, as the Akaike information criterion (AIC) penalizes models for the number of predictors they contain. As a result, the absolute value of a b coefficient in the best model is a good measure of the true absolute magnitude of the effect of that predictor, even if it does not incorporate explicitly the standard error of the b estimate. However, explicitly incorporating the standard error of the b estimates, as Towner et al. suggest, is a valid alternative to the approach we used. But Towner et al.’s correction is incomplete, because it is only applicable to the worst-case scenario where the predictors represent stochastic noise. Just as the expectation of the absolute beta of a predictor representing stochastic noise is shifted from zero to ð2s2=pÞ; the expectation of the absolute effect of a predictor with mean b and standard deviation s is