To ascertain the influences of nanostructures and amino acid compositional variations on silk mechanical properties we pooled the data for all species and constructed multiple regression models. We used the mechanical properties that our MANOVAs found to differ across treatments as the response variables and any nanostructural parameters or amino acid compositions that our MANOVAs found to differ across treatments as the predictor variables. Species and treatments were assigned as continuous predictor variables. A large number of predictor variables, interactions, and intercept terms were likely so we considered linear regression models to be too complex for interpretation. We therefore derived Y = β0 + β1(x1) + β2(x2)… βn(xn) + εi additive regression models for each response variable, where β0 is the population intercept, β1, β2. βn are the regression coefficients associated with the predictor variables x1, x2… xn and εi is the random error term associated with the ith observation . We checked all data for normality, linearity, homoscedasticity and singularity using Q-Q and scatterplots prior to constructing the models.