If your goal is to obtain p-values for the individual predictors, then using
anova on your fitted pgls models does that. AIC will give you measures of the overall goodness of fit of each model, but doesn't tell you about the significance of individual parameters. In fact, you can have a model favored by AIC that includes parameters with non-significant p-values (see Why applying model selection using AIC gives me non-significant p-values for the variables.
On the other hand, it is arguably more useful to consider which variables are included in the model selected using AIC, because those variables with non-significant p-values may still be useful. In particular, when you have collinear predictor variables, the p-values can be large even for variables that add predictive power to the model (see AIC or p-value: which one to choose for model selection?).
In practice, I would suggest reporting results from both approaches. While I always appreciate seeing the different models along with AICs, many readers and reviewers care more about seeing p-values for individual predictors. It is easy enough to report AICs along with p-values for predictors, and you may get insights about your data from looking at the AICs and p-values together (e.g., if you have a model with significant variables, and then adding a new variable improves the AIC but causes the other model variables to become non-significant, it would suggest that the new variable is correlated with the old variables and you should not necessarily conclude that the old variables don't matter, even if they have non-significant p-values in the full model).