Folks, I think it is important to recognize that the example in Andrea’s earlier post does not really address the validity of sliding semilandmark methods, because all of the data were simulated using isotropic error. Thus, the points called semilandmarks in that example were actually independent of one another at the outset. Yet a major reason for using semilandmark approaches is the fact that points along curves and surfaces covary precisely because they are describing those structures. Thus, this interdependence must be accounted for before shapes are compared between objects. The original literature on semilandmark methods makes this, and related issues quite clear. What that means is that evaluating semilandmark methods requires simulations where the points on curves are simulated with known input covariance based on the curve itself (difficult, but not impossible to do). But using independent error will not accomplish this. The result is that treating fixed landmarks as semilandmarks can lead to what some feel are unintended outcomes, just as treating semilandmarks as fixed points are known to do (illustrated nicely in Figs 1-4 of Gunz et al. 2005). But both are mis-applications of methods, not indictments of them.