Meeting Abstract
Geometric morphometric (GM) methods have revolutionized how studies into comparative morphology are undertaken. Most GM methods, however, can only be used on specimens whose shapes are fixed, barring direct analyses on specimens’ shape trajectories, that is, how the specimen’s shape changes through time. Shape trajectories differ from static shape data in that they cannot be represented as single vectors, but rather as a multivariate function from which shape vectors are derived. Here we attempt to represent the shape trajectories of two simulated organisms with radically different styles of movement as vector autoregressive moving average time series models (VARMA). Upon fitting a model to each organism, we then attempt to make statistical comparisons between the two based on the coefficients that compose the model. The advantages and disadvantages of this strategy are discussed. If successful, these methods have numerous applications in evolution, ecology, and comparative biology from comparisons of how different populations differ in locomotor style to describing microevolutionary trends.
