Abstract

Over the last three decades, geometric morphometrics has seen tremendous progress in terms of new techniques for analyzing shape variation. Statistical shape analysis provides a solid mathematical foundation, and a broad range of sophisticated tools is available for characterizing shapes and for extracting specific information that can answer a variety of biological questions. Biological datasets usually have an inherent structure that can potentially reveal important insights about the processes and mechanisms responsible for the observed variation. For instance, many organisms or their organs are symmetric, and the usually slight deviations from perfect symmetry can be characterized with morphometric methods and provide useful biological insight. Such analyses of fluctuating asymmetry can provide information on the developmental basis of integration among traits. Likewise, organisms with modular body plans consisting of repeated parts, such as most plants, provide opportuni ties to examine additional levels of variation within individuals. Many morphometric studies use samples of specimens from multiple taxa, and considering both the variation within taxa and the evolved differences among taxa permits to make inferences about evolutionary mechanisms. Adopting a multi-level approach that considers all the morphological information that can be obtained in a given study design promises rich biological insights, often for little extra effort by the investigator. My lecture will illustrate this approach with examples from animals and plants.