Postdoc University of Massachusetts, Amherst Worcester, Massachusetts, United States
Abstract Body : One of the major goals of evolutionary biology is to understand how genetic mechanisms control organismic function, thereby linking “proximate” and “ultimate” evolutionary causation. However, identification of the relationship between genes and function is often hindered by the many layers of biological complexity that lie between them. In the case of whole organism performance, these intermediates can include morphology, kinematics, and behavior. We argue that by measuring these additional levels of biological complexity (morphology, kinematics, and behavior) and including them in a structural equation model (SEM), they can be turned into an asset that strengthens our ability to detect functional consequences of genetic variation. We demonstrate the effectiveness of this statistical framework using an F2 hybrid cross between two ecologically divergent species of African cichlids. We measure their cranial morphology, feeding kinematics, and feeding performance, then construct an SEM using all these data. We find that compared to standard linear modeling, the SEM is better able to establish causal relationships between many variables and more confidently predict functional outcomes of biological variation. Additionally, our experimental design will allow us to effectively integrate genetic analyses into this same model as we expand the data set. All in all, we suggest this statistical framework carries great potential to help us understand evolutionary causation or any other integrative questions that span levels of biological complexity.
