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This workshop explores how autonomous AI agents can support scientific research in R, from conceptual distinctions between chat systems and agentic workflows to practical deployment with open-source tools (OpenCode). It is designed for researchers who want to use AI critically, securely, and effectively in data-intensive academic work.
Studies of morphological integration frequently use the mammalian skull as a model system. However, while such research often compares distantly related clades, analyses of closely related species remain scarce. Here, we investigate cranial modularity and morphological evolution across 16 well-sampled species of the subterranean rodent genus Ctenomys. Using a combination of geometric morphometrics and comparative methods, we evaluated the degree of modular structure between the proposed 2 and 3 cranial partitions (modular hypothesis) using the covariance ratio (CR). Covariance (V/CV) matrices—derived from anatomical landmark coordinates—were adjusted for repeatability to minimize sampling bias in matrix correlations. To quantify integration patterns, we adapted the eigenvalue variance method (typically applied to correlation matrices, V(λ)) for use with V/CV matrices. Linear regressions were performed to test correlations between (i) skull shape and CR values and (ii) skull shape and integration magnitude. A strong phylogenetic signal was detected for skull modularity (CR), indicating that the observed patterns of integration are deeply conserved within Ctenomys. This result suggests that modularity in these subterranean rodents is not a product of recent adaptive divergence but rather stems from ancestral developmental or genetic constraints, which may have been maintained over time due to stabilizing selection or shared functional demands across species.
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