A central problem in evolutionary biology is to explore the genetic basis of evolutionary changes in the traits of organisms, such as fin structures in fish or beak colors in birds. With the growing availability of large-scale image repositories in biology and recent advances in generative modeling, there is an opportunity to study changes in evolutionary traits of species automatically from images. We introduce a novel Hierarchical Embedding (HIER-Embed) strategy to encode the evolutionary information of a species as a composition of encodings learned at every internal node in the phylogenetic tree. We use HIER-Embeddings to condition latent diffusion models to generate synthetic images of species. Further, we introduce two novel types of perturbation operations: trait masking and trait swapping, similar in spirit to gene knockout experiments, that enable us to analyze novel changes in evolutionary traits acquired at different levels of phylogeny.