Hierarchical clustering was applied to the (a) spectral and (b–j) topological distance matrices to assess the extent to which data-driven clustering of mammalian species recapitulates traditional taxonomies based on morphology and genetics. Hierarchical clustering was implemented using the hierarchy.linkage function in the cluster module of the Scipy Python package (Virtanen et al., 2020). Details of the implementation can be found in the publicly available code repository. Specifically, hierarchical clustering was applied to the inter-species distance matrix estimated using (a) the Laplacian eigenspectra, (b) all (binary, weighted, local, and global), (c) all local (binary and weighted), (d) all global (binary and weighted), (e) all binary (local and global), (f) only binary local, (g) only binary global, (h) all weighted (local and global), (i) only weighted local, and (j) only weighted global topological features. Local features include (the average and standard deviation of) degree, clustering, betweenness, and closeness. Global features include characteristic path length, transitivity, and assortativity. Each heat map represents an inter-species distance matrix. Coloured rectangles represent the order each sample belongs to. Visual inspection of the results shows that, generally speaking, local features tend to provide a clustering solution that resembles more traditional taxonomies.