Unsupervised Machine Learning for Species Delimitation, Integrative Taxonomy, and Biodiversity Conservation

Integrative taxonomy combining data from multiple axes of biologically relevant variation is a major recent goal of systematics. Ideally, such taxonomies would be backed by similarly integrative species-delimitation analyses. Yet, most current methods rely solely or primarily on molecular data, with other layers often incorporated only in a post hoc qualitative or comparative manner. A major limitation is the difficulty of deriving and implementing quantitative parametric models linking different datasets in a unified ecological and evolutionary framework. Machine Learning methods offer flexibility in this arena by learning high-dimensional associations between observations (e.g., individual specimens) across a wide array of input features (e.g., genetics, geography, environment, and phenotype) to delineate statistical clusters. Here, I implement an unsupervised method using Self-Organizing (or "Kohonen") Maps (SOMs). Recent extensions called SuperSOMs can integrate an arbitrary number of layers, each of which exerts independent influence on the two-dimensional output clustering via empirically estimated weights. These output clusters can then be delimited into K significant units that are interpreted as species or other entities. I show an empirical example in Desmognathus salamanders with layers representing alleles, space, climate, and traits. Simulations reveal that the SOM/SuperSOM approach can detect K=1, does not over-split, reflects contributions from all layers with signal, and does not allow layer size (e.g., large genetic matrices) to overwhelm other datasets, desirable properties addressing major concerns from previous methods. Finally, I suggest that these and similar methods could integrate conservation-relevant layers such as population trends and human encroachment to delimit management units from an explicitly quantitative framework grounded in the ecology and evolution of species limits and boundaries.