Using neural networks to detect patterns in inter-specific data: An example from net-spinning caddisflies (Trichoptera: Annulipalpia)

Abstract

We introduce neural networks (NNs) as a method for detecting patterns and visually comparing multivariate inter-specific morphological data. Neural networks have relatively relaxed statistical assumptions, do not require a phylogeny, and can collapse multivariate data sets into two dimensions. The NN converts the multivariate data into vectors which are then plotted in two dimensions on a self-organizing map. Self-organizing maps visually display any hidden patterns in the data uncovered by the NN. We used a NN to study multivariate sexual dimorphism in 40 species of adult net-spinning caddisflies (Trichoptera: Annulipalpia) from North America. Utilizing eight morphological traits of adult caddisflies, the NN accurately predicted phylogenetic structure (accuracy rate: family = 92%; genus = 82%; species = 72%) and sexual dimorphism (80%) based solely on morphology. Leg traits were most important in discriminating among families and sexes whereas antennal length and eye width were most important for predicting genus and species. Overlaying the self-organizing map on the phylogenetic tree indicated that sexual dimorphism is widespread among net-spinning caddisfly taxa. Our neural network can be used to detect patterns in inter-specific biological data from any set of organisms. Future work should be aimed at developing NNs as a tool in evolutionary biology.