Evaluation of a Random Forest Model to Identify Invasive Carp Eggs Based on Morphometric Features

Abstract

Three species of invasive carp—Grass Carp Ctenopharyngodon idella, Silver Carp Hypophthalmichthys molitrix, and Bighead Carp H. nobilis—are rapidly spreading throughout North America. Monitoring their reproduction can help to determine establishment in new areas but is difficult due to challenges associated with identifying fish eggs. Recently, random forest models provided accurate identification of eggs based on morphological traits, but the models have not been validated using independent data. Our objective was to evaluate the predictive performance of egg identification models developed by Camacho et al. (2019) for classifying invasive carp eggs by using an independent data set. When invasive carp were grouped as one category, predictive accuracy was high at the following levels: family (89%), genus (90%), species (91%), and species with reduced predictor variables (94%). Invasive carp predictive accuracy decreased when we only considered observations from newly sampled locations (family: 9%; genus: 22%; species: 30%; species with reduced predictor variables: 70%), suggesting potential differences in egg characteristics among locations. Random forest models using a combination of previous and new data resulted in high predictive accuracy for invasive carp (96–98%) when invasive carp were grouped as one class for all models at the family, genus, and species levels. The two most influential predictor variables were average membrane diameter and average embryo diameter; the probability of predicting an invasive carp egg increased with these metrics. High predictive accuracy metrics suggest that these trained and validated random forest models can be used to identify invasive carp eggs based on morphometric variables. However, decreased performance at new locations suggests that more research would be beneficial to determine the models’ applicability to a larger spatial region.