Identifying Groups Using Animal Patterns: A Quantitative Comparison of Feature Extraction Methods

Many animal species have unique patterns on their body that allow for individual identification, and these patterns often have a genetic basis. This individual-level pattern variation can be leveraged in ecological applications in a number of ways, such as facilitating the identification of individuals in mark-recapture study systems, and informing conservation efforts by identifying the source populations of animals trafficked in the wildlife trade, for example. We tested four convolutional neural network architectures that researchers with access to training data can use to extract features for clustering individuals into a known number of biologically relevant groups in species with unique individual patterns. We used a common freshwater turtle species, the painted turtle (Chrysemys picta), which has intricate individual-level variation in plastron pattern. We compared the ability of our neural network approaches, traditional geometric morphometric methods, and software for identifying individuals to group individuals by clutch and population using two clustering methods. We found that no methods reliably recovered clutch membership (i.e., half or full siblings) from plastron patterns (Adjusted Rand indices < 0.266). However, individuals could be sorted into source populations with high agreement using both methods, with the convolutional autoencoder resulting in the best clustering (Adjusted Rand index = 0.858). Most of our neural network approaches outperformed (had higher Adjusted Rand indices than) the geometric morphometric methods in both cases, and both of these approaches outperformed the similarity scores calculated by the individual identification software. These results demonstrate that neural-network-derived image features can provide an effective approach for evaluating whether uniquely identifiable individuals cluster into biologically relevant groups.