Classification of animal dive tracks via automatic landmarking, principal components analysis and clustering

Abstract

The behaviour of animals and their interactions with the environment can be inferred by tracking their movement. For this reason, biologgers are an important source of ecological data, but analysing the shape of the tracks they record is difficult. In this paper we present a technique for automatically determining landmarks that can be used to analyse the shape of animal tracks. The approach uses a parametric version of the SALSA algorithm to fit regression splines to 1‐dimensional curves in N dimensions (in practice N = 2 or 3). The knots of these splines are used as landmarks in a subsequent Principal Components Analysis, and the dives classified via agglomerative clustering. We demonstrate the efficacy of this algorithm on simulated 2‐dimensional dive data, and apply our method to real 3‐dimensional whale dive data from the Behavioral Response Study (BRS) in the Bahamas. The BRS is a series of experiments to quantify shifts in behavior due to SONAR. Our analysis of 3‐dimensional track data supports an alteration in the dive behavior post‐ensonification.