Dimensionality Reduction in Animal Movement Data Using Linear Algebra and Machine Learning

Abstract

Animal movement data, often collected through GPS tracking, is inherently high-dimensional and complex, posing challenges for analysis and interpretation. Dimensionality reduction techniques, leveraging linear algebra and machine learning, offer powerful tools for extracting meaningful patterns and insights from this data. This paper explores various dimensionality reduction methods, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), t-distributed Stochastic Neighbor Embedding (t-SNE), and autoencoders. We discuss their strengths, limitations, and suitability for different research questions in animal movement ecology, such as identifying behavioral states, analyzing migratory patterns, and understanding environmental influences. A case study highlighting the use of PCA to analyze bird migration illustrates the practical application of these techniques. This paper emphasizes the importance of dimensionality reduction in transforming complex movement data into a tractable form, ultimately contributing to a deeper understanding of animal behavior and ecology.