A NOVEL COMPUTATIONAL ETHOLOGY FRAMEWORK FOR STUDYING ANIMAL BEHAVIOUR UNDER CLIMATE CHANGE (CEFABC2): A CASE STUDY OF LITTLE PENGUINS ON PHILLIP ISLAND

Abstract

Animal behaviour is a biological indicator that provides insight into a species’ interaction with its environment. Long-term records of animal behaviour form time series containing rhythms, anomalies, and trends in the activity of a species. Numerical and machine learning (ML) methods provide tools to automate the investigation of large amounts of data to detect patterns in complex behavioural time series. This thesis uses the little penguin colony on Phillip Island, Victoria, Australia as a case study, where the behaviour of this species has been recorded and analyzed over decades. To further support these studies, this thesis introduces a novel computational ethology framework on the study of animal behaviour under climate change (CEFABC2). This framework extracts and forecasts the dynamics of nightly penguin count time series, breeding success, and mean egg-laying date (MLD). CEFABC2 is built on four modules: the first module uses singular spectrum analysis (SSA) to process and reconstruct the nightly penguin count time series. From the SSA-reconstructed time series, the peaks are extracted and Gaussian Mixture Model (GMM) is implemented to estimate the peak width. The second module examines the correlations between the extracted peaks, the MLD, and breeding success. Gaussian process (GP) is applied to forecast the peaks of the nightly penguin count time series. Additionally, the performance of structural time series (STS), ARIMA, LSTM, GP, LightGBM, and Ridge models is evaluated for forecasting nightly penguin counts. The third module describes the climate of Phillip Island, using principal component analysis (PCA), K-means, and GMM to identify meteorological seasons. The fourth module integrates biological and climate variables, analyzing their correlations and forecasting breeding variables using ElasticNet, Ridge, Random Forest, and Gradient Boosting ML models. Finally, the CEFABC2 management system incorporates these components into a single tool aiming to support monitoring, forecasting, and future conservation management efforts towards the Phillip Island little penguin colony.