Comparative Analysis of MaxEnt and Deep Learning Approaches for Modeling Humpback Whale Distribution in North Iceland

Abstract

In this study, we compared the established MaxEnt and a more novel deep learning approach for modeling the distribution of humpback whales (Megaptera novaeangliae) in north Iceland. We examined the mechanisms, structures, and optimization techniques of both approaches, highlighting their differences and similarities. Monthly distribution models for Skjálfandi Bay were created, from 2018 until 2021, using presence-only sighting data and satellite remote sensing data. Search efforts and boat tracklines were utilized to create pseudo-absence points for both models. Additionally, the trained models were used to create distribution projections for the year 2022, solely based on the available environmental data. We compared the results using the established area under the curve value. The findings indicate that both approaches have their limitations and advantages. MaxEnt does not allow continuous updating within a time series, yet it mitigates the risk of overfitting by employing the maximum entropy principle. The deep learning model is more likely to overfit, but the larger weight network increases the model's capability to capture complex relationships and patterns. Ultimately, the results show that the deep learning model had a higher predictive performance in modeling both current and future humpback whale distributions. Both modeling approaches have inherent limitations, such as the low resolution of the input data, spatial biases, and the inability to fully capture the entire complexity of natural processes. Despite this, deep learning showed promising results in modeling the distribution of humpback whales and prompts further research in different study areas and applications for other mobile animal species.