Imbalance-aware Presence-only Loss Function for Species Distribution Modeling
This work tackles the challenge of accurately modeling rare species in conservation efforts, which is critical for biodiversity protection, though it appears incremental as it adapts existing loss function techniques to a specific domain.
The study addressed the problem of class imbalance in species distribution models by proposing an imbalance-aware presence-only loss function for deep learning models, which outperformed traditional loss functions across datasets and tasks, particularly improving accuracy for rare species with limited observations.
In the face of significant biodiversity decline, species distribution models (SDMs) are essential for understanding the impact of climate change on species habitats by connecting environmental conditions to species occurrences. Traditionally limited by a scarcity of species observations, these models have significantly improved in performance through the integration of larger datasets provided by citizen science initiatives. However, they still suffer from the strong class imbalance between species within these datasets, often resulting in the penalization of rare species--those most critical for conservation efforts. To tackle this issue, this study assesses the effectiveness of training deep learning models using a balanced presence-only loss function on large citizen science-based datasets. We demonstrate that this imbalance-aware loss function outperforms traditional loss functions across various datasets and tasks, particularly in accurately modeling rare species with limited observations.