  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.
