Enhancing Presence-Absence Prediction Models Using Presence-Only Data

Accurate Species Distribution Modelling (SDM) is essential for biodiversity conservation, however the limited and spatially biased nature of Presence-Absence (PA) data poses a challenge. In contrast, Presence-Only (PO) datasets are abundant but lack explicit absence records. This paper examines a two step deep learning approach to combining both PO and PA data to generate an SDM. In the first step, the model was trained on a larger PO dataset, and in the second the model was then tuned on a smaller PA dataset. Results indicate that pre-training with PO data improved the performance by 7% when subsequently fine-tuned with PA data, as measured by the samples-averaged F1-score. This approach demonstrates the potential of combining diverse data types to create more reliable species distribution models for plant biodiversity conservation.