Specie Distribution

Species distribution modeling (SDM) aims to predict where species are found based on environmental factors, crucial for conservation and biodiversity monitoring. Current research heavily utilizes deep learning architectures, often incorporating large datasets from citizen science initiatives and remote sensing, to improve prediction accuracy, particularly for rare species. Addressing challenges like data imbalance (presence-only data) and scaling models to handle massive datasets are key focuses, with advancements in algorithms and loss functions improving model performance and enabling more accurate predictions of species ranges and extinction risk. These improved SDMs provide valuable tools for conservation planning and assessing the impacts of climate change on biodiversity.