Fine-grained species classification is essential for biodiversity monitoring, enabling the detection of population changes and ecological shifts. However, distinguishing between morphologically similar species remains a significant challenge. Given the large volume of camera trap data, there is a growing need for AI-based classification models to support automated species identification. This study investigates fine-grained visual classification (FGVC) of seven visually similar antelope species by evaluating the adaptation of state-of-the-art FGVC models—HERBS, PIM, and MPSA—to this novel domain. To facilitate training and evaluation three camera trap datasets are introduced, incorporating data from expert annotations, citizen science contributions, and iNaturalist data. Five experiments systematically analyze the impact of fine-tuning strategies and dataset quality on classification performance. Results demonstrate the robustness and generalization capabilities of these models across both clean and unclean datasets, with HERBS achieving an accuracy of 99.41% on the introduced Baviaanskloof dataset. These findings highlight the potential of AI-assisted classification for wildlife monitoring and biodiversity conservation.