The field of ecological monitoring is rapidly advancing with the adoption of machine learning techniques, enabling more accurate and efficient classification of species and monitoring of biodiversity. Recent studies have focused on developing innovative methods for taxonomic classification, such as automated systems for identifying ground beetles and tree species, which have shown high accuracy rates. Additionally, researchers have been exploring the application of machine learning in aquatic environments, including the classification of aquatic invertebrates and fish feeding intensity assessment. These advancements have the potential to significantly improve conservation efforts and ecological health. Noteworthy papers include: BeetleVerse, which achieved 97% accuracy in taxonomic classification of ground beetles, and HAIL-FFIA, which introduced a novel audio-visual class-incremental learning framework for fish feeding intensity assessment. Furthermore, the development of automated pipelines for few-shot bird call classification, such as the one presented for the tooth-billed pigeon, demonstrates the potential for machine learning to support conservation efforts for rare and endangered species.