This research develops adaptable machine learning methods for wildlife monitoring using camera trap images. By clustering visually similar animal images, the system dramatically reduces the amount of manual labeling required while maintaining accuracy. The approach could enable faster, large-scale biodiversity monitoring critical for protecting endangered species worldwide.

This research shows how environmental DNA (eDNA) can rapidly and sensitively detect marine species threatened by climate change. By analysing seawater samples, the study identified over 18,800 species and revealed fine-scale ecological shifts. eDNA offers a powerful, scalable tool to monitor coastal ecosystems and protect vulnerable species as environmental conditions worsen.