Scalable biodiversity monitoring remains a critical challenge for global conservation, particularly in ecologically rich but underrepresented regions with limited data infrastructure. The AMBER project (Automated Monitoring of Biodiversity using Edge and Remote Sensing) addresses this gap by integrating lightweight, compressed machine learning models with the AMI insect-monitoring system to enable on-device species identification. We focus on moth classification as a tractable use case and evaluate two end-to-end inference pipelines: a full-featured, server-based baseline and a compressed, edge-optimised alternative. To support field deployment on low-power devices, we apply quantisation, and model distillation techniques and evaluate trade-offs between full-featured server-based inference and resource-efficient edge deployment strategies. Our results show that compressed models retain strong classification performance while drastically reducing computation and bandwidth needs, enabling scalable, real-time monitoring in remote settings. This work lays the foundation for scalable, real-time ecological monitoring through trustworthy edge AI systems.