Pollinators are essential for Europe’s biodiversity, food security, and ecosystem resilience. However, their populations are declining due to habitat loss, pesticide use, and climate change. To address this, the EU Nature Restoration Regulation (NRR) under Article 10(2) requires Member States to improve pollinator diversity and reverse declines by 2030, followed by an increasing trend of pollinator populations, measured at least every six years from 2030, until satisfactory levels are achieved.

Reliable and standardised monitoring methods are vital to assess progress toward these targets. The EU Pollinator Monitoring Scheme (EU PoMS) has been established to meet this need, collecting comparable data on pollinator species across Europe. EU PoMS will generate a large number of specimens that require identification at the species level, creating a demand for increased taxonomic capacity and innovative solutions.

The EU-funded project MAMBO (Modern Approaches to the Monitoring of Biodiversity) is contributing to this effort through the development of advanced technologies that can transform how pollinators are monitored across Europe. MAMBO’s work focuses on deploying artificial intelligence (AI) tools and insect camera traps to support large-scale, automated, and cost-effective biodiversity monitoring.

The project is advancing insect camera traps that allow automated, non-lethal, and continuous monitoring of pollinators, both nocturnal and diurnal. These devices capture high-frequency images and deliver real-time data on species presence and abundance while reducing the need for extensive field expertise. When integrated into coordinated monitoring networks, these systems can expand coverage across under-sampled and remote areas.

In addition, MAMBO is enhancing AI-powered image recognition tools that assist in identifying pollinator species such as moths, butterflies, bees, and hoverflies. Integrated into citizen science applications like ObsIdentify, these tools empower non-experts to contribute valuable data. By combining AI with public participation, this research project helps overcome taxonomic bottlenecks while engaging citizens in biodiversity monitoring.

The developed technologies align with the objectives of the EU Biodiversity Strategy for 2030, the Birds and Habitats Directives, and the Nature Restoration Regulation. They offer scalable, harmonised, and cost-effective solutions for monitoring pollinators across Europe and support evidence-based conservation policy.

For more information, please access the full policy brief available here as part of MAMBO’s Research Ideas and Outcomes Journal (RIO) collection.