A technological biodiversity monitoring toolkit for biocredits

Background

Biodiversity is in crisis, with global conservation targets repeatedly unmet. Growing awareness of biodiversity loss's economic risks intensifies the need for effective monitoring. In response, the Kunming-Montreal Global Biodiversity Framework aims to conserve at least 30% of the planet by 2030, a goal that depends on funding and private sector engagement. Biodiversity credits, or biocredits, are emerging as a financial tool for conservation, but their success requires reliable, standardized methods to measure and compare biodiversity. Current monitoring techniques are often costly, slow, and dependent on experts, highlighting the need for scalable, accessible, and technology-driven solutions that provide clear, verifiable results.

Goals and Methods

This paper evaluates the suitability of various technological solutions for monitoring biodiversity in the context of biocredits. It examines whether these technologies meet the SAGED criteria—Scalable, Accessible, Granular, Evidenceable, and capable of providing Direct measurements when possible. The authors review multiple biodiversity monitoring technologies, including satellite remote sensing, unoccupied aerial vehicles, camera trapping, passive acoustic monitoring, and environmental DNA metabarcoding, assessing their readiness for nature accounting in biocredits.

Conclusions and Takeaways

Technological solutions for biodiversity monitoring are essential to the biocredit market due to their scalability and ability to produce auditable records. While these technologies partially meet the SAGED criteria, they currently struggle to directly measure species abundances. The authors emphasize the need for a combined approach, integrating multiple techniques and human expertise for ground-truthing. Future advancements will enhance these technologies, including higher spatial resolution, expanded DNA reference databases, and improved automation for species identification. Implementing these tools now will ensure future data comparability. The authors also highlight that advancements in automation and machine learning will soon make these technologies more accessible and efficient.