BIODICAPT

Agroecological production systems depend on ecosystem services (ES) derived from biodiversity, particularly pollination and biological control of crop pests. They also aim to address two major issues: protecting and enhancing biodiversity, especially in agricultural areas, and reducing the use of pesticides. These systems combines of multiple levers, such as production diversification, biodiversity enrichment of agricultural landscapes, and the implementation of targeted agronomic practices.  Supporting the agroecological transition towards such complex systems require new standardised methods to evaluate their multi-performance of and improve their design. These methods should be able to describe and monitor multiple components on a large scale:

• Biodiversity, including a large number of taxa.
• Agricultural practices.
• Agroecological infrastructures (AEI).
• The ecological functions underlying ES.

The BIODICAPT project aims at building a biodiversity monitoring strategy that is both cost-effective and energy-efficient, based on multiple sensors that can be deployed in agricultural landscapes at different scales: local (farm), regional, and national. The project will use existing sensors and algorithms to improve their ability to characterize biodiversity, including multiple taxa, and deploy these methods on a large scale to assess biodiversity and associated services (mainly biological control, pollination, and biodiversity conservation) in response to agricultural practices and AEI.

The sensors:

• Audio recorders to characterize bird, bat, insect communities (species composition, trait composition and diversity), and the associated ES.
• Proxy- and remote sensing based images to characterize plant communities, structure, composition, ecological properties and management of AEI, associated ES, and agricultural practices at the landscape scale.

To consider a wide variety of production systems and biogeographical contexts, these sensors will be deployed on eight regional field networks already in place and experimented on a larger scale with existing institutional monitoring networks at the national level. Statistical models will be developed to analyse these multi-taxa and multi-sensor data in relation to agricultural practices, AEI, and the landscape to produce biodiversity indices. Tools will then be developed to transfer knowledge produced to farmers, citizens, and policymakers, and to support the design and management of agroecological systems beneficial to biodiversity and associated ES.