Understanding where and when biodiversity is thriving is one of the biggest challenges facing conservation today - and one that traditional monitoring methods have long struggled to meet at scale. That's where the MAMBO project has made its mark.
As the project officially concludes, MAMBO leaves behind a legacy of technological innovation, international collaboration, and practical tools for biodiversity monitoring.
MAMBO, short for Modern Approaches to the Monitoring of Biodiversity, is a Horizon Europe project bringing together 10 partners to tackle the persistent gaps in Europe's biodiversity data. Over the course of the project, MAMBO developed and tested a new generation of monitoring tools - combining artificial intelligence, computer vision, bioacoustics, remote sensing, LiDAR, drones, and citizen science - across demonstration sites in France, the United Kingdom, Germany, Denmark, the Netherlands, and Malta.
Looking back on what the project has achieved, MAMBO has created a suite of tools, available to researchers, land managers, policymakers, and citizen scientists. These outputs include:
UV light camera traps (AMI-trap) for autonomous monitoring of nocturnal pollinators, deployed in over 30 countries worldwide and capable of processing more than one million recordings per season per device.
Insect camera traps for monitoring diurnal pollinators , classifying up to 80 different taxa and generating detailed, standardised data on pollinator activity across seasons and diverse habitats.
Sound recognition models for biodiversity monitoring , covering breeding birds, amphibians, bats, and grasshoppers across Europe, performing rapid species-level identification from field audio recordings.
The Multi Source Model (MSM) , an image recognition tool trained on 35 million images that supports identification of over 41,000 taxa, now powering more than ten European biodiversity portals and performing over 76 million identifications annually.
Plant quadrat image analysis , co-developed with the GUARDEN project and available on the Pl@ntNet platform, transforming simple photographs into structured plant community data.
High-resolution species and habitat maps , offering EU-scale, 50 × 50 m resolution data on species distributions and EUNIS habitats, freely explorable via the GeoPl@ntNet platform.
Laserfarm , an open-source airborne laser scanning workflow that converts billions of LiDAR points into standardised vegetation structure layers, applied across seven demonstration sites in five countries.
A toolbox to map shrub cover and biomass from drone imagery , combining deep learning with Bayesian biomass modelling, tested at a UK rewilding site.
Beyond the technologies themselves, MAMBO has produced policy briefs exploring, e.g. how its tools can support the EU Pollinator Monitoring Scheme (EU PoMS), and has worked to connect its evidence base with major international assessment processes, including IPBES and IPCC, and with the EU Biodiversity Strategy for 2030.
MAMBO's achievements mark an important milestone in the journey toward more automated, scalable, and cost-efficient biodiversity monitoring. The tools, datasets, and partnerships built throughout the project provide a strong foundation for continued innovation across Europe's biodiversity monitoring landscape. By making these resources openly available, including through a dedicated topical collection in the RIO Journal, MAMBO ensures that the work doesn't end here, but continues to inform conservation action, policy, and research long after the project's conclusion.
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MAMBO receives funding from the European Union's Horizon Europe research and innovation programme under grant agreement No. 101060639.
Views and opinions expressed are those of the author(s) only and do not necessarily reflect those of the European Union or the European Commission. Neither the EU nor the EC can be held responsible for them.