MAMBO project aims to develop, test, and implement enabling tools for monitoring conservation status and ecological requirements of species and habitats for which knowledge gaps still exist.

Unbiased, integrated and regularly updated biodiversity and ecosystem service data is necessary for the creation of comprehensive EU policies. Despite this, efforts to monitor animals and plants remain spatially and temporally fragmented. This lack of integration regarding data and methods creates a gap in biodiversity monitoring, which can negatively impact policy-making. Today modern technologies such as drones, artificial intelligence algorithms, or remote sensing are still not widely used in biodiversity monitoring. 

MAMBO project (Modern Approaches to the Monitoring of BiOdiversity) recognises this need and aims to develop, test, and implement enabling tools for monitoring conservation status and ecological requirements of species and habitats for which knowledge gaps still exist. To do so, MAMBO will implement a multi-disciplinary approach by utilising technical expertise in the fields of computer science, remote sensing, and social science expertise on human-technology interactions, environmental economy, and citizen science. This will be combined with knowledge on species, ecology, and conservation biology. 

MAMBO aims to integrate new technologies with existing research infrastructures to create new methods of biodiversity monitoring across the EU and beyond. The project’s strong stakeholder engagement component will help identify user and policy needs in the realm of biodiversity monitoring and assess its tools at carefully selected demonstration sites across Europe. This will culminate in improved and more cost-effective monitoring schemes for species and habitats using novel technologies. 

The project kickstarted with a consortium meeting on 15-16 September in Aarhus, Denmark. The kick-off meeting welcomed representatives from 10 partnering institutions from 7 EU countries as well as the United Kingdom.

“Classification algorithms have matured to an extent where it is possible to identify organisms automatically from digital data such as images or sound,” comments project coordinator Prof. Toke T. Høye from Aarhus University. “Technical breakthroughs in the realm of high spatial resolution remote sensing set the future of ecological monitoring and can greatly enrich traditional approaches to biodiversity monitoring.” 

“You do not dance the MAMBO alone,” says work package leader Prof. Dr. Koos Biesmeijer. “Our tools will link to the diverse landscape of EU and national projects and infrastructures so that anybody interested in biodiversity monitoring can benefit from them.”

In relation to this, Project Officer Colombe Warin shares that she is looking forward to “seeing MAMBO address strategically the biodiversity decline in liaison with the Green Deal and the Biodiversity Strategy for 2030.” 

MAMBO plans to develop, evaluate, and integrate image and sound recognition-based artificial intelligence solutions for EU biodiversity monitoring from species to habitats and deliver high spatial resolution habitat extent maps. At its core the project aims to co-design novel ecological monitoring tools with researchers, policy makers, citizens, and other stakeholders. The cost-benefit analysis and testing of the tools will showcase the possibility of upscaling MAMBO’s approach and making it widely available across the EU and beyond. 

Stay tuned for more information on the new MAMBO website, which is coming soon: www.mambo-project.eu 

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This project receives funding from the EU Horizon Europe Research and Innovation Action 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.

The EU-funded project MAMBO (Modern Approaches to the Monitoring of Biodiversity) announces the release of its first policy brief, which highlights MAMBO’s contribution to the development of the European Biodiversity Observation Coordination Centre (EBOCC).

EBOCC aims to coordinate biodiversity monitoring across Europe by fostering collaboration among Member States and organisations, integrating monitoring results, and analysing data to derive indicators that support policies. By 2030, EBOCC envisions establishing harmonised data flows to conserve Europe’s ecosystems, aligning with the EU Biodiversity Strategy for 2030.

The EU Parliament and Commission have initiated actions to establish EBOCC, with its mission formulated by key stakeholders through the EuropaBON project. EBOCC focuses on supporting coordination, ensuring harmonised data flows, and analysing information at the EU level to aid conservation efforts and provide regular biodiversity updates.

Coordinated by the University of Aarhus in Denmark, the MAMBO consortium involves researchers from 10 organisations across 8 European countries. MAMBO’s work programme aims to provide the knowledge, tools and infrastructure for monitoring wildlife and their habitats more comprehensively. MAMBO has the potential to improve the ecological monitoring landscape in Europe by developing innovative monitoring tools and engaging with stakeholders. The project is mapping stakeholder landscapes, synthesising user needs, and co-designing future monitoring tools. MAMBO’s tools are meant to integrate with existing research infrastructures, and enhance the current databases with images and sounds to improve machine-learning models.

These advancements will lead to new image-based monitoring applications and improved habitat condition indicators derived from remote sensing and LiDAR data. MAMBO’s tools will feed into models that enhance biodiversity monitoring and adaptive strategies, highlighting regions with high uncertainty. Continuous cost-efficiency assessments are conducted to ensure these tools deliver maximum value.

Through these efforts, MAMBO seeks to demonstrate its impact on enhancing biodiversity monitoring, with a particular focus on its role in the development of the European Biodiversity Observation Coordination Centre (EBOCC). By doing so, MAMBO supports the overall objectives of the EU Biodiversity Strategy for 2030. 

For more information, please access the full policy brief through MAMBO’s website or the project’s collection in the Research Ideas and Outcomes Journal (RIO).

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This project receives funding from the EU Horizon Europe Research and Innovation Action 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.

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.