Demonstration sites are central to achieving MAMBO's objectives, serving as the environments where the project's innovations are tested, validated, and showcased. The EU-funded project MAMBO (Modern Approaches to the Monitoring of Biodiversity) has established a network of 12 demonstration sites across six European countries, where innovative monitoring technologies are being deployed under diverse ecological conditions.

From coastal lagoons in France to rewilding sites in the United Kingdom, and from agricultural landscapes in Germany to Mediterranean habitats in Malta, these sites represent the ecological diversity of Europe and provide crucial testing grounds for next-generation biodiversity monitoring tools. By evaluating technologies across different ecosystems and management contexts, MAMBO is developing solutions that can be deployed widely across Europe.

The demonstration network strategically balances conservation-focused sites with working agricultural landscapes. In France, Bagnas Nature Reserve features coastal lagoons, marshes, reedbeds, and dunes, while Parpalhon organic farm in Beaulieu tests technologies in active agricultural settings. In the United Kingdom, Strawberry Hill in Bedfordshire represents a 150-hectare rewilding success story, transformed from arable fields to flower-rich grassland over 25 years. The German Friedeburg LTER site spans 16 km² of heterogeneous agricultural landscape with high-value semi-natural grasslands protected under Natura 2000.

Denmark's Mols Bjerge National Park contains a biodiversity hotspot where over 4,000 species have been recorded—more than 10% of all species observed in Denmark. The Netherlands features Amsterdamse Waterleidingduinen coastal dune nature reserve and Oostvaardersplassen, a 5,400-hectare wetland and rewilding site of international importance under the Ramsar Convention. Malta contributes three Mediterranean sites: Majjistral National Park, Xrobb l-Għaġin Nature Park, and Il-Magħluq ta' Marsaskala, one of only two extant wetlands in southern Malta. 

At Friedeburg, camera traps and acoustic systems demonstrate fully autonomous monitoring of pollinators and birds within typical Central European agricultural landscapes. At Parpalhon organic farm, technologies are assessed under real farm conditions, exploring how monitoring can integrate with routine agricultural practices. The integration of field sampling with remote sensing represents a key innovation. At Strawberry Hill, the UK team combined ground-based demonstrations with remote sensing to develop models for estimating shrub biomass and carbon content.

The demonstration sites play a crucial role in MAMBO's contribution to European biodiversity monitoring infrastructure. By testing technologies across diverse ecosystems and management contexts, the project is developing tools that can integrate with existing research infrastructures and enhance current databases with images and sounds to improve machine-learning models. 

For more information on MAMBO’s demonstration sites, please access the project’s website.

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.

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.

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.

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.

Biodiversity monitoring faces significant challenges across Europe, from the labour-intensive nature of traditional field surveys to the shortage of taxonomic expertise needed to identify species. The EU-funded project MAMBO (Modern Approaches to the Monitoring of Biodiversity) addresses these challenges through innovative monitoring technologies that combine artificial intelligence, remote sensing, and automated field equipment, transforming how biodiversity is assessed across Europe.

MAMBO has developed a set of technologies designed to monitor different components of biodiversity. The AMI-trap is an autonomous light-trap system that captures high-resolution images of nocturnal insects using UV and LED light sources with motion-detection software. Capable of processing over one million recordings per season per device, the system has been deployed in more than 30 countries. For diurnal pollinators, insect camera traps record images of flowering plants and visiting insects, classifying up to 80 taxa groups at genus or species level with over 100,000 recordings per season per camera trap.

Sound recognition models identify species from audio recordings across four European animal groups: breeding birds, amphibians, bats, and grasshoppers. Compatible with equipment from smartphones to professional recorders, the models analyse recordings in three-second time windows, enabling well-standardised data collection across multiple species groups using a single method. For citizen science, the Multi Source Model (MSM) provides image recognition for over 41,000 taxa of European animals, plants, and fungi. Trained on 35 million validated images and deployed across over ten biodiversity portals, the model performs over 76 million identifications annually, with records uploaded to GBIF after validation.

The Plant Quadrat Image analysis tool, available on the Pl@ntNet platform, transforms a photograph of a 50 × 50 cm plot into a structured description of the entire plant community present, delivering plot-level results in seconds. At European scale, high-resolution species and habitat maps at 50 × 50 m spatial resolution describe all plant species and EUNIS habitats, made interactively explorable through the GeoPl@ntNet platform. The Airborne Laser Scanning tool uses Laserfarm, an open-source Python workflow, to convert LiDAR point clouds into standardised map layers capturing vegetation structure, applied to seven demonstration sites and producing full national coverage for the Netherlands.

For more information on all technologies developed within MAMBO, please visit the project’s website.

Biodiversity is enormously diverse, unevenly observed, and constantly in motion across space and time. This makes it far harder to monitor than many other parts of the environment. As a result, managing biodiversity often depends on information that is incomplete, delayed or unevenly distributed. At the very moment when rapid change makes timely knowledge more valuable, the systems for collecting, identifying, publishing, and using biodiversity data often struggle to keep pace.

To address this, six research projects – B-Cubed, BMD, OneSTOP, MAMBO, GUARDEN, and AURORA – co-wrote a policy brief that explores the causes of this persistent biodiversity data lag and identifies ways to reduce it. According to them, the problem begins in the field. Most biodiversity recording relies on volunteers and local teams, which, although invaluable, results in uneven and untimely data, often missing some metadata information. Even professional monitoring programmes still struggle with limited staff, outdated software, or incompatible standards, which slows down the flow of data even more. Additionally, a lot of already available data cannot be accessed or is hardly discoverable because it has not been openly published yet or widely distributed. This results in a bunch of small delays that lead to a bigger lag in the spread of information.

Although biodiversity data has been rapidly expanding in volume, both traditional and novel methods of biodiversity monitoring (e.g. camera traps, eDNA sampling) struggle with the same old setbacks of incompleteness and long waiting. That’s why, rather than focusing only on data collection, policies should consider other cultural, infrastructural and institutional factors that stunt the provision of up-to-date and actionable open-access observations.

“The lag from field to information is not a single technical problem but a systemic one, stretching from how data are gathered to how they are valued. As biodiversity change accelerates, the ability to see the present, not just the past, becomes essential for sound policy and effective management.”

The recommendations provided by the EU projects to address this issue include:

• Reducing institutional bottlenecks: support data pipelines that move smoothly from field collection to open publication, with shared standards and automation where possible;

• Rethinking incentives: reward data publication and timely sharing as legitimate scientific outputs;

• Strengthening identification capacity: invest in AI-assisted identification, reference collections, and expert networks to accelerate taxonomic workflows;

• Encouraging near-real-time data use: develop platforms that deliver provisional but usable data for decision-making, with clear metadata on taxonomic uncertainty.

• Embedding feedback loops: ensure that once information is used, results and corrections flow back to improve data quality over time.

Read the full policy brief to learn what each project’s role is in addressing the data lag.

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B3 - ID No 101059592, BMD - ID No 101181294, OneSTOP - ID No 101180559, MAMBO - ID No 101060639, GUARDEN - ID No 101060693 receive funding from the European Union's Horizon Europe Research and Innovation Programme. The AURORA project is funded by the Flanders Marine Institute (VLIZ). 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.