INFORMATION PRODUCTS

Information Products

One of the central objectives of OBAMA-NEXT is to transform cutting-edge marine biodiversity observations into practical, policy-relevant Information Products (IPs). These products integrate data from multiple sources (see here a detailed list of technologies) into outputs that can directly support biodiversity policy implementation through marine management and conservation decisions.

Co-design process with practitioners

The IPs are co-designed in collaboration with the Practitioners Advisory Board (PAB), which brings together policy makers, managers, and conservation practitioners from across Europe. This collaborative process ensures that each IP responds to practitioners’ needs and aligns with European policy frameworks, such as the Marine Strategy Framework Directive, the EU Biodiversity Strategy for 2030, and the EU Nature Restoration Law.

The long-term purpose

The ultimate goal of OBAMA-NEXT’s IPs is to provide scientifically sound, policy-driven, actionable knowledge that bridges the gap between advanced marine biodiversity monitoring and effective ocean governance.

OBAMA-NEXT IPs

The project will continuously update this section as soon as more IPs are validated. Below please find a list of the ongoing IPs:

Species Distribution Models for Threatened Habitats and Species

High resolution spatial predictions on the occurrence of threatened species.
Technologies: Machine Learning, Statistical Modelling, In situ data collected by divers and drop-video
Relevant to Policies: Habitats Directive, Marine Strategy Framework Directive, European Biodiversity Strategy, Global Biodiversity Framework
TRL level 9

Coastal Vegetation Mapping Using Remote Sensing

High-resolution drone-based mapping of coastal vegetation, including in situ ground truthing, model training, testing and validation.
Technologies: Satellites, Drones, Machine Learning, In situ data collection
Relevant to Policies: Habitats Directive, Marine Strategy Framework Directive, Water Framework Directive, European Biodiversity Strategy, Global Biodiversity Framework, EU Nature Restoration Law
TRL level 6

Habitat Suitability Modelling of Marine Mammals

Maps of habitat suitability and use for these marine mammal species can show how ecosystems respond to environmental change and the effectiveness of conservation actions.
Technologies: Telemetry, Earth Observation data (Copernicus)
Relevant to Policies: Habitats Directive, EU Nature Restoration Law, Maritime Spatial Planning Directive, Helsinki Convention
TRL level 6

To know more about this IP, read the publication by van Beest et al. (2025).

High-Resolution Predictive Maps of Processes Underlying Fish Reproduction

The model integrates different types of fisheries and survey data and returns estimates of how different processes underlying animal’s reproduction cycle are affected by environmental conditions at a local scale, as well as predictive maps of animal and underlying reproduction processes distribution along the area of interest. The model applies to the Baltic whitefish.
Technologies: Statistical Modelling
Relevant to Policies: Conservation and natural resources management
TRL level 5

Detecting and Quantifying Macroplastic on Beaches using Drones and AI

This IP describes how drones equipped with advanced sensors can be used to detect macroplastics on beaches. Artificial Intelligence is used to quantify the amount of litter, for instance before and after a beach clean-up event. The SeaBee drone infrastructure has done proof-of-concept studies on this, which still remains to be included in monitoring.
Technologies: Drones and Artificial Intelligence
Relevant to Policies: Barcelona Convention on Marine Litter
TRL level 6

In situ observation of phytoplankton blooms

This IP describes how high frequency fluorescence observations can be used to detect (harmful) phytoplankton blooms in situ.
Technologies: Single channel fluorometers
Relevant to policies: Marine Strategy Framework Directive, Bucharest Convention, HELCOM, OSPAR
TRL level 9

Environmental DNA run-off bioindicators

Use of microbial indicators of river run-off to provide early warning signals of coastal ecosystem change and guide monitoring and management frameworks within a European context.
Technologies: eDNA, Statistical analyses
Relevant to policies: Marine Strategy Framework Directive, Water Framework Directive, Barcelona Convention, Helsinki Convention, OSPAR Convention
TRL level 3

Environmental DNA from fish to assess estuarine ecological status

Environmental DNA metabarcoding is used to monitor estuarine fish communities for assessing the ecological status of the estuaries, instead of conventional, more invasive fishing methods. Fish is one of the biological elements that need to be monitored and assessed as stipulated by the Water Framework Directive.
Technologies: eDNA metabarcoding
Relevant to policies: Water Framework Directive
TRL level 6

To know more about this IP, read the publication by Bhendarkar et al. (2025).

Copepod mean size and total abundance (CMSTA) ecological indicators

The CMSTA indicator is a two-dimensional indicator that describes the relationship between mean zooplankton (as copepod) size and total densities. It uses a property-property plot to visualize copepod size versus density, allowing other variables to be projected to reveal patterns associated with size and abundance of copepods.
Technologies: Imaging, Machine learning
Relevant to policies: UK Marine Strategy, Marine Strategy Framework Directive
TRL level 6

Phytoplankton types at the deep Chlorophyll-a maximum (DCM)

The DCM is the ocean layer that hosts the highest phytoplankton biomass. This IP maps the DCM and characterizes the dominant phytoplankton types at this layer.
Technologies: Buoys, Glider, Ferrybox, Flow cytometry, R/V cruises, Fluorometers, Chlorophyll lab analysis, Ecosystem modelling.
Relevant to policies: Marine Strategy Framework Directive, Regional Sea Conventions (Barcelona, Bucharest, Helsinki, and OSPAR)
TRL level 6

Trophic efficiency in marine plankton food-webs

This IP focuses on monitoring phytoplankton resource-use efficiency and plankton energy/food web efficiency to evaluate marine ecosystem functioning.
Technologies: Nutrient and chlorophyll lab analysis, Flow cytometry, Image analysis of phytoplankton, R/V cruises, Size-spectra analysis of plankton
Relevant to policies: Marine Strategy Framework Directive, Regional Sea Conventions (Barcelona, Bucharest, Helsinki, and OSPAR)
TRL level 6

eDNA-based fish abundance estimates for fisheries assessment

Having precise estimates of fish abundance and biomass is the first step for ensuring a sustainable exploitation of marine resources. Nowadays, acoustics is the most widely used and relied on method for obtaining those estimations; yet, it has acknowledged biases that can affect its precision. This IP aims to integrate eDNA-based fish abundance estimates into the acoustics-based ones by combining both methodologies for an increased precision of fish biomass estimates.
Technologies: Digital or quantitative PCR
Relevant to policies: Common Fisheries Policy, Marine Strategy Framework Directive
TRL level 5

Read this publication to know more about this IP, read the publication by Claver et al. (2026).

Mediterranean mussel biomass estimation with 3D photogrammetry

The Mediterranean mussel (Mytilus galloprovincialis) is an important habitat-forming species in coastal regions of the Black Sea. However, its abundance is being affected by climate change, some human activities as well as by predation from invasive species. Measuring its biomass is critical for monitoring programmes and for assessing community structure and composition of benthic habitats.
Technologies: Scuba diving, Modelling, Photogrammetry
Relevant to policies: Bucharest Convention, Habitats Directive, Marine Strategy Framework Directive, EU Nature Restoration Regulation
TRL level 5

Large-scale mapping of blue carbon stocks

Use of machine learning to produce spatially explicit maps of organic carbon stocks across European seagrass and saltmarsh ecosystems.
Technologies: Earth Observation data, Machine Learning
Relevant to policies: Habitats Directive, EU Nature Restoration Regulation, European Biodiversity Strategy
TRL level 7

Joint species distribution modelling (HMSC) incorporating co-occurrence patterns and environmental dependencies

Community-level species distribution modelling for hard-bottom benthic communities, using HMSC to jointly predict multiple species and derive habitat/community maps with quantified uncertainty.
Technologies: Modelling
Relevant to policies: Blue Carbon–related initiatives, Convention on Biological Diversity, Habitats Directive, Marine Strategy Framework Directive, Marine Spatial Planning Directive, Marine Protected Areas and Other Effective Conservation Measures planning, Water Framework Directive, Nature Restoration Law
TRL level 8

Species distribution model for eelgrass (Zostera marina)

High-resolution spatial predictions of eelgrass occurrence and depth limits using machine learning and long-term monitoring data. Provides species distribution maps with confidence estimates to support restoration targets and marine spatial planning.
Technologies: Machine Learning, In situ monitoring (Divers /AUVs)
Relevant to policies: EU Nature Restoration Law, Habitats Directive, Marine Spatial Planning Directive, Marine Strategy Framework Directive, Water Framework Directive
TRL level 6

Data – and criteria-based identification of high-definition habitat types

This IP refines the spatial classification of multiple marine and coastal habitat types by combining geospatial data, geomorphometric analysis, remote sensing imagery, and model outputs. This IP enhances the accuracy and interpretability of habitat maps essential for biodiversity assessment and policy implementation.
Technologies: Remote sensing, Geoinformation science
Relevant to policies: EU Biodiversity Strategy 2030, Habitats Directive, Marine Strategy Framework Directive, Nature Restoration Regulation, Regional Seas Conventions (HELCOM, OSPAR), Water Framework Directive
TRL level 6

Estimating sea mammal populations using drone imagery and AI-Driven object recognition

Drones equipped with RGB sensors capture images of seal haul-out sites, which are then analysed using object detection techniques to estimate seal population sizes.
Technologies: Drones, Machine learning
Relevant to policies: Habitats Directive, Marine Strategy Framework Directive, Nature Restoration Regulation, Barcelona Convention, OSPAR Convention
TRL level 7

Mapping kelp forest regrowth using drones, after sea urchin removal restoration

Drones equipped with RGB (for visual inspection and annotation) and multispectral (MSI, for machine learning) sensors are used to monitor kelp forest recovery before and after restoration actions, such as removing sea urchins.
Technologies: Drones, ROB, Machine learning
Relevant to policies: Habitats Directive, Marine Strategy Framework Directive, Nature Restoration Regulation, Regional Sea Conventions (Barcelona, Bucharest, Helsinki, OSPAR), Water Framework Directive
TRL level 5

Integrating eDNA in marine ecosystems management: A European consensus vision

European experts involved in four EU Horizon projects (OBAMA-NEXT, BIOcean5D, MARCO-BOLO, MARBEFES) have collaborated to provide for the first time a shared European vision of how eDNA approaches can support stakeholders’ needs in the management of European marine ecosystems. Unlike previous reviews, which have focused on specific regions, taxa, or case studies, our work synthesizes the collective experience and perspectives of stakeholders and scientists across Europe. We provide a structured roadmap and practical recommendations for integrating eDNA into marine policy, emphasizing standardization, stakeholder engagement, and the operationalization of eDNA methods at a continental scale.
Technologies: eDNA metabarcoding
Relevant to policies: Animal Health Law, Bathing Water Directive (2006/7/EC), Blue carbon policy instruments relating to development of algal sector, Common Fisheries Policy, Convention for the Control and Management of Ships’ Ballast Water and Sediments, CZM: 2002 Recommendations on implementing Integrated Coastal Zone Management, Farm to Fork Strategy, IAS regulation, Integrated Maritime Policy, Invasive species regulation, Marine Strategy Framework Directive, Maritime Spatial Planning Directive, Nature Directive, Ocean Pact, Regional Sea Conventions (Barcelona, Bucharest, Helsinki, and OSPAR), Towards a Strong and Sustainable EU Algae Sector, Water Framework Directive

Mapping of biomass and carbon content of coastal vegetation using green lidar on drones

Canopy height and 3D models of submerged vegetation in the coastal zone can be estimated using airborne LiDAR technology. When combined with on-ground biomass and carbon stock measurements, biomass and carbon content can be estimated on local and regional scales. Leveraging 2D mapping to 3D habitat models improves detail level and offers insight on biodiversity and other ecosystem services.
Technologies: Drones, LiDAR (laser) scanning, Machine learning
Relevant to policies: Blue carbon policies, Marine Strategy Framework Directive
TRL level 6

Species distribution modelling with expert elicitation and Bayesian calibration

This information product presents a method to integrate traditional in situ observations and expert drawn maps on species occurrence probabilities to cost-efficiently improve the mapping of species abundance distributions.
Technologies: Species distribution models
Relevant to policies: Common Fisheries Policy, HELCOM, EU Biodiversity Strategy for 2030, Marine Strategy Framework Directive, Nature Restoration Regulation, Water Framework Directive
TRL level 6

Quantification of habitat carbon sequestration

Mapping ecosystem services (carbon sequestration) by combining remote sensing habitat type mapping with in situ measurements (eDNA, Particulate Organic Carbon) to assess the ecosystem carbon sequestration potential and capability
Technologies: Drones, eDNA, Machine learning, Carbon analysis
Relevant to policies: Marine Strategy Framework Directive
TRL level 7

Maps of regulating ecosystem services

This information product integrates large-scale biodiversity observations with machine learning models and species trait data, and quantifies regulating ecosystem services contributed by benthic species. Additionally, we used spatial prioritization to identify key ecosystem service areas for increasing their protection.
Technologies: In situ sampling
Relevant to policies: Kunming–Montreal Global Biodiversity Framework, Marine Spatial Planning directive, HELCOM
TRL level 7

Grant Agreement 101081642 – OBAMA-NEXT

OBAMA-NEXT project has been approved under HORIZON-CL6-2022-BIODIV-01-01: Observing and mapping biodiversity and ecosystems, with particular focus on coastal and marine ecosystems.Funded by the European Union. Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union or UK Research and Innovation. Neither the European Union nor the granting authority can be held responsible for them.