Modern Approaches to the Monitoring of BiOdiversity

Food, Bioeconomy & Natural ResourcesHORIZON-RIAID: 101060639
EC Contribution
€37,038
Consortium Size
10 orgs
Start Year
2022
Summary

EU policies, such as the EU biodiversity strategy 2030 and the Birds and Habitats Directives, demand unbiased, integrated and regularly updated biodiversity and ecosystem service data. However, efforts to monitor wildlife and other species groups are spatially and temporally fragmented, taxonomically biased, and lack integration in Europe. To bridge this gap, the MAMBO project will develop, test and implement enabling tools for monitoring conservation status and ecological requirements of species and habitats for which knowledge gaps still exist. MAMBO brings together the technical expertise of computer science, remote sensing, social science expertise on human-technology interactions, environmental economy, and citizen science, with the biological expertise on species, ecology, and conservation biology. MAMBO is built around stakeholder engagement and knowledge exchange (WP1) and the integration of new technology with existing research infrastructures (WP2). MAMBO will develop, test, and demonstrate new tools for monitoring species (WP3) and habitats (WP4) in a co-design process to create novel standards for species and habitat monitoring across the EU and beyond. MAMBO will work with stakeholders to identify user and policy needs for biodiversity monitoring and investigate the requirements for setting up a virtual lab to automate workflow deployment and efficient computing of the vast data streams (from on the ground sensors, and remote sensing) required to improve monitoring activities across Europe (WP4). Together with stakeholders, MAMBO will assess these new tools at demonstration sites distributed across Europe (WP5) to identify bottlenecks, analyze the cost-effectiveness of different tools, integrate data streams and upscale results (WP6). This will feed into the co-design of future, improved and more cost-effective monitoring schemes for species and habitats using novel technologies (WP7), and thus lead to a better management of protected sites and species.

Consortium (10)

Project Results (47)

Source: CORDIS, the EU research results database.

Publications (25)
A General Method for Detection and Segmentation of Terrestrial Arthropods in Images
· 2025DOI
Asger Svenning, Guillaume Mougeot, Jamie Alison, Daphne Chevalier, Nisa Luise Chavez Molina, Song-Quan Ong, Kim Bjerge, Juli Carrillo, Toke Thomas Hoeye, Quentin Geissmann
A workflow for extracting ungulate trails in wetlands using 3D point clouds obtained from airborne laser scanning
Frontiers in Remote Sensing· 2025DOI
Jinhu Wang, Perry Cornelissen, W. Daniel Kissling
Adapting a global plant identification model to detect invasive alien plant species in high-resolution road side images
Ecological Informatics· 2025DOI
Vincent Espitalier, Jean-Christophe Lombardo, Hervé Goëau, Christophe Botella, Toke Thomas Høye, Mads Dyrmann, Pierre Bonnet, Alexis Joly
Clustering and novel class recognition: evaluating bioacoustic deep learning feature extractors
Emerging Bioacousticians Days· 2025DOI
Vincent S. Kather, Burooj Ghani, Dan Stowell
Data from three camera trapping pilots in the Amsterdam Water Supply Dunes of the Netherlands
Data in Brief· 2025DOI
Julian C. Evans, Rotem Zilber, W. Daniel Kissling
InsectSet459: an open dataset of insect sounds for bioacoustic machine learning
arXiv· 2025DOI
Marius Faiß, Burooj Ghani, Dan Stowell
Performance of Computer Vision Algorithms for Fine‐Grained Classification Using Crowdsourced Insect Images
IET Computer Vision· 2025DOI
Rita Pucci, Vincent J. Kalkman, Dan Stowell
Towards edge processing of images from insect camera traps
Remote Sensing in Ecology and Conservation· 2025DOI
Kim Bjerge, Henrik Karstoft, Toke T. Høye
A deep learning pipeline for time-lapse camera monitoring of insects and their floral environments
Ecological Informatics· 2024DOI
Kim Bjerge, Henrik Karstoft, Hjalte M.R. Mann, Toke T. Høye
A predictive approach to determining the joint conservation status of species | Une approche prédictive de la détermination du statut de conservation conjoint des espèces
HAL· 2024
Joaquim Estopinan
AI Species Identification Using Image and Sound Recognition for Citizen Science, Collection Management and Biomonitoring: From Training Pipeline to Large-Scale Models
Biodiversity Information Science and Standards· 2024DOI
Laurens Hogeweg, Ni Yan, Django Brunink, Khadija Ezzaki-Chokri, Wilfred Gerritsen, Rita Pucci, Burooj Ghani, Dan Stowell, Vincent Kalkman
GeoPlant: Spatial Plant Species Prediction Dataset
· 2024DOI
Lukas Picek, Christophe Botella, Maximilien Servajean, César Leblanc, Rémi Palard, Théo Larcher, Benjamin Deneu, Diego Marcos, Pierre Bonnet, Alexis Joly
Insect Identification in the Wild: The AMI Dataset
Lecture Notes in Computer Science, Computer Vision – ECCV 2024· 2024DOI
Aditya Jain, Fagner Cunha, Michael James Bunsen, Juan Sebastián Cañas, Léonard Pasi, Nathan Pinoy, Flemming Helsing, JoAnne Russo, Marc Botham, Michael Sabourin, Jonathan Fréchette, Alexandre Anctil, Yacksecari Lopez, Eduardo Navarro, Filonila Perez Pimentel, Ana Cecilia Zamora, José Alejandro Ramirez Silva, Jonathan Gagnon, Tom August, Kim Bjerge, Alba Gomez Segura, Marc Bélisle, Yves Basset, Kent P. McFarland, David Roy, Toke Thomas Høye, Maxim Larrivée, David Rolnick
MALPOLON: A Framework for Deep Species Distribution Modeling
Machine Vision for Earth Observation· 2024DOI
Larcher, T., Picek, L., Deneu, B., Lorieul, T., Servajean, M., & Joly, A.
Modelling Species Distributions with Deep Learning to Predict Plant Extinction Risk and Assess Climate Change Impacts
· 2024DOI
Joaquim Estopinan, Pierre Bonnet, Maximilien Servajean, François Munoz, Alexis Joly
Accurate detection and identification of insects from camera trap images with deep learning
PLOS Sustainability and Transformation· 2023DOI
Kim Bjerge; Jamie Alison; Mads Dyrmann; Carsten Eie Frigaard; Hjalte M. R. Mann; Toke Thomas Høye
Comparison between transformers and convolutional models for fine-grained classification of insects
Workshop Camera Traps, AI and Ecology· 2023DOI
Rita Pucci, Vincent J. Kalkman, Dan Stowell
Diversity and Distributions
Diversity and Distributions· 2023DOI
W. Daniel Kissling, Yifang Shi
Hierarchical classification of insects with multitask learning and anomaly detection
Ecological Informatics· 2023DOI
Bjerge, Kim; Geissmann, Quentin; Alison, Jamie; Mann, Hjalte M.R.; Høye, Toke Thomas; Dyrmann, Mads; Karstoft, Henrik
Lecture Notes in Computer Science
ECIR 2023 LNCS proceedings· 2023DOI
Alexis Joly, Hervé Goëau, Stefan Kahl, Lukáš Picek, Christophe Botella, Diego Marcos, Milan Šulc, Marek Hrúz, Titouan Lorieul, Sara Si Moussi, Maximilien Servajean, Benjamin Kellenberger, Elijah Cole, Andrew Durso, Hervé Glotin, Robert Planqué, Willem-Pie
Lecture Notes in Computer Science
Experimental IR Meets Multilinguality, Multimodality, and Interaction· 2023DOI
Alexis Joly, Hervé Goëau, Stefan Kahl, Lukáš Picek, Christophe Botella, Diego Marcos, Milan Šulc, Marek Hrúz, Titouan Lorieul, Sara Si Moussi, Maximilien Servajean, Benjamin Kellenberger, Elijah Cole, Andrew Durso, Hervé Glotin, Robert Planqué, Willem-Pie
Modern Approaches to the Monitoring of Biоdiversity (MAMBO)
Research Ideas and Outcomes· 2023DOI
Høye TT, August T, Balzan MV, Biesmeijer K, Bonnet P, Breeze TD, Dominik C, Gerard F, Joly A, Kalkman V, Kissling WD, Metodiev T, Moeslund J, Potts S, Roy DB, Schweiger O, Senapathi D, Settele J, Stoev P, Stowell D
Synergizing Digital, Biological, and Participatory Sciences for Global Plant Species Identification: Enabling access to a worldwide identification service
Biodiversity Information Science and Standards· 2023DOI
Pierre Bonnet , Antoine A.A. Affouard, Jean-Christophe J.-C. Lombardo , Mathias Chouet , Hugo Gresse , Vanessa Hequet , Remi Palard , Maxime Fromholtz , Vincent Espitalier , Hervé H.G. Goëau , Benjamin Deneu , Christophe Botella , Joaquim Estopinan , Césa
The GeoLifeCLEF 2023 Dataset to evaluate plant species distribution models at high spatial resolution across Europe
ArXiv preprint - Populations and Evolution· 2023DOI
Christophe Botella, Benjamin Deneu, Diego Marcos, Maximilien Servajean, Joaquim Estopinan, et al..
Laserfarm – A high-throughput workflow for generating geospatial data products of ecosystem structure from airborne laser scanning point clouds
Ecological Informatics· 2022DOI
Kissling, W.D.; Shi, Y.; Koma, Z.; Meijer, C.; Ku, O.; Nattino, F.; Seijmonsbergen, A.C.; Grootes, M.W.
Deliverables (21)
Documents, reports
Documents, reports
Other Results (1)
Periodic Reporting for period 2 - MAMBO (Modern Approaches to the Monitoring of BiOdiversity)