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Living on the edge: assessing vulnerability of the great crested newt at its southern range margin
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Assessing the value of underwater camera trap for amphibian monitoring
Understanding how animal populations and ecological communities change over time is essential for tracking biodiversityloss and guiding environmental policy. Global assessments – such as those conducted by the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) – depend on reliable indicators built from local, field-based observations.
Collecting such data requires robust and efficient monitoring methods. Amphibians are a particularly important group in this context: they are highly sensitive to environmental change and are experiencing rapid declines worldwide. As a result, they are widely used as indicators of ecosystem health. However, existing survey methods remain limited, often relying on labour-intensive fieldwork and techniques that can disturb animals and their habitats.
To address these challenges, a previous Proof-of-Concept project (Grant reference: 12250595) funded by the Luxembourg National Research Fund (FNR) led to the development of NEWTRAP, an innovative underwater camera trap designed to automatically capture images of aquatic wildlife, particularly newts. Alongside this device, the NEWTRAP Manager web application was created to facilitate the storage and management of images and associated data. Artificial intelligence (AI) methods were also introduced to automate image analysis and interpretation.
CAMPHIBIAN is funded by the Luxembourg National Research Fund (FNR) through the BRIDGES programme (PIs: Xavier Mestdagh and Nicolas Titeux – Grant reference: 16747721). This project builds on the NEWTRAP foundation and further develop it into a versatile, user-friendly tool for freshwater wildlife monitoring – now called NEWCAM.
The system will be enhanced technologically to improve its performance, adaptability, and ease of use in a variety of field conditions. At the same time, its scientific value will be rigorously evaluated through a combination of laboratory and field experiments.
These tests will assess whether NEWCAM can provide a reliable and robust method for detecting amphibian species that are typically difficult to monitor, as well as for documenting their population size and dynamics over time.
The project brings together expertise from LIST and NHBS, with the goal of advancing NEWTCAM closer to a fully operational monitoring solution ready for broader use.
By enabling automated, non-invasive monitoring of amphibians, the project delivers important benefits for science, the economy, and society.
Scientifically, NEWTCAM supports continuous, standardized long-term monitoring and provides detailed data on abundance, behaviour, sex, life stage, and even individual identity. This strengthens ecological models, improves early detection of population declines, and enhances understanding of climate and habitat pressures.
Economically, its autonomous design reduces fieldwork and staffing needs, lowering the cost of long-term biodiversity monitoring. It also supports regulatory compliance through robust datasets and fosters innovation at the interface of ecology, engineering, and artificial intelligence.
For society, the system enables earlier and more effective conservation action while remaining fully non-invasive and ethically aligned. By supporting evidence-based decision-making, it contributes to the protection of freshwater ecosystems and the services they provide.
A new underwater camera trap for freshwater wildlife monitoring
L'Hoste L., Mestdagh X., Besnard A., Didry Y., Foucteau M., Gama A., Haas R., Minette F., Priol P., Schwartz R., Titeux N.
Methods in Ecology and Evolution, vol. 16, n° 8, pp. 1625-1635, 2025
Newtrap: Improving biodiversity surveys by enhanced handling of visual observations
Y. Didry, X. Mestdagh, and T. Tamisier
<p>in International Conference on Cooperative Design, Visualization and Engineering (CDVE 2019), pp. 277-281, 2019</p>
Automatic Picture-Matching of Crested Newts
Magnette G., Didry Y., Mestdagh X.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12983 LNCS, pp. 329-334, 2021
