Biology subjects

Courtecuisse, J.

Publications and source records attributed to Courtecuisse, J..

3 recordsLinked to original sources

bioRxiv2025

Deep learning-based photo-identification for non-invasive monitoring of animal populations: Application to penguins and tortoises

We describe DL-ID, a deep learning-based framework for the individual identification of animals from images with higher accuracy and characteristics that make it more robust in challenging conditions, such as field studies, than widely used algorithms. This is demonstrated through testing with small datasets involving two species that exhibit distinctive morphological features: Humboldt penguins (Spheniscus humboldti Meyen, 1834) and Hermanns tortoises (Testudo hermanni Gmelin, 1789). We define a confidence index based on entropy within the model that significantly improves handling of the open set recognition problem, offering a new way to discriminate unknown individuals. DL-IDs accuracy of 87%, even with as few as four images per class, contrasts with the usual practice in deep learning of building large datasets to assess performance. The model outperforms traditional photo-identification methods like Wild-ID and I3S Pattern, offering a significant advancement for research and conservation efforts. Its efficiency and adaptability indicate its potential in real-time monitoring, opening new possibilities for wildlife conservation. Instead of designing a complex new deep learning model, we focused on adapting existing methods to address a relevant ecological problem. Our approach effectively tackles issues like limited training data and recognizing new individuals in field studies. Notably, it performs well even with small datasets, making it particularly useful for data-limited ecological research. This makes our approach a valuable step forward in applying deep learning to ecological studies.

ecology↗

bioRxiv2024

Declining juvenile survival of Adelie penguins in Antarctica

As summer sea ice around Antarctica reaches modern lows, quantifying the demographic response of polar species to such environmental changes becomes critical. To achieve this, synthesizing results across species ranges and elucidating the environmental factors driving population dynamics are key. Adelie penguins are considered reliable indicators of changes in Antarctica but the processes through which sea ice and other environmental factors shape their population dynamics are still unclear, especially for critical age groups such as juveniles. Using a 17-year dataset of Adelie penguins electronically tagged in Adelie Land (Antarctica), we found that juvenile survival probability was most impacted by sea ice concentration near their natal colony right after fledging, with lower ice concentrations detrimental to survival. Importantly, we found that juvenile survival declined by 32% from 2007 to 2020, mirroring trends at other distant colonies. The emergence of similar patterns at opposite ends of the continent may be an early signal for shifts in population trends expected from climate change.

ecology↗

bioRxiv2023

RFIDeep: Unfolding the Potential of Deep Learning forRadio-Frequency Identification

O_LIAutomatic monitoring of wildlife is becoming a critical tool in the field of ecology. In particular, Radio-Frequency IDentification (RFID) is now a widespread technology to assess the phenology, breeding, and survival of many species. While RFID produces massive datasets, no established fast and accurate methods are yet available for this type of data processing. Deep learning approaches have been used to overcome similar problems in other scientific fields and hence might hold the potential to overcome these analytical challenges and unlock the full potential of RFID studies. C_LIO_LIWe present a deep learning workflow, coined "RFIDeep", to derive ecological features, such as breeding status and outcome, from RFID mark-recapture data. To demonstrate the performance of RFIDeep with complex datasets, we used a long-term automatic monitoring of a long-lived seabird that breeds in densely packed colonies, hence with many daily entries and exits. C_LIO_LITo determine individual breeding status and phenology and for each breeding season, we first developed a one-dimensional convolution neural network (1D-CNN) architecture. Second, to account for variance in breeding phenology and technical limitations of field data acquisition, we built a new data augmentation step mimicking a shift in breeding dates and missing RFID detections, a common issue with RFIDs. Third, to identify the segments of the breeding activity used during classification, we also included a visualisation tool, which allows users to understand what is usually considered a "black box" step of deep learning. With these three steps, we achieved a high accuracy for all breeding parameters: breeding status accuracy = 96.3%; phenological accuracy = 86.9%; breeding success accuracy = 97.3%. C_LIO_LIRFIDeep has unfolded the potential of artificial intelligence for tracking changes in animal populations, multiplying the benefit of automated mark-recapture monitoring of undisturbed wildlife populations. RFIDeep is an open source code to facilitate the use, adaptation, or enhancement of RFID data in a wide variety of species. In addition to a tremendous time saving for analyzing these large datasets, our study shows the capacities of CNN models to autonomously detect ecologically meaningful patterns in data through visualisation techniques, which are seldom used in ecology. C_LI

ecology↗