Th.--Lyon 1. / Bibliogr. p. 160-168. Index. Résumé en anglais et en français.

In order to improve our knowledge of the mechanisms of ageing in animals, the main objective of the thesis was to understand the modulation of such mechanisms by the individual social role, within different social organisations. This objective thus addresses two main questions: i) describing the covariation of the degree of social complexity with ageing patterns; ii) highlighting the underlying cellular and molecular processes. Thanks to complementary and diversified studies (behavioural observations, dosage of the oxidative balance, qPCR measurement of telomere length, proteomics, metabolomics), the present thesis showed that sociality plays a role on ageing at many levels. In the zebra finch, social stress caused by aggression of the conspecifics induces oxidative stress and reduces telomere length in adults. In the sociable weaver, the social environment is of crucial importance during pre- and post-hatch development on the medium term survival of the chicks. Finally, in ants, we were able to show a positive relationship between the degree of sociality and maximum potential life span: this link was caste specific, being only significant for the most social queens. This is inline with a recent review by Lucas and Keller (2020) which concluded that the benefits of sociality are most sensitive for high levels of sociality and particularly in reproductive individuals. With regard to the molecular mechanisms of ageing,we were able to establish a causal chain between social stress, oxidative response and telomere erosion in zebra finches.The role of telomeres as a predictor of offspring survival has been confirmed (over at least 5 years) in the sociable weaver,a cooperative breeder bird. However, this link was not true in queen ants where the longest lived were those with the shortest telomeres. The co-evolution of anti-cancer mechanisms and longevity seems to be conserved since similar strategies are found in taxa as diverse as ants and rodents. On the other hand, and contrary to previous studies conducted on ants, we found that oxidative stress might be a marker of individual ageing. We suggest that the proxies of oxidative stress used so far in ants have been misleading or at least incomplete. Thus, understanding the physiological ageing particularities of ants and other social insects might require finding new relevant and specific markers. Finally, the sirtuins and mTOR signalling pathways, key precursors of which we have detected in ants, are molecular crossroads capable of activating or inhibiting cellular metabolism depending on the cell energy state. According to the studies carried out to date, these signalling pathways are among the first to be able to slow down the effects of ageing and extend life expectancy.However, specific studies need to be carried out to understand their fine regulation and thus assess the universality of these mechanisms in animal ageing. Based on our findings, we propose three points to be further addressed to better understand the mechanisms of ageing in social insects: i) the setup of experiments testing the effectiveness of energy trade-offs involving immunity or digestion metabolism; ii) measuring the telomerase activity among castes of various species in order to explore the telomere and telomere independent roles played by this enzyme in ageing; iii) the need to think about individual longitudinal follow-up and to study wild populations, after the first necessary stages in laboratory. / Doctorat en Sciences / Un résumé grand public en français est disponible au début du manuscrit, juste après les remerciements. / info:eu-repo/semantics/nonPublished

Les réseaux neuronaux profonds sont des outils prometteurs pour l'évaluation de la biodiversité aviaire, en particulier pour la détection des chants et la classification acoustique des espèces. Toutefois, on connaît mal l’étendue de leur capacité de généralisation face à la variation intraspécifique présente dans les chants d’oiseaux, ce qui pourrait mener à des biais. Notre étude porte sur l'évaluation des performances de BirdNET, un réseau neuronal profond, pour le traitement d’un corpus d'enregistrements audio caractérisés par une variation intraspécifique significative, en utilisant l’exemple du chant du bruant des prés (Passerculus sandwichensis). Dans la population de l'île de Kent, au Nouveau-Brunswick, les individus sont suivis et enregistrés grâce à leurs bagues de couleur et la présence de microdialectes est solidement documentée. Nous avons recueilli et annoté 69 606 chants provenant de 52 individus et analysé ces données à l'aide d’une version récente de BirdNET. Nos résultats révèlent que BirdNET démontre une précision globale suffisante, prédisant correctement 81,9 % des chants, ce qui dépasse les résultats rapportés par ses développeurs. Toutefois, nous avons observé une variation considérable dans les scores de confiance et les taux de prédiction exactes entre les individus, ce qui suggère des biais potentiels. Cependant, nos recherches n'ont pas mis en évidence de variation entre les résultats des différents microdialectes, ce qui souligne la relative robustesse de l'algorithme. Nous avançons que la variation observée entre les individus est due au fait que certains d’entre eux chantent systématiquement plus près des microphones, résultant en des chants plus clairs donc plus faciles à identifier. Pour mieux comprendre le processus de prise de décision de BirdNET, nous avons tenté de produire des cartes d'activation de classe, qui constituent un outil précieux pour identifier les éléments d’un chant qui déterminent une prédiction. Cependant, il ne nous a pas été possible d’obtenir des cartes d’activation de classe d’après la version actuellement disponible du code de BirdNET sans avoir recours à des connaissances avancées en informatique. L'accès à des outils explicatifs adaptés aux innovations récentes dans les architectures de réseaux neuronaux 4 profonds serait crucial pour mieux interpréter les résultats et renforcer la confiance des utilisateurs. Nos résultats soulignent la nécessité de poursuivre les recherches sur la capacité de généralisation des réseaux neuronaux profonds pour la bioacoustique en utilisant des ensembles de données monospécifiques portant sur de plus longues périodes ou des aires de répartition géographique plus vastes. En outre, l'extension de cette étude à des espèces ayant des répertoires plus importants ou des différences plus subtiles entre le chant des individus pourrait nous informer davantage sur les limites et le potentiel des algorithmes d'apprentissage profond pour la détection et la classification acoustiques des espèces. En conclusion, notre étude démontre les performances prometteuses de BirdNET pour le traitement d'un large corpus de chants de bruants des prés, et confirme son potentiel en tant qu'outil précieux pour l'évaluation de la biodiversité aviaire. Les biais dus aux techniques d’enregistrement et la variation dans les taux de succès observés entre les individus méritent d'être étudiés plus en détail. / Machine learning, particularly deep neural networks, has gained prominence as a valuable tool in ecological studies and wildlife conservation planning. In the field of avian biodiversity assessment, deep neural networks have shown remarkable promise, particularly in acoustic species detection and classification. Despite their success, a critical knowledge gap exists concerning the generalization ability of these algorithms across intraspecific variation in bird song. This raises concerns about potential biases and misinterpretation of results. This study focuses on evaluating the performance of BirdNET, a deep neural network, in processing audio recordings characterized by significant intraspecific variation in the Savannah Sparrow (Passerculus sandwichensis) song. Savannah Sparrows are an ideal candidate for this investigation, given their well-studied population on Kent Island, New Brunswick, Canada. Each male sings a unique, unchanging song throughout its life, and the population exhibits well-documented geographical microdialects. We collected a large corpus of Savannah Sparrow songs using autonomous and focal recorders on Kent Island, yielding a total of 69,606 manually annotated songs from 52 different sparrows. We analyzed the audio data using BirdNET-Analyzer. The resulting confidence scores were used to assess the algorithm's performance across microdialects and individual birds. Our results revealed that BirdNET exhibited considerable overall accuracy, correctly predicting 81.9% of the songs, which surpassed the results reported by the developers of BirdNET. We observed variations in BirdNET's confidence scores among individual birds, suggesting potential biases in its classifications. However, our investigation indicated no evidence of distinct biases towards specific microdialects, highlighting the algorithm's relative robustness across these groups. We suspect that the variation observed amongst individuals is caused by the fact that some were singing consistently closer to microphones, yielding clearer songs. To gain insights into BirdNET's decision-making process, we sought to employ class activation maps, a valuable tool for identifying essential song elements contributing to species predictions. However, we were unable to produce class activation maps from the current version of BirdNET 6 without advanced computer science skills. Access to informative tools adapted to recent innovations in deep neural network architectures for bioacoustic applications is crucial for understanding and interpreting results better. Such tools would enhance user confidence and favour accountability for conservation decisions based on these predictions. Our findings underscore the need for further research investigating the generalization capacity of deep neural networks in bioacoustics on single-species datasets with more extensive intraspecific variation and broader geographical ranges. Additionally, expanding this investigation to species with larger song repertoires or more subtle inter-individual song differences could provide valuable insights into the limits and potential of deep learning algorithms for acoustic species detection and classification. In conclusion, our study demonstrates BirdNET's promising performance in processing a large corpus of Savannah Sparrow songs, highlighting its potential as a valuable tool for avian biodiversity assessment. Biases and variations in confidence scores observed across individual birds warrant further investigation.