Analyses environnementales spatiales à l'aide d'approches fonctionnelles : application aux données acoustiques multifréquentielles halieutiques

In the field of functional statistics applied to the environment, the focus of this research lies in the analysis of data presented in functional form. Functional statistics explores a sector of statistics dedicated to handling functional data, providing methods for dimension reduction, supervised and unsupervised learning, while taking into account the temporal and/or spatial dependencies inherent in such data. The rise of modern technologies has made these types of data increasingly accessible, especially in fields such as environmental sciences. A concrete example of the application of functional statistics lies in fisheries acoustics techniques, which enable the collection of spatial and temporal samples of marine organisms at different depths and spatial scales, without requiring intrusion. Within this research, a set of multi-frequency acoustic data, extracted by scientific fishery echosounders, has been meticulously analyzed to explore the spatial structure of aggregations of micronektonic marine organisms, commonly referred to as "Sound Scattering Layers" (SSL). The examination of the characteristics of these complex biological entities, such as their thickness, relative density, and depth, was conducted in correlation with their pelagic environment. The detailed representation of this environment was made possible through the use of a multiparametric system towed behind the vessel (ScanFish). In this approach, we initiated the analysis using standard multivariate statistical methods, and then exploited techniques of functional data analysis, with or without consideration of spatial dimension. In our initial exploratory analysis, multivariate Functional Principal Component Analysis provided precise information about parameter variation along depths, unlike classical Principal Component Analysis. In the context of regression tasks, our analyses, whether integrating spatial dimension or not, revealed interactions between SSL characteristics and key environmental variables on a spatial scale. Significant geographical distinctions were observed among SSLs in our dataset, i.e., between northern and southern zones, as well as between coastal and offshore zones during this study. These conclusions remain relevant in the second part of the thesis, where a recent supervised learning method is used, leveraging the concept of signatures extracted from environmental data. The final, more methodological contribution introduces a new approach to principal component analysis for multivariate spatial functional data. Compared to multivariate functional principal component analysis, this methodology is suited for exploring and reducing the dimensionality of dependent spatial data. Thethesis findings highlight the crucial importance of spatial-functional statistical analysis in ecological research involving spatially complex entities. These results underscore the added value of considering spatial dimension in the analysis of these complex biological phenomena. Beyond our specific case study, the application of functional data analysis opens promising avenues for a wide range of ecological studies involving massive spatial dataAu sein du domaine de la statistique fonctionnelle appliquée à l’environnement, l’accent de cette recherche est mis sur l’analyse de données présentées sous forme fonctionnelle. La statistique fonctionnelle explore un secteur de la statistique dédié à la manipulation de données fonctionnelles, fournissant des méthodes pour la réduction de dimension, l’apprentissage supervisé et non supervisé, tout en prenant en compte les dépendances temporelles et/ou spatiales inhérentes à ces données. L’essor des technologies modernes a rendu ces types de données de plus en plus accessibles, en particulier dans des domaines tels que les sciences de l’environnement. Un exemple concret d’application de la statistique fonctionnelle réside dans les techniques d’acoustique des pêches, qui permettent d’obtenir des échantillons spatiaux et temporels d’organismes marins à différentes profondeurs et échelles spatiales, sans nécessiter d’intrusion.Au sein de cette recherche, un ensemble de données acoustiques multi-fréquences, extraites par des échosondeurs scientifiques halieutiques, a été minutieusement analysé pour explorer la structure spatiale des agrégations d’organismes marins micronectoniques, communément désignées sous le terme de couches diffusantes et en anglais de "Sound Scattering Layers" (SSL). L’examen des caractéristiques de ces entités biologiques complexes, telles que leur épaisseur, leur densité relative, et leur profondeur, a été réalisé en corrélation avec leur environnement pélagique. La représentation fine de cet environnement a été rendue possible grâce à l’utilisation d’un système multiparamétrique tracté derrière le navire (ScanFish). Dans cette démarche, nous avons initié l’analyse en recourant à des méthodes standards de statistique multivariée, pour ensuite exploiter des techniques de l’analyse de données fonctionnelles, avec ou sans prise en compte de la dimension spatiale.Dans notre première analyse exploratoire, l’Analyse en Composantes Principales Fonctionnelle multivariée a fourni des informations précises sur la variation des paramètres le long des profondeurs, contrairement à l’Analyse en Composantes Principales classique. Dans le cadre des tâches de régression, nos analyses, qu’elles intègrent ou non la dimension spatiale, ont mis en évidence des interactions entre les caractéristiques des SSL et les variables environnementales clés à l’échelle spatiale. Des distinctions géographiques significatives ont été constatées entre les SSL de notre jeu de données, i.e. entre les zones septentrionales et méridionales, ainsi qu’entre ceux des zones côtières et hauturières au cours de cette étude. Ces conclusions demeurent pertinentes dans la seconde partie de la thèse, où une méthode d’apprentissage supervisé récente est employée, exploitant la notion de signatures extraites des données environnementales. La dernière contribution, plutôt méthodologique, introduit une nouvelle approche d’analyse en composante principale pour les données fonctionnelles multivariées spatiales. Comparée à l’analyse en composante principale fonctionnelle multivariée classique, cette méthodologie est adaptée à l’exploration et à la réduction de la dimension des données spatiales dépendantes.Les conclusions de la thèse mettent en lumière l’importance cruciale de l’analyse statistique spatiale-fonctionnelle dans les recherches écologiques portant sur des entités spatialement complexes. Ces résultats mettent en évidence la valeur ajoutée de la prise en compte de la dimension spatiale dans l’analyse de ces phénomènes biologiques complexes. Au-delà de notre étude de cas spécifique, l’application de l’analyse de données fonctionnelles ouvre des perspectives prometteuses pour un large éventail d’études écologiques impliquant des données spatiales massive

Analyses environnementales spatiales à l'aide d'approches fonctionnelles : application aux données acoustiques multifréquentielles halieutiques

In the field of functional statistics applied to the environment, the focus of this research lies in the analysis of data presented in functional form. Functional statistics explores a sector of statistics dedicated to handling functional data, providing methods for dimension reduction, supervised and unsupervised learning, while taking into account the temporal and/or spatial dependencies inherent in such data. The rise of modern technologies has made these types of data increasingly accessible, especially in fields such as environmental sciences. A concrete example of the application of functional statistics lies in fisheries acoustics techniques, which enable the collection of spatial and temporal samples of marine organisms at different depths and spatial scales, without requiring intrusion. Within this research, a set of multi-frequency acoustic data, extracted by scientific fishery echosounders, has been meticulously analyzed to explore the spatial structure of aggregations of micronektonic marine organisms, commonly referred to as "Sound Scattering Layers" (SSL). The examination of the characteristics of these complex biological entities, such as their thickness, relative density, and depth, was conducted in correlation with their pelagic environment. The detailed representation of this environment was made possible through the use of a multiparametric system towed behind the vessel (ScanFish). In this approach, we initiated the analysis using standard multivariate statistical methods, and then exploited techniques of functional data analysis, with or without consideration of spatial dimension. In our initial exploratory analysis, multivariate Functional Principal Component Analysis provided precise information about parameter variation along depths, unlike classical Principal Component Analysis. In the context of regression tasks, our analyses, whether integrating spatial dimension or not, revealed interactions between SSL characteristics and key environmental variables on a spatial scale. Significant geographical distinctions were observed among SSLs in our dataset, i.e., between northern and southern zones, as well as between coastal and offshore zones during this study. These conclusions remain relevant in the second part of the thesis, where a recent supervised learning method is used, leveraging the concept of signatures extracted from environmental data. The final, more methodological contribution introduces a new approach to principal component analysis for multivariate spatial functional data. Compared to multivariate functional principal component analysis, this methodology is suited for exploring and reducing the dimensionality of dependent spatial data. Thethesis findings highlight the crucial importance of spatial-functional statistical analysis in ecological research involving spatially complex entities. These results underscore the added value of considering spatial dimension in the analysis of these complex biological phenomena. Beyond our specific case study, the application of functional data analysis opens promising avenues for a wide range of ecological studies involving massive spatial dataAu sein du domaine de la statistique fonctionnelle appliquée à l’environnement, l’accent de cette recherche est mis sur l’analyse de données présentées sous forme fonctionnelle. La statistique fonctionnelle explore un secteur de la statistique dédié à la manipulation de données fonctionnelles, fournissant des méthodes pour la réduction de dimension, l’apprentissage supervisé et non supervisé, tout en prenant en compte les dépendances temporelles et/ou spatiales inhérentes à ces données. L’essor des technologies modernes a rendu ces types de données de plus en plus accessibles, en particulier dans des domaines tels que les sciences de l’environnement. Un exemple concret d’application de la statistique fonctionnelle réside dans les techniques d’acoustique des pêches, qui permettent d’obtenir des échantillons spatiaux et temporels d’organismes marins à différentes profondeurs et échelles spatiales, sans nécessiter d’intrusion.Au sein de cette recherche, un ensemble de données acoustiques multi-fréquences, extraites par des échosondeurs scientifiques halieutiques, a été minutieusement analysé pour explorer la structure spatiale des agrégations d’organismes marins micronectoniques, communément désignées sous le terme de couches diffusantes et en anglais de "Sound Scattering Layers" (SSL). L’examen des caractéristiques de ces entités biologiques complexes, telles que leur épaisseur, leur densité relative, et leur profondeur, a été réalisé en corrélation avec leur environnement pélagique. La représentation fine de cet environnement a été rendue possible grâce à l’utilisation d’un système multiparamétrique tracté derrière le navire (ScanFish). Dans cette démarche, nous avons initié l’analyse en recourant à des méthodes standards de statistique multivariée, pour ensuite exploiter des techniques de l’analyse de données fonctionnelles, avec ou sans prise en compte de la dimension spatiale.Dans notre première analyse exploratoire, l’Analyse en Composantes Principales Fonctionnelle multivariée a fourni des informations précises sur la variation des paramètres le long des profondeurs, contrairement à l’Analyse en Composantes Principales classique. Dans le cadre des tâches de régression, nos analyses, qu’elles intègrent ou non la dimension spatiale, ont mis en évidence des interactions entre les caractéristiques des SSL et les variables environnementales clés à l’échelle spatiale. Des distinctions géographiques significatives ont été constatées entre les SSL de notre jeu de données, i.e. entre les zones septentrionales et méridionales, ainsi qu’entre ceux des zones côtières et hauturières au cours de cette étude. Ces conclusions demeurent pertinentes dans la seconde partie de la thèse, où une méthode d’apprentissage supervisé récente est employée, exploitant la notion de signatures extraites des données environnementales. La dernière contribution, plutôt méthodologique, introduit une nouvelle approche d’analyse en composante principale pour les données fonctionnelles multivariées spatiales. Comparée à l’analyse en composante principale fonctionnelle multivariée classique, cette méthodologie est adaptée à l’exploration et à la réduction de la dimension des données spatiales dépendantes.Les conclusions de la thèse mettent en lumière l’importance cruciale de l’analyse statistique spatiale-fonctionnelle dans les recherches écologiques portant sur des entités spatialement complexes. Ces résultats mettent en évidence la valeur ajoutée de la prise en compte de la dimension spatiale dans l’analyse de ces phénomènes biologiques complexes. Au-delà de notre étude de cas spécifique, l’application de l’analyse de données fonctionnelles ouvre des perspectives prometteuses pour un large éventail d’études écologiques impliquant des données spatiales massive

Spatial functionnal analysis of acoustic fisheries data

International audienceIn this work, we are interested in the application of functional, spatial and classical statistical methods (multivariate functional principal component analysis, classical principal component analysis, classical, spatial and functional regression) on acoustic data (Sv) and environmental (temperature, fluorescence, salinity and turbidity) from the AWA campaign carried out on the West African coast. The goal of this project is to study the impact of environmental parameters on the distribution of the layers extracted from the Sv. First we considered the aggregated aspect of the environmental data in the analyzes then their functional nature to carry out a functional modeling

Demonstrating the relevance of spatial-functional statistical analysis in marine ecological studies : the case of environmental variations in micronektonic layers

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Demonstrating the relevance of spatial-functional statistical analysis in marine ecological studies: The case of environmental variations in micronektonic layers

In this study, we conducted an analysis of a multifrequency acoustics dataset acquired from scientific echosounders in the West African water. Our objective was to explore the spatial arrangement of marine organism aggregations. We investigated various attributes of these intricate biological entities, such as thickness, relative density, and depth, in relation to their surroundings. These environmental conditions were represented at a fine scale using a towed multiparameter system. This study is closely intertwined with two key domains: Fisheries acoustics techniques and functional data analysis. Fisheries acoustics techniques facilitate the collection of high-resolution spatial and temporal data concerning marine organisms at various depths and spatial scales, all without causing any disturbance. On the other hand, spatial-functional data analysis is a statistical approach for examining data characterised by functional attributes distributed across a spatial domain. This analysis encompasses dimension reduction techniques, as well as supervised and unsupervised methods, which take into consideration spatial dependencies within extensive datasets. We began by applying multivariate statistical techniques and subsequently employed Functional Data Analysis (FDA). In the modeling section, we introduced the spatial dimension with the spatial coordinates as covariates in the General Additive Model (GAM) and Functional Generalized Spectral Additive Model (FGSAM) models, aiming to underscore its relevance in those contexts. In an exploratory phase, Multivariate Functional Principal Component Analysis provided detailed insights into the variations of parameters at different depths, a capability not offered by traditional Principal Component Analysis. When it came to regression tasks, we explored the interactions between descriptors of Sound Scattering Layers and key environmental variables, both with and without considering spatial dimensions. Our findings revealed significant distinctions between northern and southern Sound Scattering Layers, as well as between coastal and high-sea regions. The use of the spatial locations enhanced the performance of GAM and FGSAM, particularly in the case of salinity, reflecting the influence of water mixing and seawater temperature. The multifaceted effects of environmental variations on Sound Scattering Layers underscore the importance of spatial-functional statistical analysis in ecological studies involving complex, spatially functional objects. Beyond the scope of this specific case study, the application of functional data analysis shows promise for a wide array of ecological studies dealing with extensive spatial datasets

Study of the spatial variability of marine pollution around the peninsula of Cape Verde

International audienceMarine pollution, the scourge of modern times, is due to the runoff of domestic and industrial waters as well as to various anthropogenic activities, i.e. products and objects deliberately or accidentally discharged into the sea. The samples taken from 11 sites on the Cap-Vert peninsula in Senegal, indicate the presence of certain polluting substances in varying amounts. The objective of this work is to study the correlations between the physical, microbiological and chemical parameters in order to highlight the similarities between the sites and, if possible, to determine the most relevant parameter(s) to characterize the pollution. PCA results have shown that some sites appear to be less chemically polluted than others that are more polluted with eutrophication and chemicals (e.g., copper, mercury). From a physical point of view, for example, we observe that the characteristics of sediments (large silt, clays, fine silt) are related to certain chemical parameters.The AFC performed between the overall toxicity of the sediments and the microbiological quality of the water shows that the site of Ouakam has a medium toxicity and a good microbiological quality while that of Cambérène and the Vivier are characterized respectively by bad and good quality but also by low toxicity at both sites. The two sites of Hann (Hann1 and Hann2), Soumbédioune, Ngor, Yoff Tonghor and Dakar Le Dantec are characterized by high toxicity and poor microbiological quality. Those in the Madeleine Islands and the Port of Dakar are characterized by high toxicity and bad microbiological quality. Moreover, as expected Soumbédioune appears as the most polluted sites in terms of microbiological load. The interest of the multivariate approach (ACP and AFC) is then discussed in this type of analysis

Pollution assessment around a big city in West Africa reveals high concentrations of microplastics and microbiologic contamination

International audienceMarine pollution around West African big cities is of major concerns. Nevertheless, few attempts have been performed so far particularly on microplastic assessment. We had led first survey targeting microplastic in West African coastal waters (2016); and evaluated on the same sites microbiological contamination as well as marine sediment toxicity and mercury content. Thus, neuston marine water samples were collected over Dakar a highly populated West African city. The average abundance was around 258 954 microplastic particles per km2 and 37 442 for macroplastics. One station, downstream from the major wastewater plant, contained high abundance of microplastic particles of over 945 000 and 190 000 macroplastics. The offshore station had a lower abundance of microplastics and macroplastics. It was observed that the stations found with highest level of microbiological pollution were related to highest microplastics abundance and the presence of major effluents, suggesting wastewaters inputs and microbiological pollution favoured by microplastics and macroplastics as vector. No correlation was observed between microplastics and/or macroplastics and sediment toxicity neither mercury level, which appeared low in all studied sites. However, high level of ecotoxicity were often found near effluents. Such results are a first step within the framework of encouraging awareness and actions in West Africa

Study of the spatial variability of marine pollution around the peninsula of Cape Verde

International audienceMarine pollution, the scourge of modern times, is due to the runoff of domestic and industrial waters as well as to various anthropogenic activities, i.e. products and objects deliberately or accidentally discharged into the sea. The samples taken from 11 sites on the Cap-Vert peninsula in Senegal, indicate the presence of certain polluting substances in varying amounts. The objective of this work is to study the correlations between the physical, microbiological and chemical parameters in order to highlight the similarities between the sites and, if possible, to determine the most relevant parameter(s) to characterize the pollution. PCA results have shown that some sites appear to be less chemically polluted than others that are more polluted with eutrophication and chemicals (e.g., copper, mercury). From a physical point of view, for example, we observe that the characteristics of sediments (large silt, clays, fine silt) are related to certain chemical parameters.The AFC performed between the overall toxicity of the sediments and the microbiological quality of the water shows that the site of Ouakam has a medium toxicity and a good microbiological quality while that of Cambérène and the Vivier are characterized respectively by bad and good quality but also by low toxicity at both sites. The two sites of Hann (Hann1 and Hann2), Soumbédioune, Ngor, Yoff Tonghor and Dakar Le Dantec are characterized by high toxicity and poor microbiological quality. Those in the Madeleine Islands and the Port of Dakar are characterized by high toxicity and bad microbiological quality. Moreover, as expected Soumbédioune appears as the most polluted sites in terms of microbiological load. The interest of the multivariate approach (ACP and AFC) is then discussed in this type of analysis

Assessment of the global toxicity of marine sediments from the Dakar peninsula (Senegal, West Africa)

International audienceMarine pollution in West Africa is major threat particularly around coastal megacities. We assess the chemical and ecotoxicological quality of the marine sediments in various submerged sampling sites of Dakar. Analysis revealed that sediments were slightly basic in which fine and coarse sands predominated. High percentages of total organic carbon were found sometime above 6%. Higher levels of heavy metal were reported than in previous studies. Chromium and nickel were above the Probable Effect Concentration. Low trophic level appeared not affected by the overall toxicity, while medium trophic level was more affected. Indeed, the vast majority (91%) of sites studied revealed a net percentage of Magallana gigas embryolarval developmental abnormality over 20%. The assessment of the global toxicity of marine sediments from the Dakar sites Studied (n = 11) seemed, almost, as a whole, to be in a poor ecotoxicological state calling to take measures to improve the sanitary condition of this marine feature