Status and size of Pied Avocet Recurvirostra avosetta populations in East Africa, with a first coastal breeding record

Several populations of Pied Avocet are understood to overlap in East Africa, yet the specific movements and size of each of them remains largely unclear. A review of current literature, combined with waterbird counts and recent citizen science data, suggests that potentially three populations occur in the region (Palaearctic, southern origin, and resident), and that the resident population is substantially smaller than previous estimates suggested. A new breeding record at the Kenyan coast, which only constitutes the fourth confirmed breeding location of Pied Avocet in Kenya and the first for the East African coast, demonstrates a potential overlap of Palaearctic migrants and East African residents, which may breed opportunistically along the coast. More resources are needed to carry out standardized and regular national monitoring counts in order to further elucidate the origin, movement, and numbers of Pied Avocets in East Africa

Integrating complex and diverse spatial datasets: Applications to hydrogeophysics

Around the world, groundwater is vital for humankind, yet threatened by anthropogenic deterioration. Sustainable groundwater management is thus crucial and relies on numerical models for adequately forecasting groundwater conditions. A key step in the construction of such models is to determine hydrogeological parameters that describe the behaviour of the water in the aquifer (i.e. water-bearing rock). In recent years, the increasing application of geophysical techniques to characterize the aquifer has improved this endeavour. However, the integration of geophysical data with hydrogeological parameters remains a challenging task. This is due to fundamental differences in coverage and resolution as well as the complex and non-unique interrelation of the measured parameters. Geostatistics have proven to be a useful framework to integrate these spatial datasets. This thesis takes a broader perspective to address this topic, which can be summarized as: “developing computationally efficient and accurate methodologies for the integration of spatial datasets, which are variable in terms of coverage and resolution, and related through complex, site-dependent and/or non-unique relationship”. The contribution presented in this thesis can be partitioned into three stages. The initial stage is concerned with the first part of the problem statement taking a more general and theoretical geostatistical approach. More specifically, it aims to improve the efficiency and accuracy of Sequential Gaussian Simulation (SGS), which is a widely used geostatistical method employed to generate Gaussian fields. It populates a grid by consecutively visiting each node and sampling a value in a local conditional distribution. In the first project, we look at the impact of the type of simulation path, that is, the strategy defining the order in which the nodes are simulated. It is shown that declustering paths, which maximize the distance between consecutively simulated nodes, present the best reproduction of spatial structure. The second project assesses the computational gain and resulting biases of using a constant path for multiple realizations. Results show that these biases are minimal and easily surpassed by the high computational gains, which in turn allow for increasing the neighbourhood size and thus reducing the overall magnitude of biases. In a second stage, an improved version of Bayesian Sequential Simulation (BSS) is proposed. BSS integrates a known secondary variable in the stochastic simulation of a primary variable. The method is based on SGS with the addition that, for each simulated node, the conditional distribution is combined with a distribution coming from the known value of the collocated secondary variable. Our proposition is to generalize this combination by assigning a log-linear weight to each distribution. A key novelty of this work is to design a weighting scheme that adapts its values along the simulation to account for the variation of dependence between both sources of information. Tests are performed for a hydrogeophysical case study consisting of simulating hydraulic conductivity using surface-based electrical resistivity tomography as the secondary variable. This case study shows that the proposed weighting scheme considerably improves the realizations in terms of reproducing the spatial structure while maintaining a good agreement between primary and secondary variables. In the third and final stage, we develop a methodology capable of downscaling tomographic images resulting from smoothness-constrained inversions of geophysical data. The key idea is to use the resolution matrix, computed during the inversion, to quantify the smoothing of the tomogram through a linear mapping. Using area-to-point kriging, it is then possible to simulate fine-scale realizations of electrical conductivity constrained to the tomogram through the previously computed linear mapping. The method developed is able to provide multiple realizations at a relatively low computational cost. These realizations accurately reproduce the spatial structure and the correspondence to the tomogram. -- Bien que vitales pour l’humanité, les eaux souterraines sont menacées à travers le monde par la détérioration anthropique. Leur gestion durable, cruciale pour préserver la ressource en eau, repose en partie sur des modèles numériques permettant de prévoir l’état de l’eau dans les aquifères, c’est-à-dire des roches contenant les eaux souterraines. Une étape clé dans la construction de tels modèles consiste à déterminer les paramètres hydrogéologiques décrivant le comportement de l’eau dans l’aquifère. Au cours des dernières années, l’appli- cation croissante de techniques géophysiques à la caractérisation des aquifères a permis une meilleure description de leurs paramètres hydrogéologiques. Cependant, l’intégration de telles données reste une tâche difficile à cause (1) des différences de couverture et de résolution entre les types de mesures et (2), de l’interaction complexe et non unique des paramètres en question. Dans ce contexte, la géostatistique fournit un cadre utile permettant l’intégration de ces données spatiales. Cette thèse adopte une perspective plus large, qui peut être résumée comme suit : «développer des méthodologies efficaces et précises pour l’intégration de données spatiales, variables en termes de couverture et de résolution, et liées par des relations complexes non-uniques et dépendant du site d’étude. Les contributions principales de ce travail de thèse peuvent être divisées en trois étapes, brièvement résumées ci-dessous. L’étape initiale concerne la première partie de l’énoncé du problème et adopte une approche géostatistique générale et théorique. Elle consiste à améliorer l’efficacité et la précision d’une méthode géostatistique largement utilisée pour générer des champs gaussiens : la simulation gaussienne séquentielle (SGS). Le but de cet algorithme est de remplir une grille en visitant consécutivement chaque nœud et en échantillonnant une valeur dans une distribution conditionnelle locale. Dans un premier temps, nous examinons l’impact du type de chemin utilisé pour réaliser la simulation, c’est-à-dire la stratégie définissant l’ordre dans lequel les nœuds sont simulés. Nous montrons que les chemins dits de “dégroupement”, c’est-à-dire qui maximisent la distance entre les nœuds simulés consécutivement, conduisent à une meilleure reproduction de la structure spatiale dans les résultats de la simulation. Dans un deuxième temps, nous évaluons le gain en temps de calcul et les biais résultants de l’utilisation d’un chemin constant lors de plusieurs réalisations. Les résultats montrent que les biais résultant sont minimaux et facilement surpassés par un gain considérable en temps de calcul. Ceci permet d’augmenter la taille du voisinage utilisé lors de la simulation, et, au final, de réduire l’ampleur globale des biais dans les différentes réalisations. La seconde étape consiste à développer une version améliorée de la simulation séquentielle bayésienne (SSB). Cette méthode de simulation permet d’intégrer une variable secondaire connue dans la simulation stochastique d’une variable primaire. Elle est fondée sur une simulation SGS à laquelle s’ajoute, pour chaque nœud simulé, l’intégration d’une variable secondaire co-localisée. Pour cela, la distribution conditionnelle issue de la simulation SGS est combinée avec une distribution provenant de la valeur connue de la variable secondaire. Notre proposition consiste à généraliser cette combinaison en attribuant un poids log-linéaire à chaque distribution. La nouveauté essentielle consiste alors à concevoir un schéma de pondération qui adapte la valeur des poids au cours de la simulation pour tenir compte de la variation de dépendance entre les deux sources d’information. Pour évaluer les gains obtenus par cette nouvelle approche, des tests sont effectués à partir d’une étude de cas hydrogéophysique consistant à simuler la conductivité hydraulique en utilisant comme source secondaire la tomographie en surface de résistivité électrique. Cette étude de cas montre que le schéma de pondération proposé améliore considérablement la reproduction de la structure spatiale tout en maintenant en accord les variables primaires et secondaires. Enfin, la troisième étape consiste à développer une méthodologie capable d’augmenter la résolution des images tomographiques résultant d’inversions de données géophysiques soumises à des contraintes de lissage. L’idée clé est d’utiliser la matrice de résolution, calculée lors de l’inversion pour quantifier le lissage du tomogramme à travers un mapping linéaire. En utilisant le krigeage “zone-à-point”, il est alors possible de simuler des réalisations à une échelle fine de la conductivité électrique contraintes au tomogramme par le mapping linéaire précédemment calculé. La méthode développée est capable de fournir plusieurs réalisations à un coût de calcul relativement faible. Ces réalisations reproduisent fidèlement la structure spatiale et la correspondance avec le tomogramme

A Geostatistical Approach to Estimate High Resolution Nocturnal Bird Migration Densities from a Weather Radar Network

Quantifying nocturnal bird migration at high resolution is essential for (1) understanding the phenology of migration and its drivers, (2) identifying critical spatio-temporal protection zones for migratory birds, and (3) assessing the risk of collision with artificial structures. We propose a tailored geostatistical model to interpolate migration intensity monitored by a network of weather radars. The model is applied to data collected in autumn 2016 from 69 European weather radars. To validate the model, we performed a cross-validation and also compared our interpolation results with independent measurements of two bird radars. Our model estimated bird densities at high resolution (0.2° latitude–longitude, 15 min) and assessed the associated uncertainty. Within the area covered by the radar network, we estimated that around 120 million birds were simultaneously in flight (10–90 quantiles: 107–134). Local estimations can be easily visualized and retrieved from a dedicated interactive website. This proof-of-concept study demonstrates that a network of weather radar is able to quantify bird migration at high resolution and accuracy. The model presented has the ability to monitor population of migratory birds at scales ranging from regional to continental in space and daily to yearly in time. Near-real-time estimation should soon be possible with an update of the infrastructure and processing software

GeoPressureR: Global Positioning by Atmospheric Pressure

R package to determine the position of a bird based on the data retrieved from multi-sensor geolocators.To cite package "GeoPressureR" in publications use

Investigating the influence of the extreme Indian Ocean Dipole on the 2020 influx of Red-necked Phalaropes Phalaropus lobatus in Kenya

Ocean currents have wide-ranging impacts on seabird movement and survival. By extension, the extreme oscillations they are subject to, such as extreme Indian Ocean Dipole (IOD) events, can also be expected to dramatically influence seabird populations. This study links the extreme IOD event that occurred in 2019–2020 to the unusually high number of Red-necked Phalarope sightings observed in February 2020. We show that the extreme IOD event resulted in low net primary productivity (a measure of plankton growth) offshore from the Somalia-Kenyan coast, where Phalaropes have been tracked in previous winters. We suggest that Phalaropes were therefore forced to move closer to the coast to find food at river estuaries, thus explaining the influx in February 2020. This study calls for closer monitoring of seabird populations in East Africa, particularly during extreme IOD events, which are expected to become more common in the future

Investigating the influence of the extreme Indian Ocean Dipole on the 2020 influx of Red-necked Phalaropes Phalaropus lobatus in Kenya

Ocean currents have wide-ranging impacts on seabird movement and survival. By extension, the extreme oscillations they are subject to, such as extreme Indian Ocean Dipole (IOD) events, can also be expected to dramatically influence seabird populations. This study links the extreme IOD event that occurred in 2019–2020 to the unusually high number of Red-necked Phalarope sightings observed in February 2020. We show that the extreme IOD event resulted in low net primary productivity (a measure of plankton growth) offshore from the Somalia-Kenyan coast, where Phalaropes have been tracked in previous winters. We suggest that Phalaropes were therefore forced to move closer to the coast to find food at river estuaries, thus explaining the influx in February 2020. This study calls for closer monitoring of seabird populations in East Africa, particularly during extreme IOD events, which are expected to become more common in the future

Investigating the influence of the extreme Indian Ocean Dipole on the 2020 influx of Red-necked Phalaropes Phalaropus lobatus in Kenya

Ocean currents have wide-ranging impacts on seabird movement and survival. By extension, the extreme oscillations they are subject to, such as extreme Indian Ocean Dipole (IOD) events, can also be expected to dramatically influence seabird populations. This study links the extreme IOD event that occurred in 2019–2020 to the unusually high number of Red-necked Phalarope sightings observed in February 2020. We show that the extreme IOD event resulted in low net primary productivity (a measure of plankton growth) offshore from the Somalia-Kenyan coast, where Phalaropes have been tracked in previous winters. We suggest that Phalaropes were therefore forced to move closer to the coast to find food at river estuaries, thus explaining the influx in February 2020. This study calls for closer monitoring of seabird populations in East Africa, particularly during extreme IOD events, which are expected to become more common in the future

Investigating the influence of the extreme Indian Ocean Dipole on the 2020 influx of Red-necked Phalaropes Phalaropus lobatus in Kenya

Ocean currents have wide-ranging impacts on seabird movement and survival. By extension, the extreme oscillations they are subject to, such as extreme Indian Ocean Dipole (IOD) events, can also be expected to dramatically influence seabird populations. This study links the extreme IOD event that occurred in 2019–2020 to the unusually high number of Red-necked Phalarope sightings observed in February 2020. We show that the extreme IOD event resulted in low net primary productivity (a measure of plankton growth) offshore from the Somalia-Kenyan coast, where Phalaropes have been tracked in previous winters. We suggest that Phalaropes were therefore forced to move closer to the coast to find food at river estuaries, thus explaining the influx in February 2020. This study calls for closer monitoring of seabird populations in East Africa, particularly during extreme IOD events, which are expected to become more common in the future