25 research outputs found
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: âdeveloping 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 relationshipâ. 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.
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Bien que vitales pour lâhumanitĂ©, les eaux souterraines sont menacĂ©es Ă travers le monde par la dĂ©tĂ©rioration anthropique. Leur gestion durable, cruciale pour prĂ©server la ressource en eau, repose en partie sur des modĂšles numĂ©riques permettant de prĂ©voir lâĂ©tat de lâeau dans les aquifĂšres, câest-Ă -dire des roches contenant les eaux souterraines. Une Ă©tape clĂ© dans la construction de tels modĂšles consiste Ă dĂ©terminer les paramĂštres hydrogĂ©ologiques dĂ©crivant le comportement de lâeau dans lâaquifĂšre. Au cours des derniĂšres annĂ©es, lâappli- cation croissante de techniques gĂ©ophysiques Ă la caractĂ©risation des aquifĂšres a permis une meilleure description de leurs paramĂštres hydrogĂ©ologiques. Cependant, lâintĂ©gration de telles donnĂ©es reste une tĂąche difficile Ă cause (1) des diffĂ©rences de couverture et de rĂ©solution entre les types de mesures et (2), de lâinteraction complexe et non unique des paramĂštres en question. Dans ce contexte, la gĂ©ostatistique fournit un cadre utile permettant lâintĂ©gration de ces donnĂ©es spatiales. Cette thĂšse adopte une perspective plus large, qui peut ĂȘtre rĂ©sumĂ©e comme suit : «dĂ©velopper des mĂ©thodologies efficaces et prĂ©cises pour lâintĂ©gration de donnĂ©es spatiales, variables en termes de couverture et de rĂ©solution, et liĂ©es par des relations complexes non-uniques et dĂ©pendant du site dâĂ©tude. Les contributions principales de ce travail de thĂšse peuvent ĂȘtre divisĂ©es en trois Ă©tapes, briĂšvement rĂ©sumĂ©es ci-dessous.
LâĂ©tape initiale concerne la premiĂšre partie de lâĂ©noncĂ© du problĂšme et adopte une approche gĂ©ostatistique gĂ©nĂ©rale et thĂ©orique. Elle consiste Ă amĂ©liorer lâefficacitĂ© et la prĂ©cision dâune mĂ©thode gĂ©ostatistique largement utilisĂ©e pour gĂ©nĂ©rer des champs gaussiens : la simulation gaussienne sĂ©quentielle (SGS). Le but de cet algorithme est de remplir une grille en visitant consĂ©cutivement chaque nĆud et en Ă©chantillonnant une valeur dans une distribution conditionnelle locale. Dans un premier temps, nous examinons lâimpact du type de chemin utilisĂ© pour rĂ©aliser la simulation, câest-Ă -dire la stratĂ©gie dĂ©finissant lâordre dans lequel les nĆuds sont simulĂ©s. Nous montrons que les chemins dits de âdĂ©groupementâ, câest-Ă -dire
qui maximisent la distance entre les nĆuds simulĂ©s consĂ©cutivement, conduisent Ă une meilleure reproduction de la structure spatiale dans les rĂ©sultats de la simulation. Dans un deuxiĂšme temps, nous Ă©valuons le gain en temps de calcul et les biais rĂ©sultants de lâutilisation dâun chemin constant lors de plusieurs rĂ©alisations. Les rĂ©sultats montrent que les biais rĂ©sultant sont minimaux et facilement surpassĂ©s par un gain considĂ©rable en temps de calcul. Ceci permet dâaugmenter la taille du voisinage utilisĂ© lors de la simulation, et, au final, de rĂ©duire lâampleur globale des biais dans les diffĂ©rentes rĂ©alisations.
La seconde Ă©tape consiste Ă dĂ©velopper une version amĂ©liorĂ©e de la simulation sĂ©quentielle bayĂ©sienne (SSB). Cette mĂ©thode de simulation permet dâintĂ©grer une variable secondaire connue dans la simulation stochastique dâune variable primaire. Elle est fondĂ©e sur une simulation SGS Ă laquelle sâajoute, pour chaque nĆud simulĂ©, lâintĂ©gration dâune variable secondaire co-localisĂ©e. Pour cela, la distribution conditionnelle issue de la simulation SGS est combinĂ©e avec une distribution provenant de la valeur connue de la variable secondaire. Notre proposition consiste Ă gĂ©nĂ©raliser cette combinaison en attribuant un poids log-linĂ©aire Ă chaque distribution. La nouveautĂ© essentielle consiste alors Ă concevoir un schĂ©ma de pondĂ©ration qui adapte la valeur des poids au cours de la simulation pour tenir compte de la variation de dĂ©pendance entre les deux sources dâinformation. Pour Ă©valuer les gains obtenus par cette nouvelle approche, des tests sont effectuĂ©s Ă partir dâune Ă©tude de cas hydrogĂ©ophysique consistant Ă simuler la conductivitĂ© hydraulique en utilisant comme source secondaire la tomographie en surface de rĂ©sistivitĂ© Ă©lectrique. Cette Ă©tude de cas montre que le schĂ©ma de pondĂ©ration proposĂ© amĂ©liore considĂ©rablement la reproduction de la structure spatiale tout en maintenant en accord les variables primaires et secondaires.
Enfin, la troisiĂšme Ă©tape consiste Ă dĂ©velopper une mĂ©thodologie capable dâaugmenter la rĂ©solution des images tomographiques rĂ©sultant dâinversions de donnĂ©es gĂ©ophysiques soumises Ă des contraintes de lissage. LâidĂ©e clĂ© est dâutiliser la matrice de rĂ©solution, calculĂ©e lors de lâinversion pour quantifier le lissage du tomogramme Ă travers un mapping linĂ©aire. En utilisant le krigeage âzone-Ă -pointâ, il est alors possible de simuler des rĂ©alisations Ă une Ă©chelle fine de la conductivitĂ© Ă©lectrique contraintes au tomogramme par le mapping linĂ©aire prĂ©cĂ©demment calculĂ©. La mĂ©thode dĂ©veloppĂ©e est capable de fournir plusieurs rĂ©alisations Ă un coĂ»t de calcul relativement faible. Ces rĂ©alisations reproduisent fidĂšlement 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° latitudeâlongitude, 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 (10â90 quantiles: 107â134). 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
Migration routes and timing of European Nightjars (Caprimulgus europaeus) breeding in eastern Mongolia
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 2019â2020 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 2019â2020 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 2019â2020 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 2019â2020 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