Temporal and spatial variability of fin whale's (Balaenoptera physalus) distribution in the Pelagos Sanctuary: Northwestern Mediterranean Sea

Dissertação de Mestrado, Estudos Integrados dos Oceanos, 20 de Março de 2014, Universidade dos Açores.

Segmentation of striatal brain structures from high resolution pet images

Dissertation presented at the Faculty of Science and Technology of the New University of Lisbon in fulfillment of the requirements for the Masters degree in Electrical Engineering and Computers

Extracting temporal and spatial distributions information about marine mucilage phenomenon based on Modis satellite images; a case study of the Tyrrhenian and the Adriatic Sea, 2010-2012

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Use of high-resolution geophysical data to characterize heterogeneous aquifers: influence of data integration method on hydrological predictions

The integration of geophysical data into the subsurface characterization problem has been shown in many cases to significantly improve hydrological knowledge by providing information at spatial scales and locations that is unattainable using conventional hydrological measurement techniques. The investigation of exactly how much benefit can be brought by geophysical data in terms of its effect on hydrological predictions, however, has received considerably less attention in the literature. Here, we examine the potential hydrological benefits brought by a recently introduced simulated annealing (SA) conditional stochastic simulation method designed for the assimilation of diverse hydrogeophysical data sets. We consider the specific case of integrating crosshole ground-penetrating radar (GPR) and borehole porosity log data to characterize the porosity distribution in saturated heterogeneous aquifers. In many cases, porosity is linked to hydraulic conductivity and thus to flow and transport behavior. To perform our evaluation, we first generate a number of synthetic porosity fields exhibiting varying degrees of spatial continuity and structural complexity. Next, we simulate the collection of crosshole GPR data between several boreholes in these fields, and the collection of porosity log data at the borehole locations. The inverted GPR data, together with the porosity logs, are then used to reconstruct the porosity field using the SA-based method, along with a number of other more elementary approaches. Assuming that the grid-cell-scale relationship between porosity and hydraulic conductivity is unique and known, the porosity realizations are then used in groundwater flow and contaminant transport simulations to assess the benefits and limitations of the different approaches.

Predicting spatial patterns of plant biodiversity : from species to communities

Understanding the distribution and composition of species assemblages and being able to predict them in space and time are highly important tasks io investigate the fate of biodiversity in the current global changes context. Species distribution models are tools that have proven useful to predict the potential distribution of species by relating their occurrences to environmental variables. Species assemblages can then be predicted by combining the prediction of individual species models. In the first part of my thesis, I tested the importance of new environmental predictors to improve species distribution prediction. I showed that edaphic variables, above all soil pH and nitrogen content could be important in species distribution models. In a second chapter, I tested the influence of different resolution of predictors on the predictive ability of species distribution models. I showed that fine resolution predictors could ameliorate the models for some species by giving a better estimation of the micro-topographic condition that species tolerate, but that fine resolution predictors for climatic factors still need to be ameliorated. The second goal of my thesis was to test the ability of empirical models to predict species assemblages' characteristics such as species richness or functional attributes. I showed that species richness could be modelled efficiently and that the resulting prediction gave a more realistic estimate of the number of species than when obtaining it by stacking outputs of single species distribution models. Regarding the prediction of functional characteristics (plant height, leaf surface, seed mass) of plant assemblages, mean and extreme values of functional traits were better predictable than indices reflecting the diversity of traits in the community. This approach proved interesting to understand which environmental conditions influence particular aspects of the vegetation functioning. It could also be useful to predict climate change impacts on the vegetation. In the last part of my thesis, I studied the capacity of stacked species distribution models to predict the plant assemblages. I showed that this method tended to over-predict the number of species and that the composition of the community was not predicted exactly either. Finally, I combined the results of macro- ecological models obtained in the preceding chapters with stacked species distribution models and showed that this approach reduced significantly the number of species predicted and that the prediction of the composition is also ameliorated in some cases. These results showed that this method is promising. It needs now to be tested on further data sets. - Comprendre la manière dont les plantes se répartissent dans l'environnement et s'organisent en communauté est une question primordiale dans le contexte actuel de changements globaux. Cette connaissance peut nous aider à sauvegarder la diversité des espèces et les écosystèmes. Des méthodes statistiques nous permettent de prédire la distribution des espèces de plantes dans l'espace géographique et dans le temps. Ces modèles de distribution d'espèces, relient les occurrences d'une espèce avec des variables environnementales pour décrire sa distribution potentielle. Cette méthode a fait ses preuves pour ce qui est de la prédiction d'espèces individuelles. Plus récemment plusieurs tentatives de cumul de modèles d'espèces individuelles ont été réalisées afin de prédire la composition des communautés végétales. Le premier objectif de mon travail est d'améliorer les modèles de distribution en testant l'importance de nouvelles variables prédictives. Parmi différentes variables édaphiques, le pH et la teneur en azote du sol se sont avérés des facteurs non négligeables pour prédire la distribution des plantes. Je démontre aussi dans un second chapitre que les prédicteurs environnementaux à fine résolution permettent de refléter les conditions micro-topographiques subies par les plantes mais qu'ils doivent encore être améliorés avant de pouvoir être employés de manière efficace dans les modèles. Le deuxième objectif de ce travail consistait à étudier le développement de modèles prédictifs pour des attributs des communautés végétales tels que, par exemple, la richesse en espèces rencontrée à chaque point. Je démontre qu'il est possible de prédire par ce biais des valeurs de richesse spécifiques plus réalistes qu'en sommant les prédictions obtenues précédemment pour des espèces individuelles. J'ai également prédit dans l'espace et dans le temps des caractéristiques de la végétation telles que sa hauteur moyenne, minimale et maximale. Cette approche peut être utile pour comprendre quels facteurs environnementaux promeuvent différents types de végétation ainsi que pour évaluer les changements à attendre au niveau de la végétation dans le futur sous différents régimes de changements climatiques. Dans une troisième partie de ma thèse, j'ai exploré la possibilité de prédire les assemblages de plantes premièrement en cumulant les prédictions obtenues à partir de modèles individuels pour chaque espèce. Cette méthode a le défaut de prédire trop d'espèces par rapport à ce qui est observé en réalité. J'ai finalement employé le modèle de richesse en espèce développé précédemment pour contraindre les résultats du modèle d'assemblage de plantes. Cela a permis l'amélioration des modèles en réduisant la sur-prédiction et en améliorant la prédiction de la composition en espèces. Cette méthode semble prometteuse mais de nouveaux tests sont nécessaires pour bien évaluer ses capacités.

Land use improves spatial predictions of mountain plant abundance but not presence-absence

Question Does a land-use variable improve spatial predictions of plant species presence-absence and abundance models at the regional scale in a mountain landscape? Location Western Swiss Alps. Methods Presence-absence generalized linear models (GLM) and abundance ordinal logistic regression models (LRM) were fitted to data on 78 mountain plant species, with topo-climatic and/or land-use variables available at a 25-m resolution. The additional contribution of land use when added to topo-climatic models was evaluated by: (1) assessing the changes in model fit and (2) predictive power, (3) partitioning the deviance respectively explained by the topo-climatic variables and the land-use variable through variation partitioning, and (5) comparing spatial projections. Results Land use significantly improved the fit of presence-absence models but not their predictive power. In contrast, land use significantly improved both the fit and predictive power of abundance models. Variation partitioning also showed that the individual contribution of land use to the deviance explained by presence-absence models was, on average, weak for both GLM and LRM (3.7% and 4.5%, respectively), but changes in spatial projections could nevertheless be important for some species. Conclusions In this mountain area and at our regional scale, land use is important for predicting abundance, but not presence-absence. The importance of adding land-use information depends on the species considered. Even without a marked effect on model fit and predictive performance, adding land use can affect spatial projections of both presence-absence and abundance models.

Measuring ecological niche overlap from occurrence and spatial environmental data

1. Aim - Concerns over how global change will influence species distributions, in conjunction with increased emphasis on understanding niche dynamics in evolutionary and community contexts, highlight the growing need for robust methods to quantify niche differences between or within taxa. We propose a statistical framework to describe and compare environmental niches from occurrence and spatial environmental data.¦2. Location - Europe, North America, South America¦3. Methods - The framework applies kernel smoothers to densities of species occurrence in gridded environmental space to calculate metrics of niche overlap and test hypotheses regarding niche conservatism. We use this framework and simulated species with predefined distributions and amounts of niche overlap to evaluate several ordination and species distribution modeling techniques for quantifying niche overlap. We illustrate the approach with data on two well-studied invasive species.¦4. Results - We show that niche overlap can be accurately detected with the framework when variables driving the distributions are known. The method is robust to known and previously undocumented biases related to the dependence of species occurrences on the frequency of environmental conditions that occur across geographic space. The use of a kernel smoother makes the process of moving from geographical space to multivariate environmental space independent of both sampling effort and arbitrary choice of resolution in environmental space. However, the use of ordination and species distribution model techniques for selecting, combining and weighting variables on which niche overlap is calculated provide contrasting results.¦5. Main conclusions - The framework meets the increasing need for robust methods to quantify niche differences. It is appropriate to study niche differences between species, subspecies or intraspecific lineages that differ in their geographical distributions. Alternatively, it can be used to measure the degree to which the environmental niche of a species or intraspecific lineage has changed over time.

Integration of high-resolution geophysical data for the characterization of alluvial aquifers : new approaches and implications for hydrological predictions

Abstract Accurate characterization of the spatial distribution of hydrological properties in heterogeneous aquifers at a range of scales is a key prerequisite for reliable modeling of subsurface contaminant transport, and is essential for designing effective and cost-efficient groundwater management and remediation strategies. To this end, high-resolution geophysical methods have shown significant potential to bridge a critical gap in subsurface resolution and coverage between traditional hydrological measurement techniques such as borehole log/core analyses and tracer or pumping tests. An important and still largely unresolved issue, however, is how to best quantitatively integrate geophysical data into a characterization study in order to estimate the spatial distribution of one or more pertinent hydrological parameters, thus improving hydrological predictions. Recognizing the importance of this issue, the aim of the research presented in this thesis was to first develop a strategy for the assimilation of several types of hydrogeophysical data having varying degrees of resolution, subsurface coverage, and sensitivity to the hydrologic parameter of interest. In this regard a novel simulated annealing (SA)-based conditional simulation approach was developed and then tested in its ability to generate realizations of porosity given crosshole ground-penetrating radar (GPR) and neutron porosity log data. This was done successfully for both synthetic and field data sets. A subsequent issue that needed to be addressed involved assessing the potential benefits and implications of the resulting porosity realizations in terms of groundwater flow and contaminant transport. This was investigated synthetically assuming first that the relationship between porosity and hydraulic conductivity was well-defined. Then, the relationship was itself investigated in the context of a calibration procedure using hypothetical tracer test data. Essentially, the relationship best predicting the observed tracer test measurements was determined given the geophysically derived porosity structure. Both of these investigations showed that the SA-based approach, in general, allows much more reliable hydrological predictions than other more elementary techniques considered. Further, the developed calibration procedure was seen to be very effective, even at the scale of tomographic resolution, for predictions of transport. This also held true at locations within the aquifer where only geophysical data were available. This is significant because the acquisition of hydrological tracer test measurements is clearly more complicated and expensive than the acquisition of geophysical measurements. Although the above methodologies were tested using porosity logs and GPR data, the findings are expected to remain valid for a large number of pertinent combinations of geophysical and borehole log data of comparable resolution and sensitivity to the hydrological target parameter. Moreover, the obtained results allow us to have confidence for future developments in integration methodologies for geophysical and hydrological data to improve the 3-D estimation of hydrological properties.

The geophysical electromagnetic prospecting in the spatial location of the travertine deposits of the Banyoles Depression (Girona)

The results of the application of the geophysical electromagnetic prospection methods in the resolution of the problems of the spatial location of the travertine quaternary formations of the Banyoles depression are presented

Attenuated asymmetry of functional connectivity in schizophrenia: a high-resolution EEG study.

The interhemispheric asymmetries that originate from connectivity-related structuring of the cortex are compromised in schizophrenia (SZ). Under the assumption that such abnormalities affect functional connectivity, we analyzed its correlate-EEG synchronization-in SZ patients and matched controls. We applied multivariate synchronization measures based on Laplacian EEG and tuned to various spatial scales. Compared to the controls who had rightward asymmetry at a local level (EEG power), rightward anterior and leftward posterior asymmetries at an intraregional level (1st and 2nd order S-estimator), and rightward global asymmetry (hemispheric S-estimator), SZ patients showed generally attenuated asymmetry, the effect being strongest for intraregional synchronization in the alpha and beta bands. The abnormalities of asymmetry increased with the duration of the disease and correlated with the negative symptoms. We discuss the tentative links between these findings and gross anatomical asymmetries, including the cerebral torque and gyrification pattern, in normal subjects and SZ patients.

Plant species richness and environmental heterogeneity in a mountain landscape: effects of variability and spatial configuration

The loss of biodiversity has become a matter of urgent concern and a better understanding of local drivers is crucial for conservation. Although environmental heterogeneity is recognized as an important determinant of biodiversity, this has rarely been tested using field data at management scale. We propose and provide evidence for the simple hypothesis that local species diversity is related to spatial environmental heterogeneity. Species partition the environment into habitats. Biodiversity is therefore expected to be influenced by two aspects of spatial heterogeneity: 1) the variability of environmental conditions, which will affect the number of types of habitat, and 2) the spatial configuration of habitats, which will affect the rates of ecological processes, such as dispersal or competition. Earlier, simulation experiments predicted that both aspects of heterogeneity will influence plant species richness at a particular site. For the first time, these predictions were tested for plant communities using field data, which we collected in a wooded pasture in the Swiss Jura mountains using a four-level hierarchical sampling design. Richness generally increased with increasing environmental variability and "roughness" (i.e. decreasing spatial aggregation). Effects occurred at all scales, but the nature of the effect changed with scale, suggesting a change in the underlying mechanisms, which will need to be taken into account if scaling up to larger landscapes. Although we found significant effects of environmental heterogeneity, other factors such as history could also be important determinants. If a relationship between environmental heterogeneity and species richness can be shown to be general, recently available high-resolution environmental data can be used to complement the assessment of patterns of local richness and improve the prediction of the effects of land use change based on mean site conditions or land use history.

Characterization of gravitational rock slope deformations at different spatial scales based on field, remote sensing and numerical approaches

Rock slope instabilities such as rock slides, rock avalanche or deep-seated gravitational slope deformations are widespread in Alpine valleys. These phenomena represent at the same time a main factor that control the mountain belts erosion and also a significant natural hazard that creates important losses to the mountain communities. However, the potential geometrical and dynamic connections linking outcrop and slope-scale instabilities are often unknown. A more detailed definition of the potential links will be essential to improve the comprehension of the destabilization processes and to dispose of a more complete hazard characterization of the rock instabilities at different spatial scales. In order to propose an integrated approach in the study of the rock slope instabilities, three main themes were analysed in this PhD thesis: (1) the inventory and the spatial distribution of rock slope deformations at regional scale and their influence on the landscape evolution, (2) the influence of brittle and ductile tectonic structures on rock slope instabilities development and (3) the characterization of hazard posed by potential rock slope instabilities through the development of conceptual instability models. To prose and integrated approach for the analyses of these topics, several techniques were adopted. In particular, high resolution digital elevation models revealed to be fundamental tools that were employed during the different stages of the rock slope instability assessment. A special attention was spent in the application of digital elevation model for detailed geometrical modelling of past and potential instabilities and for the rock slope monitoring at different spatial scales. Detailed field analyses and numerical models were performed to complete and verify the remote sensing approach. In the first part of this thesis, large slope instabilities in Rhone valley (Switzerland) were mapped in order to dispose of a first overview of tectonic and climatic factors influencing their distribution and their characteristics. Our analyses demonstrate the key influence of neotectonic activity and the glacial conditioning on the spatial distribution of the rock slope deformations. Besides, the volumes of rock instabilities identified along the main Rhone valley, were then used to propose the first estimate of the postglacial denudation and filling of the Rhone valley associated to large gravitational movements. In the second part of the thesis, detailed structural analyses of the Frank slide and the Sierre rock avalanche were performed to characterize the influence of brittle and ductile tectonic structures on the geometry and on the failure mechanism of large instabilities. Our observations indicated that the geometric characteristics and the variation of the rock mass quality associated to ductile tectonic structures, that are often ignored landslide study, represent important factors that can drastically influence the extension and the failure mechanism of rock slope instabilities. In the last part of the thesis, the failure mechanisms and the hazard associated to five potential instabilities were analysed in detail. These case studies clearly highlighted the importance to incorporate different analyses and monitoring techniques to dispose of reliable and hazard scenarios. This information associated to the development of a conceptual instability model represents the primary data for an integrated risk management of rock slope instabilities. - Les mouvements de versant tels que les chutes de blocs, les éboulements ou encore les phénomènes plus lents comme les déformations gravitaires profondes de versant représentent des manifestations courantes en régions montagneuses. Les mouvements de versant sont à la fois un des facteurs principaux contrôlant la destruction progressive des chaines orogéniques mais aussi un danger naturel concret qui peut provoquer des dommages importants. Pourtant, les phénomènes gravitaires sont rarement analysés dans leur globalité et les rapports géométriques et mécaniques qui lient les instabilités à l'échelle du versant aux instabilités locales restent encore mal définis. Une meilleure caractérisation de ces liens pourrait pourtant représenter un apport substantiel dans la compréhension des processus de déstabilisation des versants et améliorer la caractérisation des dangers gravitaires à toutes les échelles spatiales. Dans le but de proposer un approche plus globale à la problématique des mouvements gravitaires, ce travail de thèse propose trois axes de recherche principaux: (1) l'inventaire et l'analyse de la distribution spatiale des grandes instabilités rocheuses à l'échelle régionale, (2) l'analyse des structures tectoniques cassantes et ductiles en relation avec les mécanismes de rupture des grandes instabilités rocheuses et (3) la caractérisation des aléas rocheux par une approche multidisciplinaire visant à développer un modèle conceptuel de l'instabilité et une meilleure appréciation du danger . Pour analyser les différentes problématiques traitées dans cette thèse, différentes techniques ont été utilisées. En particulier, le modèle numérique de terrain s'est révélé être un outil indispensable pour la majorité des analyses effectuées, en partant de l'identification de l'instabilité jusqu'au suivi des mouvements. Les analyses de terrain et des modélisations numériques ont ensuite permis de compléter les informations issues du modèle numérique de terrain. Dans la première partie de cette thèse, les mouvements gravitaires rocheux dans la vallée du Rhône (Suisse) ont été cartographiés pour étudier leur répartition en fonction des variables géologiques et morphologiques régionales. En particulier, les analyses ont mis en évidence l'influence de l'activité néotectonique et des phases glaciaires sur la distribution des zones à forte densité d'instabilités rocheuses. Les volumes des instabilités rocheuses identifiées le long de la vallée principale ont été ensuite utilisés pour estimer le taux de dénudations postglaciaire et le remplissage de la vallée du Rhône lié aux grands mouvements gravitaires. Dans la deuxième partie, l'étude de l'agencement structural des avalanches rocheuses de Sierre (Suisse) et de Frank (Canada) a permis de mieux caractériser l'influence passive des structures tectoniques sur la géométrie des instabilités. En particulier, les structures issues d'une tectonique ductile, souvent ignorées dans l'étude des instabilités gravitaires, ont été identifiées comme des structures très importantes qui contrôlent les mécanismes de rupture des instabilités à différentes échelles. Dans la dernière partie de la thèse, cinq instabilités rocheuses différentes ont été étudiées par une approche multidisciplinaire visant à mieux caractériser l'aléa et à développer un modèle conceptuel trois dimensionnel de ces instabilités. A l'aide de ces analyses on a pu mettre en évidence la nécessité d'incorporer différentes techniques d'analyses et de surveillance pour une gestion plus objective du risque associée aux grandes instabilités rocheuses.

Diffraction theory of Fresnel lenses encoded in low-resolution devices

A mathematical model that describes the behavior of low-resolution Fresnel lenses encoded in any low-resolution device (e.g., a spatial light modulator) is developed. The effects of low-resolution codification, such the appearance of new secondary lenses, are studied for a general case. General expressions for the phase of these lenses are developed, showing that each lens behaves as if it were encoded through all pixels of the low-resolution device. Simple expressions for the light distribution in the focal plane and its dependence on the encoded focal length are developed and commented on in detail. For a given codification device an optimum focal length is found for best lens performance. An optimization method for codification of a single lens with a short focal length is proposed.

Diffraction theory of optimized low-resolution Fresnel encoded lenses

A mathematical model describing the behavior of low-resolution Fresnel encoded lenses (LRFEL's) encoded in any low-resolution device (e.g., a spatial light modulator) has recently been developed. From this model, an LRFEL with a short focal length was optimized by our imposing the maximum intensity of light onto the optical axis. With this model, analytical expressions for the light-amplitude distribution, the diffraction efficiency, and the frequency response of the optimized LRFEL's are derived.

Analysis and spatial modeling of the indoor radon Swiss data

The present research deals with an important public health threat, which is the pollution created by radon gas accumulation inside dwellings. The spatial modeling of indoor radon in Switzerland is particularly complex and challenging because of many influencing factors that should be taken into account. Indoor radon data analysis must be addressed from both a statistical and a spatial point of view. As a multivariate process, it was important at first to define the influence of each factor. In particular, it was important to define the influence of geology as being closely associated to indoor radon. This association was indeed observed for the Swiss data but not probed to be the sole determinant for the spatial modeling. The statistical analysis of data, both at univariate and multivariate level, was followed by an exploratory spatial analysis. Many tools proposed in the literature were tested and adapted, including fractality, declustering and moving windows methods. The use of Quan-tité Morisita Index (QMI) as a procedure to evaluate data clustering in function of the radon level was proposed. The existing methods of declustering were revised and applied in an attempt to approach the global histogram parameters. The exploratory phase comes along with the definition of multiple scales of interest for indoor radon mapping in Switzerland. The analysis was done with a top-to-down resolution approach, from regional to local lev¬els in order to find the appropriate scales for modeling. In this sense, data partition was optimized in order to cope with stationary conditions of geostatistical models. Common methods of spatial modeling such as Κ Nearest Neighbors (KNN), variography and General Regression Neural Networks (GRNN) were proposed as exploratory tools. In the following section, different spatial interpolation methods were applied for a par-ticular dataset. A bottom to top method complexity approach was adopted and the results were analyzed together in order to find common definitions of continuity and neighborhood parameters. Additionally, a data filter based on cross-validation was tested with the purpose of reducing noise at local scale (the CVMF). At the end of the chapter, a series of test for data consistency and methods robustness were performed. This lead to conclude about the importance of data splitting and the limitation of generalization methods for reproducing statistical distributions. The last section was dedicated to modeling methods with probabilistic interpretations. Data transformation and simulations thus allowed the use of multigaussian models and helped take the indoor radon pollution data uncertainty into consideration. The catego-rization transform was presented as a solution for extreme values modeling through clas-sification. Simulation scenarios were proposed, including an alternative proposal for the reproduction of the global histogram based on the sampling domain. The sequential Gaussian simulation (SGS) was presented as the method giving the most complete information, while classification performed in a more robust way. An error measure was defined in relation to the decision function for data classification hardening. Within the classification methods, probabilistic neural networks (PNN) show to be better adapted for modeling of high threshold categorization and for automation. Support vector machines (SVM) on the contrary performed well under balanced category conditions. In general, it was concluded that a particular prediction or estimation method is not better under all conditions of scale and neighborhood definitions. Simulations should be the basis, while other methods can provide complementary information to accomplish an efficient indoor radon decision making.