Variation in the proportion of flower visitors of Arum maculatum along its distributional range in relation with community‐based climatic niche analyses

Here, we investigate the geographical constancy in the specificity level of the specialized lure-and-trap pollination antagonism involving the widespread European Arum maculatum and its associated Psychodid pollinators. Until now, studies concurred in demonstrating that one single insect species, Psychoda phalaenoides, efficiently cross-pollinated plants; researches were, however, performed locally in western Europe. In this study we characterize for the first time the flower visitors' composition at the scale of the distribution range of A. maculatum by intensively collecting plants and insects throughout the European continent. We further correlate local climatic characteristics with the community composition of visiting arthropods.Our results show that flowers are generally visited by P. phalaenoides females, but not over the whole distribution range of the plant. In some regions this fly species is less frequent or even absent and another species, Psycha grisescens, becomes the prevailing visitor. This variability is geographically structured and can be explained by climatic factors: the proportion of P. grisescens increases with higher annual precipitations and lower precipitations in the warmest trimester, two characteristics typical of the Mediterranean zone. Climate thus seems driving the specificity of this interaction, by potentially affecting the phenology of one or both interacting species, or even of volatile and heat production in the plant. This result therefore challenges the specificity of other presumably one-to-one interactions covering wide distribution ranges, and provides an example of the direct effect that the abiotic environment can have on the fate of plant-insect interactions.

Kernel-based Mapping of Meteorological Fields in Complex Orography

Due to the advances in sensor networks and remote sensing technologies, the acquisition and storage rates of meteorological and climatological data increases every day and ask for novel and efficient processing algorithms. A fundamental problem of data analysis and modeling is the spatial prediction of meteorological variables in complex orography, which serves among others to extended climatological analyses, for the assimilation of data into numerical weather prediction models, for preparing inputs to hydrological models and for real time monitoring and short-term forecasting of weather.In this thesis, a new framework for spatial estimation is proposed by taking advantage of a class of algorithms emerging from the statistical learning theory. Nonparametric kernel-based methods for nonlinear data classification, regression and target detection, known as support vector machines (SVM), are adapted for mapping of meteorological variables in complex orography.With the advent of high resolution digital elevation models, the field of spatial prediction met new horizons. In fact, by exploiting image processing tools along with physical heuristics, an incredible number of terrain features which account for the topographic conditions at multiple spatial scales can be extracted. Such features are highly relevant for the mapping of meteorological variables because they control a considerable part of the spatial variability of meteorological fields in the complex Alpine orography. For instance, patterns of orographic rainfall, wind speed and cold air pools are known to be correlated with particular terrain forms, e.g. convex/concave surfaces and upwind sides of mountain slopes.Kernel-based methods are employed to learn the nonlinear statistical dependence which links the multidimensional space of geographical and topographic explanatory variables to the variable of interest, that is the wind speed as measured at the weather stations or the occurrence of orographic rainfall patterns as extracted from sequences of radar images. Compared to low dimensional models integrating only the geographical coordinates, the proposed framework opens a way to regionalize meteorological variables which are multidimensional in nature and rarely show spatial auto-correlation in the original space making the use of classical geostatistics tangled.The challenges which are explored during the thesis are manifolds. First, the complexity of models is optimized to impose appropriate smoothness properties and reduce the impact of noisy measurements. Secondly, a multiple kernel extension of SVM is considered to select the multiscale features which explain most of the spatial variability of wind speed. Then, SVM target detection methods are implemented to describe the orographic conditions which cause persistent and stationary rainfall patterns. Finally, the optimal splitting of the data is studied to estimate realistic performances and confidence intervals characterizing the uncertainty of predictions.The resulting maps of average wind speeds find applications within renewable resources assessment and opens a route to decrease the temporal scale of analysis to meet hydrological requirements. Furthermore, the maps depicting the susceptibility to orographic rainfall enhancement can be used to improve current radar-based quantitative precipitation estimation and forecasting systems and to generate stochastic ensembles of precipitation fields conditioned upon the orography.

Association of statins with inflammatory cytokines: a population-based Colaus study.

PURPOSE: A pleiotropic effect of statins has been reported in numerous studies. However, the association between statin use and inflammatory cytokines is controversial. We examined the associations between statin use and C-reactive protein (CRP), tumour necrosis factor α (TNF-α), interleukin-1β (IL-1β) and interleukin-6 (IL-6) in a healthy Caucasian population. METHODS: Cross-sectional study of 6184 participants aged 35-75years from Lausanne, Switzerland. Cytokines were assessed by multiplexed particle-based flow cytometric assay. Self-reported history of medication was collected for statins and other medication. 99 participants without cytokine data were excluded. RESULTS: Among the 6085 participants, 2289 (37.6%), 451 (7.4%) and 43 (0.7%) had IL-1β, IL-6 and TNF-α levels below detection limits, respectively. On multivariate analysis adjusting for age, gender, smoking status, body mass index, hypertension, diabetes, baseline cardiovascular disease, total cholesterol, anti-inflammatory use, other cytokine modifying drugs and other drugs, participants on statins had significantly lower CRP levels (adjusted mean±standard error: 1.22±1.05 vs. 1.38±1.04mg/L for use and non-use, respectively, p<0.01 on log-transformed data). Conversely, no association was found between statin use and IL-1β (p=0.91), IL-6 (p=0.25) or TNF-α (p=0.28) levels. On multivariate analysis, individuals in the statin group (β coefficient=-0.12; 95% CI=-0.21, -0.03) had lower levels of CRP as compared to those in the reference group (i.e. those not using statin). However, no significant associations were observed between IL-1β, IL-6 and TNF-α and statins. CONCLUSION: Individuals on statins have lower CRP levels; conversely, no effect was found for IL-1β, IL-6 and TNF-α levels.