The decline of diadromous fish in Western Europe inland waters: mains causes and consequence

Relative to the overwhelming information available on marine fisheries, inland systems have received less attention within the global fisheries crisis. The present situation however, raises serious concerns and this chapter is an attempt to summarize the status of Western European inland fisheries focused on some of the most valuable species targeted in Western Europe: diadromous fishes, including shads, salmonids and the European eel. These species have been reported to be declining over the last decades and the underlying causes appear to be related with human impact on habitat, water quality deterioration, river regularizations, introduction of invasive species, and overexploitation whereas the effects of climate change are still under debate. Overall, these species not only have economic importance but also play fundamental ecological roles in inland aquatic habitats including nutrient cycling, trophic dynamics and overall productivity. Consequently, a decline of migratory fish populations may have important direct and future consequences on the economy. Nevertheless, it also means that fewer species are present to perform critical functions and the consequences may be severe when species with disproportionately influence on biogeochemical cycles, energy fluxes and trophic dynamics are lost. In view of this, the sustainable future of inland fisheries will certainly include a compromise with biodiversity maintenance. Since for different species and types of habitat the major impacts differ, some case studies are examined and management proposals are discussed

Aggregated functional data model for Near-Infrared Spectroscopy calibration and prediction

Calibration and prediction for NIR spectroscopy data are performed based on a functional interpretation of the Beer-Lambert formula. Considering that, for each chemical sample, the resulting spectrum is a continuous curve obtained as the summation of overlapped absorption spectra from each analyte plus a Gaussian error, we assume that each individual spectrum can be expanded as a linear combination of B-splines basis. Calibration is then performed using two procedures for estimating the individual analytes curves: basis smoothing and smoothing splines. Prediction is done by minimizing the square error of prediction. To assess the variance of the predicted values, we use a leave-one-out jackknife technique. Departures from the standard error models are discussed through a simulation study, in particular, how correlated errors impact on the calibration step and consequently on the analytes' concentration prediction. Finally, the performance of our methodology is demonstrated through the analysis of two publicly available datasets.Comment: 27 pages, 7 figures, 7 table

Estimação por minima distancia de Hellinger

Orientador : Jose Antonio CordeiroDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matematica, Estatistica e Ciencia da ComputaçãoResumo: Não informadoAbstract: Not informedMestradoMestre em Estatístic

A Hierarchical Model for Aggregated Functional Data

In many areas of science one aims to estimate latent sub-population mean curves based only on observations of aggregated population curves. By aggregated curves we mean linear combination of functional data that cannot be observed individually. We assume that several aggregated curves with linear independent coefficients are available. More specifically, we assume each aggregated curve is an independent partial realization of a Gaussian process with mean modeled through a weighted linear combination of the disaggregated curves. We model the mean of the Gaussian processes as a smooth function approximated by a function belonging to a finite dimensional space ${\cal H}_K$ which is spanned by $K$ B-splines basis functions. We explore two different specifications of the covariance function of the Gaussian process: one that assumes a constant variance across the domain of the process, and a more general variance structure which is itself modelled as a smooth function, providing a nonstationary covariance function. Inference procedure is performed following the Bayesian paradigm allowing experts' opinion to be considered when estimating the disaggregated curves. Moreover, it naturally provides the uncertainty associated with the parameters estimates and fitted values. Our model is suitable for a wide range of applications. We concentrate on two different real examples: calibration problem for NIR spectroscopy data and an analysis of distribution of energy among different type of consumers.Comment: 29 pages, 12 figure