The characteristics of precipitation in regional climate model simulations deviate considerably from those of the observed data; therefore, bias correction is a standard part of most climate change impact assessment studies. The standard approach is that the corrections are calibrated and applied separately for individual spatial points and meteorological variables. For this reason, the correlation and covariance structures of the observed and corrected data differ, although the individual observed and corrected data sets correspond well in their statistical indicators. This inconsistency may affect impact studies using corrected simulations.This study presents a new approach to the bias correction utilizing principal components in combination with quantile mapping, which allows for the correction of multivariate data sets. The proposed procedure significantly reduces the bias in covariance and correlation structures, as well as that in the distribution of individual variables. This is in contrast to standard quantile mapping, which only corrects the individual distributions, and leaves the dependence structure biased.

Summary1. Accurately measuring the rate of spread for expanding populations is important for reliably predicting their future spread, as well as for evaluating the effect of different conditions and management activities on that rate of spread. 2. Although a number of methods have been developed for such measurement, all these are designed only for one-or two-dimensional spread. Species dispersing along rivers, however, require specific methods due to the distinctly branching structure of river networks. 3. In this study, we analyse data regarding Eurasian beavers' modern recolonization of the Czech Republic. We developed a new methodology for quantifying spread of species dispersing along streams based on representation of the river network by means of a weighted graph. 4. We defined two different network-based spread rate measures, one estimating the rate of range expansion, with the range defined as the total length of occupied streams, and the second, named range diameter, quantifying the progress along one or several main streams. In addition, we estimated the population growth rates, and, dividing the population size by the range size, we measured the density of beaver records within their overall range. Using linear regression, we compared four beaver populations under different environmental conditions in terms of each of these measures. Finally, we discuss the differences between our method and the classical approaches. 5. Our method provided substantially higher spread rate values than did the classical methods. Both population growth and range expansion were found to follow logistic growth. In cases of there being no considerable barriers in dispersal routes, the rate of progress along main streams did not differ significantly among populations. In homogeneous environments, population densities remained relatively constant over time even though overall population sizes increased. This indicates that at large spatial scales, the population growth of beavers occurs through progressive space filling rather than increasing population density.

Size-assortative mating (SAM) is a widespread phenomenon related to individual fitness. In our study, we examined: (i) the appearance of SAM, and (ii) the effect of sex ratio on intensity of SAM in wild populations of two explosively breeding anurans, common frogs,Rana temporaria, and common toads,Bufo bufo. Despite a higher male-biased operational sex ratio (OSR) in toads, the body lengths of the paired males and females were significantly correlated only in frogs. Increasing male-male competition, assessed via the OSR, resulted in a stronger correlation also in frogs only. Thus, great variability in the presence and intensity of SAM has been observed within both studied species.

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Abstract. Simulations of regional or global climate models are often used for climate change impact assessment. To eliminate systematic errors, which are inherent to all climate model simulations, a number of post-processing (statistical downscaling) methods have been proposed recently. In addition to basic statistical properties of simulated variables, some of these methods also consider a dependence structure between or within variables. In the present paper we assess the changes in cross- and auto-correlation structures of daily precipitation in six regional climate model simulations. In addition the effect of outliers is explored making a distinction between ordinary outliers (i.e. values exceptionally small or large) and dependence outliers (values deviating from dependence structures). It is demonstrated that correlation estimates can be strongly influenced by a few outliers even in large datasets. In turn, any statistical downscaling method relying on sample correlation can therefore provide misleading results. An exploratory procedure is proposed to detect the dependence outliers in multivariate data and to quantify their impact on correlation structures.