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Factorial analyses are frequently used in ecological studies to identify different groups, which is a valid approach if those variables which show the highest variability also best differentiate among groups. Here we present VIDTAXA, an algorithm designed to identify statistically different groups in Principal Components and Correspondence analyses, without previous knowledge of the various potential groups present in the dataset. VIDTAXA is freely available as a function of the VARSEDIG package, which is available as an RWizard application and as an R package on CRAN. As a demonstration of VIDTAXA's potential, we used the algorithm in an example of phenetic taxonomy, for the identification of statistically different taxa in a number of marine Scorpaeniform species. This algorithm. however, it may be used with any kind of data, whether qualitative or quantitative, for the identification of statistically different groups in ecological studies.

1. Environmental impact assessment (EIA) studies are most frequently focused on assessing the possible impacts of a proposal and identifying alternatives, in order to minimize environmental damage. Little attention has been paid, however, to the development of statistical methods for evaluation of potential changes in the environment after the impact, particularly when control sites are not available. 2. Here, the EIA algorithm is presented, available as an RWizard ( Guisande et al., 2014 ) application on http://www.ipez.es/RWizard . This algorithm provides tools for the detection of variables which change significantly, following environmental impact (EI), once the time at which the impact occurred is available. 3. In the algorithm, the following steps are taken: 1) variables are prioritized by their discrimination capacity to distinguish between the before and after of the EI; 2) estimation of the Area Under the Curve (AUC) of Receiver Operating Characteristic curves (ROC curves) is obtained for the before and after of the EI, 3) comparison of the AUC of the ROC curves (before and after the EI), with an AUC of 0.5 (the situation in which two distributions are equal), 4) identification, with a Monte-Carlo test, of the sampling sites for each sampling time, following the EI, which are significantly different than before the EI (hereinafter, outliers), 6) the augmented Dickey-Fuller test is used to determine whether there is a significant increasing trend in the percentage of outliers after the EI. 4. As a demonstration of the potential of the EIA algorithm, monthly climatic and quarterly agronomic data from sampling sites near the Sogamoso reservoir (Santander, Colombia) were used. Throughout the manuscript, the main strengths of the EIA algorithm are shown, which can be summarized as follows: 1) it allows a real, multivariate approach 2) it may be used with any kind of experimental design (Before-After, Before-After-Control-Impactor, Impact vs. Reference Sites analysis, and even designs with information from before the EI, if impact and control sites are available), 3) it overcomes the shortcomings of often-used statistical methods in EIA studies for identification of changes following an EI, and finally, 4) it allows for differentiation between natural and impact-induced variability on different temporal scales.

The aim of the SINENVAP algorithm is to facilitate the estimation of spatial interpolations within polygons, by using simple, ordinary, and universal kriging. This algorithm is available as a function of the EcoIndR package, which is available as an RWizard application and an R package on CRAN. The main strengths of this algorithm include: the possibility of using different file formats for polygon variable and coordinate inputs (CSV, EXCEL, RData, shape or ASC), compatibility with UTM or decimal coordinates, estimation of optimal grid cell size, the possibility of selecting only points inside polygons from the entire dataset, the application of a jitter function or to estimate the mean value of the variable for duplicated coordinates, reservation a percentage of data for validation, selection of those grid coordinates nearest the data coordinates reserved for validation, the possibility of fitting 13 different models into the semivariogram, automatic selection of the model that best predicts the data reserved for validation through the use of seven accuracy measures, the possibility of using countries, regions, departments, river basins, or even alpha shape distribution as polygons, and finally, depiction of contour plots with the spatial interpolation of the variable and the error within polygons. The spatial interpolation of the temperature in North America and the distribution of a virtual species are used as examples of this algorithm's potential to perform spatial interpolations on both large and small scales.