A new approach for evaluating internal cluster validation indices

A vast number of different methods are available for unsupervised classification. Since no algorithm and parameter setting performs best in all types of data, there is a need for cluster validation to select the actually best-performing algorithm. Several indices were proposed for this purpose without using any additional (external) information. These internal validation indices can be evaluated by applying them to classifications of datasets with a known cluster structure. Evaluation approaches differ in how they use the information on the ground-truth classification. This paper reviews these approaches, considering their advantages and disadvantages, and then suggests a new approach

Cautionary note on calculating standardized effect size (SES) in randomization test

In community ecology, randomization tests with problem specific test statistics (e.g., nestedness, functional diversity, etc.) are often applied. Researchers in such studies may want not only to detect the significant departure from randomness, but also to measure the effect size (i.e., the magnitude of this departure). Measuring the effect size is necessary, for instance, when the roles of different assembly forces (e.g., environmental filtering, competition) are compared among sites. The standard method is to calculate standardized effect size (SES), i.e., to compute the departure from the mean of random communities divided by their standard deviations. Standardized effect size is a useful measure if the test statistic (e.g., nestedness index, phylogenetic or functional diversity) in the random communities follows a symmetric distribution. In this paper, I would like to call attention to the fact that SES may give us misleading information if the distribution is asymmetric (skewed). For symmetric distribution median and mean values are equal (i.e., SES = 0 indicates p = 0.5). However, this condition does not hold for skewed distributions. For symmetric distributions departure from the mean shows the extremity of the value, regardless of the sign of departure, while in asymmetric distributions the same deviation can be highly probable and extremely improbable, depending on its sign. To avoid these problems, I recommend checking symmetry of null-distribution before calculating the SES value. If the distribution is skewed, I recommend either log-transformation of the test statistic, or using probit-transformed p-value as effect size measure. | Supporting Information Supporting Information </supplementary-material

Morphological plasticity in the rhizome system of Solidago gigantea (Asteraceae): Comparison of populations in a wet and a dry habitat

This study was motivated by the fact that although the plasticity of its above-ground organs is obvious in natural conditions and there are many data on the plasticity of Solidago?s rhizome system in glasshouse experiments, there are no data on below-ground plasticity under natural conditions. We compared the morphology of rhizomes in two, contrasting habitats. We found that rhizome system responded to environmental conditions: in the dry habitat, ramets developed more but shorter rhizomes compared to the wet habitat. The decrease in rhizome length can be explained by the decrease in the size of above-ground organs, but the increase of rhizome number cannot. The most important regulating factor of rhizome growth is probably its mechanical restriction by the root biomass of other species

Kékperjés cseres-tölgyesek a belső-somogyi homokvidék déli részén

Oak woods were investigated, formed on aeolian sandy soil, in a mesophilic deciduous forest zone. These forests are dominated by Quercus robur. Forty phytosociological relevés were analysed examining similarities with each other and with Quercus cerris-Quercus robur associations earlier described in Carpathian Basin. According to the results, all samples belong to Molinio litoralis - Quercetum cerris Szodfridt et Tallós ex Borhidi 1996. The phytosociological characteristics of these oak forests are summarized using the 40 new phytosociological relevés

Testing the ability of functional diversity indices to detect trait convergence and divergence using individual-based simulation

The quest for 'assembly rules', that is the processes shaping the species composition of communities, is a central issue in community ecology. Nevertheless, so far there is no general agreement on a framework to detect assembly rules in real-life data: several key elements are still missing or heavily disputed, including the choice of the appropriate test statistic (e.g. functional diversity index) and randomization strategy for each major assembly process. Simulation studies based on artificial communities can help to explore the usefulness of different approaches in detecting assembly rules. Nevertheless, the currently dominant approach to simulate artificial communities (i.e. selecting species from a pool based solely on trait values) oversimplifies the complex processes involved in community assembly and thus fails to produce realistic patterns. Consequently, its value for testing methodologies is seriously limited. In this study, we implemented a flexible, individual-based algorithm simulating real-life community processes (individuals are born, survive, compete for resources, reproduce and die), to generate artificial species composition data. With the help of this algorithm, we estimated the type I error rates and the statistical power of five different diversity indices (FRic, Rao's quadratic entropy, FEve, the variance of functional distances, and the variance of nearest-neighbour distances) in combination with three randomization strategies (randomization of trait values in the whole data set, within-plots and within the range of trait values occurring in each plot) for detecting two underlying assembly processes (habitat filtering and limiting similarity). We also tested the influence of all adjustable simulation parameters on the simulation results in a sensitivity analysis framework. The results of the sensitivity analysis show that the individual-based simulation framework proposed here can be used for creating artificial community data with realistic pattern of trait values. Based on the results, Rao's quadratic entropy performed best for detecting both habitat filtering (trait convergence) and limiting similarity (trait divergence). Functional richness may also be suitable for detect traiting convergence. Functional evenness and variance of nearest-neighbour distances, however, should not be used for finding assembly rules. © 2015 British Ecological Society