Numerical studies of compositional gradients in oreophilous vegetation (northern Apennines,Italy)

Summary A numerical study of a set of phytosociological data from oreophilous vegetation in the Apennines (Italy) has been performed. Ranking, classification and ordination methods have been used. The advantages of the joint use of the different methods is diseussed. The results confirm a compositional gradient related to degradation due to grazing, and another, related to base-richness in the soil. Compositional variation does not appear strongly disjointed.Nomenclature follows Zangheri (1976)This work was supported by a grant from the Italian CNR, Pfoject Environment Quality. We wish to thank Professor L. Orlóci for discussions and facllities, and Drs. D. Ubaldi and C. Ferrari for participation in the fieldwork.     

Assessment of Multiple Ordinal Endpoints

Ranking multivariate ordinal data and applying a non‐parametric test is an analytical approach commonly employed to compare treatments. We study three types of ranking and demonstrate how to combine them. The ranking methods rest upon partial orders of the multidimensional measurements or upon the sum of ranks. Since their usage is simple as regards statistical assumptions and technical realization, they are also adapted for health professionals without deep statistical knowledge. Our goal is discussing differences between the approaches and disclosing possible statistical consequences of their usage (© 2009 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Identifying the methodological characteristics of European green city rankings

City rankings that aim to measure the environmental sustainability of European cities may contribute to the evaluation and development of environmental policy of European cities. The objective of this study is to identify and evaluate the methodological characteristics of these city rankings. First, a methodology was developed to systematically identify methodological characteristics of city rankings within different steps of the ranking development process. Second, six city rankings (European Energy Award, European Green Capital Award, European Green City Index, European Soot-free City Ranking, RES Champions League, Urban Ecosystem Europe) were examined. Official websites and any methodological documents found on those websites were content analyzed using the developed methodology. Interviews with representatives of the city rankings were conducted to acquire any additional information. Results showed that the city rankings varied greatly with respect to their methodological characteristics and that all city rankings had methodological weaknesses. Developers of city rankings are advised to use the methodology developed in this study to find methodological weaknesses and improve their ranking. In addition, developers ought to be more transparent about the methodological characteristics of their city rankings. End-users of city rankings are advised to use the developed methodology to identify and evaluate the methodological characteristics of city rankings before deciding to act on ranking results.     

A Method to Rank Multiple Trait Performance of Biological Entities

Performance of biological entities are defined, in general by k inter-dependent traits whose error variances are heterogeneous. A method of arriving at “Performance Scores” for the entities along with a multivariate test of significance of k-trait differences is described. They are used to compute, for each entity, a “Performance Index” on which a ranking of the relative performance is obtained. The improvement in the present method over an earlier one is illustrated with a numerical example.     

Identifying conservation priorities when data are scanty: A case study with small mammals in Italy

Species prioritisation is an important component of conservation strategies. However, identifying species that are threatened is not easy for many taxa that lack detailed information on distribution and population trends. We propose a ranking system for small mammals, based on their degree of vulnerability and their conservation value. Scores were derived from data on life history traits and ecological requirements of individual species, with respect to their sensitivity to changes in landscape and the composition and qualities of ecosystems. Twelve variables were considered, related to the distribution, demography, ecological adaptability, and their endemism and taxonomic diversification. Rodents with the highest score values were either characteristic of mountain habitats (Apodemus alpicola, Chionomys nivalis and Marmota marmota), typical of lowlands (Micromys minutus) or forest species (dormice), and they were also short living, with few reproduction events. Top ranking Soricomorpha were endemic (Crocidura sicula, C. pachyura), range restricted (Sorex alpinus, Talpa caeca) and habitat specialists (Neomys fodiens, N. anomalus), and were further characterised by low reproduction, low dispersal ability, and restricted elevation range. The factors used in the score system were able to emphasise localised endemisms that could be recognised in the future whenever subspecies should be promoted to the rank of species. Soricomorpha, highlighted in the IUCN national red list as nearly threatened or for the absence of information, ranked at the top of our list. The methodological framework proposed here could be used when a pool of species needs to be evaluated for further investigation or conservation actions, helping by focusing on species that are more sensitive to habitat changes or have an intrinsic conservation value.     

Ranking species based on the components of equivocation information

Summary The results presented in this paper suggest that for species to be weighted according to their suitability to characterize isolated groups of relevés in a phytosociological table, the equivocation information may serve as a suitable weight. The appropriate formulations are derived and computed for some data from a salt marsh community.Contribution from the Working Group for Data-Processing in Phytosociology, International Society for Vegetation Science.The author thanks the National Research Council of Canada for support given.     

On the probability of correct selection for large k populations, with application to microarray data

One frontier of modern statistical research is the problems arising from data sets with extremely large k (>1000) populations, e.g. microarray and neuroimaging data. For many such problems the focus shifts from testing for significance to selecting, filtering, or screening. Classical Ranking and Selection Methodology (RSM) studied the probability of correct selection (PCS). PCS is the probability that the "best" (t = 1) of k populations is truly selected, according to some specified criteria of best. This paper extends and adapts two selection goals from the RSM literature that are suitable for large k problems (d-best and G-best selection). It is then shown how estimation of PCS for selecting multiple (t > 1) populations with d-best and G-best selection can be implemented to provide a useful measure of the quality of a given selection. A simulation study and the application of the proposed method to a benchmark microarray data set show it is an effective and versatile tool for assessing the probability that a particular gene selection or gene filtering step truly obtains the best genes. Moreover, the proposed method is fully general and may be applied to any such extremely large k problem.