Impacts of urban sprawl on species richness of plants,butterflies, gastropods and birds: not only built-up area matters

Urban growth is a major factor of global environmental change and has important impacts on biodiversity, such as changes in species composition and biotic homogenization. Most previous studies have focused on effects of urban area as a general measure of urbanization, and on few or single taxa. Here, we analyzed the impacts of the different components of urban sprawl (i.e., scattered and widespread urban growth) on species richness of a variety of taxonomic groups covering mosses, vascular plants, gastropods, butterflies, and birds at the habitat and landscape scales. Besides urban area, we considered the average age, imperviousness, and dispersion degree of urban area, along with human population density, to disentangle the effects of the different components of urban sprawl on biodiversity. The study was carried out in the Swiss Plateau that has undergone substantial urban sprawl in recent decades.Vascular plants and birds showed the strongest responses to urban sprawl, especially at the landscape scale, with non-native and ruderal plants proliferating and common generalist birds increasing at the expense of specialist birds as urban sprawl grew. Overall, urban area had the greatest contribution on such impacts, but additional effects of urban dispersion (i.e., increase of non-native plants) and human population density (i.e., increases of ruderal plants and common generalist birds) were found. Our findings support the hypothesis that negative impacts of urban sprawl on biodiversity can be reduced by compacting urban growth while still avoiding the formation of very densely populated areas.     

Who’s About to Leave? A Global Survey of Aspirations and Intentions to Migrate

This article analyses aspirations and intentions to migrate based on Gallup World Poll for the period 2010–2015. We estimate individual‐level traits associated with aspirations and intentions to migrate across groups of countries in different regions and with different income levels. This paper brings together previous hypotheses regarding migration aspirations and tests them under several specifications, while keeping separate findings according to migration aspirations and intentions to appreciate differences between them. Being dissatisfied with one’s own standard of living is associated with a higher probability of desiring to move, while the relation with the actual preparation to migrate is less clear. Some individual traits remain significant across (almost) all specifications: being male, foreign‐born, highly educated, and having networks abroad are associated with higher probability of preparing for international migration. Aspects related to one’s economic situation are not consistently significant across specifications, signalling the importance of contextual analyses for these factors.     

Behavioural Addictions in Adolescents and Young Adults: Results from a Prevalence Study

Our study aims to assess the prevalence of behavioural addictions in an adolescent population, evaluating the effects of gender and age, and to assess the correlations among different behavioural addictions. 2853 high school students were assessed in order to evaluate the prevalence of behavioural addictions such as Pathological Gambling (PG), Compulsive Buying (CB), Exercise Addiction (EA), Internet Addiction (IA), and Work Addiction (WA), in a population of Italian adolescents. The South Oaks Gambling Screen-Revised Adolescent (SOGS-RA), the Compulsive Buying Scale (CBS), the Exercise Addiction Inventory (EAI), the Internet Addiction Test (IAT), and the Work Addiction Risk Test (WART), were compiled anonymously by the students. Overall prevalence was 7.0% for PG, 11.3% for CB, 1.2% for IA, 7.6% for WA, 8.5% for EA. PG and EA were more common among boys, while gender had no effect on the other conditions. CB was more common among younger (<18 years old) students. The scores of all of these scales were significantly correlated. The strong correlation among different addictive behaviours is in line with the hypothesis of a common psychopathological dimension underlying these phenomena. Further studies are needed to assess personality traits and other clinical disorders associated with these problems behaviours.     

Interculturalism in Italian primary schools with a high concentration of immigrant students

The present article focuses on quantitative research carried out on a statistically representative sample of Italian primary schools with a high concentration of immigrant students. Research data show that schools with a higher number of immigrant students offer a greater number and a wider variety of intercultural initiatives. The presence of immigrant students serves as an effective stimulus in enhancing intercultural approaches. Furthermore, the opinions of head teachers about the proposal of ‘separate classes’ for immigrant students and the proposed ‘limit of 30% on the presence’ of immigrant students are negative. These judgements do not change in a statistically significant manner in relation to concentration of immigrant students: in particular, higher rates of immigrant students are not related to more positive opinions. Continuing education on intercultural issues is widely adopted, mainly on behalf of teachers and less often on behalf of head teachers and other school personnel. Finally, activities by ‘linguistic facilitators’ and ‘linguistic and cultural mediators’ are not significantly related to a decrease in perceived concern regarding problematic situations.     

Bayesian outlier testing using the predictive distribution for a linear model op constant intraclass form

The problem of testing suspected outliers from a linear model with constant intraclass correlation is considered from a Bayesian viewpoint. The main objective of this paper is to develop an outlier test procedure based on the predictive distribution of suspected outlier observations given a set of existing inlier observations. The test procedure is easily performed with the usual F and t distributions.     

Parameter estimation of state space models for univariate observations

This paper contributes to the problem of estimation of state space model parameters by proposing estimators for the mean, the autoregressive parameters and the noise variances which, contrarily to maximum likelihood, may be calculated without assuming any specific distribution for the errors. The estimators suggested widen the scope of the application of the generalized method of moments to some heteroscedastic models, as in the case of state-space models with varying coefficients, and give sufficient conditions for their consistency. The paper includes a simulation study comparing the proposed estimators with maximum likelihood estimators. Finally, these methods are applied to the calibration of the meteorological radar and estimation of area rainfall.     

Multivariate geostatistical mapping of radioactive contamination in the Maddalena Archipelago (Sardinia, Italy): spatial special issue

To improve the quality of prediction of radioactive contamination, geostatistical methods, and in particular multivariate geostatistical models, are increasingly being used. These methods, however, are optimal only in the case in which the data may be assumed Gaussian and do not properly cope with data measurements that are discrete, nonnegative or show some degree of skewness. To deal with these situations, here we consider a hierarchical model in which non-Gaussian variables of different kind are handled simultaneously. We show that when observations are assumed to be conditionally distributed as Poisson and Gamma, variograms and cross-variograms have convenient simple forms, and estimation of the parameters of the model can be carried out by Monte Carlo EM. This work was inspired by radioactive contamination data from the Maddalena Archipelago (Sardinia, Italy).     

Approximate Bayesian inference in semi-mechanistic models

Inference of interaction networks represented by systems of differential equations is a challenging problem in many scientific disciplines. In the present article, we follow a semi-mechanistic modelling approach based on gradient matching. We investigate the extent to which key factors, including the kinetic model, statistical formulation and numerical methods, impact upon performance at network reconstruction. We emphasize general lessons for computational statisticians when faced with the challenge of model selection, and we assess the accuracy of various alternative paradigms, including recent widely applicable information criteria and different numerical procedures for approximating Bayes factors. We conduct the comparative evaluation with a novel inferential pipeline that systematically disambiguates confounding factors via an ANOVA scheme.