Generalized biodiversity assessment by Bayesian nested random effects models with spyke-and-slab priors

We analyze variations in alpha-diversity of benthic macroinvertebrate communities in an Italian lagoon system using Bayesian hierarchical models with nested random effects. Our aim is to understand how spatial scales influence microhabitat definition. Tsallis entropy measures diversity and spike-and-slab regression selects predictors

I modelli geo-additivi per l'analisi del grado di salinita' di un suolo

: In questo studio un modello geo-additivo e' stato utilizzato per analizzare la distribuzione spaziale del tasso di assorbimento del sodio nella zona costiera di Muravera-Villaputzu (CA) per individuare le cause che ne hanno determinato leccessivo grado di salinita', dannoso per le colture agrumicole praticate nella zona. I modelli geo-additivi rappresentano unevoluzione del kriging universale e consentono di considerare esplicitamente i legami non lineari tra la risposta e le covariate e la correlazione spaziale descritta mediante una funzione di autocorrelazione.

Spatially correlated mixed-effects models for the analysis of soil water retention.

The knowledge of hydraulic properties of soil is necessary in many environmental applications and land planning. These properties, however, are difficult to determine and often they demand high labour costs, for which the tendency is to estimate them on the base of other more easily measurable or already available soil data. The level of detail reached using this method is not always satisfactory for some applications to basin scale, where variables to measure the morphologic property of the landscape are required. This study is proposed to characterize the spatial distribution of the water retention of a soil on wide scale using data relative to the physical, topographical and chemical characteristics of the soil within a model based approach.Linear Mixed Models, Spatial Continuous Autoregressive Correlation Structure, Soil Water Retention.

Assessing the role of the spatial scale in the analysis of lagoon biodiversity. A case-study on the macrobenthic fauna of the Po River Delta

The analysis of benthic assemblages is a valuable tool to describe the ecological status of transitional water ecosystems, but species are extremely sensitive and respond to both microhabitat and seasonal differences. The identification of changes in the composition of the macrobenthic community in specific microhabitats can then be used as an “early warning” for environmental changes which may affect the economic and ecological importance of lagoons, through their provision of Ecosystem Services. From a conservational point of view, the appropriate definition of the spatial aggregation level of microhabitats or local communities is of crucial importance. The main objective of this work is to assess the role of the spatial scale in the analysis of lagoon biodiversity. First, we analyze the variation in the sample coverage for alternative aggregations of the monitoring stations in three lagoons of the Po River Delta. Then, we analyze the variation of a class of entropy indices by mixed effects models, properly accounting for the fixed effects of biotic and abiotic factors and random effects ruled by nested sources of variability corresponding to alternative definitions of local communities. Finally, we address biodiversity partitioning by a generalized diversity measure, namely the Tsallis entropy, and for alternative definitions of the local communities. The main results obtained by the proposed statistical protocol are presented, discussed and framed in the ecological context

A multivariate circular-linear hidden Markov model for distributions-oriented wind forecast verication

Winds from the North-West quadrant and lack of precipitation are known to lead to an increase of PM10 concentrations in a residential neighborhood of the city of Taranto (Apulia, Italy). In 2012 the local government prescribed a reduction of industrial emissions by 10% every time such meteorological conditions are forecasted 72 hours in advance. Wind prediction is addressed using the Weather Research and Forecasting (WRF) atmospheric simulation system by the Regional Environmental Protection Agency (ARPA Puglia). In the framework of distributions-oriented forecast verication, we investigate the ability of the WRF system to properly predict the local wind speed and direction allowing dierent performances for unknown wind regimes. Ground-observed and WRF-predicted wind speed and direction at a relevant location are jointly modeled as a 4-dimensional time series with a nite number of states (wind regimes) characterized by homogeneous distributional behavior. Observed and simulated wind data are made of two circular (direction) and two linear (speed) variables, then the 4-dimensional time series is jointly modeled by a mixture of projected-skew normal distributions with time-independent states, where the temporal evolution of the state membership follows a rst order Markov process. Parameter estimates are obtained by a Bayesian MCMCbased method and results provide useful insights on wind regimes corresponding to dierent performances of WRF predictions

EURECOM:Monthly Bulletin of European Community Economic and Financial News. July/August 1991 Vol. 3, No. 7

Winds from the North-West quadrant and lack of precipitation are known to lead to an increase of PM10 concentrations over a residential neighborhood in the city of Taranto (Italy). In 2012 the local government prescribed a reduction of industrial emissions by 10% every time such meteorological conditions are forecasted 72 hours in advance. Wind forecasting is addressed using the Weather Research and Forecasting (WRF) atmospheric simulation system by the Regional Environmental Protection Agency. In the context of distributions-oriented forecast verification, we propose a comprehensive modelbased inferential approach to investigate the ability of the WRF system to forecast the local wind speed and direction allowing different performances for unknown weather regimes. Ground-observed and WRF-forecasted wind speed and direction at a relevant location are jointly modeled as a 4-dimensional time series with an unknown finite number of states characterized by homogeneous distributional behavior. The proposed model relies on a mixture of joint projected and skew normal distributions with time-dependent states, where the temporal evolution of the state membership follows a first order Markov process. Parameter estimates, including the number of states, are obtained by a Bayesian MCMC-based method. Results provide useful insights on the performance of WRF forecasts in relation to different combinations of wind speed and direction

Bayesian Measurement Error Correction in Structured Additive Distributional Regression with an Application to the Analysis of Sensor Data on Soil-Plant Variability

The flexibility of the Bayesian approach to account for covariates with measurement error is combined with semiparametric regression models for a class of continuous, discrete and mixed univariate response distributions with potentially all parameters depending on a structured additive predictor. Markov chain Monte Carlo enables a modular and numerically efficient implementation of Bayesian measurement error correction based on the imputation of unobserved error-free covariate values. We allow for very general measurement errors, including correlated replicates with heterogeneous variances. The proposal is first assessed by a simulation trial, then it is applied to the assessment of a soil-plant relationship crucial for implementing efficient agricultural management practices. Observations on multi-depth soil information forage ground-cover for a seven hectares Alfalfa stand in South Italy were obtained using sensors with very refined spatial resolution. Estimating a functional relation between ground-cover and soil with these data involves addressing issues linked to the spatial and temporal misalignment and the large data size. We propose a preliminary spatial interpolation on a lattice covering the field and subsequent analysis by a structured additive distributional regression model accounting for measurement error in the soil covariate. Results are interpreted and commented in connection to possible Alfalfa management strategies

General Health, Psychological Well-Being and Distress of Youth Immigrants in Italy

In seeking to ease the rehabilitation of refugees there has generally been a failure to take account of the complexity of the refugees’ experience of suffering and loss. In this their psychological and emotional well-being as well as the social and economic aspects of the question have frequently been of only peripheral concern, and the response to the psychological impact of violence has been primarily focused on the concept of Post-Traumatic Stress Disorder (PTSD). This approach assumes a pathological response to stress that is both universal across different cultures and centred on the potential of pathologizing coping strategies that might be essential not only for survival but also for psychological well-being

A statistical protocol to describe differences among nutrient utilization patterns of Fusarium spp. and Trichoderma gamsii

The Biolog® Phenotype MicroArrays™ (PM) system offers a simple and cheap tool to rapidly providing a high throughput of information about the phenotypes of fungal isolates in a short lapse of time. In order to improve the use of the PM system in fungal ecology studies, in the present work we propose a new statistical protocol based on two approaches, i.e. a functional PCA to describe similarity patterns of growth curves and a Bayesian GAMs to allow inferences on specific growth features, in order to analyse nutrient fungal utilization in a model system including four causal agents of FHB, the natural competitor Fusarium oxysporum and the beneficial isolate Trichoderma gamsii T6085. Analysis of data collected by the Biolog® Phenotype MicroArrays™ (PM) in our biological system showed a different nutritional competitive potential of the four pathogens, as well as an intermediate behaviour of the natural competitor and of our biocontrol agent. This protocol, applicable to different fungal phenotypical studies both at isolate and community level, allows a full exploitation of data obtained by PM system and provides important information about the nutritional pattern of a single isolate compared to those of other fungi, a key information to be exploited in biocontrol strategies