The ecological status classification of aquatic ecosystems requires separate quantification of natural and anthropogenic sources of environmental variability. A clustering of ecosystems into ecosystem types (i.e. Typology) is used in order to minimise natural variability. Among transitional water quality elements, benthic macroinvertebrates are the most exposed to natural variability patterns due to their life cycles and space-use behavior. Here, we address the ecological status classification issue for Mediterranean and Black Sea lagoons, using benthic macroinvertebrates, from a set of 12 reference lagoons. Two main classification approaches have been proposed in literature: the a-priori approach, based on standard multimetric indices, and the a-posteriori approach, based on linear mixed models. It may happen that different indices are in disagreement with respect to lagoon classification. We propose a Bayesian hierarchical model in which the multimetric indices are jointly modeled through a multivariate normal mixture. Each mixture component is estimated as function of covariates of interest and corresponds to an ecological status. We compare the proposed model with the a-priori and a-posteriori approaches highlighting pros and cons of each method.

In transitional waters and among Water Framework Directive (WFD) (European Parliament, 2000) quality elements, benthic macroinvertebrates are the most exposed to natural variability patterns characteristic of these ecosystems due to their life cycles and space-use behaviour. Here, we address the ecological status classification issue for Mediterranean and Black Sea lagoons, using benthic macroinvertebrates from a set of 13 reference lagoons. Two main classification approaches have been proposed in literature: the a priori approach, based on standard classification boundaries, and the a posteriori approach, basedon classification boundaries set according to linear mixed models. However, the two approaches take into account only partially and at a different extent the natural variability of ecosystem properties, which may lead to inaccuracies of the classification procedure. Moreover, the different proposed multimetric indices are likely to respond differently to different source of stress and natural variability components, adding uncertainty to the classification procedure. We propose a Bayesian hierarchical model in which the multimetric indices are jointly modelled through a multivariate normal mixture, in order to emphasize clusters of ecosystems resulting from the integration of different multimetric indices. Each mixture component is estimated as function of covariates of interest and corresponds to a cluster of sites assumedto be a proxy of a class of ecological status. This assumption has been validated on a set of ecosystems already classified into ecological status classes with standard methods. We apply the model to available data and compare the classification obtained by the proposed model with those given by the a priori and a posteriori approaches, highlighting pros and cons of each method.

The EU Water Framework Directive recognizes benthic macroinvertebrates as good biological indicators of the quality of transitional waters as they are mainly exposed to natural variability patterns characteristic of these ecosystems, due to their life cycles and space-use behavior. Here, we address the classification of the ecological status of three lagoons in Apulia (I), using three multimetric indices based on benthic macroinvertebrates (namely M-AMBI, BITS and ISS), that are likely to respond differently to different sources of stress and natural variability. Lagoon classification is usually based on the discretization of such indices by standard classification boundaries with only partial consideration of the natural variability of ecosystem properties and possible inaccuracies of the classification procedures. We first consider a Bayesian hierarchical model to study the effects of abiotic covariates and external anthropogenic pressure indicators on the multimetric indices, taking into account their correlation structure. In order to further investigate the possible contrasting behavior of the three indices in terms of lagoon classification, we propose a cumulative proportional odds model for the discretized version of the indices as function of the same explanatory ecological variables. This model allows to understand how abiotic variables and anthropogenic pressures affect the classification into different ecological status and to evaluate the agreement between indices in terms of classification. Both models have been estimated in a fully Bayesian framework by a Monte Carlo Markov Chain posterior simulation algorithm.

Winds from the North-West quadrant and lack of precipitation are known to lead to an increase of PM10 concentrations in the city of Taranto. In 2012 the Apulia 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). We investigate the ability of the WRF system to properly predict the local wind speed and direction allowing different performances for unknown weather regimes. Observed and WRF-predicted wind speed and direction at a relevant location are jointly modeled as a 4-dimensional time series with a finite 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-dependent states, where the temporal evolution of the state membership follows a first order Markov process. Parameter estimates are obtained by a Bayesian MCMC-based method and results provide useful insights on wind regimes corresponding to different performances of WRF predictions.

This study analyzes air quality data in the Taranto municipal area. This is a high environmental risk region being characterized by the massive presence of industrial sites with elevated environmental impact activities. We focus on three pollutants formed by combustion processes and related to meteorological conditions, namely particulate matter, sulphur dioxide, and nitrogen dioxide. Preliminary analysis involved addressing several data problems. First of all an imputation technique was considered to cope with the large number of missing data. Missing data imputation was addressed by a leave-one-out procedure based on the recursive Bayesian estimation and prediction of spatial linear mixed effects (LME) models enriched by a time-recursive prior structure. Secondly, a unique daily weather database at the city level was obtained combining data from three stations, characterized by gaps and unreliable measurements. Spatio-temporal modeling of the multivariate normalized daily pollution data was then performed within a Bayesian hierarchical framework, including time varying weather covariates and a semiparametric spatial covariance structure. Daily estimates of the pollutants’ concentration surfaces allow us to identify areas of higher concentration (hot spots), possibly related to specific anthropic activities.

First, we congratulate the authors for their extremely interesting work that sheds new light on Gaussian Markov random-filed (GMRF) modelling. A connection is established with Matérn Gaussian fields through a weak solution of a linear fractional stochastic partial differential equation (SPDE) driven by a Gaussian white noise process. One of the main theoretical results is stated in theorems 3 and 4, where the authors prove the weak convergence of the finite Hilbert representation of the solution for positive integer values of the parameter α to the continuous solution. Recently Bolin and Lindgren (2011) have characterized a class of non-Gaussian random fields as the solution to a nested SPDE driven by Gaussian white noise. Could the authors comment on the possible further extension of their results to the case of SPDEs driven by more general non-Gaussian laws as stable or Lévy processes, in view of obtaining tools suitable for rates or concentrations, which do not typically follow a normal distribution? When d = 2, the integer α restriction of theorems 3 and 4 implies that also the parameter ν is integer in the Matérn class specification. Within this class, the exponential covariance function, one of the most popular in many applied fields, corresponds to the value ν = 0.5. Is the exponential covariance model definitively excluded by the approach proposed? In the discussion the authors recognize that “the approach comes with an implementation and pre-processing cost for setting up the models, as it involves the SPDE solution, triangulations and GMRF representations”. We would be interested in some more comments on the possible qualifications of this cost from an applied statistical point of view. A more detailed comparison of the GMRF solution to competing modelling approaches from a predictive perspective would also be of considerable interest. Here we mainly refer to low-rank (Banerjee et al., 2008; Cressie and Johannesson, 2008; Eidsvik et al., 2010; Crainiceanu et al.,2008) and process convolution methods (Higdon, 1998; Higdon et al., 1999) when estimation is carried out with the same technique (either integrated nested Laplace approximation or Markov chain Monte Carlo methods). Incidentally we think that the geographical interpretation of figure 6(b) would benefit from a more detailed description.

The Santa Maria di Leuca (SML) cold-water coral (CWC) province is a proposed priority conservation area according to several conservation initiatives in the Mediterranean Sea. Part of it is a Fisheries Restricted Area (FRA). Anthropogenic impacts due to fishing on this FRA were investigated using a towed camera system during 2005. The geographic distribution of fishing effort in the SML CWC province was examined through an observers' program of longline and trawl fishing activities during 2009 and 2010 and Vessel Monitoring by satellite System (VMS) data from 2008 to 2013. Using the video system, it was possible to observe evidence of impacts in the FRA due to longlines, proved by remains of lines on the bottoms and/or entangled in corals, and those due to trawl nets, proved by trawl door scars on the bottom. The application of Generalized Liner Models indicates that the impacts due to longline were significantly related to a geographic site characterized by carbonate mounds while those from trawl net were significantly related to the soft bottoms, consisting of bioturbated fine-grained sediments. The presence of waste of various types was also observed in the FRA; plastic was the most widespread waste and was significantly related to a macrohabitat characterized by the presence of corals. The geographic distribution of fishing effort for each type of fishing were rather superimposed in the two years of the observers' program and six years of VMS data with a significantly greater fishing effort outside the FRA than inside this area. The trawlers generally fished on the muddy bottoms of the upper and middle slope within the SML CWC province and near and inside the northward limit of the FRA. The longliners fished mainly on the shelf in north and off the FRA. The coral by-catch was only recorded during 2009 in 26% of the trawl hauls. No coral by-catch was recorded from longlining in either year. The catches from longlining mainly consisted of Chelidonichthys lucerna, Merluccius merluccius and Conger conger while those from trawling mostly consisted of Aristeus antennatus, Aristaeomorpha foliacea and M. merluccius. The information collected during the observers' program and VMS data indicated greater impact due to trawling than longlining. The conservation and effective management of this vulnerable marine ecosystem remain difficult.

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.

The Water Framework Directive (WFD) recognizes benthic macroinvertebrates as a good biological quality element for transitional waters as they are the most exposed to natural variability patterns characteristic of these ecosystems, due to their life cycles and space-use behavior. In this paper we consider the performance of three multimetric indices (namely M-AMBI, BITS and ISS) based on benthic macroinvertebrates abundances, aiming at assessing the ecological status of lagoons and likely to respond differently to different sources of stress and natural variability. In order to investigate the possible contrasting behavior of the three multimetric indices, we propose a Bayesian hierarchical model in which they are jointly modeled as functions of abiotic covariates, external anthropogenic pressure indicators and lagoon effects. The proposed model is applied to data from three lagoons in Apulia and assessed using multiple diagnostic tools. The joint sensitivity of lagoon quality evaluations to available covariates is thus investigated.

The Water Framework Directive (WFD) recognizes benthic macroinvertebrates as a good biological quality element for transitional waters as they are the most exposed to natural variability patterns characteristic of these ecosystems, due to their life cycles and space-use behavior. Here, we address the ecological status classification issue for three lagoons in Apulia, using benthic macroinvertebrates and three proposed multimetric indices (namely M-AMBI, BITS and ISS), likely to respond differently to different sources of stress and natural variability. Lagoon classification is based on discretization by standard classification boundaries with only partial consideration of the natural variability of ecosystem properties and possible inaccuracies of the classification procedures. In order to investigate the possible contrasting behavior of the three classifications, we propose Bayesian hierarchical models in which the multimetric indices and their discrete counterparts are jointly modeled as function of abiotic covariates, external anthropogenic pressures indicators and spatio-temporal effects.

We evaluate the spatiotemporal changes in the density of a particular species of crustacean known as deep-water rose shrimp, Parapenaeus longirostris, based on biological sample data collected during trawl surveys carried out from 1995 to 2006 as part of the international project MEDITS (MEDiterranean International Trawl Surveys). As is the case for many biological variables, density data are continuous and characterized by unusually large amounts of zeros, accompanied by a skewed distribution of the remaining values. Here we analyze the normalized density data by a Bayesian delta-normal semiparametric additive model including the effects of covariates, using penalized regression with low-rank thin-plate splines for nonlinear spatial and temporal effects. Modeling the zero and nonzero values by two joint processes, as we propose in this work, allows to obtain great flexibility and easily handling of complex likelihood functions, avoiding inaccurate statistical inferences due to misclassification of the high proportion of exact zeros in the model. Bayesian model estimation is obtained by Markov chain Monte Carlo simulations, suitably specifying the complex likelihood function of the zero-inflated density data. The study highlights relevant nonlinear spatial and temporal effects and the influence of the annual Mediterranean oscillations index and of the sea surface temperature on the distribution of the deep-water rose shrimp density.

An analysis of air quality data is provided for the municipal area of Taranto (Italy) characterized by high environmental risks as decreed by the Italian government in the 90s. In the context of an agreement between Dipartimento di Scienze Statistiche - Universit`a degli Studi di Bari and the local regional environmental protection agency air quality, data were provided concerning six monitoring stations and covering years from 2005 to 2007. In this paper we analyze the daily concentrations of three pollutants highly relevant in such an industrial area, namely SO2, NO2 and PM10, with the aim of reconstructing daily pollutants concentration surfaces for the town area. Taking into account the large amount of sparse missing data and the non normality affecting pollutants’ concentrations, we propose a full Bayesian separable space-time hierarchical model for each pollutant concentration series. The proposed model allows to embed missing data imputation and prediction of pollutant concentration.We critically discuss the results, highlighting advantages and disadvantages of the proposed methodology.

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.

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.

We analyze variations in α-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.

In 2009 the limit value of benzo(a)pyrene (BaP) in ambient air of 1.0 ng/m3 has been exceeded in the urban district of Taranto near to the industrial area, where a several large plants are located, including an integrated cycle steel plant. To identify emission sources and quantify relative contribution to the PAHs levels; to estimate health impact associated to PAHs exposure in general population. Multivariate receptor models have been used. Concentration of PAHs measured in 4 location in Taranto in 2008-2009 have been analyzed. 5 different models estimated profiles of unknown sources and identified significant chemical species. To compute the lung cancer risk the WHO unit risk estimate for BaP (8.7 x 10(5) ng/m3) has been adopted. Models employed identify 3 to 4 emission sources. Estimated profiles have been compared with measured ones. Based on the average annual BaP level measured (1.3 ng/m3), 2 attributable cancer cases in the district Taranto population are estimated to result from a life-time exposure. Among different emissive sources, the analysis identifies theoretical sources whose profiles, compared with observed data, allow to identify dominant contributions to PAHs pollution and to design corrective actions to reduce environmental and health impact.

We present a multivariate receptor model for identifying the spatial location of major PM10 pollution sources through the concentrations at multiple monitoring stations. We build on a mixed multiplicative log-normal factor model adjusting the source contributions for meteorological covariates and for temporal correlation and considering source profiles as compositional Gaussian random fields, to account for the variability induced by the spatial distribution of the monitoring sites. Taking a Bayesian approach to estimation, the proposed hierarchical model is implemented and used to analyze average daily PM10 concentration measurements from 13 monitoring sites in Taranto, Italy, for the period April–December 2005. Three major sources of pollution are identified and characterized in terms of their spatial and temporal behavior and in relation to meteorological data.

In questo lavoro proponiamo una rassegna di studi sulla qualità dell‟aria nell‟area metropolitana di Taranto. Le misure delle concentrazioni di PM10 rese disponibili da ARPA Puglia presentavano elementi di complessità quali dati mancanti e diversi criteri di validazione per diverse reti di centraline. Questo duplice problema è stato affrontato con tre strategie alternative, di cui si è confrontata la performance, sfruttando la natura spaziale dei dati nell‟ambito del paradigma inferenziale bayesiano [1]. Successivamente si è considerata l‟interpolazione spazio-temporale delle superfici di concentrazione giornaliere di PM10 sull‟area considerata, tramite un modello basato su kriging bayesiano e caratterizzato dall‟uso di dati meteorologici e da una correlazione spaziale semiparametrica [2]. Analogamente si sono ricostruite le superfici giornaliere delle concentrazioni di tre inquinanti (SO2, NO2 e PM10) visti come un unico oggetto multivariato [3]. La considerazione, per ciascun inquinante, di modelli gerarchici bayesiani univariati con struttura spazio-temporale separabile, ha permesso di imputare i dati mancanti nell‟ambito del procedimento di stima [4]. Allo scopo di collocare sul territorio le principali sorgenti emissive di PM10 [5], si è proposto un modello a recettori multivariato spaziale che considera la correlazione temporale e l‟influenza della meteorologia sui contributi delle sorgenti e la correlazione spaziale nell‟ambito di profili emissivi composizionali [6]. Infine si è effettuata un‟analisi della diffusione spaziale e dell‟evoluzione temporale di 46 contaminanti del PM10 (diossine, PCB, IPA) basata sulle concentrazioni mensili in corrispondenza di tre centraline di monitoraggio. A causa dell‟elevata dimensionalità dei dati, si è adottata una strategia descrittiva basata su tecniche di riduzione riconducibili al diagramma duale [7]. Bibliografia [1] A. Pollice, G. Jona Lasinio, Journal Of Data Science, 43-59, 7 (2009). [2] A. Pollice, G. Jona Lasinio, Environmental Monitoring and Assessment, 177-190, 162 (2010). [3] A. Pollice, G. Jona Lasinio, Environmetrics, 741-754, 21 (2010). [4] S. Arima, L. Cretarola, G. Jona Lasinio, A. Pollice, Statistical Methods & Applications, 75-91, 21 (2012). [5] A. Pollice, Environmetrics, 35-41, 22 (2011). [6] A. Pollice , G. Jona Lasinio, Environmental and Ecological Statistics, doi: 10.1007/s10651-011- 0173-0 (2011). [7] A. Pollice, V. Esposito, Spatial2 - Spatial Data Methods for Environmental and Ecological Processes. Foggia - Baia delle Zagare (I), 1-2 settembre 2011 (2011).

Plant roots are a major pool of total carbon in the planet, and their dynamics are directly relevant to greenhouse gas balance. Composted wastes are increasingly used in agriculture for environmental and economic reasons, but their role as a substitute for traditional fertilizers needs to be tested on all plant components. Compost application was compared to traditional fertilization for its effect on roots of Sorghum bicolor Moench x S. sudanense (Piper) Stapf. in a three-year experiment (2007-2009) in Southern Italy. Plant roots were monitored through sequential images taken with a digital microscope from 4 transparent acrylic access tubes per treatment, buried in the soil at 45° angle up to the soil depth of 60 cm from the surface. Roots on each 166 mm x 124.5 mm image were analysed and attributed to three categories: white, dark and gone. Total roots length, and surface area and average roots diameter were measured on each frame for the three root categories. Roots measurements distributions were assumed to follow an exponential dispersion model, namely the Tweedie distribution, in order to account for zero-inflation in the relative data. Generalized Additive Models (GAM’s) were used to evaluate the nonlinear longitudinal variation of roots measurements along “days after sowing”, together with the possible effects of fertilization treatment, depth and year and eventually adjusting for the confounding effect of the atmospheric temperature. GAM’s parameters were estimated by penalized likelihood maximization with smoothing degree given by generalized cross validation minimization. Residuals and likelihood ratio tests were used for model validation and comparison.

Random walks (RW’s) appeared in the mathematical and statistical literature in 1905 when Karl Pearson, in a letter to the journal Nature, introduced the name for the first time. They are a simple kind of stochastic processes and describe the random movements of an object in a set of possible positions. RW’s are Markov processes as the conditional distribution of a future state given the present and the past depends only on the present state. As a consequence the classification of Markov chains as irreducible, recurrent and periodic can be applied to characterize their limiting behavior. An important role in this regard is played by asymptotic results in probability theory concerning sums of i.i.d. random variables, such as the laws of large numbers and the central limit theorem. At present a large number of papers in environmental sciences make explicit or implicit use of RW based models. Their application has mainly to do with studies of animal movements and microscopic motility and of particle diffusion in fluids. The implicit use of RW models arises when computational algorithms or complex, possibly hierarchical, statistical models are employed.

In pollution source apportionment studies, multivariate receptor models heavily rely on statistical factor analytic techniques to estimate the source-specific contributions from a large number of observed chemical concentrations. The scope of this paper is to offer a review of some recent statistical literature in order to describe the main features and recent advances of this field, advice on the possible “statistical risks” in using standard methods and finally show how some theoretical and practical failures of the commonly used methodologies can be addressed by proper statistical modeling and estimation tools. The topics addressed include: the estimation of the number of sources, model identifiability issues, the consideration of the temporal dependence in the data and systematic effects of physical factors such as meteorological conditions, possible extensions to spatial data collected by multiple receptors and the assessment of source specific health effects.

Alfalfa is a highly productive and fertility-building forage crop; itsperformance, can be highly variable as influenced by within-field soilspatial variability. Characterising the relations between soil and for-age-variation is important for optimal management. The aim of thiswork was to model the relationship between soil electrical resistivity(ER) and plant productivity in an alfalfa (Medicago sativaL.) field inSouthern Italy. ER mapping was accomplished by a multi-depth auto-matic resistivity profiler. Plant productivity was assessed through nor-malised difference vegetation index (NDVI) at 2 dates. A non-linearrelationship between NDVI and deep soil ER was modelled within theframework of generalised additive models. The best model explained70% of the total variability. Soil profiles at six locations selected alonga gradient of ER showed differences related to texture (ranging fromclay to sandy-clay loam), gravel content (0 to 55%) and to the presenceof a petrocalcic horizon. Our results prove that multi-depth ER can beused to localise permanent soil features that drive plant productivity.

In Environmental Epidemiology studies, the effects of the presence of a source of pollution on the population health can be evaluated by models that consider the distance from the source as a possible risk factor. We introduce a hierarchical Bayesian model in order to investigate the association between the risk of multiple pathologies and the presence of a single pollution source. Our approach provides the possibility to incorporate spatial effects and other confounding factors within a logistic regression model. Spatial effects are decomposed into the sum of a disease-specific parametric component accounting for the distance from the point source and a common semi-parametric component that can be interpreted as a residual spatial variation. The model is applied to data from a spatial case–control study to evaluate the association of the incidence of different cancers with the residential location in the neighborhood of a petrochemical plant in the Brindisi area (Italy).

Dioxins and dioxin-like compounds are byproducts of industrial processes, commonly regarded as highly toxic persistent organic pollutants. Polycyclic aromatic hydrocarbons occur in oil, coal, and tar deposits and are produced as byproducts of fuel burning, coke-making, and metal smelting. We propose an analysis of the spatial diffusion and temporal evolution of 46 congeners, based on monthly concentration data for the period October 2008 - December 2010 at three monitoring stations. Given the high dimensionality of the data, a descriptive strategy was adopted based on the duality diagram approach, a unifying framework including classical multivariate statistical methods that has become a valuable tool for combining data collected from different sources and using different methods.

Spatio-temporal analysis of biodiversity indices estimated in the bathyal demersal species assemblages of the Ionian Sea has been performed. Data were collected during 16 trawl surveys carried out from 1995 to 2010 as part of the international MEDITS project funded by EC. In the Apulian sector a significant increase of species richness and a significant decrease of evenness have been detected. In the Southern Calabrian sector a significant decrease of evenness has been detected while a positive trend has been found for the Simpson index. GAM’s have then been applied to explain the dependence of the indices in terms of time and space.

In order to evaluate the spatio-temporal fluctuations of an aquatic population in relation to anthropogenic and environmental factors, we consider density, biomass and size of a crustacean species particularly diffused in the North-Western Ionian Sea: Parapenaeus longirostris (Lucas, 1846). Data from twelve trawl surveys (1995-2006) were analyzed by two different spatio-temporal statistical models accounting for a complex region comprised between the coastline and a specified depth contour. First generalized additive models (GAM’s) were used with soap film smoothers (Wood et al., 2008). While conventional smoothing performs badly when used over complicated regions, soap film smoothing avoids errors across boundaries, using a set of basis functions for the interior region and another one for the boundary. These smoothers, that can be represented in terms of a low rank basis and one or two quadratic penalties, employ a global tuning parameter and one for each boundary and are estimated by penalized likelihood maximization with smoothing degree given by generalized cross validation minimization. Covariates that proved to be significant within the GAM’s were used in a Bayesian implementation combining the Stochastic Partial Differential Equation (SPDE) representation of a Gaussian process with the Integrated Nested Laplace Approximation (INLA). The SPDE approach approximates a Gaussian Markov Random Field substituting the spatio-temporal covariance function and the corresponding dense covariance matrix with a sparse precision matrix for a neighbourhood structure. With respect to MCMC methods INLA provides more accurate deterministic approximations to posterior marginal distributions and a more computational efficient algorithm for Bayesian inference.

In this paper, an analysis of air quality data is provided for the municipal area of Taranto (southern Italy) characterized by high environmental risks as formally decreed by the Italian government in the 1990s with two administrative measures. This is due to the massive presence of industrial sites with elevated environmental impact activities along the NW boundary of the city conurbation. The aforementioned activities have effects on the environment and on public health, as a number of epidemiological researches concerning this area reconfirm. The present study is focused on particulate matter as measured by PM10 concentrations at 13 monitoring stations, equipped with analogous instruments based on the Beta absorption technology, either reporting hourly, two-hourly, or daily measurements. Daily estimates of the PM10 concentration surfaces are obtained in order to identify areas of higher concentration (hot spots), possibly related to specific anthropic activities. Preliminary analysis involved addressing several data problems: (1) due to the use of two different validation techniques, a calibration procedure was devised to allow for data comparability; (2) imputation techniques were considered to cope with the large number of missing data, due to both different working periods and occasional malfunctions of PM10 sensors; and (3) reliable weather covariates (wind speed and direction, pressure, temperature, etc.) were obtained and considered within the analysis. Spatiotemporal modelling was addressed by a Bayesian kriging-based model proposed by Le and Zidek (2006) characterized by the use of time varying covariates and a semiparametric covariance structure. Advantages and disadvantages of the model are highlighted and assessed in terms of fit and performance. Estimated daily PM10 concentration surfaces are suitable for the interpretation of time trends and for identifying concentration peaks within the urban area.

Spatio-temporal analysis of biodiversity indices estimated in the bathyal demersal species assemblages of the Ionian Sea has been performed. Data were collected during 16 trawl surveys carried out from 1995 to 2010 as part of the international MEDITS project funded by EC. In the Apulian sector a significant increase of species richness and a significant decrease of evenness have been detected. In the Southern Calabrian sector a significant decrease of evenness has been detected while a positive trend has been found for the Simpson index. GAM’s have then been applied to explain the dependence of the indices in terms of time and space.

In theMediterrean Sea the population features of demersal resources fluctuate over spatial and temporal scales due to the variability of abiotic and biotic factors as well as to human activities. The two shrimps Parapenaeus longirostris and Aristaeomorpha foliacea are among the most important deep-sea demersal resources in the North-Western Ionian Sea. Their changes in terms of density, biomass andmedian length induced by anthropogenic and environmental variables (fishing effort, sea surface temperature, precipitations, Winter North Atlantic Oscillation (NAO) and Annual MediterraneanOscillation (MO) indices) were investigated. Biological data were collected during trawl surveys carried out from 1995 to 2006 as part of the international program MEDITS (International Bottom Trawl Survey in the Mediterranean). Generalized AdditiveModels were used to evaluate the spatio-temporal variation of both species, together with the possible nonlinear effects of biotic and abiotic factors. Density and biomass were assumed to be distributed according to a member of the Tweedie family in order to account for zero-inflation in the relative data. Spacetime interaction was consideredwithin a non-separablemodel with smooth spatio-temporal component based on tensor product splines. The results show significant spatio-temporal and depth effects in the three population parameters of these resources. Winter NAO index significantly influenced the density, biomass and length of P. longirostris. Sea surface temperature significantly influenced the size of this species and the three population features of A. foliacea. The size of this shrimp resulted also influenced negatively by fishing effort and positively by the MO index.

tN-stable isotope analysis of macroalgae has become a popular method for the monitoring of nitrogenpollution in aquatic ecosystems. Basing on changes in their ı15N, macroalgae have been successfullyused as biological traps to intercept nitrogen inputs. As different nitrogen sources differ in their isotopicsignature, this technique provides useful information on the origin of pollutants and their extension inthe water body. However, isotopic fractionation potentially resulting from microbial nitrogen processing,and indirect isotopic variations due to effects of physicochemical conditions on algal nutrient uptake andmetabolism, may affect anthropogenic N isotopic values during transportation and assimilation. This inturn can affect the observed isotopic signature in the algal tissue, inducing isotopic variations not relatedto the origin of assimilated nitrogen, representing a “background noise” in isotope-based water pollutionstudies.In this study, we focused on three neighbouring coastal lakes (Caprolace, Fogliano and Sabaudia lakes)located south of Rome (Italy). Lakes were characterized by differences in terms of anthropogenic pressure(i.e. urbanization, cultivated crops, livestock grazing) and potential “background noise” levels (i.e. nutri-ent concentration, pH, microbial concentration). Our aim was to assess nitrogen isotopic variations infragments of Ulva lactuca specimens after 48 h of submersion to identify and locate the origins of nitro-gen pollutants affecting each lake. ı15N were obtained for replicated specimens of U. lactuca spatiallydistributed to cover the entire surface of each lake, previously collected from a benchmark, unpollutedsite. In order to reduce the environmental background noise on isotopic observations, a Bayesian hierar-chical model relating isotopic variation to environmental covariates and random spatial effects was usedto describe and understand the distribution of isotopic signals in each lake.Our procedure (i) allowed to remove background noise and confounding effects from the observedisotopic signals; (ii) allowed to detect “hidden” pollution sources that would not be detected when notaccounting for the confounding effect of environmental background noise; (iii) produced maps of thethree lakes providing a clear representation of the isotopic signal variation even where background noisewas high. Maps were useful to locate nitrogen pollution sources, identify the origin of the dissolvednitrogen and quantify the extent of pollutants, showing localized organic pollution impacting Sabaudiaand Fogliano, but not Caprolace. This method provided a clear characterization of both intra- and inter-lake anthropogenic pressure gradients, representing a powerful approach to the ecological indicationand nitrogen pollution management in complex systems, as transitional waterbodies are.

The WFD, adopted by the European Community requires that Member States achieve and maintain a good ecological status of all water bodies by 2015. In the marine context, the ecological status has to be quantied applying indexes based on appropriate key biological elements. The CARLIT index is a car- tographic monitoring tool enabling the EQR2 to be calculated using macroalgae in coastal hard bottoms as a key biological element. Here we investigate the role of Cystoseira amentacea var.stricta: a key macroalgae involved in the index denition. We analyze the relation between the algae presence and geomorphological character- istics of Pontine Islands coast through standard logistic regression and autologistc models to account for spatial correlation.

In the ecological field, the sampling of abundance data is often characterized by the zero inflation of population distributions. Constrained zero-inflated GAM’s (COZIGAM) are obtained assuming that the probability of non-zero inflation and the mean non-zero-inflated population abundance are linearly related. Models of this class have been applied to a spatio-temporal case study concerning the deep-water rose shrimp, Parapenaeus longirostris (Lucas, 1846). Abundance data were collected during 16 experimental trawl surveys conducted from 1995 to 2010 in the Ionian Sea. The sampling design adopted was random-stratified by depth, with proportional allocation of hauls to the area of each depth range and geographical sector. Density index (N/km2) and length (mm) were considered for each haul identified by time, depth, geographic coordinates and geographical sector.

Spatial information on vineyard soil properties can be useful in precision viticulture. In this paper a combination of high resolution soil spatial information of soil electrical resistivity (ER) and ancillary topographic attributes, such as elevation and slope, were integrated to assess the spatial variability patterns of vegetative growth and yield of a commercial vineyard (Vitis vinifera L. cv. Tempranillo) located in the wine-producing region of La Rioja, Spain. High resolution continuous geoelectrical mapping was accomplished by an Automatic Resistivity Profiler (ARP) on-the-go sensor with an on-board GPS system; rolling electrodes enabled ER to be measured for a depth of investigation approximately up to 0.5, 1 and 2 m. Regression analysis and cluster analysis algorithm were used to jointly process soil resistivity data, landscape attributes and grapevine variables. ER showed a structured variability that matched well with trunk circumference spatial pattern and yield. Based on resistivity and a simple terrain attribute uniform management units were delineated. Once a spatial relationship to target variables is found, the integration of point measurement with continuous soil resistivity mapping is a useful technique to identify within-plots areas of vineyard with similar status.