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Coralligenous reefs are considered as one of the most important benthic ecosystems in the Mediterranean Sea in terms of biodiversity, ecosystem functioning, and aesthetic value. Bryozoans deserve special attention within the coralligenous framework fauna: they are among the most common animals, showing high diversity values and playing different functional roles. Distribution, spatial variability, and demographic features of five erect bryozoan taxa Adeonella sp., Myriapora truncata, Pentapora fascialis, Reteporella sp., and Smittina cervicornis were studied in the central Tyrrhenian Sea. Abundance, size, and relationship with other taxa/morphological groups of benthic organisms were studied in coralligenous reefs between 35 and 40 m depth. High density of colonies was reported especially in association to gorgonians and algae dominated assemblages. Size distributions of the five bryozoans were characterized by small colonies; only two species (P. fascialis and S. cervicornis) showed colonies larger than 50 cm2. Structure of coralligenous assemblages and disturbances outlined different ecological requirements of the five studied taxa. Results provide baseline information for monitoring trajectories of change of these organisms in the coralligenous ecosystem. To date, this work represents the first study exploring ecology and distribution patterns of common erect bryozoans in the Mediterranean Sea.

We consider modeling of angular or directional data viewed as a linear variable wrapped onto a unit circle. In particular, we focus on the spatio-temporal context, motivated by a collection of wave directions obtained as computer model output developed dynamically over a collection of spatial locations. We propose a novel wrapped skew Gaussian process which enriches the class of wrapped Gaussian process. The wrapped skew Gaussian process enables more flexible marginal distributions than the symmetric ones arising under the wrapped Gaussian process and it allows straightforward interpretation of parameters. We clarify that replication through time enables criticism of the wrapped process in favor of the wrapped skew process. We formulate a hierarchical model incorporating this process and show how to introduce appropriate latent variables in order to enable efficient fitting to dynamic spatial directional data. We also show how to implement kriging and forecasting under this model. We provide a simulation example as a proof of concept as well as a real data example. Both examples reveal consequential improvement in predictive performance for the wrapped skew Gaussian specification compared with the earlier wrapped Gaussian version.

N-stable isotope analysis of macroalgae has become a popular method for the monitoring of nitrogen pollution in aquatic ecosystems. Basing on changes in their δ15N, macroalgae have been successfully used as biological traps to intercept nitrogen inputs. As different nitrogen sources differ in their isotopic signature, this technique provides useful information on the origin of pollutants and their extension in the 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 and metabolism, may affect anthropogenic N isotopic values during transportation and assimilation. This in turn can affect the observed isotopic signature in the algal tissue, inducing isotopic variations not related to the origin of assimilated nitrogen, representing a “background noise” in isotope-based water pollution studies. 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. nutrient concentration, pH, microbial concentration). Our aim was to assess nitrogen isotopic variations in fragments of Ulva lactuca specimens after 48 h of submersion to identify and locate the origins of nitrogen pollutants affecting each lake. δ15N were obtained for replicated specimens of U. lactuca spatially distributed to cover the entire surface of each lake, previously collected from a benchmark, unpolluted site. In order to reduce the environmental background noise on isotopic observations, a Bayesian hierarchical model relating isotopic variation to environmental covariates and random spatial effects was used to describe and understand the distribution of isotopic signals in each lake. Our procedure (i) allowed to remove background noise and confounding effects from the observed isotopic signals; (ii) allowed to detect “hidden” pollution sources that would not be detected when not accounting for the confounding effect of environmental background noise; (iii) produced maps of the three lakes providing a clear representation of the isotopic signal variation even where background noise was high. Maps were useful to locate nitrogen pollution sources, identify the origin of the dissolved nitrogen and quantify the extent of pollutants, showing localized organic pollution impacting Sabaudia and 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 indication and nitrogen pollution management in complex systems, as transitional waterbodies are.

We investigated the space-time dynamics of N pollution in a Mediterranean gulf (Gulf of Gaeta) by means of δ15N variation in seaweed fronds (Ulva lactuca) previously collected from an unpolluted habitat. We used a comprehensive deployment grid that enabled the generation of isotopic seascapes (isoseascapes) describing the topography of N pollution in coastal waters and identifying N input hotspots and their pathways of dispersion at sea. The δ15N values of U. lactuca increased during 48h of exposure to the gulf waters, indicating anthropogenic N inputs from wastewater-derived sources. Comparison of the isoseascapes between two years differing in terms of rainfall identified coastal and offshore areas that were vulnerable to freshwater-transported nutrients, consistent with terrestrial hydromorphology and sea surface-water circulation. Isoseacapes were robust enough to reduce deployment effort, representing a powerful tool for monitoring and management strategies and useful for Environmental Protection Agencies, the main target audience of applied ecological research.

We introduce a multivariate hidden Markov model to jointly cluster time-series observations with different support, that is, circular and linear. Relying on the general projected normal distribution, our approach allows for bimodal and/or skewed cluster-specific distributions for the circular variable. Furthermore, we relax the independence assumption between the circular and linear components observed at the same time. Such an assumption is generally used to alleviate the computational burden involved in the parameter estimation step, but it is hard to justify in empirical applications. We carry out a simulation study using different data-generation schemes to investigate model behavior, focusing on well recovering the hidden structure. Finally, the model is used to fit a real data example on a bivariate time series of wind speed and direction. Copyright © 2015 John Wiley & Sons, Ltd.

We investigated the space-time dynamics of N pollution in a Mediterranean gulf (Gulf of Gaeta) by means of δ

The coralligenous habitats found in the Mediterranean Sea are hotspots comparable in biodiversity to tropical reefs. Coralligenous reefs are vulnerable to many human pressures, thus they are among the most threatened habitats in the Mediterranean Sea. In this study, we assessed the impacts on coralligenous habitats of activities associated with salvaging the wreck of the Costa Concordia cruise ship. After its partial foundering in 2012, the Costa Concordia remained adjacent to the eastern coast of Giglio Island (Tuscany, Italy), in the Tyrrhenian Sea, for over two years. Its salvage required high-impact engineering works, during the course of which monitoring of benthic communities was undertaken. We performed Rapid Visual Assessment (RVA) sampling (using recorded video) from 17 stations located between 35 and 76 m depth and characterized by coralligenous habitats. Sampling activity was performed during the summers of 2012, 2013, and 2014. In parallel, chemical and physical water parameters were measured continuously from summer 2012 to the end of summer 2014, in order to detect any perturbation in natural conditions caused by salvage activities. We assessed the ecological quality of coralligenous habitats by applying the COARSE (COralligenous Assessment by ReefScape Estimate) index, based on the RVA approach. Slight modifications were applied to one of the descriptors of the COARSE index in order to adjust for study site features. There was clear evidence of a reduction in coralligenous habitats quality. Assemblages, slope, type of pressure, and distance from the source of disturbance played a pivotal role in characterizing bottom quality. The index was shown to have an easy and cost-effective application, even in waters deeper than its calibration specification; furthermore, the modification reported here may increase its potential applications.

The flexibility of the Bayesian approach to account for covariates with measurement error is combined with semiparametric regression models. We consider 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 and 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 aggregation 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.

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., Comment: Keywords: Lagoon biodiversity; Macrobenthic fauna; Tsallis entropy; Biodiversity partitioning; Mixed effects models

The effects of scuba diving activities on coralligenous concretions builders in the Mediterranean Sea are reported for a non-marine protected area (non-MPA). Five erect bryozoan species, which are particularly vulnerable due to their fragile structure, were investigated: Adeonella calveti , Myriapora truncata , Pentapora fascialis , Reteporella grimaldii , and Smittina cervicornis . These species were sampled at frequently and rarely visited diving sites to the maximum depths reachable with the most common recreational diving certifications (i.e., 40 m). To do this, a non-destructive photographic sampling technique was used to minimize any impact on the benthic communities. For each sample, the number of colonies, their width, coverage, type of injury and degree of exposure were quantified using image analysis software. The number of species differed between the frequently and rarely visited diving sites, and frequently visited sites had significantly lower number of colonies and reduced colony width. Species-specific response to stress was associated with colony morphology and skeleton rigidity. Both the type of injury and colony position within the coralligenous habitat were affected by the presence of scuba divers. A linear model was proposed to analyse the response of bryozoans to scuba diving pressure. The results reported here suggest that P. fascialis and R. grimaldii could serve as robust species indicators of diving stress.

International audience; Feeding habits, diet overlap and morphological correlates of four juvenile species of the genus Diplodus were investigated during their settlement periods, along the Tyrrhenian coast. Stomach content analysis showed that the diets of D. sargus and D. puntazzo mainly comprised benthic prey such as harpacticoid copepods, amphipods and polychaetes. On the other hand, D. vulgaris and D. annularis fed mainly on planktonic prey such as ciclopoids, calanoids copepods and fish larvae. A biologically significant diet overlap, calculated using the Schoener index, was recorded between D. sargus and D. puntazzo and between D. vulgaris and D. annularis. Morphological characters related to feeding such as gape height and gut length with their relative growth patterns suggested that different trophic preferences have led to a morphological diversification of feeding structures. Therefore, a geometric morphometric outline method, namely Elliptic Fourier Analysis (EFA) was used to examine shape modification of the head and body regions. The multivariate analyses performed on shape descriptors demonstrated that the four species were morphologically distinct due to different feeding habits: the two species which feed mainly on benthic prey presented a discoidal shape, with broad profiles and rounded head; by contrast, the other two species which relied mostly on planktonic prey, presented a streamlined and more elongated body shape.

The intensification of agricultural land use and urbanisation has increased nutrient loads in aquatic ecosystems. Nitrogen loads can alter ecosystem structure and functioning, resulting in increased algal productivity, algal blooms and eutrophication. The principal aim of the present paper is to extend the use of epilithic δ 15 N signatures to a lake ecosystem in order to evaluate the potential impact of anthropogenic nitrogen discharges (organic and inorganic) that can also reach coastal waters. Epilithic associations were collected from volcanic rocks in different seasons in shallow water along the entire perimeter of Lake Bracciano and analysed for their nitrogen stable isotope signatures. Furthermore, some stones were moved from an unpolluted site to a polluted one in order to verify the effect on the nitrogen signature of the epilithic association. The epilithon’s δ 15 N signatures provided strong evidence of the space-time variability of N inputs. The differing quality of nitrogen loads was reflected in high isotopic variation within the lake, especially at the beginning of summer (1.7‰ ≤ δ 15 N ≤ 13.3‰), while in winter, when anthropogenic pressure was lowest, the δ 15 N signature variation was less accentuated (3.1‰ ≤ δ 15 N ≤ 7.6‰). At all sampling times, spatial variability was found to be related to the various human activities along the lake shore (especially tourism and agriculture), while seasonal variation at all sampling sites was related to the intensity of anthropogenic pressures (higher in summer and lower in winter). Our results showed that epilithic algal associations and the physicochemical properties of the water did not influence the δ 15 N signature, which in contrast was strongly related to the site-specific effect of human activities around the lake. Thus, the distribution of δ 15 N across space and time can be used to direct nutrient reduction strategies in the region and can assist in monitoring the effectiveness of environmental protection measures.

Macroalgal assemblages dominated by the turf-forming alga Phyllophora crispa are described in detail for the first time in the Central Mediterranean Sea. This particular form of algal growth, which comprises an upper mixed layer of multiple algal species with a basal stratum formed by entangled thalli of P. crispa, was observed for the first time in 2012 along the promontory of Punta del Lazzaretto (Giglio Island, Italy). In this study, this assemblage was analysed to document the diversity of macroalgae and invertebrate associated communities and assess their distribution along a depth gradient. The algae forming turfs grow directly on the rock at low depth up to 10–15 m depth, while they grow above P. crispa from 15 m to 35 m depth, resulting in luxuriant beds covering up to 100% of the substrate. Multivariate analysis revealed clear differences regarding algae and invertebrate species richness and abundance between shallow and deep strata because of the dominance of Phyllophora crispa at depths greater than 20 m. The long laminal thalli of P. crispa favoured sessile fauna colonization, while the vagile species were principally linked to the architectural complexity of the turf layer created by the P. crispa, which increased the microhabitat diversity and favoured sediment deposition within the turf layer. The complex structures of these turf assemblages and their widespread distribution along the whole coast of the island suggest a well-established condition of the communities linked to the high natural sedimentation rate observed in the area.

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.

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.

In this work we propose the use of functional data analysis (FDA) to deal with a very large dataset of atmospheric aerosol size distribution resolved in both space and time. Data come from a mobile measurement platform in the town of Perugia (Central Italy). An OPC (Optical Particle Counter) is integrated on a cabin of the Minimetrò, an urban transportation system, that moves along a monorail on a line transect of the town. The OPC takes a sample of air every six seconds and counts the number of particles of urban aerosols with a diameter between 0.28 μm and 10 μm and classifies such particles into 21 size bins according to their diameter. Here, we adopt a 2D functional data representation for each of the 21 spatiotemporal series. In fact, space is unidimensional since it is measured as the distance on the monorail from the base station of the Minimetrò. FDA allows for a reduction of the dimensionality of each dataset and accounts for the high space-time resolution of the data. Functional cluster analysis is then performed to search for similarities among the 21 size channels in terms of their spatiotemporal pattern. Results provide a good classification of the 21 size bins into a relatively small number of groups (between three and four) according to the season of the year. Groups including coarser particles have more similar patterns, while those including finer particles show a more different behavior according to the period of the year. Such features are consistent with the physics of atmospheric aerosol and the highlighted patterns provide a very useful ground for prospective model-based studies.

Circular data arise in many areas of application. Recently, there has been interest in looking at circular data collected separately over time and over space. Here, we extend some of this work to the spatio-temporal setting, introducing space-time dependence. We accommodate covariates, implement full kriging and forecasting, and also allow for a nugget which can be time dependent. We work within a Bayesian framework, introducing suitable latent variables to facilitate Markov chain Monte Carlo (MCMC) model fitting. The Bayesian framework enables us to implement full inference, obtaining predictive distributions for kriging and forecasting. We offer comparison between the less flexible but more interpretable wrapped Gaussian process and the more flexible but less interpretable projected Gaussian process. We do this illustratively using both simulated data and data from computer model output for wave directions in the Adriatic Sea off the coast of Italy.

We propose a new statistical protocol for the estimation of precipitation using lightning data. We first identify rainy events using a scan statistics, then we estimate Rainfall Lighting Ratio (RLR) to convert lightning number into rain volume given the storm intensity. Then we build a hierarchical Bayesian model aiming at the prediction of 15- and 30-minutes cumulated precipitation at unobserved locations and time using information on lightning in the same area. More specifically, we build a Bayesian hierarchical model in which precipitation is modeled as function of lightning count and space time variation is handled using specific structured (random) effects. The mean component of the model relates precipitation and lightning assuming that the number of lightning recorded on a regular grid depends on the number of lightning occurring in neighboring cells. We analyze several model formulations where storms propagation speed, spatial dependence and time variation incorporates different descriptions of the phenomena at hand. The space-time variation is assumed separable. The study area is located in Central Italy, where two storms, that differ for duration and intensity, are presented., 37 pages, 10 figures, 15 tables; Corrected typos

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, based on 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 assumed to 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.

In response to the drought experienced in Southern Italy a rain seeding project has been setup and developed during the years 1989-1994. The initiative was taken with the purpose of applying existing methods of rain enhancement technology to regions of south Italy including Puglia. The aim of this study is to provide statistical support for the evaluation of the experimental part of the project. In particular our aim is to reconstruct rainfall fields by combining two data sources: rainfall intensity as measured by ground raingauges and radar reflectivity. A difficulty in modeling the rainfall data here comes from rounding of many recorded rainguages. The rounding of the rainfall measurements make the data essentially discrete and models based on continuous distributions are not suitable for modeling these discrete data. In this study we extend two recently developed spatio-temporal models for continuous data to accommodate rounded rainfall measurements taking discrete values with positive probabilities. We use MCMC methods to implement the models and obtain forecasts in space and time together with their standard errors. We compare the two models using predictive Bayesian methods. The benefits of our modeling extensions are seen in accurate predictions of dry periods with no positive prediction standard errors.

Presence-only data are referred to situations in which a censoring mechanism acts on a binary response which can be partially observed only with respect to one outcome, usually denoting the presence of an attribute of interest. A typical example is the recording of species presence in ecological surveys. In this work a Bayesian approach to the analysis of presence-only data based on a two levels scheme is presented. A probability law and a case-control design are combined to handle the double source of uncertainty: one due to censoring and the other one due to sampling. In the paper, through the use of a stratified sampling design with non-overlapping strata, a new formulation of the logistic model for presence-only data is proposed. In particular, the logistic regression with linear predictor is considered. Estimation is carried out with a new Markov Chain Monte Carlo algorithm with data augmentation, which does not require the a priori knowledge of the population prevalence. The performance of the new algorithm is validated by means of extensive simulation experiments using three scenarios and comparison with optimal benchmarks. An application to data existing in literature is reported in order to discuss the model behaviour in real world situations together with the results of an original study on termites occurrences data.

Twenty-three freshwater springs of the Po plain (northern Italy) were surveyed and visited multiple times during 2011–2013 to assess water quality and algal taxa present. Despite poor water conditions of this human-altered floodplain, a relatively high number of taxa referable to the freshwater red algae, Batrachospermum and Sheathia, were observed. The identity of specimens collected was based on morphology and confirmed with sequence data. The specimens belonged to Sheathia arcuata, S. boryana, Batrachospermum atrum, B. gelatinosum and B. gelatinosum f. spermatoinvolucrum. This is the first report of B. gelatinosum f. spermatoinvolucrum outside North America. Niche analysis revealed a marked difference in the physical and chemical preferences of the freshwater red algae recorded. The occurrence of S. boryana was limited to relatively low nitrate and higher oxygen concentrations. Conversely, the spatial distribution of the other four taxa indicated that they were able to tolerate high concentrations of nitrate (up to 53.6 mg l–1) and low rates of dissolved oxygen saturation (as low as 3%). All the specimens of B. gelatinosum collected had few to many trichogynes deformed by a basal protuberance or knob, a structure previously unobserved in this species. No other taxa showed any deformities. The ecological analyses confirmed the existence of a relationship between the environmental conditions of the springs and the occurrence of knobs.

The effect of temperature increase on the stock of the deep-sea pink shrimp (Parapenaeus longirostris) was analysed along the western coasts of Italy (North Tyrrhenian–Ligurian Sea: Geographical Sub-Area 9). This crustacean is currently one of the most important commercial species of the trawl fisheries in the Mediterranean Sea. Landings of the species in the North Tyrrhenian–Ligurian Sea have grown consistently during the last years following a rapid increase in the stock size. Since the deep-sea pink shrimp stock is exploited on the same fishing ground of other heavily overexploited stocks in a full mixed and poorly selective fishery, its condition seems to be largely independent of the current fishing exploitation pattern suggesting a positive role of climate change on the dynamic of the stock. To test this hypothesis we investigated the effect of sea surface temperature (SST) on density and distribution of P. longirostris by means of general additive models (GAMs). Two different models were developed for the whole stock and for the recruits (CL The best GAM for the whole stock explained 67.1% of the total deviance, showing a clear increase in density in concomitance with the expansion of the stock northward. We found a significant positive effect of the min SST of all seasons, as expected considering that P. longirostris spawn all year round, with the highest influence played by summer min SST, either in the same or previous year. The best model for recruits explained 64.9% of the total deviance. Recruitment increased linearly with the density of spawners showing a positive temporal trend and an expansion northward. The observed trend in recruitment appeared significantly linked to minimum SST in summer and during the autumn of the previous year, with a positive effect on shrimp density over 23.5 °C and 14 °C respectively. The climate-related changes in recruitment appeared to be the key process driving the dynamics of the stock, involving either higher production or survival in the planktonic stages, or owing to changes in the quality/quantity of nursery habitats.

It is valuable to have a better understanding of factors that influence sea motion and to provide more accurate forecasts. In particular, we are motivated by data on wave heights and outgoing wave directions over a region in the Adriatic sea during the time of a storm, with the overarching goal of understanding the association between wave directions and wave heights to enable improved prediction of wave behavior. Our contribution is to develop a fully model-based approach to capture joint structured spatial and temporal dependence between a linear and an angular variable. Model fitting is carried out using a suitable data augmented Markov chain Monte Carlo (MCMC) algorithm. We illustrate with data outputs from a deterministic wave model for a region in the Adriatic Sea. The proposed joint model framework enables both spatial interpolation and temporal forecasting.

Timing, microhabitat selection and behavior from the onset of settlement to recruitment to the adult population of juvenile fishes of the genus Diplodus (Pisces: Sparidae) were investigated along a rocky coastline in the Central Mediterranean Sea. The settlement periods in Diplodus sargus and Diplodus annularis were concentrated in spring, between late May and early June, and the recruits leave the nursery grounds in late September–October. Juvenile fishes of Diplodus puntazzo and Diplodus vulgaris showed a partial time overlapping, sharing the same zones in winter and early spring, from February to May. Multiple correspondence analysis showed that sea breams settle in well-defined habitats. The smallest juveniles of D. sargus and D. puntazzo settled primarily in the shallowest sheltered pebbly areas, located in sciaphilous crannies covered by red algae. Diplodus vulgaris settlers were observed on a wider range of substrata: rock on sand, gravel and pebbles without algal cover or large boulders, generally in deeper waters. The intermediate-size juveniles of D. sargus, D. puntazzo and D. vulgaris showed a preference for rocky substrata with substantial algal cover, with arborescent structures (Phaeophyceae). Diplodus annularis juveniles showed high fidelity to seagrass beds (Posidonia oceanica). The home range increased over time in all species, highlighting a loss of substrate specificity: larger juveniles were even observed in deeper and different microhabitats outside nursery grounds. This study suggests that shallow infra-littoral rocky communities with photophilic algae play a key role in recruitment of sparid fishes, affecting the distribution and abundance of juvenile fishes and therefore determining the renewal of populations and the structure of adult assemblages.

Mixed models (linear and nonlinear) belong to a class of models in which some of the effects are fixed and some are random; formalization of these models is easily achieved in a hierarchical Bayesian framework. Here we propose a space-time mixed model to link rain measures and lightning counts in a given area of Central Italy.

The ecological status classification of aquatic ecosystems using biological indices requires a number of steps, including the description and standardisation of the indices’ natural variability. Here, we address this point with reference to selected Mediterranean and Black Sea lagoons, using benthic macroinvertebrates in order to: (i) explore the drivers and extent of the indices’ natural variability; (ii) evaluate lagoon type-specific reference conditions and related classification boundaries; (iii) test the classification strength of the derived boundaries; and, (iv) propose recommendations for optimising ecological status classification. The considered indices showed large variation between and within the reference lagoons on both spatial and temporal scales. Among the tested descriptors of the proposed lagoon typologies, surface area, confinement and water salinity were found to be significant sources of index variability. Type-specific reference conditions and classification boundaries were then defined, improving the accuracy of ecological status assessment. At the lagoon level, classification strength increased up to 100 % in reference (least disturbed) lagoons and up to 83 % in an independent validation set of highly disturbed sites. Nevertheless, a certain degree of uncertainty was still found to affect classification at the study site level. Recommendations concerning the application of the various approaches to type-specific reference conditions and classification boundaries are given.

When a large amount of spatial data is available computational and modeling challenges arise and they are often labeled as “big n problem”. In this work we present a brief review of the literature. Then we focus on two approaches, respectively based on stochastic partial differential equations and integrated nested Laplace approximation, and on the tapering of the spatial covariance matrix. The fitting and predictive abilities of using the two methods in conjunction with Kriging interpolation are compared in a simulation study.

Directional data arise in various contexts such as oceanography (wave directions) and meteorology (wind directions), as well as with measurements on a periodic scale (weekdays, hours, etc.). Our contribution is to introduce a model-based approach to handle periodic data in the case of measurements taken at spatial locations, anticipating structured dependence between these measurements. We formulate a wrapped Gaussian spatial process model for this setting, induced from a customary linear Gaussian process. We build a hierarchical model to handle this situation and show that the fitting of such a model is possible using standard Markov chain Monte Carlo methods. Our approach enables spatial interpolation (and can accommodate measurement error). We illustrate with a set of wave direction data from the Adriatic coast of Italy, generated through a complex computer model., Comment: Published in at http://dx.doi.org/10.1214/12-AOAS576 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

In this work we propose a review of researches developed in recent years by statisticians and biologists working on lagoons and coastal areas. We briefly illustrate the use of: (1) spatial statistical tools to fill gaps in the data, (2) linear models (LM’s) and GAM’s to investigate the relationships between food web metrics and environmental variables, (3) LM’s, GAM’s, GAMM’s and linear mixed models to investigate the relationships between ecological multimetric indices and environmental parameters and (4) a model based approach for the classification of the ecological status of lagoons. Points (1) and (2) have been developed in collaboration with the Environmental Biology department in Rome, based on data from an aquatic ecosystem in South Sardinia, in order to address two questions: to what extent the effect of disturbance in the terrestrial landscape spreads through the nearest aquatic ecosystem? Which aquatic community metric is more suitable to indicate the propagation distance of terrestrial disturbance? The other two topics come from the collaboration with Environmental Biology department at university of Salento, working on the classification of the ecological status of Mediterranean and Black Sea lagoons. Here the goal is to understand the behaviour and classification ability of multimetric indices (MAMBI, AMBI, BAT, BITS and ISS). In (4) we try to solve the disagreement often encoutered among alternative lagoon classifications, proposing a joint Bayesian hierarchical model for four multimetric indices, based on multivariate normal mixtures. In this model each mixture component is estimated as a function of covariates of interest and corresponds to an ecological status.

Small intestine contrast ultrasonography (SICUS) has emerged as a valuable tool in the detection of intestinal damage in Crohn's disease (CD). Our aim was to develop a numerical index quantitating small bowel damage as detected by SICUS in patients with an established diagnosis of CD. One hundred and ten patients with ileal or ileocolonic CD were prospectively enrolled and followed up for one year. Disease activity was assessed by CDAI and CRP levels. Study variables included bowel wall thickness, lumen diameter, lesion length and number of lesion site. Fistula, mesenteric adipose tissue alteration, abscess and lymphnodes were also considered. Bowel segments were considered as a hollow cylinder. Standardized variations of variables were combined into a statistical and mathematical model to create an algorithm scoring an index value ranging from 0 to 200. Index was subdivided into a severity scale with 5 classes from the lower (A) to the higher score (E). Median lesion index value was significantly higher (p0.005) in patients with a CDAI150 and in patients with CRP5 mg/l (p=0.003). Patients classified in class E and D at SICUS underwent surgery within one year follow up more frequently than those in class C, B and A (p0.0001). We propose a new index for assessment of small bowel lesions in CD (SLIC: sonographic lesion index for CD) developed by using SICUS. This index may turn ultrasonography in CD from a descriptive qualitative assessment to a quantitative numerical index suitable for comparison studies.

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 PM10, SO2 and NO2. 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 models enriched by a time-recursive prior structure. Secondly a unique daily weather database at the city level was obtained combining data from 3 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 semi-parametric spatial covariance structure. Daily estimates of the pollutants’ concentration surfaces allow to identify areas of higher concentration (hot spots), possibly related to specific anthropic activities.

An analysis of air quality data is provided for the municipal area of Taranto characterized by high environmental risks, due to the massive presence of industrial sites with elevated environmental impact activities. The present study is focused on particulate matter as measured by PM10 concentrations. Preliminary analysis involved addressing several data prob- lems, mainly: (i) an imputation techniques were considered to cope with the large number of missing data, due to both different working periods for groups of monitoring stations and occasional malfunction of PM10 sensors; (ii) due to the use of different validation techniques for each of the three monitoring networks, a calibration procedure was devised to allow for data comparability. Missing data imputation and calibration were addressed by three alternative procedures sharing a leave-one-out type mechanism and based on ad hoc exploratory tools and on the recursive Bayesian estimation and prediction of spatial linear mixed effects models. The three procedures are introduced by motivating issues and compared in terms of performance. This paper is motivated by an analysis of air quality data for the municipal area of Taranto (southern Italy) characterized by high environmental risks as formally decreed by the Italian government in the '90s 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 of Taranto conurbation. Such activities include one of the largest iron production plants in Europe, an oil-refinery, cement production, fuel storage, power stations, waste materials management, mining industry and many others. Some other highly environmental impacting activities are more integrated within the urban area and have to do with the presence of a large commercial harbour and quite a few military plants (a NATO base, an old arsenal and fuel and munitions storages).

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.

In this paper we present a first attempt to define a statistically coherent protocol for Environmental Risk Assessment (ERA), considered as a classification problem. Our approach moves from an idea developed in the pattern recognition literature. Several independent classifiers, each working on a subset of the covariates space, produce a set of corresponding units classifications. Then a gate, modulating the partial results, produces a final, combined classification. We propose a combined classification strategy based on rank transformations and Bayesian mixture classifiers. Although the use of Bayesian mixture models in classification problems is quite common in many fields of application, the novelty of our proposal concerns the use of truncated Gaussian components to model the behaviour of the rank variables in a multidimensional setting. We approach the general problem by partitioning the covariate space into several subspaces, each one representing one environmental dimension; we consider three environmental dimensions (Air, Water and Waste), represented by several pressure indicators. The evaluation of environmental risk for the Tuscany municipalities is our motivating example. Copyright © 2007 John Wiley & Sons, Ltd.

Professor Antoniadis deserves our warmest compliments for his complete and very clear review of this topic. In our discussion we want to point out several connections between wavelets (and their use) and Bayesian nonparametric statistics.

Burkholderia cenocepacia, Burkholderia ambifaria, and Burkholderia pyrrocinia are the Burkholderia cepacia complex (Bcc) species most frequently associated with roots of crop plants. To investigate the ecophysiological diversity of these species, metabolic profiling of maize rhizosphere isolates was carried out by means of the Biolog system, using GN2 and SFN2 plates and different parameters related to optical density (OD). The metabolic profiles produced by the SFN2 and GN2 plates were identical, but the SFN2's narrower range of OD values and significantly longer reaction times made these plates less suitable for differentiation of isolates. Principal component analysis of maximum OD (ODM) and maximum substrate oxidation rate (muM) data generated by GN2 plates allowed the selection of a reduced number of carbon sources. Statistical analysis of ODM values highlighted marked differences between the metabolic profiles of B. cenocepacia and B. ambifaria, whereas metabolic profiles of B. pyrrocinia clustered very often with those of B. cenocepacia. Analysis of the mu(M) parameter resulted in a slightly lower differentiation among the three Bcc species and a higher metabolic diversity within the single species, in particular within B. cenocepacia. Finally, B. cenocepacia and B. pyrrocinia showed generally higher oxidation rates than B. ambifaria on those GN2 substrates that commonly occur in maize root exudates.

Predicting species occurrence using a modelling approach based on a geographic information system (GIS) represents a new methodological tool which can be used to endorse conservation policies, on condition that models are tested for reliability. Habitat suitability models are often used to predict species occurrence through the modelling of proper environmental variables. A major constraint in building large-scale models of species distribution is the availability of data and therefore the deductive approach is adopted. This approach is suitable for bio-diversity assessment as it can be applied to a great number of species, it does however require statistical validation, both to test the accuracy of the input information and to deal with presence data (often affected by high spatial and temporal variability). Available data are, in the large majority, represented by a small sample collected without an ad hoc sampling design and without information on species absence. Reliable information on absence is not easily obtained with animal species due to elusive behaviour, poorly accessible habitats and their activity patterns. Therefore, absence data are to be considered ambiguous. Hence the need to develop statistical tools to deal with presence-only data. Moreover, presence data are rarely geo-referenced with high precision. Often information on species presence is spatially degraded as referred to polygon within which the exact species location cannot be recovered. Consequently, the validation procedure should statistically consider this uncertainty. In this paper we suggest that a compositional procedurebe used when presence-only data are affected by strong spatial and temporal variability, and that a multinomial(based) procedurebe used when presence-only data are geo-referenced with high precision during an ad hoc field survey. The two procedures have been tested on a large number of deductive models built to represent the distribution of vertebrate species in Italy. Results show that both procedures allow the assessment of the calibration properties of the models. The reliability of the two methods is confirmed by a model rejection pattern, determined by ecological factors, scale issues, spatial accuracy and sample size. Both procedures have a wide range of applications being threshold independent. © 2004 Elsevier B.V. All rights reserved.

A space-time, univariate dataset is assumed to have been sampled from a 3-dimensional Markov Random Field where the data dependence structure is modeled through pairwise interaction parameters. The likelihood function depends upon (1) an undirected, 3-dimensional graph, where edges connect observation points, and (2) the parameter dimension that captures possible space-time anisotropy of data interaction. Automatic model selection to discriminate both the graph and the model dimension is suggested on the basis of a penalized Pseudo-likelihood function. In most cases, the procedure can be implemented using standard statistical packages capable of GLM estimation. Weak consistency of the criterion is shown to hold under mild and easily verifiable sufficient conditions. Its performance in small samples is studied providing simulation results.

In this paper we report a brief account of an application to spatial data, observed on an irregular grid, of an exploratory technique based on the diagonalization of cross-variogram matrices. Our aim is to describe the behavior of a multivariate set of spatial data in a dimensionally reduced space in such a way that the information on the spatial variation is preserved. We adapt an exploratory technique built for the analysis of quantitative data to frequency data. We give special attention to the choice of a distance measure that well describes the type of “connection” between sites we consider for the analysis of this specific situation. The application aims to the characterization of districts in the city of Rome according to the electoral behavior of their inhabitants, special attention is given to the increasing phenomenon of abstentions.

In this paper we describe an exploratory technique based on the diagonalization of cross-variogram matrices. Our aim is to describe the behavior of a multivariate set of spatial data in a dimensionally reduced space in such a way that the information on the spatial variation is preserved. Furthermore we propose a definition for the range of «variograms» in the multivariate case. Simulation studies and an application to botanical data collected on line transects are reported.

In this paper we analyse an ordination method in which spatial contiguity information is included. The original method, proposed by Ver Hoef and Glenn-Lewin (1989), is extended, using graphs to describe spatial contiguity for several sampling schemes where locations are given. Extensions for two dimensional grids and for the case of discrete measures of abundance are analysed. Applications to simulated transect frequency data and real data collected on a regular grid are reported.