• Energy Research

Sediments are fundamental resources for productive activities like plant nursing, which are also likely to be responsible of their loss. In contrast, other activities like the dredging of canals and waterways involve the extraction and continuous accumulation of sediments. Most dredged sediments are polluted, and need to be stocked and transported to landfills, with extremely high costs for transport and management. To address these problems, a low-cost remediation methodology was previously developed to decontaminate sediments which were tested for use in plant nursery field plantations located in Pistoia (Italy). The phytoremediated sediments were mixed in percentages of 33% and 50% with alluvial soil, which itself was used as control. We studied the characteristics of these mixtures, and the physiological response and growth of Viburnum tinus L. grown on each substrate, as well as its corresponding root ball. Substrates with sediments showed quick water infiltration and no waterlogging, in sharp contrast to what was observed in autumn in the control. Despite a rainy summer, V. tinus demonstrated a good acclimation to the different substrates, showing the lowest leaf water potentials in mixed substrates and no signs of stress. No differences in leaf carbon assimilation or transpiration were observed among substrates, while in late August plants grown on substrates with sediments showed a higher performance index for energy conservation from photons absorbed by PSII to the reduction of intersystem electron acceptors. In the 50% mixture, there was also an enhancement of electron transport from PSII to PSI. Moreover, no differences in growth and biomass were found. Plants in all substrates showed some thin-root mortality, likely due to the persistent rainfall, though a higher number of plants with dead roots was observed in control. Thanks to the dense and fibrous root apparatus of V. tinus, the mixture with 33% sediments produced satisfactory results even for the root ball, resulting in less deformation and a lower breakage percentage.

AbstractGlobal change is expected to have complex effects on the distribution and transmission patterns of zoonotic parasites. Modelling habitat suitability for parasites with complex life cycles is essential to further our understanding of how disease systems respond to environmental changes, and to make spatial predictions of their future distributions. However, the limited availability of high quality occurrence data with high spatial resolution often constrains these investigations. Using 449 reliable occurrence records for Echinococcus multilocularis from across Europe published over the last 35 years, we modelled habitat suitability for this parasite, the aetiological agent of alveolar echinococcosis, in order to describe its environmental niche, predict its current and future distribution under three global change scenarios, and quantify the probability of occurrence for each European country. Using a machine learning approach, we developed large‐scale (25 × 25 km) species distribution models based on seven sets of predictors, each set representing a distinct biological hypothesis supported by current knowledge of the autecology of the parasite. The best‐supported hypothesis included climatic, orographic and land‐use/land‐cover variables such as the temperature of the coldest quarter, forest cover, urban cover and the precipitation seasonality. Future projections suggested the appearance of highly suitable areas for E. multilocularis towards northern latitudes and in the whole Alpine region under all scenarios, while decreases in habitat suitability were predicted for central Europe. Our spatially explicit predictions of habitat suitability shed light on the complex responses of parasites to ongoing global changes.

Since the 1990s, Common Cranes migrating in autumn through Italy have increased significantly both in number and in flock size. In the present study we provided a countrywide profile of autumn crane migration across Italy between 2001 and 2007 (486 records). To investigate the association of climatic characteristics with temporal and spatial migration patterns, we used weather data and climate anomalies over 60 years (1948-2007; NCEP/NCAR Reanalysis Project database). Autumn migration showed different phenological patterns along two main migratory routes: 1) a Southern Italy route and 2) a Northern Italy route. The Southern route, across the lower Adriatic Sea was only partially described before, and more inferred than documented, whereas the Northern route, across the Po River plain, resulted as a new flyway, never described before. Crane migrations along the Northern route occurred 7 to 14 days earlier than along the Southern one. Along both routes, we detected mass migration events concurring with particular weather conditions: the use of Southern route was associated with southward winds in the Balkans, the records along Northern route with high pressure and favourable westward winds in Central Europe and in the main stop-over site (Hortobagy) of likely origin. In the last 60 years, the occurrence of the latter weather configurations has slightly, but consistently, increased, suggesting that the Northern route may have recently established as an alternative route for the cranes migrating from Eastern Europe, joining the two traditional continental routes (the West-European, and the Baltic-Hungarian).

Assessing future climate trends and global warming requires the analysis of long and heterogeneous time series. Gaps in the data series, due to malfunctioning or poorly calibrated instrumentation, can introduce a bias that should be considered in the final assessment. Therefore data quality is a strategic issue for the reliability of this kind of studies and particularly missing data management strategy is a key aspect. This study focuses on uncertainty introduced on climate change trend analysis by the use of incomplete temperature time series recorded by weather stations. Two data quality approaches are evaluated: replacement or filling gap techniques by interpolation and exclusion criteria based on the maximum acceptable number of missing values in a time series. In this study the regression method based on a neighboring station to estimate missing data of another station has been chosen among several interpolation methods. Several complete temperature time series are used to simulate the occurrence of random and consecutive missing values and compare the uncertainty of trend estimation. The two methods are applied to the incomplete data series and the uncertainty in trend analysis results introduced by the two methods is evaluated and compared.