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Management of Plasmopara viticola: from the tradition to the innovation
No full textThis case explores grapevine downy mildew caused by Plasmopara viticola by examining its life cycle and impact on viticulture. It also briefly discusses the challenges and importance of breeding resistant grapevine varieties. Particular attention has been given to the use of chemical plant protection products and their limitations. Finally, this work illustrates the potential of the development of plant protection bioproducts as an alternative to chemical input for the management of grapevine downy milde
Environmental detection and the use of non-lethal sampling for diagnosis and detection of Renibacterium salmoninarum
No full textBacterial kidney disease (BKD) is a serious and notifiable disease caused by the gram-positive bacterium Renibacterium salmoninarum (RS). The disease is chronic and insidious, making it difficult to detect before the infection is widespread in the population. Effective detection of infected fish and populations is essential to manage the disease. In the autumn of 2024, BKD was detected at a land-based fish farm in Iceland that raises Atlantic salmon from roe to slaughter. The disease has been detected intermittently since 2017, and RS appears to persist in the facility. The facility uses both freshwater and seawater from several intakes and testing of roe have never yielded positive results. Repeated sampling of fish, water and the environment were performed from 25.09.2024 and throughout 2025 with the aims of determining which sample material and analysis methods will improve the sensitivity for detecting R. salmoninarum in Atlantic salmon with various stages of infection and disease, to what extent detection of bacteria in water corresponds to infection and disease status of the fish, and how the bacterium potentially enter, spreads and persists in the farming environment. 63 salmon from 6 fish groups and 8 tanks have been necropsied. Tissue was taken for RT-qPCR and histological analysis from the liver, gills, skin, spleen, kidney, and heart. PCR-analysis of homogenate of the head kidney and swabs from skin, cloaca, gills, and kidney were performed. Water and swabs from locations considered likely infection areas were analysed with RT-qPCR, with the aim of tracing a potential source of infection. So far, 56 swabs have been taken from the production environment, in addition to 22 water samples from 7 different locations. Preliminary results showed that 5 fish from the same tank and fish group had grossly observable multifocal white nodules in one or more tissues. RS was detected in most examined samples, including swabs from the gills, skin, and cloaca in all fish. Genetic material from RS was also detected in 16 fish without macroscopic changes consistent with BKD (Ct value: 18.04-32,91). Only effluent water from the tank containing diseased fish were positive for the bacterium (Ct value: 31.93). RS was widely detected in the production environment (27/56 swab samples, Ct value: 29.03-33.25). Further sampling, including bacterial culture of environmental samples is ongoing. We will present the preliminary results from this study, including tissue distribution of bacteria and lesions and correlation between fish, water and environmental detection of RS
Design and implementation of an educational role-play activity about a bioeconomy scenario
No full textBioeconomy is gaining interest among researchers and economic operators all over Europe, but the knowledge of the topic among young people is still partial, particularly for secondary school students, as emerged from a survey in collaboration with Bioregions Facility. A novel 2-hour activity has been designed and proposed to eight secondary school classes in Italy. The principles that guided the designing phase were based on the European Sustainability Competence Framework GreenComp. A first hour of lecture give the basis to perform a role-play in the second phase, which focuses on a simulation of a group work announced to vote for the implementation of a new biogas plant in a touristic zone. The students were asked to personify the different stakeholders and their particular interests until the final voting. The activity aimed to foster competence into critical thinking, systemic thinking, and collective actions needed to achieve an effective change towards sustainability
Preliminary results of genetic analysis of Dreissena polymorpha (zebra mussel) populations in Northern and Central Italy
Get PDFInter-period and inter-season variability of zooplankton of a mountain lake with an emphasis on under-ice communities
Get PDFOnly recently the under-ice period is recognised as a time of plankton activity. However, most studies on under-ice zooplankton focus on low-land lakes over relatively short temporal scales; long-term under-ice zooplankton dynamics in mountain lakes remain largely unknown. We compared the under-ice zooplankton community (one sampling per year; n = 11) to that of the ice-free seasons (monthly samplings) of mountain Lake Tovel. The considered timeframe (2001–2023) covered the period of an ecosystem change linked to intensified autumn mixing. To avoid confounding the effect of this ecosystem change with a seasonal effect, we first investigated the general patterns of abundance and biomass of rotifers and crustaceans for the years considered to determine the periods conforming to the ecosystem change (period 1: weak mixing; period 2: strong mixing). Rotifers and crustaceans were sampled by a net (50 μm mesh size) from 35 m depth to the surface. Rotifer abundance and biomass were higher in period 1 (the years 2001, 2003, 2011, 2014 and 2016) than in period 2 (the years 2017, 2018, 2019, 2021, 2022 and 2023) conforming to the ecosystem change. We applied a taxonomic and functional perspective on zooplankton diversity as complementary approaches to investigate season and period differences. With two-way ANOVA (period and season effect) considering temporal dependence and heterogeneity of data, taxonomic indices (abundance, biomass, species richness) and functional indices (effective number of functional groups, effective number of common functional groups, functional richness) of rotifers showed higher mean values during period 1 than period 2, while taxonomic indices of crustaceans (species richness, effective number of common species, effective number of dominant species) showed the opposite pattern. Thus, the ecosystem change linked to altered mixing patterns favoured crustaceans over rotifers. NMDS ordinations with taxonomic (species identity) and functional diversity (functional entities and community weighted mean trait values) of rotifers showed a splitting between periods but generally not between seasons. The prevailing absence of seasonal differences was linked to the lake's cold-water temperature year-round. The similarity of rotifer functional feeding preferences of the last 2 years of period 2 (2022 and 2023) to that of period 1 indicated that mixing intensity was declining again, a result not shown by taxonomy. From the last autumn to under-ice sampling, Filinia terminalis, Kellicottia longispina, Keratella hiemalis, Polyarthra dolichoptera, Synchaeta kitina, S. lakowitziana, Bosmina longirostris and nauplii and adults of Cyclops strenuus increased in abundance that we attributed to reproduction under ice. Under-ice indicator taxa were only found during period 2, namely F. terminalis and S. lakowitziana. Our study showed that in mountain Lake Tovel the under-ice period is characterised by a similar high zooplankton diversity as during the ice-free period. Studies like ours, covering both taxonomic and functional diversity, are more needed to understand under-ice zooplankton communities
Tree species classification using time series of sentinel-2 images and weak labelled data
No full textMapping tree species plays a significant role in forestry and ecology applications, particularly in heterogeneous forest environments with complex terrain and diverse ecological conditions. The availability of Sentinel-2 (S2) satellite data, with its high spatial (10 m), spectral (13 bands), and temporal resolution (revisit time of 5 days), has revolutionised land-cover mapping. These dense image time series offer an effective framework for automatically classifying tree species of large forested areas because they can capture seasonal phenological changes, which are critical for distinguishing species with similar spectral signatures. However, this process relies on robust and accurate training data, which are often unavailable for large forest areas, where field surveys are impractical due to the time and manual labour required. Previous studies have successfully mapped tree species using methods that focused on the classification of few species, often only relying on labeled pixels randomly selected from either very homogeneous areas or identified on the basis of the average spectral reflectance value. While effective, these approaches are less suitable for complex landscapes with high species diversity and mixed forests, where training data are limited, especially for rare species. To address this limitation, we adapt a method previously used in land-cover classification and test its effectiveness for tree species mapping. This method automatically filters and extracts reliable pixel-level training samples from weak thematic data of relatively homogenous forest areas. Our approach leverages forest inventory data, which represent tree species distribution as percentages within forestry units. A forestry unit refers to an area, often delineated by natural or administrative boundaries, within which the tree species composition and forest characteristics are recorded. This forest inventory data is then integrated with S2 satellite imagery to generate a high-quality training dataset for mapping 18 tree species classes in the province of Trento, Italy. To create the classes, minor species frequently co-occurring with overlapping canopies were merged into broader classes, while major species were kept as separate classes. Purity thresholds were then defined for each class to identify almost “pure” forestry units in the forest inventory data, where spectral signatures predominantly represent a specific tree species. From these selected forestry units, S2 data from the summer months of 2019 were sampled. Buffers were created along spatial boundaries to exclude edge regions and minimize their effects during sampling. To improve representativeness while preserving within-class variability, the sampled data underwent unsupervised filtering. This was applied by clustering sampled pixels within each forestry unit using k-means clustering based on the S2 spectral reflectances and keeping only the points from the dominant cluster. Subsequently, a consistency analysis was performed by removing forest units with spectral characteristics far from the distribution of the related tree species class. Finally, the resulting dataset was downsampled by using elevation as a stratification layer to obtain a balanced distribution of classes. To evaluate the effectiveness of the filtering methods, Linear Discriminant Analysis (LDA) was conducted. The results revealed greater centroid distances among classes after filtering, indicating improved class separability. The refined dataset was then employed to train a Support Vector Machine (SVM) model, which has been previously proved successfully in similar studies, to map the distribution of tree species classes in the province of Trento at 10 m resolution. The use of the proposed filtering methods improved classification performance, increasing the average cross-validation accuracy from 77.38% to 84.11% and the Kappa statistic from 0.76 to 0.85. Test accuracies for the ten most abundant classes ranged from 75% to 93%. Preliminary validation using an independent set of individually sampled trees yielded accuracies of up to 80% for the most abundant species, though rare species exhibited lower accuracy due to limited training data. The preliminary results point out the potential of the proposed methodology to address one of the most pressing challenges in large-scale forest mapping: the scarcity of high-quality training data. By leveraging freely available S2 imagery and widely accessible forest inventory data, this approach provides a replicable framework for producing high-resolution, ecologically meaningful tree species maps. The methodology is scalable and adaptable to diverse forest environments, thus it represents a valuable tool for supporting automated forest management and ecological conservatio
