Conservation management, aimed at mitigating the ongoing biodiversity loss, critically relies on ecosystem monitoring to enable accurate estimates of species distributions and population sizes. Environmental DNA (eDNA) analysis has become increasingly popular for non-invasive and high-throughput species assessment, including fish species diversity. Beyond the established sample collection protocols, natural samplers of eDNA (nsDNA) – organisms that trap environmental genetic material in their tissues – show considerable promise, with recent work especially demonstrating the remarkable effectiveness of sea sponges. Here, the potential of jellyfish (phylum Cnidaria) to serve as motile, marine, pelagic natural samplers of eDNA was investigated through DNA metabarcoding using fish specific primers. Jellyfish are opportunistic marine predators known to consume fish eggs and larvae and their presence has been associated with certain fish species, making them potentially useful for open water fish assessment. Four species, the many-ribbed jellyfish (Aequorea forskalea, Class Hydrozoa), the moon jelly (Aurelia solida, Class Scyphozoa, Order Semaeostomeae), the barrel jellyfish (Rhizostoma pulmo Class Scyphozoa, Order Rhizostomeae) and the fried-egg jellyfish Cotylorhiza tuberculata (Class Scyphozoa, Order Rhizostomeae) were collected from the Italian waters of the Gulf of Trieste (northern Adriatic Sea) and nsDNA isolated, sequenced and analysed. Across all species, 28 fish Molecular Operational Taxonomic Units (MOTUs) were detected including pelagic species, benthic species likely spawning at the time of sampling, and species known to associate with the presence of jellyfish. We highlight the potential of jellyfish as tools for enhancing biodiversity monitoring, particularly in remote and inaccessible areas where conventional surveys may be difficult to employ.
Understanding the temporal evolution of volcanic activity is crucial for eruption forecasting and hazard assessment. We use an unsupervised machine learning method, Deep Embedded Clustering, to classify daily seismic spectrograms of Mount Etna between November 2020 and November 2021, a period that includes two major lava fountain sequences and quiescent phases. Using data from the horizontal components at two summit stations, we identify four clusters corresponding to distinct seismic regimes associated with different volcanic phases: (1) quiescence or non-dominant seismic features related to fluid dynamics, (2) fluid pressurisation indicated by elevated Long Period (LP) events, (3) preparatory phase, and (4) eruptive lava fountain episodes. These clusters closely match expert-defined volcanic phases and are validated against independent volcanic state indicators, including LP event catalogues, RMS amplitude trends, and eruption logs. Notably, a preparatory phase is observed before the lava fountains of February 2021, likely linked to the volcano’s recharging phase. After the first eruptive sequence, a cluster dominated by LP events emerges, which may reflect fluid pressurisation within the volcanic system. The approach also identifies ambiguous days that reflect mixed behaviour. These results demonstrate the potential of unsupervised learning as a reliable and supportive tool for volcanic monitoring and eruption forecasting.
Climate change is intensifying the frequency and severity of environmental hazards, with distinct impacts in urban and rural areas. Cities can experience amplified risks due to the urban heat island effect and increased exposure to extremes. We analyze climate extreme indices using Euro-CORDEX regional climate models that include urban representations, focusing on 40 cities and their surrounding rural areas. Our findings highlight that city-scale hazards intensify with warming across the domain, while precipitation responses are strongly regional. Mediterranean cities exhibit robust drying and longer dry spells, whereas central and northern European cities experience the strongest and most widespread increases in heavy-precipitation extremes. Urban–rural contrasts strengthen several hazard metrics, particularly minimum-temperature extremes, with the strongest signals found in inland cities, underscoring the need for targeted adaptation measures. Understanding how global warming impacts these hazard indices is crucial for developing climate-resilient policies and strategies tailored to both urban and rural settings.
Fault strength spatial variability controls how earthquakes initiate, propagate, and arrest, yet remains poorly resolved in complex tectonic settings. The southeastern Alps constitute one of the most seismically hazardous regions in Central Europe, with active fault systems, a history of damaging earthquakes, and ongoing tectonic deformation. We analyze more than 9,200 small-to-moderate earthquakes (0 ≤ ML ≤ 4.5) recorded between 2016 and 2025 to image lateral variations in crustal stress using the Energy Index (EI), a moment-energy parameter sensitive to rupture efficiency. By extending the RAMONES framework to this region, we detect pronounced east–west contrasts in fault mechanical behavior: high EI values in the west mark zones of reduced frictional strength, whereas low EI in the east suggests mechanically stronger, segmented fault domains. These spatial patterns align with independent geophysical indicators (VP/VS, QP), indicating a strong link between mechanical segmentation, material properties, and permeability structure. Our results demonstrate that small earthquakes carry diagnostic signatures of fault-zone strength and segmentation, providing a scalable tool to resolve stress heterogeneity and refine seismic hazard models in structurally complex regions.
We present a comprehensive analysis of earthquake source parameters in the Southeastern Alps, a tectonically complex region located at the junction of the Eastern Alps and the Dinarides. Using single station spectral inversion of S-wave displacement spectra from 1521 well-recorded earthquakes (1.3 <= MW <= 4.3) occurring between 2016 and 2023, we estimated seismic moment, corner frequency, static stress drop, apparent stress, radiated energy, and seismic efficiency. Our results reveal a small deviation from self-similar scaling condition, with static stress drop values ranging mostly from 0.1 to 10 MPa (median approximate to 0.84 MPa) and apparent stress stabilizing above 1 MPa for moderate events. The Savage-Wood efficiency values suggest a dominant overshoot rupture regime, indicating that only a fraction of the available stress is radiated as seismic energy. The spatial patterns of stress drop and attenuation correlate with the underlying tectonic domains. Regions characterized by strong, competent crust exhibit higher stress drops and lower attenuation, whereas areas with fractured, fluid-rich fault zones show lower stress drops and stronger attenuation. This study highlights the value of high-resolution spectral analysis and dense seismic networks for characterizing rupture processes and provides a new regional reference dataset for ground motion prediction and seismic hazard assessment in Southeastern Alps and comparable intraplate environments.
