Antarctic penguins’ guano represents a complex mixture of nutrients and chemical compounds. A large amount of guano produced during the breeding season can flow into seawater, near the colonies, altering the chemical balance of coastal environments. However, information on how guano input may alter marine community structures and dynamics in Antarctic ecosystems remains scarce. This exploratory investigation assesses the influence of guano, as a chemical stressor, on habitat selection (spatial avoidance behavior) in three key Antarctic marine invertebrate species: the amphipods Cheirimedon femoratus and Oediceroides lahillei, and Antarctic krill Euphausia superba. We employed a linear non-forced exposure system simulating a chemically heterogeneous environment, combining: (i) model organisms; (ii) two exposure scenarios, a linear guano gradient and guano as chemical barrier; (iii) guano from two penguin species, Chinstrap and Gentoo; (iv) two light conditions (outdoor and darkness); and (v) two exposure times (5 and 8 h). After 5 h, both amphipod species exhibited significant avoidance responses to guano under the conditions tested. Krill did not show a response related to guano; they consistently aggregated at the system’s extremes, reflecting krill’s complex spatial dynamics. These findings indicate that guano can alter amphipod distribution by triggering avoidance responses, potentially affecting distribution patterns and leading to localized population declines. As a pilot assessment, this study highlights the need to integrate behavior-based endpoints and non-forced exposure approaches to understand the ecological effects of chemically complex inputs in Antarctic marine ecosystems.
Groundwater in Mediterranean coastal aquifers supplies a large part of the demand for freshwater but is increasingly threatened by seawater intrusion and anthropogenic pollution. During the springs of 2023 and 2024, six coastal carbonate aquifers in Mallorca were sampled to assess the present-day spatial and vertical variability of salinization and pollution. Hydrochemistry showed elevated Cl- (2140-18,800 mg/L), Na+ (1317-10,983 mg/L) and SO₄2- (440-2890 mg/L), with high electrical conductivity (7480-53,850 μS/cm in 2023 and 16,200-42,000 μS/cm in 2024). Nutrients (NO₃-, NH₄+, PO₄3-, NO₂-) and fecal indicators (Escherichia coli, Enterococci) were detected. Vertical profiles showed salinity increases with depth (except at Drac de Santanyí). The ƒsea index indicated marine intrusion in >96% of samples. Modified Piper indices (GQIPiper(mix), GQIPiper(dom)) and GQISWI values (28-56 in 2023; 32-51 in 2024) pointed to dominant to mixed NaCl facies. PCA and hierarchical clustering revealed marked hydrochemical heterogeneity among sites, with differences between 2023 and 2024 and site-specific anomalies associated with freshwater inputs and anthropogenic pressure. Overall, the results document widespread brackish to saline groundwater conditions and the co-occurrence of microbiological contamination in Mallorca's coastal aquifers, highlighting their high vulnerability to salinization and water-quality degradation. This work presents a data-driven, site-specific conceptual model of the marine intrusion system in Mediterranean coastal aquifers, characterized by a laterally extensive brackish zone overlying saline groundwater and a limited or locally absent freshwater lens near the coast. These findings underscore the need for integrated groundwater management, including salinity monitoring, regulation of abstraction, and improved wastewater treatment, to mitigate ecological and public-health risks in Mediterranean coastal aquifer systems.
In the last decade, advanced AI methods were applied to radiology, providing tools for clinical practice. Regulations across countries are a relevant topic, considering that AI tools must be regarded as medical devices. We describe the regulatory scenarios in the EU, USA, and China. For the EU, we considered the 2017 Medical Device Regulation, including AI tools as “active” medical devices, the 2018 General Data Protection Regulation, protecting data privacy, and the risk-based approach by the 2024 AI Act. For the USA, we considered the three FDA premarket pathways: the 510(k)-clearance demonstrating substantial equivalence, the De Novo classification for novel devices without predicates, and the Premarket Approval process for high-risk applications demanding rigorous clinical evidence; recent regulations regarded lifecycle management, post-marketing surveillance and adaptive algorithms, underscoring the importance of real-world evidence of AI tool performance. For China, the role of the 2022 Guidance for classification and definition of AI medical software by the National Medical Products Administration is illustrated, describing how to determine whether a tool is an AI-enabled medical device, categorizing the associated risk level. The NMPA published six premarket technical review guides related to AI-enabled medical devices in radiology and medical imaging; protection of patient privacy is enforced by the law and de-identification is mandatory for manufacturers. Regulations in these three scenarios show meaningful convergences about patient’s data protection, risk assessment and classification, ensuring equity and generalizability, transparency and explainability, and the need of human oversight. The radiology community will act in a world scenario more homogeneous than expected. Question Regulatory fragmentation across the EU, USA, and China creates uncertainty for radiology AI development, validation, and clinical adoption, requiring clearer international harmonization. Findings Despite differences, regulations in the EU, USA, and China converge on core requirements: patient data protection, risk classification, transparency, bias mitigation, and human oversight. Clinical relevance By highlighting convergences across major jurisdictions, this review informs radiologists and developers on safe integration of AI tools, ensuring patient safety, equity, and trustworthy adoption in clinical practice.
Species distribution models (SDMs) are one of the most common statistical methods to assess species occupancy and geographic distribution patterns. With the increasing complexity of ecological data, many methodological approaches have been developed, often accessible through command-line interfaces or graphical user interfaces (GUIs). However, few species distribution modeling tools are designed to be well-documented, user-friendly, flexible, and reproducible. Here we introduce GLOSSA, an open-source R package and Shiny app designed for species distribution modeling using species occurrence and environmental data. GLOSSA's user-friendly interface guides users through steps including data uploading, processing, model fitting, spatial and temporal projections, and interactive visualization of results. The app also calculates variable importance, generates response curves with environmental variables, and performs cross-validation. At its core, GLOSSA modeling approach is based on Bayesian Additive Regression Trees (BART), an innovative machine learning method. We present the functionality and versatility of GLOSSA through three case studies, addressing a range of ecological scenarios at regional and global scales. Along with comprehensive documentation, examples, and tutorials, these case studies illustrate how an intuitive graphical interface can make species distribution modeling accessible to a broad audience. GLOSSA stands out as an easy-to-use tool for species distribution modeling, providing an intuitive interface, detailed documentation, flexible modeling, and interactive result exploration and export options. Additionally, its outputs can be used directly to inform marine ecosystem models (MEMs), enhancing its utility in ecological research and applications.
The evolutionary relationships among decapodiform lineages (cuttlefish and diverse types of squid) remain uncertain, with implications for the origin of internalized structures (for example, gladius, cuttlebone and coiled shell) derived from the ancestral chambered shell as well as the ecological shifts between the deep ocean and shallow coastal habitats. To address these questions, we adopted a phylogenomic approach that integrated three new high-quality genome sequences with available genomic and transcriptomic datasets. Our analyses support a novel topology that separates a clade of open-ocean lineages (Oegopsida and Spirulida, together Acorneata) from a clade comprising the remaining coastal and shallow-water orders (Sepiida, Myopsida, Idiosepiida and Sepiolida, together Corneata). Molecular clock estimates suggest a rapid cladogenesis of modern decapodiform orders in the deep open ocean during the mid-Cretaceous, consistent with fossil data. This early diversification set a 'long fuse' that led to the explosive radiation of squid and cuttlefish into coastal and shallow-water environments as they recovered from the Cretaceous-Palaeogene extinction event.
