Volcanic monitoring in Antarctica provides dual benefits by facilitating the study of highly volcanically active yet underexplored regions and by serving as an essential tool for maintaining the safety of both national and international Antarctic bases. Deception Island, one of the most volcanically active areas on the Antarctic continent, is now equipped with an enhanced monitoring system, modernized by the Spanish National Geographic Institute (IGN), which assumed its management in September 2020, pursuant to a protocol established between the Spanish Ministry of Transport, Mobility, and Urban Agenda and Ministry of Science and Innovation. In order to meet the challenging conditions of the polar region, six permanent stainless-steel shelters have been installed over the course of the last four Spanish Antarctic campaigns. Each shelter is outfitted with batteries, solar panels, Wi-Fi and data acquisition systems for seismic and Global Navigation Satellite System (GNSS) stations. This monitoring network also comprises a station dedicated to measuring temperature within a thermal located where there is the anomaly on the island, a visual surveillance camera, seven seismic stations and six GNSS stations. All data collected are transmitted in real time to IGN headquarters in Madrid through Gabriel de Castilla Base, operated by the Spanish Army. Due to the growing volume of recorded data, it became necessary to upgrade transmission systems, leading to the installation of a next-generation satellite telemetry system during the 2023–2024 campaign. All seismic information is continuously and immediately analysed by the IGN’s National Seismic Network and is stored at the National Polar Data Center, housed within the Marine Technical Unit of the Spanish National Research Council, in accordance with the guidelines of the Spanish Polar Committee. The establishment of this infrastructure on Deception Island delivers a robust operational model that can be replicated in other polar settings, thereby contributing to the advancement of volcanic monitoring across the continent.

The current state of Arctic research and its community continues to exhibit high levels of political polarization and fragmentation by establishing a number of questions regarding the societal relevance of the process and its results for the benefit of present and future generations. The ASSW2025 (Arctic Science Summit Week) conference devoted to the ICARP IV (International Conference on Arctic Research Planning) process took place in Boulder, Colorado (March 2025), and had special importance in this regard. The conference was a summary meeting of the Arctic research planning process for the next 10 years and set the foundation for the upcoming IPY-5 (International Polar Year). The Summit, attended by nearly 1200 international scientists and researchers, Indigenous Knowledge holders, and community members from across the Arctic and beyond, exhibited the lowest ever attendance of Russian Arctic scientists representing Russian science organizations. This group studies and advocates for almost half of the Arctic’s social and human capital (roughly 2.4 million people live in Arctic Russia). In order to preserve scientific discourse and guarantee the societal and environmental benefits of science for the fragile socio-ecological systems of polar regions and their delicate geopolitics, effective transition strategies and approaches should be taken into consideration where possible.

Numerous fish microremains, including diverse chondrichthyans, have been recovered for the first time in the Carboniferous–lower Permian of the Naqing, Narao, and Shanglong deep-water sections from Guizhou Province, South China, as well as in the Serpukhovian–lower Bashkirian of the Sholaksay section from Kazakhstan. The richest assemblage was found in the Bashkirian strata of the three South China sections. These findings are the first reliable record of Pennsylvanian fishes in China. The fish assemblage from the Sholaksay section and the Bashkirian of China closely resemble those from the same interval in the Aksu section, Uzbekistan. All studied fish faunas from these three Asian regions are associated with the Paleotethys. Notably, while Bashkirian chondrichthyan faunas generally exhibit low taxonomical diversity in many regions of the world, the deep-water fish assemblages from South China, Kazakhstan, and Uzbekistan are characterized by high taxonomical diversity. The fish fauna includes widely distributed chondrichthyan taxa; however, Gissarodus occurs only in these three Asian regions.

Good air quality is a critical determinant of public health, influencing life expectancy, respiratory health, work productivity, and the prevention of chronic diseases. This study presents a novel approach to classifying the Air Quality Index (AQI) using deep learning techniques, specifically convolutional neural networks (CNNs). We collected and curated a dataset comprising 11,000 digital images from three distinct regions in Indonesia—Jakarta, Malang, and Semarang—ensuring uniformity through standardized acquisition settings. The images were categorized into four air quality classes: good, moderate, unhealthy for sensitive groups, and unhealthy. We designed and implemented a CNN architecture optimized for AQI classification. The model achieved an impressive accuracy of 99.81% using K-fold cross-validation. In addition, the model’s interpretative capabilities were examined using techniques such as Grad-CAM, providing valuable insights into how the CNN identifies and classifies air quality conditions based on image features. These findings underscore the effectiveness of CNNs for AQI classification and highlight the potential for future work to incorporate a more diverse set of digital images captured from various perspectives to enhance dataset complexity and model robustness. The dataset is publicly accessible at https://doi.org/10.5281/zenodo.15727522.

Despite the lesser preservation of bat fossils compared to karstic sites, fluviolacustrine deposits are of crucial importance for assessing the local palaeodiversity of bats. This was confirmed by three faunas from the upper Miocene of Slovakia (Borský Svätý Jur, MN9; Studienka A, MN9; Krásno, MN11), which comprise seven species—Miostrellus cf. Miostrellus noctuloides (Lartet, 1851), Myotis cf. Myotis murinoides (Lartet, 1851), ‘Otonycteris’ sp. indet., Miniopterus sp. indet., Vespertilionidae gen. indet. sp. indet. 1, Vespertilionidae gen. indet. sp. indet. 2, and Rhinolophus cf. Rhinolophus grivensis Depéret, 1892. Bats represent ~ 1.40% of the small mammal remains from Borský Svätý Jur, ~ 0.25% of those from Studienka A, and ~ 1.78% of those from Krásno. The MN11 fauna from Krásno consists of six distinct species, which is unusual for a Miocene fluviolacustrine deposit. Most likely, this is the consequence of the relatively high frequency of bats in the taphocoenosis, therefore providing a more complete and more accurate picture of the Carpathian late Miocene bat faunas. This material encourages the study of fluviolacustrine deposits to unravel the cryptic diversity of Neogene taxa.

Paleo-ecological niche modeling (paleoENM) estimates the niches and distributions of extinct species using fossil paleo-coordinates and local environmental data. While general circulation models (GCMs) have been used to estimate climate conditions in deep time, primarily for terrestrial vertebrates, variations in paleo-elevation models used in GCM construction can influence paleoENM outcomes. This study (1) examines the impact of the Cretaceous–Paleogene (K-Pg) mass extinction on the niche dimensions of the marine invertebrate group Turritellinae (Cerithoidea: Turritellidae) and (2) compares two paleo-elevation models’ effects on GCM-based species’ distribution predictions. Fossil occurrence data from the Maastrichtian and Danian periods were collected from the Paleobiology Database (PBDB), museum collections, and published literature. Environmental data were extracted from HadCM3L GCM simulations using Scotese- and Getech-based paleogeographic and pCO2 boundary conditions. We estimated the niche dimensions of turritellines using maximum entropy (MaxEnt) and performed ordination analysis using kernel density estimation. MaxEnt model metrics showed that the Getech-based GCM outperformed the Scotese-based GCM. Geographic projections revealed minor differences in suitable habitat between the Maastrichtian and Danian in the Getech-based GCM, but overinflated predictions in the Scotese-based GCM. Niche overlap between the Maastrichtian and Danian was high, with both GCMs supporting niche similarity and equivalency. Our results suggest that differences in elevation model boundary conditions affected predicted distribution and niche patterns. This study offers a novel approach to understanding ecological persistence in invertebrates after mass extinction events, examines the robustness of GCM boundary conditions in paleoENM studies, and provides a framework for future paleoecological research on fossil invertebrates.

Species recognition is an essential part of biological and paleontological study. In gastropods, although species are genetic entities, shell morphology continues to be used as the primary source of information to recognize most species. While there are few directly tested cases, variations in conchological characters for modern species are expected to reflect underlying genetic differences that define a biological species, an assumption that is also applied to identify species in the fossil record. Additionally, how consistently shell shape differentiates gastropod species remains poorly understood. In this study, shell shape of Recent and Pliocene–Pleistocene fossil specimens of well-known intertidal gastropods (Littorinidae, periwinkles: †Littorina petricola, Littorina keenae, and the sister-species pair Littorina plena and Littorina scutulata) from the east Pacific was analyzed using landmark-based morphometrics and compared with published molecular data. For the extant species, there is a general positive relationship between shell shape and genetic differences. Discriminant function analyses indicate distantly related species can be more reliably recognized from their shells, while closely related species have a higher error. Fossils and recent specimens were classified with similar consistency. More work is needed to illuminate whether this case applies more widely.