Search Results (6,311)

Human activity-driven territorial spatial competition profoundly affects ecosystem service value (ESV). However, the spatiotemporal patterns of “urban–agricultural–ecological space” (UAES) competition in China’s key agricultural regions and their quantitative effects on ESV have not been systematically investigated. Therefore, this study first constructed a “UAES competition–ESV response” analytical framework and selected Henan Province, a representative key agricultural region in China, as the study area. Subsequently, utilizing land-use remote sensing monitoring data from five periods (1980 to 2020), this study systematically analyzed the spatiotemporal competition characteristics of UAES in Henan Province and its impact on ESV using GIS spatial analysis method, the Geo-informatic Tupu method, and improved ESV evaluation model. The results indicate that from 1980 to 2020, Henan Province experienced a gradual shrinkage of agricultural space, rapid urban expansion, and a slight decline in ecological space. Urban encroachment on agricultural land is the primary spatial competition manifestation, which is most pronounced in the core area of the Central Plains Urban Agglomeration. This urban expansion and subsequent agricultural encroachment on ecological land are key ESV loss drivers, causing losses of USD 812.41 million and USD 1663.24 million, respectively. The indirect ESV loss from cropland displacement substantially exceeded direct losses from urban expansion. This study provides critical insights into the trade-offs between urban expansion, agricultural development, and ecological protection in agricultural regions undergoing urbanization. The findings inform spatial planning and ecological conservation strategies in Henan Province and other similar agricultural regions. Full article

Studying bird, insect, and other wildlife interactions with photovoltaic (PV) solar energy facilities is difficult due to limited multi-season, multi-site data. Researchers can address such data gaps by combining passive monitoring and artificial intelligence (AI). As a part of the development of AI-enabled avian–solar monitoring software, we collected over 19,000 h of daytime videos at five PV sites across three U.S. regions between 2019 and 2024. We applied a moving object detection and tracking (MODT Version 1) AI model we developed earlier to 4373 h of the footage to extract moving objects in video frames, and human reviewers interpreted the model output and identified 68,646 bird, 25,968 insect, and 169 other wildlife instances to generate the training/validation dataset. We analyzed the data by site, region, and season, considering ground cover and landscapes. Songbirds were most common, with raptors as the next most frequent group. Most notably, no bird collisions were confirmed in our observations collected from the videos. Birds most often flew over or near panels, with the highest observations in the Midwest and Northeast (approximately 30 observations per hour on average) and fewer in the desert Southwest. Other behaviors included perching, foraging, and nesting. Bird abundance peaked during breeding and migration seasons. AI-assisted video monitoring proved effective for non-invasively studying flying wildlife at solar facilities to inform ecologically mindful energy development. Full article

The State of Alaska, USA, has a long and controversial history of controlling predators to enhance ungulate populations, including moose (Alces alces). Moose management is complicated by a dual system in which the Federal and State governments prioritize harvesting moose for human consumption over other considerations, such as trophy or sport hunting, but have conflicting regulations regarding who is eligible to harvest moose. Wildlife management for the State is overseen by the Alaska Board of Game (BOG), with advice from the Alaska Department of Fish and Game (ADFG). In accordance with its Intensive Management Policy, the BOG establishes regulations promoting the harvest of moose and other ungulates for human consumption. This typically occurs by controlling bears (Ursus americanus and U. arctos) and gray wolves (Canis lupus) in anticipation of increasing ungulate harvests, often without adequate information on the status and ecology of predator or ungulate populations. We provide a narrative and integrative review of moose population dynamics to help resolve those issues. We argue that the current management of moose and their predators in Alaska does not encompass a full range of management options and fails to consider or implement important aspects of their population dynamics. Predators maintain some moose populations at a low density, reducing the harvest of moose but promoting large-antlered individuals, which are of value to Alaska’s professional guide and tourism industries. Using modern models of population dynamics of moose and other ungulates, we argue that if the proximity of the moose population to K (the ecological carrying capacity) is known, management strategies that increase the human harvest of moose and also promote large-antlered trophies are not mutually exclusive. We list life history and population characteristics to help determine the nutritional status of moose populations in relation to K, thereby guiding wise management of that valuable resource. We also recommend an adaptive management approach to assessing the effects of such activities. We caution, however, that to wisely manage these important wildlife resources, more information on the dynamics of moose and their predators is necessary. A system that embraces more biology and fewer politics would provide greater opportunities to employ the best science in the management of moose and their predators. Full article

Until recently, the Karpathos water frog (Pelophylax cerigensis) was considered endemic to Karpathos Island (Greece) and has recently been reclassified by the IUCN as Endangered (EN), having been previously assessed as Critically Endangered (CR). The species faces severe threats primarily associated with the scarcity of freshwater bodies in the southern Aegean Sea. Over the past decade, demographic assessments have revealed a marked population decline, driven by the intensifying effects of climate change, including reduced rainfall, and increasing summer temperatures. In addition, the few natural ponds that persist during the dry summer months are often shared with the Levantine freshwater crab (Potamon potamios), resulting in increased frog mortality due to predation. Despite these challenges, recent developments provide cautious optimism. These include the construction of a dam in southern Karpathos and the taxonomic reassessment of the water frog population on the neighboring island of Rhodes as conspecific with P. cerigensis. In response to the species’ precarious status, the Hellenic Herpetological Society designed and implemented a National Action Plan aimed at the protection and conservation of the Karpathos water frog. The Action Plan includes a series of targeted mitigation measures, such as the construction of artificial ponds to retain water during the summer, as well as a hydrological study addressing the seasonal drying of the ecologically important Eleimonitria spring. A key component of the Action Plan involves education and outreach initiatives targeting primary school students, local residents, and visitors, highlighting the frog’s ecological importance and conservation needs. Informational brochures will be distributed across the island to raise awareness of the species’ conservation status and the importance of safeguarding its habitat. The implementation of this Action Plan aims to secure the long-term survival of the Karpathos water frog and to strengthen integrated conservation efforts across its extremely limited range. Full article

Soil erosion undermines the sustainable development of land—a vital resource for human survival. Research into the spatiotemporal dynamics of soil erosion is therefore crucial for formulating effective soil and water conservation strategies and advancing ecological protection efforts. In the domain of soil erosion research, the Universal Soil Loss Equation and Revised Universal Soil Loss Equation (USLE/RUSLE) model represent the dominant approach for quantifying soil erosion volumes. While this methodology yields reliable outcomes, it fails to incorporate an assessment of the relative significance of the factors embedded within the model. This study selected the Henan section of the Yellow River Basin as the research area, using monthly remote sensing data from 2010 to 2025 as the main data source. Taking into account factors such as rainfall, slope, elevation, vegetation coverage, and hydrological conservation measures, the RUSLE model was used to calculate and combine Geographic Information System (GIS) geographic detectors for quantitative analysis of soil erosion factors. The results showed the following: (1) The average soil erosion modulus in the study area from 2010 to 2025 was mainly micro and mild erosion. (2) Soil erosion exhibits a certain periodicity, with a year of significant soil erosion occurring every 3–4 years. The overall trend of soil erosion is a decrease. (3) Geographic detector analysis shows that slope has the greatest impact on soil erosion, with larger slopes leading to more severe soil erosion. The influence of each factor ranges from large to small as slope > water conservation measures > rainfall > vegetation coverage > elevation. (4) The interaction between factors can enhance the influence on soil erosion, and the interaction between vegetation cover factors and other factors significantly increases the influence; after interacting with various factors, the slope factor will significantly increase the influence of soil erosion. The research results can provide technical support and decision-making basis for ecological protection in the Yellow River Basin, such as through soil and water conservation, returning farmland to forests, and slope greening; The dominant factors and obvious interaction factors in the research area can provide a scientific basis for subsequent scholars to optimize the parameters of regional models. Full article

Land use and land cover change (LULC) is a critical catalyst for global climate patterns, environmental conditions, and ecological dynamics. Remote sensing and geographic information system (GIS) methods have accelerated research on the impacts and variability of climate change. In ecologically sensitive karst regions, LULC poses significant challenges to sustainable urbanization. As a representative karst mountain city in China, Guiyang has undergone rapid spatial transformation, yet quantitative studies on its long-term LULC trajectories within an integrated spatial modeling framework remain insufficient. This study analyzed LULC dynamics in Guiyang from 2007 to 2022 and projected changes for 2027, 2032, 2037, and 2042. Using the CA-ANN model within the QGIS MOLUSCE plugin, we calibrated the model with multi-temporal LULC data and nine spatial drivers, including topographic, proximity, and socioeconomic factors. The model structure was optimized through iterative testing, resulting in a final configuration of 8 hidden layers and 500 iterations. This setup achieved high validation accuracy during training, with a hindcast simulation overall accuracy of 84.42% and a Kappa coefficient of 0.73 for simulating the 2022 land cover. Future projections indicate that impervious surfaces will continue to expand in a spatially constrained manner, reaching 332.82 km² by 2042, while shrubland area will sharply decrease to 10.75 km². Cultivated land and forest areas show relative stability with fluctuations. The projected patterns may exacerbate risks associated with surface runoff and ecological fragmentation due to established linkages between land use/cover change and ecosystem services. Through spatially explicit, multi-temporal scenario simulations, the findings underscore the urgent need in Guiyang’s unique karst setting to deeply integrate land-use planning with ecological conservation strategies, so as to strengthen regional ecological resilience. Full article

Castanea henryi is an important economic tree species in China. Its nutrient-rich nuts play a key role in raising farmers’ income in mountainous areas, promoting forestry industry development, and maintaining ecological balance, thereby providing significant economic and ecological value. To systematically elucidate the genetic characteristics of major C. henryi cultivars in China, this study conducted phenotypic trait measurements on 42 cultivars collected from Taining and Jian’ou in Fujian Province. Combined with whole-genome resequencing technology and using the C. henryi genome as a reference, systematic analyses were carried out. The results indicated that the Jian’ou group (HJO) generally exhibited superior performance in key fruit phenotypic traits compared to the Taining group (HTNC), with greater phenotypic diversity observed within the HJO group. Clustering analysis of phenotypic traits further revealed a cross-geographic convergent clustering pattern among the 42 C. henryi cultivars. Further analysis revealed that the overall genetic diversity of the 42 C. henryi cultivars was relatively low (observed heterozygosity: HJO = 0.0275, HTNC = 0.0194). Notably, parameters such as heterozygosity, minor allele frequency, nucleotide polymorphism, and polymorphic information content were slightly higher in the Jian’ou group compared to the Taining group. Divergent selection signal analysis (Fst top 5%) identified 3129 genomic regions under divergent selection. Genes within these regions showed homology to 1205 Arabidopsis thaliana genes, reflecting adaptive divergence driven by differential historical selection pressures between the two groups. Population genetic structure analysis indicated that the two regional groups exhibit high genetic similarity and low differentiation. This study reveals low genetic diversity and high genetic background homogeneity among C. henryi cultivars, findings that could inform the design of future breeding strategies. Full article

During a taxonomic revision on Caridina of East and Southeast Asia, a group of amphidromous and anchialine Caridina species, i.e., Caridina serratirostris, C. celebensis, C. rubella, C. troglodytes, C. magnovis, C. rintelenorum, and C. henriettae, widely distributed in the Indo-Pacific region has been found to exhibit distinct and unique morphological characters, e.g., rostra moderately long and straight, armed with many dorsal teeth, at least six of them on the carapace; telson terminating in a posteromedian projection; stylocerite long, reaching to or beyond the end of the basal segment of the antennular peduncle; and preanal carina with a spine, and with slender walking legs. Genetically, the group forms a monophyletic clade, either alone or together with the genus Marosina. The clade is well-separated from other species/species groups of Caridina. By taking a conservative taxonomic approach, the genus Marosina is thus redefined to accommodate members of the C. serratirostris species group. The revised genus Marosina can be separated from the typical Caridina species (represented by Caridina typus species group) by the structure of the endopod of the male first pleopod, which does not have an appendix interna, and the long stylocerite, which reaches to or beyond the end of the basal segment of the antennular peduncle, and the high number of postorbital teeth on the carapace. The paper provides a revised diagnosis of Marosina, with a key to all species assigned to it. Diagnosis, taxonomic remarks, habitat, and distribution information for all species are provided. The ecology, biogeography, and conservation of the genus are briefly discussed. Full article

Hydrocarbon-based rocket fuels, particularly kerosene grades T-1 and RG-1 used during launch vehicle operations, represent a persistent source of soil contamination in areas impacted by rocket stages. This study quantitatively evaluates the response and recovery dynamics of soil microbial communities in Calcisol (Loamic) soils from the U-25 impact area near the “Baikonur” Cosmodrome (Kazakhstan) under controlled kerosene contamination. Eleven soil samples were monitored over 90 days, including one uncontaminated control and ten samples exposed to increasing concentrations of T-1 or RG-1 (100–15,000 mg/kg). Microbial indicators included total microbial count, actinomycetes, microscopic fungi, and spore-forming bacteria, expressed as CFU/g (mean ± SD, n = 3). Acute exposure caused significant reductions in total microbial abundance (28–58%) and microscopic fungi (43–75%, p ≤ 0.05), indicating pronounced short-term toxicity. By Day 90, bacterial and actinomycete populations exhibited partial to complete recovery, with some treatments exceeding control values, suggesting metabolic adaptation and hydrocarbon utilization. In contrast, fungal populations remained consistently suppressed throughout the experiment, indicating prolonged ecological stress. No strict dose–response relationship was observed, highlighting the influence of soil physicochemical properties on microbial resilience and hydrocarbon bioavailability. These findings identify microscopic fungi as the most sensitive indicators of kerosene contamination, suggesting that indigenous bacterial and actinomycete communities play a key role in natural attenuation. The results provide quantitative thresholds relevant for environmental monitoring and support the development of microbiologically informed bioremediation strategies in areas impacted by rocket launches. Full article

Professionals working in biological conservation seek to understand, manage, and restore populations of native organisms using many techniques. A common approach for this discipline is using long-term data collections to inform decision making. However, several quantitative issues complicate statistical analysis of monitoring datasets and can reduce the utility of results for conservation decision making. Integrating results from multiple analyses applied to the same dataset (i.e., approaching the same biological problem using different techniques) is one way to address concerns related to field data that violate statistical assumptions. This process allows data analysts, researchers, and managers to assemble insights based on the weight of evidence. Here we tested whether three different statistical techniques [(1) multiple logistic regression on original data, (2) multiple logistic regression on standardized data (i.e., mean of 0 and standard deviation of 1), and (3) random forest analysis] identified a similar hierarchy for selecting natural and anthropogenic habitat regressors. Our examination of how environmental variables affected Plains Minnow (Hybognathus placitus), a state-threatened fish, is relevant to other taxa and locations. We gained useful information from redundancies (i.e., agreements across analyses). New directions also emerged by addressing ambiguities (i.e., disagreements among results across analyses). When multiple analyses were integrated into one ecological story, a clearer interpretation emerged. Viewing different statistical tests as facilitators that provide mutual advantages can advance the understanding and application of statistical analyses applied to non-experimental field datasets. Full article

Water resources are of critical importance across all ecological, social, and economic realms. Accurate extraction of water bodies is of significance to estimate the spatial coverage of water resources and to mitigate water-related disasters. Single-modal remote sensing images are often insufficient for accurate water body extraction due to limitations in spectral information, weather conditions, and speckle noises. Furthermore, state-of-the-art deep learning models may be constrained by data extensibility, feature transferability, model scalability, and task producibility. This manuscript presents an integrated GeoAI framework that enhances foundation models for efficient water body extraction with multi-modal remote sensing images. The proposed framework consists of a data augmentation module tailored for optical and synthetic aperture radar (SAR) remote sensing images, as well as extraction modules augmented by three popular foundation models, namely SAM, SAMRS, and CROMA. Specifically, optical and SAR images are preprocessed and augmented independently, encoded through foundation model backbones, and subsequently decoded to generate water body segmentation masks under single-modal and multi-modal settings. Full article

This paper examines the critical role of youth engagement in building urban climate resilience in secondary cities of West Africa, with a specific focus on Bo City, Sierra Leone. As one of the world’s most climate-vulnerable countries, Sierra Leone faces significant challenges exacerbated in urban environments where infrastructure gaps, rapid population growth, climate migration, and limited resources intersect with intensifying climate impacts (rising temperature, extreme weather events, rising sea levels, and socio-economic health impacts). We describe a pathway to invest in the adaptive capacity of this community by developing and implementing a Youth Climate Science Hub designed to inform and empower secondary school students as future climate leaders. Drawing on theories of social–ecological resilience and transformative education, we analyze how youth-centered approaches can bridge the knowledge–action gap in urban climate adaptation. The initiative represents an innovative practice-based example for building resilience in secondary cities expected to receive climate migrants while demonstrating the power of youth mobilization in creating locally appropriate climate solutions. Full article

Background/Objectives: Antimicrobial resistance (AMR) in wastewater represents a valuable reservoir of information for wastewater-based epidemiology (WBE) and a major environmental and public health concern, as wastewater treatment plants (WWTPs) are recognized hotspots for the accumulation and dissemination of antimicrobial resistance genes (ARGs). Within the One Health framework, and to better understand the contribution to AMR spread and the potential of metagenomic surveillance, this study aimed to characterize the taxonomic, functional, and resistome profiles of three WWTPs in Sicily, specifically those located in Catania, Giarre, and Syracuse. Methods: Sixty-nine composite influent samples were collected between February 2022 and December 2023. Shotgun metagenomic sequencing was performed on the Illumina NovaSeq platform. Bioinformatic analyses were conducted to assess microbial community composition, functional pathways, and ARG prevalence across sites. Results: Dominant genera included Aliarcobacter, Bacteroides, and Acinetobacter. Site-specific taxonomic variations reflected differences in local microbial ecology. Functional profiling revealed enrichment in membrane-associated, ribosomal, and energy metabolism pathways, consistent with the expected functional redundancy of wastewater microbiomes. Resistome analysis detected a diverse and ubiquitous array of ARGs, dominated by β-lactam and macrolide resistance genes, followed by aminoglycoside, sulphonamide, and tetracycline classes. Conclusions: These findings highlight urban wastewater as a relevant reservoir and dissemination route for AMR and support the integration of metagenomic approaches into wastewater surveillance programs. By providing region-specific, integrated taxonomic, functional, and resistome data from Sicilian WWTPs, this study contributes to the growing body of evidence supporting WBE as a valuable tool for AMR monitoring and One Health-oriented risk assessment. Full article

The previous discovery of genomes of Polinton-like viruses (PLVs) associated with viruses of Chrysochromulina parva stimulated this research to determine the biological nature of these putative viral hyperparasites. Purification of C. parva viruses to enable co-infection experiments led to the discovery of a previously unknown virus, CpV-BQ3, which, based on sequence information and electron microscopy, is a species of Tethysvirus, a genus within the Megaviricetes. Purification and TEM imaging of CpV-PLV Moe revealed naked icosahedral particles morphologically similar to other cultivated virophages and PLVs. Mixed-infection experiments with the putative Polinton-like virus CpV-PLV Moe demonstrated that CpV-BQ3 supports its replication, whereas the putative Phycodnavirus CpV-BQ1 does not. Further, experimental infections with differing proportions of the Moe and its helper virus CpV-BQ3 revealed a dose-effect whereby high levels of Moe had a greater negative impact on BQ3 replication compared to lower levels. Conversely, high levels of Moe relative to BQ3 provided greater protection for C. parva, allowing enhanced cell survival, whereas low doses of Moe did not prevent cell lysis. Overall, the results of this study demonstrated the intimate relationship of CpV-PLV Moe with the newly discovered virus, CpV-BQ3, and C. parva, and illustrate the complex ecology of algal viruses. Full article

Remote sensing plays a vital role in monitoring environmental change in Antarctica, offering non-invasive insights into ice dynamics, biodiversity, and fragile ecosystems. Harsh conditions, limited field access, and logistical challenges result in sparse, noisy, and often unlabelled datasets, posing major obstacles for machine learning (ML) approaches. Data scarcity remains a fundamental challenge for uncrewed aerial vehicle (UAV)-based ecological monitoring. While ML models in other Earth observation domains demonstrate state-of-the-art performance, their applicability in Antarctic and polar regions’ settings is limited. This paper reviews the intersection of ML and UAV-based remote sensing in Antarctica under extreme data constraints. We surveyed recent strategies designed to overcome these limitations, including self-supervised learning, physics-informed modelling, and foundation models. Results highlight a notable gap, as polar environments remain excluded from global datasets and benchmarks due to the extensive data requirements of large-scale models. Opportunities exist where multimodal and multi-scale generalisation can enhance cross-domain adaption to data-scarce use cases. Unlike prior reviews on general remote sensing or task-specific polar studies, this work uniquely underscores the need for Antarctic representation in global ML advances, positioning Antarctica as a frontier testbed for machine learning in extreme, inaccessible, and under-resourced fields. Full article