Earth and Environmental Sciences

Rangelands provide essential ecological functions such as biomass production, carbon and water regulation, nutrient cycling, and biodiversity conservation, yet their integrity is increasingly threatened by climate change and grazing pressures. Reliable assessment of ecosystem functions (aboveground biomass, soil conservation factor, soil organic carbon, soil respiration, and microbial biomass carbon) and multifunctionality is therefore critical for sustainable management. This study comparatively evaluated two widely used assessment approaches, the Rangeland Health Assessment (RHA) and Landscape Function Analysis (LFA), across five bioclimatic rangelands in Iran. Ecosystem multifunctionality was quantified using standardized measures of key functions, and linear models were applied to examine the explanatory power of RHA and LFA metrics. Results showed that the explanatory power of models (R²) generally increased with the number of functions included, although the magnitude of this increase varied across regions and metrics. Structural indicators such as rangeland health, soil/site stability, and hydrologic function showed the highest ability to explain multifunctionality in Mediterranean and Semi-steppe regions. In contrast, Desert and Cold regions exhibited weaker and more variable relationships. Overall, the findings demonstrate that targeted combinations of key functions, together with sensitive structural indicators, can substantially improve monitoring efficiency and provide a robust scientific basis for rangeland management under diverse climatic conditions.

This study investigates the enhanced degradation of Alizarin Red S (ARS) from aqueous solutions using an ultrasound-assisted persulfate (PS/US) system, combining experimental design and artificial intelligence-driven optimization. ARS, a persistent anthraquinone dye, poses significant environmental and health risks due to its toxic and non-biodegradable nature. Traditional wastewater treatment methods often fail to degrade such dyes effectively, necessitating advanced oxidation processes (AOPs). Here, persulfate (PS) activated by ultrasound (US) was employed to generate highly reactive sulfate (SO 4 ·− ) and hydroxyl ( · OH) radicals, achieving efficient ARS degradation. A Box-Behnken Design (BBD) coupled with Response Surface Methodology (RSM) was utilized to optimize key operational parameters, including contact time (50–70 min), PS concentration (0.1–0.7 mM), initial ARS concentration (5.0–15 mg/L), US frequency (20–60 kHz), and pH (7.0–11). The quadratic model demonstrated high predictive accuracy (R 2 = 0.96), with contact time and PS concentration identified as the most influential factors. Artificial intelligence techniques, including RANSAC regression and Monte Carlo simulation, were applied to model and optimize the process, achieving a degradation efficiency of 89.32% under optimal conditions (57.5 min, 0.43 mM PS, 8.5 mg/L ARS, 20.2 kHz, pH 8.1). Kinetic analysis revealed pseudo-first-order degradation (rate constants: 0.0196–0.0279 min -1 ), with sulfate radicals contributing dominantly (67.2%) to ARS oxidation. Scavenger experiments and EPR spectroscopy confirmed the synergistic role of SO 4 ·− and · OH radicals. The presence of inorganic anions (e.g., carbonate, bicarbonate) reduced efficiency, highlighting the need for pre-treatment in complex matrices. This study presents a robust, scalable approach for ARS degradation, integrating sonochemistry, sulfate radical-based AOPs, and AI-driven optimization to advance sustainable wastewater treatment technologies.

This study describes groundwater potential zones (GWPZs) and identifies suitable artificial recharge sites within the Rushikulya River Basin, Odisha, India, integrating geospatial technology and a multi-criteria decision-making (MCDM) framework. Analytic Hierarchy process (AHP) and fuzzy-AHP were employed to integrate ten hydrogeologically significant parameters, viz., drainage density, geomorphology, geology, lineament density, land use/land cover, precipitation, proximity to rivers, slope, soil, and topographic wetness index. The fuzzy membership was established to alleviate epistemic ambiguity and linguistic vagueness inherent in hydro-environmental systems. Delineated GWPZs were classified as high (4.47%), moderate (93.17%), and low (2.36%). Model performance was validated using 128 well locations. Additionally, 89 potential artificial recharge sites were identified and ranked based on recharge suitability. To overcome hydro-centric approaches, a novel socially sensitive hydrogeologically suitable recharge zones (SSHSRZ) was established to enhance the hydrogeological accuracy of the Fuzzy-AHP model with socio-economic (population density, poverty index, and irrigation intensity) dimensions into the decision architecture. The proposed hydro-socio-economic framework, coupled with geospatial modelling, serves as a decision-support tool for prioritising groundwater recharge interventions and promoting equitable groundwater management in semi-arid hard-rock regions by providing a scalable, adaptable, and resilience-focused approach to sustainable aquifer stewardship worldwide.

Ecological security assessment is of fundamental importance for the conservation and sustainable management of fragile grassland ecosystems. Traditional assessment methods are frequently limited by subjective indicator weighting and inadequate consideration of spatial autocorrelation, which constrains their reliability in evaluating complex grassland systems. This study examined the spatiotemporal patterns of grassland ecological security in Northwest Sichuan, China, from 2000 to 2020, by integrating the Driver-Pressure-State-Impact-Response (DPSIR) framework, a Genetic Algorithm-optimized Projection Pursuit Model (GA-PPM) and spatial autocorrelation analysis. The GA-PPM was applied to achieve objective weighting of key indicators and identify spatial heterogeneity in ecological security. Results revealed that ecologically vulnerable zones were mainly distributed in the northeastern and southwestern parts of the study area. Slope and population density were identified as the dominant driving factors, illustrating the combined constraints of topographic characteristics and anthropogenic disturbances (e.g., grazing and tourism). Based on these ecological insights, study propose targeted strategies including differentiated zoning management and ecological restoration to mitigate grassland degradation. This integrated approach provides both a methodological basis and practical reference for ecological security assessment in other fragile grassland regions globally.

Coastal wetlands, including mangroves, seagrass beds, and coral reefs, provide critical blue carbon sequestration services and nursery habitats that support fishery resources. However, the bidirectional coupling between blue carbon stocks and fishery abundance remain poorly quantified, particularly under accelerating environmental change in tropical marginal seas. This study presents the first coupled LSTM framework for bidirectional blue carbon-fishery prediction in tropical coastal wetlands. We developed a coupled Long Short-Term Memory (LSTM) neural network model to predict the bidirectional relationship between blue carbon stocks and fishery resource abundance in the coastal ecosystems of Guangdong Province and Hainan Island, China. Field surveys were conducted at 15 representative sites from 2018 to 2025, generating 96 monthly observations of environmental variables (sea surface temperature, salinity, dissolved oxygen, turbidity, nutrients), biological indicators (mangrove above-ground biomass, soil organic carbon, seagrass coverage, coral cover), and fishery metrics (catch per unit effort, species richness, juvenile abundance). The LSTM model achieved superior predictive performance compared to baseline methods, with root mean square error of 0.142 Mg C ha⁻¹ for blue carbon prediction (R² = 0.93) and 0.108 kg h⁻¹ for CPUE prediction (R² = 0.89). Feature importance analysis revealed that mangrove soil organic carbon was the strongest predictor of fishery CPUE (Shapley value = 0.187), while sea surface temperature exerted the greatest influence on blue carbon stock variability. Scenario simulations for 2025-2026 indicate that under a moderate warming scenario (SST increase of 0.8 °C), blue carbon stocks are projected to decline by 7.2% (95% CI: 5.8-8.6%), with associated fishery CPUE reductions of 11.4% (95% CI: 9.2-13.6%). These findings provide a novel predictive framework for ecosystem-based management of tropical coastal wetlands and highlight the vulnerability of coupled social-ecological systems to climate change.

This study evaluates the effectiveness of integrating remote sensing data with field observations and machine learning techniques for estimating and mapping available phosphorus (Pav) and exchangeable potassium (Kex) in the Gonbad Kavous region. A total of 394 soil samples collected from the surface layer (0-15 cm depth) were analyzed for Pav and Kex concentrations. Sentinel-2 satellite imagery, together with environmental covariates, was used as predictor variables to model the spatial distribution of soil nutrients. Four machine learning algorithms Random Forest (RF), Support Vector Machine (SVM), Boosted Regression Trees (BRT), and Generalized Linear Model (GLM) were trained and validated for digital soil nutrient mapping. Among these models, RF achieved the highest predictive performance for both Kex (R² = 0.79, RMSE = 13.39) and Pav (R² = 0.83, RMSE = 2.60), outperforming the other approaches. The SVM model also demonstrated satisfactory performance in capturing spatial variability, while GLM showed comparatively lower accuracy. The results confirm the strong potential of combining Sentinel-2-derived spectral information with machine learning algorithms for high-resolution digital soil mapping. The generated spatial distribution maps of Pav and Kex provide valuable insights for soil fertility assessment and can support precision agriculture, nutrient management planning, and sustainable land management practices in semi-arid agricultural regions. Importantly, this study highlights the robustness of ensemble-based learning methods, particularly Random Forest, for predicting soil nutrient variability using multi-source geospatial data.

<title>Abstract</title> Wetlands are highly productive and biologically diverse systems that enhance water quality, control erosion, maintain stream flows, sequester carbon, and provide a home to at least one-third of all threatened and endangered species. Rainfall and temperature naturally and directly impact them, but other elements that influence these variables include transpiration, evaporation, and vegetation cover. To investigate the impact upon wetlands in the Madhubani district of Bihar, this study utilizes several multi-dimensional variables, viz., actual and potential evapotranspiration (ET and PET), gross and net primary productivity (GPP and NPP), annual rainfall, the Modified Normalized Difference Water Index (MNDWI), and the Soil-Adjusted Vegetation Index (SAVI). Satellite data of Terra-MODIS 8-day composite (500m) and Landsat series data from the USGS have been used for mapping the changes in wetlands in the Arc-GIS Pro environment. Modified Mann-Kendall (mMK) test and Sen slope have been employed for estimating rainfall trends.The study finds that annual rainfall in Madhubani has decreased over the period 1994–2024, negatively impacting the wetland ecology. The primary productivity witnessed a decreasing trend of GPP and NPP during the years 2002–2024, which resulted in a decreasing trend of seasonal and annual patterns of ET and PET. The past two decades have seen a decline in the spatial distribution of wetlands in Madhubani, resulting from declining trends of annual rainfall, evapotranspiration, and primary productivity.

Northern spotted owls were spared from logging. Now, scientists are making a last stand to save them from a new threat.

Reporting should consider data harmonization, supply chain metrics, and pollution hotspot identification.

Freezing redirects the fate of iron in Earth's cryosphere.

Phages often degrade the genome of their bacterial host to individual nucleotides. Here we describe Metis, a bacterial defense system that directly senses phage-mediated host genome degradation. Metis aborts phage infection once it detects the modified mono-nucleotide m 6 dAMP. As methylation of deoxyadenosines usually occurs on the DNA polymer, accumulation of m 6 dAMP signals that the host genome has been degraded. In type I Metis, sensing of m 6 dAMP activates an NAD + diphosphatase, leading to NAD + depletion and cessation of the infection process; while the effector in type II Metis is a membrane-spanning protein whose toxicity is triggered in response to the modified mono-nucleotide. We further show that Metis defense depends on endogenous DNA methylases, and that phages can escape Metis via mutations that inactivate host genome degradation.

Abstract Over geological time, the growth of the ocean floor involves magmatic and tectonic extension 1 at mid-ocean ridges (MORs). Because seismogeodetic monitoring of these submarine plate boundaries remains challenging 2–7 , little is known about how these systems operate on yearly timescales. Here we report the first, to our knowledge, in situ observation of a rifting event at a MOR segment that combines hydroacoustic, direct-path ranging and bottom-pressure measurements, with repeated seafloor mapping. This event started on 26 April 2024 at the axis of the Southeast Indian Ridge (SEIR) near 37° S, two months after instruments had been deployed across the ridge axis and nearby Amsterdam transform fault (TF). The event began as a rapidly migrating swarm of extensional seismicity along the axial valley. It caused 4 m of subsidence of the valley floor and more than a metre of horizontal extension across the valley. We interpret this as the deflation of a sill-like reservoir feeding propagating dykes along the ridge axis. The dykes eventually led to the outpouring of about 160 million m 3 of lava at the seafloor in about 16 days, while inducing both seismic and aseismic slip on valley-bounding normal faults and finally triggering seismic activity on the abutting TFs. Large-scale aseismic slip induced by magmatic processes could therefore be the primary mechanism by which MOR normal faults accrue their displacement, which would account for their well-documented seismic deficit 8,9 .

Arctic sea-ice decline is fundamentally altering the momentum transfer between the atmosphere and ocean, with important implications for the climate system. However, capturing the complexity of air-ice-ocean interactions across varying timescales remains a challenge for climate models. Here, we show that Arctic Ekman pumping is organized into seven distinct physical regimes identified using a probabilistic clustering framework applied to a 26-year state estimate. These regimes reflect different combinations of wind, ice, and geostrophic forcing, alongside a residual component that becomes more prominent during seasonal sea-ice transitions. Spatially, the Beaufort Sea is characterized by frequent transitions and persistent ice-geostrophic coupled regimes, whereas the Nordic and Eurasian marginal seas exhibit greater variability in regime stability over time. This regime framework provides a physically grounded link between atmospheric forcing and surface momentum balance, offering a process-based perspective on how changing ice cover may influence Arctic upper-ocean dynamics.

Abstract Biological dinitrogen (N 2 ) fixation sustains productivity in oligotrophic oceans and is now also thought to contribute substantially to the nitrogen supply in the warming Arctic. Here we demonstrate significant N 2 fixation by particle-associated diazotrophs in subsurface waters of the Barents Sea. Comparing our findings with subtropical studies reveals particle-associated non-cyanobacterial diazotrophs as the primary N 2 fixers in subsurface Arctic waters of the Barents Sea, contrasting with diverse communities in warmer regions. As the Arctic shifts towards oligotrophication, understanding the magnitude and controls of particle-associated N 2 fixation will provide critical insights into future nitrogen supply required to sustain productivity in the rapidly changing Arctic Ocean. However, particle-associated N 2 fixation may be a distinctive feature of the Barents Sea, where in contrast to other Arctic shelves the seasonal and long-term trends in nitrogen dynamics are heterogeneously determined by changes in the external Atlantic Water supply, sea-ice extent, and terrestrial inputs. In this context, the role of particle-associated N 2 fixation across the wider Arctic Ocean will require further investigation.

Observations from satellites and climate-model simulations show that moderate volcanic eruptions and extreme wildfires have humidified the stratosphere since 2005. The aerosol-mediated effects of these eruptions and fires explain about one-third of the observed stratospheric water-vapour trend, a contribution similar to that of surface warming. Moderate eruptions and extreme fires account for about one-third of the upward trend in stratospheric moisture.

Cloud attenuation due to cloud particles can be a critical issue for next-generation sub-terahertz (sub-THz), terahertz (THz), and free-space optical (FSO) satellite communications. Accurate estimation of cloud attenuation under various cloud conditions is essential for the design of satellite communication systems; accordingly, numerous cloud attenuation estimation models have been proposed. The widely used ITU-R model proposed up to 200 GHz is based on liquid water content and does not explicitly consider ice crystals. Previous studies have often assumed water droplets to be dominant, potentially leading to unrealistic cloud attenuation estimates, especially in multilayered cloud systems where ice crystals can exist over a wide temperature range. In addition, approaches based on reanalysis data and numerical weather prediction models rely on parameterized cloud representations and may involve substantial uncertainties. These limitations highlight the need for observationally grounded evaluations that explicitly account for both water droplets and ice crystals. In this study, cloud attenuation was estimated up to wavelengths of approximately 20 [Formula: see text] (corresponding to about 10 THz) using ten cloud microphysical data obtained from cloud particle sensor (CPS) sondes observed in Okinawa, Japan, during the Baiu (East Asia rainy season) in 2016, 2017, and 2025. CPS sondes enable the vertical measurement of the number of cloud particles along the sonde flight path and allow for their rough classification into water droplets and ice crystals, making them particularly well-suited to addressing the aforementioned challenges. Cloud attenuation was estimated based on in situ observational data obtained using CPS sondes. To the best of our knowledge, this study is the first to estimate cloud attenuation for satellite communications using in situ observational data of cloud particles. The results demonstrate that cloud attenuation due to ice crystals becomes dominant, particularly at wavelengths shorter than 1.0 mm, challenging the conventional picture that cloud attenuation is dominated by water droplets. This finding was obtained by explicitly considering the size distribution of ice crystals. A key contribution of this study is the identification of limitations in conventional cloud attenuation models for satellite communications using in situ cloud particle observations. Expanding similar studies globally could lead to the development of more accurate and widely applicable cloud attenuation models.