Influence of Stipa spp. on near-surface aeolian process: A wind tunnel simulation.

Host species as key drivers of Symbiodiniaceae assemblages: Coral and free-living diversity in a Southwestern Atlantic oceanic island.

Rainstorm regulates phosphorus composition and migration along an ecological continuum of mudflat-mangrove-creek.

Dialogue between science, law, and traditional knowledge regarding global environmental problems.

Enhanced phytoremediation of Cd-As co-contaminated farmland using hydrogen-rich water: Mechanisms and efficiency evaluation in Solanum nigrum L․.

Expanding horizons of ecological and evolutionary research in urban heat islands.

In recent years, global climate change and changing environmental policy in North America have put forests at an all-time risk for devastating wildfires. Specifically, in 2024, a megafire known officially as the Jasper Complex Fire devastated the town of Jasper and the surrounding national park. The fire burned around 95,000 acres and caused around one billion CAD in damages. This paper primarily focuses on the Jasper Megafire and its causes, specifically dealing with economic changes to Parks Canada. By using information and statistics from climate models in Jasper National Park, wildland policy changes to Parks Canada under the UCP, and residential concerns in the Jasper townsite, this paper analyzes the environmental and political causes of the Jasper Megafire through many years of global environmental change and budget cuts. Moreover, interviews with a resident revealed deep concerns with Parks Canada mentioning ineffective prevention strategies and buildup of fire fuel. The intent of this paper is to call for stronger fire management and policy to protect Canadian forests.

ABSTRACT Assessing the carbon sequestration potential of agricultural soils with accuracy is essential for global food security and climate change mitigation. We aimed to improve the Rothamsted Carbon (RothC) model for application in Andosol paddy fields by integrating two RothC-based models previously developed in Japan: one tailored for paddy fields and one for Andosol upland fields. The core refinement involved adjusting the decomposition rate of the humified organic matter (HUM) pool within the paddy model using the phosphate adsorption coefficient (PAC). Validation was performed using long-term continuous field data (11–48 years) from five Andosol paddy sites throughout Japan, encompassing control plots (chemical fertilizer only) and plots with varying organic matter amendments. Comparing simulated and observed soil organic carbon (SOC) stocks (to 30 cm), the performance of the improved Andosol-Paddy model showed substantial gains relative to the paddy-specific RothC model. The improved model yielded an average root mean square error (RMSE) of 9.99 and an average absolute mean error (ME) of 7.49, with values < 50% of the original model error (RMSE: 20.32; absolute ME: 16.69). This evidence clearly demonstrates that incorporating PAC-based HUM decomposition into the paddy-specific RothC model significantly elevates the predictive accuracy of SOC dynamics in Andosol paddy fields.

Expediting the construction of the new energy systems is crucial to achieving an energy transition, decreasing carbon dioxide emissions, and alleviating global warming. Utilizing Chinese province panel data from 2000 to 2022, we examined the causal effect of diversity in financial ecosystems on the new energy systems and utilized instrumental variable estimates to mitigate endogeneity. We constructed a new energy systems indicator framework, employed the entropy approach to assign weights to each indicator objectively, and quantitatively assessed the level and attributes of the new energy systems. The findings indicate that diversity in financial ecosystems plays a crucial role in developing new energy systems. Diversity in financial ecosystems impacts the development of new energy systems via the consequences of technology innovation and digital effects. These effects are more concentrated in areas without external power inputs, with a long transportation history, and characterized by low bird biodiversity. The study's findings offer a viable Chinese approach for global energy transition and successful climate change mitigation.

As a result of prolonged excessive anthropogenic impact, there is a degradation of meadow and pasture agrobiocenoses, and global climate change is exacerbating the situation by reducing the hydrothermal coefficient. In general, it can be stated that cattle breeding in the Central Black Earth Region has a fairly high development potential, which is due to a combination of factors, including favorable natural and climatic conditions, a high level of government support, and a growing demand for the industry's products from processing enterprises. At the same time, the increase in the number of beef cattle kept on pasture has an additional negative impact on pastures. Adopted federal and regional programs for restoring the fertility of agricultural lands can be implemented using zoned varieties of perennial grasses.

Land-use and land-cover change (LULCC) is one of the key drivers altering terrestrial ecosystem carbon storage. Accurate simulation of LULCC is crucial for assessing ecosystem sustainability and formulating global climate change mitigation strategies. Within this context, this study proposes a novel deep learning model integrating U-Net architecture, an attention mechanism, and ConvLSTM-termed U-Net-Attention-ConvLSTM (UNA-CL), to enhance the accuracy of LULCC simulation. The model's effectiveness was validated using land use and land cover (LULC) data from Kunming (2000-2020) and compared with a widely applied convolutional neural network (CNN) model (CNNA-CL) and Random Forest-Cellular Automata (RF-CA) model. Furthermore, this study coupled the UNA-CL model with the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, which is designed for ecosystem service assessment, to jointly reveal the spatiotemporal evolution characteristics of future LULC patterns and carbon storage. The results indicate that (1) the UNA-CL model outperformed the comparative models in classification accuracy, achieving an overall accuracy (OA) of 94.36%, which is 5.72% and 0.5% higher than the CNNA-CL and RF-CA models, respectively; (2) in terms of spatial allocation accuracy, the UNA-CL model not only accurately simulated land cover categories with complex distribution patterns but also mitigated the simulation bias caused by spatial heterogeneity in the RF-CA model; (3) from 2000 to 2030, the net increase in carbon storage was 3.74 Mega tons (Mt), exhibiting a trend of increase followed by decrease. Specifically, the conversion of grassland and cultivated land to forest land led to an accumulation of 3.81 Mt during 2000-2020. However, from 2020 to 2030, a combination of forest land loss and continued construction land expansion resulted in a net decrease of 0.07 Mt.

Marine ranching, as a pivotal strategy for enhancing the ocean’s carbon sequestration potential, offers significant potential to mitigate nearshore fishery depletion and restore marine ecosystems amid the global carbon neutrality agenda. However, the mechanistic pathways linking sediment total organic carbon (TOC) to various environmental factors in tropical marine ranches remain insufficiently quantified. This study selected the Wuzhizhou Island Marine Ranch in Hainan Province—a representative tropical marine ranch—as the research site. Field investigations and sampling were conducted during the dry (March 2024) and wet (September 2024) seasons to quantify TOC in surface sediments and associated environmental variables. A two-step analytical framework, integrating Principal Component Analysis (PCA) and Generalized Additive Models (GAM), was employed to elucidate the environmental drivers governing the spatiotemporal dynamics of TOC. The results show that the surface sediment TOC at Wuzhizhou Island Marine Ranch exhibits a distinct spatial gradient—Core Reef &gt; Atoll &gt; Control &gt; Estuarine, and a pronounced seasonal pattern with elevated concentrations in the dry season relative to the wet season. The spatiotemporal differentiation of TOC is mainly driven by a gradient (explaining 52.1% of variation) that encompasses processes related to carbon accumulation from terrestrial inputs and primary production, as well as organic matter degradation promoted by nutrients and higher water temperatures. Sediment total nitrogen (TN) emerges as the primary environmental driver of TOC distribution, contributing up to 46.9% of the variance at an extremely significant level (p &lt; 0.001). Furthermore, total phosphorus (TP), pH, and water temperature (WT) have relatively minor influences on the distribution of sedimentary TOC. Our study offers a crucial reference for elucidating the key processes governing the carbon cycle in tropical marine ranches and provides essential theoretical support for optimizing ocean carbon sink strategies in the context of global climate change.

In Latin America, Food and Nutritional Insecurity (FNI) is a challenge, particularly in households that receive social assistance programs, where food scarcity affects up to 50% of these households. Environmental degradation and climate change are significant contributors to FNI, underscoring the importance of ongoing monitoring. In this paper, we present the roadmap for a higher-impact project to support Food and Nutrition Security (FNS) policymaking in Latin America. The ultimate goal is to improve FNS in communities affected by climate change through the development of an interactive platform that evidences the identification of variables, a transnational data acquisition program, the development of predictive models, and the assessment of climate vulnerability in the region. Additionally, an open data platform, together with the dashboard and a virtual assistant, is being developed for monitoring FNS indicators in Latin America. The project was awarded the ALSEA prize and addresses technological and regional challenges through a multidisciplinary and international team. Effective coordination between space agencies, academia, government, and the productive sector is required to ensure that project results are usable and add value at the local level. The Supporting Evidence for the Proposed Approach presented in this paper are promising and pave the way for future developments that extend not only the geographic scope but also the dimensional analysis.

Global climate change has increased the frequency and intensity of heat waves, posing a significant threat to livestock production. During heat exposure, the disruption of intestinal barrier integrity is a pivotal event in the pathogenesis of heat stress-induced intestinal injury. Endoplasmic reticulum (ER) stress and mitochondrial dysfunction are key consequences of heat stress at the cellular level. However, direct causal evidence linking ER stress to mitochondrial dysfunction in heat-stressed enterocytes remains limited. To investigate this, we used an integrated transcriptomic, metabolomic, and functional validation strategy to assess mitochondrial bioenergetics and cellular ultrastructure in porcine intestinal epithelial (IPEC-J2) cells under acute heat stress. Transcriptomic analysis revealed extensive reprogramming, highlighting the significant enrichment of pathways related to protein processing in the endoplasmic reticulum, apoptosis, and MAPK signaling. Untargeted metabolomics identified significant perturbations in amino acid and energy metabolism, as well as altered bile acid profiles. Functional assessments confirmed that heat stress severely impaired mitochondrial bioenergetics, as evidenced by reduced maximal respiration and ATP production, and induced ultrastructural damage to mitochondria. The pharmacological inhibition of ER stress by 4-phenylbutyric acid (4-PBA) significantly attenuated the mitochondrial bioenergetic impairment and ultrastructural damage, whereas ER stress induction recapitulated these defects. We demonstrate that heat stress induces profound transcriptional and metabolic remodeling characterized by ER stress activation, which critically mediates subsequent mitochondrial bioenergetic dysfunction and ultrastructural damage. Our findings suggest that targeting ER stress may represent a promising therapeutic strategy to ameliorate enterocyte mitochondrial dysfunction and mitigate heat stress-induced intestinal injury in livestock.

Heatwaves frequently coincided with ozone to form one of the most imminent health threats under global climate change. Identifying hotspots of compound heatwave and ozone pollution events (CHOEs) has crucial significance in developing targeted mitigation strategies, which is largely unknown. We therefore performed the comprehensive global and regional analyses on the spatiotemporal variation and death burden of CHOEs during 2000-2021. In addition to CHOE exposure, we estimated its population exposure through multiplying the exposure by total population. We observed that the frequency, duration, and intensity of CHOEs and their population exposures significantly increased in most midlatitudes, particularly in the Middle East and North Africa and South Asia. The population exposures increased at an accelerating pace and were mainly driven by the CHOE itself. We estimated that the death burden of CHOEs increased over time especially in the Middle East and North Africa, with a population attributable fraction of 0.99% in 2000 to 2.01% in 2021. Our findings add novel evidence that CHOEs markedly increased and posed substantial death burdens both regionally and globally in the past two decades. This evidence highlights urgent needs to develop regional targeted mitigation and adaptation actions to reduce health risks due to CHOEs, particularly in hotspots.

The Altai Mountains, a complex mountain system of Central Asia, is particularly sensitive to global change. Under increasing human activities and continuing climate change, the range of animals may show expansion or contraction. In this study, we evaluated and predicted the distribution dynamics of 27 animal species and the resulted change of species richness in the Altai Mountains by using MaxEnt model in the current and future periods. The results show that most species are predicted to mainly distribute in the northwest of the Altai Mountains under current conditions. In the future, habitats located in the central region may be largely lost. Most species tend to shift their ranges towards higher altitudes or latitudes. Human activities, snow cover and precipitation of coldest quarter are the most important predictors explaining the potential distributions of most species. As global climate change continues to alter potentially suitable habitats, we recommend to establish a transboundary protected area across the four countries (China, Kazakhstan, Mongolia and Russia) in the central region of the Altai Mountains. Additionally, we suggest reducing potential anthropogenic impacts on wildlife and their habitats by regulating human activities.

The rising frequency of extreme natural disasters under global climate change presents significant uncertainty and safety challenges for emergency logistics facility planning, particularly in mountainous regions. Addressing the limitations of traditional location studies that often overlook terrain complexity and disaster risks, this study proposes an integrated framework for mountainous emergency logistics site selection, combining Geographic Information Systems (GIS) spatial analysis with Multi-Criteria Decision-Making (MCDM) methods. Using 88 counties and districts in Guizhou Province as a case study, the framework incorporates four criterion-level dimensions and eight indicator-level factors, and employs GIS spatial analysis alongside a hybrid BWM-EWM weighting scheme and an improved TOPSIS evaluation to generate a suitability map. Sensitivity analysis confirms the robustness of the model. The results reveal an east-strong-west-weak spatial pattern of suitability, with population density and transportation accessibility as dominant factors, terrain imposing fundamental constraints, and natural disaster risk providing critical differentiation for decision-making.

Enhancing agricultural carbon productivity under the dual-carbon goal is important for mitigating global climate change, and the development of digital technology provides a new impetus for the low-carbon transformation of agriculture and the green development of the economy. Using the panel data of 30 provinces in China from 2013 to 2023, the projection pursuit model based on the accelerated genetic algorithm and the emission coefficient method are used to measure the development levels of rural digital technology and agricultural carbon productivity, respectively, and the panel fixed-effect model and spatial Durbin model are used to study the impact of digital technology development on agricultural carbon productivity. The results showed that: ① The development of digital technology in China had a spatial distribution pattern of decreasing gradient from east to west, and the agricultural carbon productivity in the southwest region had a high-high clustering pattern, with both showing an increasing trend year by year. ② The development of rural digital technology had a significant effect on the improvement of agricultural carbon productivity. ③ The heterogeneity test showed that the effect of digital technology on the improvement of agricultural carbon productivity was "western &gt; central &gt; eastern" and "balanced grain production and marketing area &gt; main grain production area &gt; main grain marketing area." ④The development of digital technology had a positive spatial spillover effect on agricultural carbon productivity in neighboring regions, with intensive spillovers within 300 km and detectable effects extending to 700 km of the spillover effect.

As an important indicator for measuring the carbon sequestration capacity of ecosystems，carbon storage is of great significance for alleviating global climate change. By taking advantage of machine learning and ecosystem service models，an integrated analysis framework based on the InVEST-Ridge Regression-PLUS model was constructed to conduct a quantitative analysis of the spatio-temporal evolution characteristics and driving mechanisms of carbon storage in the Yunnan-Guizhou Plateau from 2000 to 2020，and future scenarios were designed to predict the changing trends of regional carbon storage under different land use paths. The results show that：Firstly，from 2000 to 2020，the carbon storage in the Yunnan-Guizhou Plateau generally presented a slow growth trend，and the growth rate continued to decline，showing a distribution pattern of "higher in the south and lower in the north." Secondly，vegetation coverage was a crucial determining factor for carbon storage in this area，and the conversion between different land use types affected the spatial distribution of carbon storage. Thirdly，in the future scenario simulation，the carbon storage under the carbon sink enhancement scenario performed best，effectively verifying the effects of ecological projects such as the conversion of farmland to forest and grassland restoration，providing a scientific basis for the dynamic assessment and optimization of carbon storage in the Yunnan-Guizhou Plateau and similar karst areas.

The environmental hazards caused by fierce global climate change have driven the search for new routes to mitigate increasing CO2 emissions. Specifically, adsorption is highlighted herein for its great industrialization potential. Among the prevalent carbon capture materials, zeolites are highly favored due to their high capacity, selectivity, and robustness. While the effects of pore size, composition, morphology, and particle size of zeolites are commonly emphasized for CO2 adsorption, the influences of active site (normally metal cations) distribution are frequently neglected. In this review, the spatial distributions of different metal cations in diverse zeolites, from small- to large-pore ones, are first summarized and then associated with their CO2 adsorption properties. Accordingly, the targeted modification strategies for adjusting metal cation position and distribution in zeolites are introduced, such as one-pot anchoring of heteroatoms into the zeolite windows. Thereafter, the typical and advanced characterization methods are elucidated to reveal the fine structure in zeolites as well as the site-adsorption relationships. Finally, a comprehensive summary and outlook for future spotlights in carbon capture field are provided, including the application of computational chemistry and artificial intelligence-assisted methodologies in determining active sites and adsorption mechanism, along with the potential solutions for the challenge of water vapor competition in zeolites.