Effects of climate change on the metabolic ecology of small yellow croaker (Larimichthys polyactis) based on dynamic energy budget (DEB) model.

Modeling the fate of organic micropollutants in agricultural soils: Laboratory parameterization and field-scale implications.

Multi-criteria evaluation of blue nature-based solutions: Suitability of Posidonia oceanica in the Mediterranean Sea under climate scenarios

Are intertidal habitats keeping up with nutrient export? Insights from modelling climate and management scenarios.

Integrated water and nitrogen management sustains rice yield and efficiency under changing climate scenarios

Effects of persistent thermal plumes on Zostera muelleri reproductive effort, seed bank densities and seed bank viability.

Global climate change-driven poleward shifts in suitable habitat distribution and niche differentiation of benthic euryhaline Lateolabrax species.

Soil water and carbon dynamics of barley - pea intercropping in a temperate environment under projected climate change.

Warming climate intensifies systemic neurobehavioral-metabolic disruption induced by polyethylene microplastics in Tenebrio molitor.

Vegetation inventories characterizing potential fuels represents critical information underpinning wildfire management and emergency response planning. Available fuels can be characterized in terms of burn probability, which describes the degree to which a set of biotic and abiotic conditions corresponds to known or simulated burned areas. Changes in future climate are expected to result in corresponding shifts in burn probability. In this study, existing burn probability models based on climate, vegetation, and topographic conditions were used as inputs with variables from four future climate scenarios to examine the spatiotemporal distribution of burn probability in the 21st century. Changes were calculated and analyzed for all pixels in forest-dominated ecozones in Canada and 160 forest-adjacent communities by comparing future projections to contemporary values of burn probability. By 2100, overall median projected burn probability increased by 17% across scenarios, ranging from 4 - 60% across individual ecozones. Burn probability likewise increased for the majority of forest-adjacent communities, although the magnitude of the increase was highly variable. The results of this study show the spatiotemporal distribution of changes in burn probability under future climate scenarios and provide valuable information for those interested in implementing mitigation techniques (e.g., prescribed burning, thinning, creation of defensible spaces or firebreaks) to reduce the impacts of future fires.

Climate warming is one of the most pressing global changes, with profound consequences for biodiversity, ecosystem functioning, and the provision of ecosystem services. Although warming is expected to alter soil nutrient cycling and plant community structure, the mechanisms through which it reshapes ecosystem multifunctionality (EMF) remain insufficiently understood. Here, we conducted a 3-year field warming experiment in an alpine grassland to assess how warming influences plant diversity, soil nutrients, and their joint effects on EMF. We found that plant α-diversity declined in both control and warming groups in 2021 and partially recovered by 2023, though recovery was weaker under warming. In contrast, β-diversity (turnover) showed a continuous increasing trend under warming across years, although differences from the control were not statistically significant. EMF, evaluated with single- and multi-threshold approaches, exhibited a consistent decline, with warming accelerating this reduction and producing more complex bimodal fluctuations within intermediate threshold ranges (55–75% and 80–90%). Warming also restructured the functional drivers of EMF: soil organic carbon (SOC) and available nitrogen (AN) emerged as dominant regulators, whereas the contributions of total nitrogen and turnover weakened. Collectively, these findings demonstrate that warming not only alters biodiversity patterns and ecosystem functions but also reshapes the soil–plant–function feedbacks that sustain EMF. By identifying SOC and AN as critical mediators, this study highlights a mechanistic pathway through which climate warming may undermine ecosystem resilience and long-term sustainability, providing insights essential for predicting terrestrial ecosystem responses under future climate scenarios.

Climate change poses a severe long-term threat to endemic species. Ecologists must have a comprehensive understanding of habitat suitability and environmental variables that control their distribution to minimize biodiversity loss and improve conservation strategies effectively. The MaxEnt model is commonly applied to predict species distribution based on occurrence data and environmental variables. This study investigated the suitable habitats of the endemic Quercus afares in Algeria, evaluated shifts in its range under climate change scenarios and identified the key ecological factors determining its distribution. The results showed that the Area Under the Curve (AUC = 0.992) indicated excellent performance of our MaxEnt model. The major environmental predictor for Quercus afares was the Martonne aridity index (Idm), which had the most useful information. Future scenarios indicate that the highly suitable habitat for Quercus afares is expected to range between 0.2% and 0.14%. The average elevation of suitable habitat changes according to each climate scenario, ranging from 1,086.5 to 1,276.5m. The highly suitable habitat shifts towards the northeast in most future climate scenarios. Our findings represent a decision support tool and contribute to developing effective conservation and management measures of Quercus afares in Algeria.

Accurate optimization of plant population and foliar application doses of growth regulators like GA3 can play a crucial role in enhancing growth and flower dynamics of flowering plants under different climate scenarios. An experiment was conducted to investigate how different plant populations and foliar applications of GA3 at different concentrations influenced the vegetative growth performance and flowering performance of cocksflower. The experiment followed a Randomized Complete Block Design (RCBD) with 3 replications and consisted of two factors. Factor A: Three levels of plant population: P1: 12 plants plot-1 (160,000 plants ha-1), P2: 15 plants plot-1 (200,000 plants ha-1), P3: 18 plants plot-1 (240,000 plants ha-1) and Factor B: G0 = 0 ppm GA3, G1 = 50 ppm GA3, G2 = 100 ppm GA3, and G3 = 150 ppm GA3, respectively. Plant population and GA3 levels resulted significant variations for most of the parameters. P1G2 consistently showed superior growth across all stages. It recorded the tallest plants (33.16, 53.15, and 69.12 cm), the longest stems (13.28, 20.81, and 35.52 cm), and the longest spikes (9.18, 14.75, and 25.09 cm), highest spikelet numbers (2.46, 3.46, and 4.53 per spike), at 15, 30, and 45 DAT, respectively confirming its overall growth advantage. P1G2 produced the highest spikes per plant with 8.40, and this supported 100.80 spikes per plot and 1344000 spikes per hectare. The results demonstrated that optimum plant population and GA3 usage of 100 ppm by foliar application give better results for growth and flowering performance of cocksflower.

Urban flooding has emerged as one of the most critical challenges in rapidly developing cities, driven by climate change, intense rainfall events, and increasing land-use pressures. Accurate flood hazard mapping is essential for informed urban planning and disaster risk reduction, yet traditional approaches often face limitations in capturing complex hydrodynamic processes and ensuring interpretability for decision-makers. This study presents an integrated methodology that combines two-dimensional HEC-RAS hydrodynamic modelling with explainable machine learning (XAI) techniques for flood hazard zonation in urban watersheds. The HEC-RAS model successfully simulated flood depths and flow velocities, validated against observed data with a strong correlation coefficient (R2 = 0.92) and low error indices. Machine learning models were tested using rainfall intensity, land use, slope, and proximity to rivers as predictors, with Random Forest achieving the highest performance (91% accuracy). To address the ‘black-box’ limitation, Shapley Additive Explanations (SHAP) were applied, identifying rainfall intensity and river proximity as the most significant drivers of flood risk. An integrated hazard map was developed by combining hydrodynamic outputs with Random Forest predictions. Validation against historical flood records yielded an overall accuracy of 89% and a Kappa statistic of 0.84, confirming the robustness of the approach. Sensitivity and statistical analyses further highlighted the impacts of rainfall variability and land-use change on flood susceptibility. The findings demonstrate that integrating hydrodynamic modelling with explainable AI enhances the accuracy, interpretability, and practical utility of flood hazard mapping, offering a valuable framework for urban planners and policymakers in managing flood risks under dynamic climate and land-use scenarios.

Climate change poses one of the most pressing challenges to global sustainability, necessitating comprehensive mitigation strategies informed by robust scientific analysis. This article examines the role of advanced modeling techniques in enhancing climate change mitigation efforts across multiple scales and sectors. We explore recent developments in integrated assessment models, machine learning algorithms, and high-resolution climate simulations that enable more accurate projections of future climate scenarios and their socioeconomic impacts. The study discusses how these sophisticated computational approaches facilitate the evaluation of mitigation pathways, including renewable energy transitions, carbon capture technologies, and nature-based solutions. Particular attention is given to the integration of uncertainty quantification methods and the coupling of physical climate models with economic and land-use models to support evidence-based policy decisions. Case studies demonstrate the application of ensemble modeling techniques, deep learning frameworks, and scenario analysis in identifying cost-effective mitigation strategies at regional and global levels. Results indicate that advanced modeling approaches significantly improve the accuracy of emission reduction projections and enhance our understanding of feedback mechanisms within the climate system. The article also addresses current limitations in data availability, computational constraints, and the challenges of downscaling global projections to local contexts. We conclude that continued refinement of modeling techniques, combined with improved interdisciplinary collaboration and stakeholder engagement, is essential for designing effective climate mitigation policies that can achieve the goals outlined in international climate agreements.

Abstract This study examines the impact of climate change, defined as long-term changes in temperature and precipitation patterns due to natural and human factors, on women's employment in Burkina Faso, highlighting labour market participation and gender disparities. Using a static computable general equilibrium model calibrated with a gender-specific social accounting matrix, it evaluates two climate scenarios: a 2.4°C temperature increase and a 7.5 per cent decrease in precipitation by 2050. The results indicate that these climate shocks significantly reduce women's employment opportunities. The supply of paid labour for women may decrease by 3.9 per cent, with skilled women experiencing greater job losses than their unskilled counterparts. In rural areas, the domestic workload could increase by up to 0.28 per cent, further limiting women's labour market participation. These changes reinforce gender inequalities and contribute to a decline in real GDP. To counter these effects, investments in climate-resilient agriculture, water and energy infrastructure, and women's entrepreneurship are essential. Gender-responsive policies are needed to promote inclusive economic growth and reduce employment disparities.

Abstract Oceans are the primary source of atmospheric bromoform (CHBr 3 ) and dibromomethane (CH 2 Br 2 ), with implications for tropospheric chemistry and the ozone layer. Nevertheless, socio‐economic developments are changing the oceans' biological characteristics, which could impact the magnitude and distribution of oceanic emissions in the future. In this work, we couple a machine learning (ML) framework to the Community Earth System Model (CESM) data of the Coupled Model Intercomparison Project (CMIP) and estimate the monthly sea surface concentrations of CHBr 3 and CH 2 Br 2 between 2015 and 2100, under different climate change scenarios. We use these estimates to run CESM version 2 (CESM2), with comprehensive halogen chemistry, and calculate present‐day global emissions of 269–271 Gg Br and 61–65 Gg Br for CHBr 3 and CH 2 Br 2 , respectively, based on different scenarios. Furthermore, we project 14%–40% and 8%–23% increases for global mean emissions of CHBr 3 and CH 2 Br 2 , respectively, by 2100; where more stringent scenarios lead to smaller enhancements. Regionally, there are uncertainties within the magnitudes and signs of the changes that depend on the climate scenarios considered. Nevertheless, the largest enhancements, under all scenarios, were predicted over the western tropical Pacific Ocean, tropical Atlantic Ocean, and Indian Ocean. We attribute these changes primarily to biological parameters rather than physical parameters. These changes project a 0.47–1.13 ppt Br increase from the combined source gases (CHBr 3 and CH 2 Br 2 ) in the upper troposphere by 2100, which could impact the stratospheric ozone budget. Overall, this study highlights the far‐reaching influence of human activities on natural oceanic emissions and atmospheric chemistry.

Climate change can affect the dispersion of air pollutants, and it is important to investigate how the emission sources would contribute to air pollution in the context of future climate scenarios. The main objective of this study is to examine the impact of climate-induced changes on dispersion and concentration of local air pollutants related with aircraft operations in airport. The dispersion modelling was implemented considering current and future climate conditions (Copernicus data for 2050). The emission inventory for NO2 from aircraft was compiled for winter and summer periods using current publicly available flight-tracking data and emission factors provided by the European Environment Agency. The methodology is applied to Lisbon International Airport allowing quantification of climate-induced changes on local air pollution near the airport. Although the highest NO2 levels related to aircraft activities occur in winter, the most pronounced difference between the two climate scenarios is obtained for summer, with an increase up to 73% in the daily average concentration under future climate conditions. The area exceeding the EU daily average limit value expands by 59% in summer, while a slight 4% decrease is obtained for winter. Additionally, the data were analysed considering modelling receptor point co-located with the current measurements and showing that at some hours the airport may contribute up to 25% of NO2 observed in densely populated area. This study highlights the importance of climate change in shaping future airport-related air pollution, emphasizing the need for effective air quality management strategies in the context of climate change.

Climate change poses an unprecedented threat to global biodiversity, with birds serving as critical indicators of ecosystem responses. This study assessed the impacts of climate change on 29 endangered bird species in South Korea, a critical stopover region within the East Asian-Australasian Flyway (EAAF). Using Random Forest models, we predicted current (2010 baseline) and future species distributions under two climate scenarios (SSP2-4.5 and SSP5-8.5) for four time periods (2030s, 2050s, 2070s, and 2090s). Model performance was robust, with a mean AUC of 0.844 ± 0.122 across all species and 72.4% of species achieving AUC ≥ 0.80. Elevation emerged as the most influential predictor for 44.8% of species, followed by precipitation of the driest month (17.2%) and distance to water bodies (10.3%). Current species richness patterns showed spatial heterogeneity, with higher concentrations along coastal wetlands, particularly in the western and southern coasts and Jeju Island. Under SSP2-4.5, species richness patterns remained relatively stable through 2090, while SSP5-8.5 projected more dramatic shifts, particularly after 2070. Coastal regions and national parks exhibited differential responses, with some areas showing increases and others experiencing declines in species richness. High-elevation national parks, including Mt. Hallasan, Mt. Seoraksan, and Mt. Odaesan, demonstrated potential to serve as climate refugia, maintaining relatively stable species richness under both scenarios. Our spatial analysis at municipality and national park levels identified priority conservation areas and emphasized the need for climate refugium identification, habitat connectivity along elevational gradients, and adaptive management strategies. The findings provide actionable guidance for science-based conservation planning and contribute to international efforts to protect migratory birds along the EAAF. Urgent conservation measures are needed to safeguard coastal wetlands and establish ecological corridors to facilitate species range shifts under changing climatic conditions.

Potamon ibericum , a freshwater crab species, is highly sensitive to environmental changes, especially water temperature and flow regimes. This sensitivity makes it an excellent bioindicator for assessing the health and stability of freshwater ecosystems under climate change scenarios. However, its limited dispersal ability makes it vulnerable to habitat fragmentation and climate-induced range shifts. Therefore, predicting its future habitat suitability is crucial for early conservation planning. This study utilizes current and future (2060–2080) climate variables along with species distribution modeling (SMD) tools. By collecting presence records of the species from various datasets and published articles, we examined its potential distribution. The results indicated that temperature seasonality is the most significant factor influencing the species’ distribution. Additionally, with increasing climatic changes, the species’ altitude range shifts to higher elevations, averaging between 1,600 and 1,900 meters above sea level. We also assessed the degree of overlap between Iran’s protected areas and the current and future suitable habitats for the species. The findings revealed that the most important refuge is the Central Alborz Protected Area, which encompasses approximately 1,803 square kilometers of suitable habitat. However, future projections under the most severe climate scenarios suggest that less than one-fifth of the suitable habitat will remain within protected areas. In general, P. ibericum may face the risk of extinction and significant loss of suitable habitat in Iran due to extreme future climatic conditions. Protecting this sensitive and ecologically important species within freshwater ecosystems is vital, and immediate management actions are necessary to ensure its conservation.