Climate Scenarios Research Articles

Assessing Climate Change Impact on Habitat Suitability and Ecological Connectivity of Wych Elm (Ulmus glabra Huds.) in Türkiye

Understanding how climate change influences the geographical distribution of species within an ecological niche is essential for predicting habitat shifts and informing conservation efforts. This study evaluates the impact of climate change on habitat suitability and ecological connectivity of wych elm (Ulmus glabra Huds.) in Türkiye. The study explores the future distribution of U. glabra and how its connectivity is affected by habitat fragmentation arising from changing climatic conditions. Contextually, this paper aims to achieve two primary objectives: estimating the potential geographical ranges of U. glabra under different climate scenarios and assessing alterations in ecological connections between current and future habitats. The maximum entropy (MaxEnt) model was used along with Morphological Spatial Pattern Analysis (MSPA), and the Probability of Connectivity (PC) index was applied to show possible transformations in distribution patterns of U. glabra over time. The findings suggest that there will be a reduction in the suitability of locations for the species. Moreover, it is expected that under future climate scenarios, ecological connectivity will decline, especially from 2061 to 2100 in the SSP585 scenario. Notably, significant alterations are anticipated during the latter half of the twenty-first century, mainly outside the coastal areas of the Black Sea, where extensive regions would become unsuitable. Additionally, the species is projected to shift its range, decreasing its presence in inland regions while expanding along the coasts. The results show the vulnerability of this species against climate change, thereby demanding adaptive conservation measures to preserve it within the forest ecosystems of Türkiye.

Integrating Machine Learning and Genetic Algorithms to Optimize Building Energy and Thermal Efficiency Under Historical and Future Climate Scenarios

As the global energy demand rises and climate change creates more challenges, optimizing the performance of non-residential buildings becomes essential. Traditional simulation-based optimization methods often fall short due to computational inefficiency and their time-consuming nature, limiting their practical application. This study introduces a new optimization framework that integrates Bayesian optimization, XGBoost algorithms, and multi-objective genetic algorithms (GA) to enhance building performance metrics—total energy (TE), indoor overheating degree (IOD), and predicted percentage dissatisfied (PPD)—for historical (2020), mid-future (2050), and future (2080) scenarios. The framework employs IOD as a key performance indicator (KPI) to optimize building design and operation. While traditional indices such as the predicted mean vote (PMV) and the thermal sensation vote (TSV) are widely used, they often fail to capture individual comfort variations and the dynamic nature of thermal conditions. IOD addresses these gaps by providing a comprehensive and objective measure of thermal discomfort, quantifying both the frequency and severity of overheating events. Alongside IOD, the energy use intensity (EUI) index is used to assess energy consumption per unit area, providing critical insights into energy efficiency. The integration of IOD with EUI and PPD enhances the overall assessment of building performance, creating a more precise and holistic framework. This combination ensures that energy efficiency, thermal comfort, and occupant well-being are optimized in tandem. By addressing a significant gap in existing methodologies, the current approach combines advanced optimization techniques with modern simulation tools such as EnergyPlus, resulting in a more efficient and accurate model to optimize building performance. This framework reduces computational time and enhances practical application. Utilizing SHAP (SHapley Additive Explanations) analysis, this research identified key design factors that influence performance metrics. Specifically, the window-to-wall ratio (WWR) impacts TE by increasing energy consumption through higher heat gain and cooling demand. Outdoor temperature (Tout) has a complex effect on TE depending on seasonal conditions, while indoor temperature (Tin) has a minor impact on TE. For PPD, Tout is a major negative factor, indicating that improved natural ventilation can reduce thermal discomfort, whereas higher Tin and larger open areas exacerbate it. Regarding IOD, both WWR and Tin significantly affect internal heat gains, with larger windows and higher indoor temperatures contributing to increased heat and reduced thermal comfort. Tout also has a positive impact on IOD, with its effect varying over time. This study demonstrates that as climate conditions evolve, the effects of WWR and open areas on TE become more pronounced, highlighting the need for effective management of building envelopes and HVAC systems.

Future Scenarios of Forest Carbon Sink in a Typical Subtropical County

In the context of achieving global carbon neutrality, forests play a pivotal role in sequestering atmospheric CO2, particularly in China, where forest management is central to national climate strategies. This study evaluates the forest carbon sink capacity in Zixi County, a subtropical region, under varying climate scenarios (SSP2-4.5 and SSP5-8.5). Using the Forest-DNDC (Denitrification–Decomposition) model, combined with high-precision climate data and a random forest model, we simulate forest carbon density and forest carbon sink under different management strategies. The results indicate that under the baseline scenario, forest carbon density in Zixi County increases by 31% over 42 years under the SSP2-4.5 climate scenario and by 28.6% under SSP5-8.5. In the enhancing economic scenario, carbon density increases by 8.5% under SSP2-4.5 and by 7.2% under SSP5-8.5. For the natural development scenario, a significant increase of 130% is observed under SSP2-4.5, while SSP5-8.5 shows an increase of 120%. Spatially, forest carbon sinks in Zixi County total 843,152 T C in 2020, 542,852 T C in 2030, and 877,802 T C in 2060 under the baseline SSP2-4.5 scenario; under SSP5-8.5, these values are 841,321 T C in 2020, 531,301 T C in 2030, and 1,016,402 T C in 2060. In the enhancing economic scenario, the total carbon sink is 34,650 T C in both 2020 and 2030, increasing to 427,351 T C in 2060 under SSP2-4.5, while under SSP5-8.5, it is 46,200 T C in 2020, 34,650 T C in 2030, and 415,801 T C in 2060. The natural development scenario shows the total carbon sink under SSP2-4.5 as 11,157,332 T C in 2020, 3,441,910 T C in 2030, and 1,409,104 T C in 2060, and under SSP5-8.5, it is 10,903,231 T C in 2020, 3,337,960 T C in 2030, and 1,131,903 T C in 2060. Spatial analysis reveals that elevation and forest type significantly affect carbon density, with high-altitude areas and forests dominated by Chinese fir and broadleaf species showing higher carbon accumulation. The findings highlight the importance of targeted forest management, prioritizing species with higher carbon sequestration potential and considering spatial heterogeneity. These strategies, applied locally, can contribute to broader national and global carbon neutrality efforts.

Assessing the impacts of land use and climate change on the distribution patterns of Ulex europaeus L. (Fabaceae) in the Canary Islands

AbstractBiological invasions are one of the major threats to biodiversity, but their impact is particularly detrimental on oceanic islands like the Canary Archipelago. The common gorse (Ulex europaeus L. (Fabaceae)) is a highly invasive shrub with established populations in Tenerife, the sole island of the archipelago where it is present. Understanding the habitat preferences of U. europaeus is essential for predicting its current and potential future distribution across Tenerife and other Canary Islands, guiding effective local management practices. In 2019 and 2020, we surveyed different populations of U. europaeus in Tenerife, retrieving information on its abundance, presence of other invasive species and most frequent natural and anthropogenic characteristics of the landscape. We used this information to build explanatory and predictive models to identify the key natural and anthropogenic drivers of U. europaeus abundance in Tenerife and estimate the potential distribution of the species across the whole archipelago under current and future climatic conditions. Our findings showed that U. europaeus thrives in humid areas impacted by human activities where other invasive species persist. Both current and future climatic conditions do not support the presence of the species in the more arid islands of the archipelago (i.e., Fuerteventura and Lanzarote), rather highlight that the windwards, and thus more humid, areas of the other islands might support the species also under future warmer climatic scenarios. These findings deepen our understanding of U. europaeus local dynamics and are crucial to inform targeted management strategies to mitigate its impact across the Canary Archipelago and, ultimately, oceanic islands.

Identifying key monitoring areas for tree insect pest risks in China under climate change.

Climate change can exacerbate pest population growth, posing significant threats to ecosystem functions and services, social development, and food security. Risk assessment is a valuable tool for effective pest management that identifies potential pest expansion and ecosystem dispersal patterns. We applied a habitat suitability model coupled with priority protection planning software to determine key monitoring areas (KMA) for tree insect pest risks under climate change and used forest ecoregions and nature reserves to assess the ecological risk of insect pest invasion. Finally, we collated the prevention and control measures for reducing future pest invasions. The KMA for tree insect pests in our current and future climate is mainly concentrated in eastern and southern China. However, with climate change, the KMA gradually expands from southeastern to northeastern China. In the current and future climate scenarios, ecoregions requiring high monitoring levels were restricted to the eastern and southern coastal areas of China, and nature reserves requiring the highest monitoring levels were mainly distributed in southeastern China. Tree insect pest invasion assessment using ecoregions and nature reserves identified that future climates increase the risk of pest invasions in forest ecoregions and nature reserves, especially in northeastern China. The increased risk and severity of tree insect pest invasions require implementing monitoring and preventative measures in these areas. We effectively assessed the pest invasion risks using forest ecoregions and nature reserves under climate change. Our assessments suggest that monitoring and early prevention should focus on southeastern and northeastern China.

The influence of aridity on plant intraspecific chemical diversity supports adaptive differentiation and convergent evolution.

Plants synthesize a broad array of specialized chemical compounds that mediate their interactions with the surrounding environment. Some of this chemical diversity is functional and subject to natural selection, but the factors underlying chemical evolution at the intraspecific level remain largely unknown. Here, we combined chemical, environmental and genetic data to investigate the effect of aridity on the expression of chemotypes in the invasive shrub Senecio pterophorus. We studied the variation in pyrrolizidine alkaloids (PAs), a group of specialized metabolites widespread across the families Boraginaceae, Asteraceae and Fabaceae, from native populations spanning a cline of aridity and from three cross-continental introductions, under natural and common garden conditions. We examined whether the relationship between chemistry and aridity was compatible with a process of adaptive differentiation using a method that partitions the variance and covariance by controlling for the population neutral genetic structure. We found a consistent shift from retrorsine-like to seneciphylline-like compounds under increasing aridity in both natural and controlled conditions in coherence with the biosynthetic pathways. This pattern was independent of the neutral genetic structure and occurred along the environmental gradient in the native range and in a convergent manner in all nonnative regions, which suggests adaptive differentiation in response to aridity. Our findings show that the diversity of PAs in S. pterophorus has been partially shaped by aridity. Investigating how abiotic factors influence chemical evolution is key to elucidating the plant responses in future climate scenarios and the cascading effects on other trophic levels.

Corrosion on cultural heritage buildings in Jordan in current situation and in future climate scenarios

This study examines the impact of air pollution on Jordan’s cultural heritage sites, focusing on key pollutants (SO2, HNO3, O3, PM10) and climate conditions. Using 2019 data and future projections for 2040–2059 and 2080–2099, the research reveals significant material corrosion in urban areas like Amman and Irbid, driven by pollutants such as SO₂ and PM10. Random Forest Analysis identifies these pollutants as primary contributors to material degradation. Future scenarios indicate corrosion rates exceeding safe limits across Jordan, underscoring the need for proactive conservation strategies. This work highlights the critical role of air quality management in protecting cultural heritage, especially under climate change pressures, and provides guidance for national policy development.

Effects of short- and long-term thermal stress on developmental stages and adults vis-à-vis reproductive physiology of Spodoptera litura

Abstract Temperature is a critical environmental factor influencing insect development, physiology, and distribution. Global warming is predicted to increase the frequency and severity of extreme heat events, influencing insects differently across life stages. This study investigates the effects of short- and long-term thermal stress on the tobacco cutworm, Spodoptera litura, a polyphagous pest damaging over 389 plant species. We exposed eggs, larvae, pupae, and adults to elevated temperatures of 42°C and 46°C for four hours and one hour, respectively, in single and repeated occasions for two, four, two and two days for egg, larva, pupa and adults, respectively. Our results demonstrate significant variation in biological and reproductive fitness parameters across treatments. Notably, thermal stress skewed the sex ratio towards females. We also observed impacts on offspring survival and potential negative carry-over effects. Overall, the effects of heat stress were dependent on temperature, exposure duration, and life stage, with adults exhibiting the highest tolerance. These findings suggest that S. litura possesses a degree of heat tolerance that may enable it to survive summer heat waves, but its offspring and other life stages could be more vulnerable under future climate scenarios.

AI for Climate Action: Leveraging Artificial Intelligence to Address Climate Change Challenges

This comprehensive article explores the transformative role of Artificial Intelligence (AI) in addressing the global climate crisis, focusing on its applications in climate modeling, renewable energy optimization, and disaster response. Through an extensive literature review, case studies, and expert interviews, we examine how AI technologies are revolutionizing climate prediction accuracy, enhancing data processing capabilities, and enabling sophisticated climate scenario simulations. The article delves into AI's contributions to renewable energy forecasting, smart grid management, and energy storage optimization, highlighting its potential to accelerate the transition to sustainable energy systems. We also investigate AI-driven approaches to disaster response and resilience, including early warning systems, resource allocation during climate-related disasters, and post-disaster recovery planning. While acknowledging the significant advancements AI brings to climate action, this study also addresses the challenges and limitations, such as data quality issues, ethical considerations, and technical barriers. Looking ahead, we discuss emerging AI technologies, their integration with other solutions, and the policy implications for effective climate change mitigation and adaptation. This research underscores the critical role of AI in combating climate change, while emphasizing the need for responsible development and deployment of these technologies to ensure a sustainable and resilient future.

Predicting future impacts of climate and land use change on streamflow in the middle reaches of China's Yellow River

With increasing temperatures, changing weather patterns and ongoing development, it is becoming increasingly important to clarify the evolution mechanism of future regional streamflow processes and their controlling factors. In this study, an integrated framework for watershed streamflow prediction based on a Global Climate Model (GCM), the Patch-generating Land Use Simulation model (PLUS), and the Soil and Water Assessment Tool (SWAT) was proposed in the middle Yellow River. The results indicate that, compared with the baseline period (1989–2018), levels of precipitation and maximum and minimum temperatures are expected to increase in the next 30 years, resulting in a warmer and wetter regional climate. Under various climate scenarios, the annual streamflow is projected to increase by 49.2–115.1%. The acreage of various land types may have tended to be saturated, and the main land types such as cropland, forest and grassland have little change (−6.6%-0.6%), so the impact on streamflow will be correspondingly reduced. Under various land use scenarios, the annual streamflow is projected to increase by 5.0%–7.3%. The annual average streamflow trends under the combined climate and land use scenarios are consistent with the climate change scenarios, while the mean values corresponding to the combined scenarios are higher than those of the single scenario. Findings show that climate change is the main driver influencing streamflow, with a contribution of 86.3%–95.1%. This study deepens understanding of the change pattern and influence mechanism of the streamflow process, which can provide a scientific basis for the development and refinement of regional ecological construction plans.

Potential impact of climate change on water productivity and water footprints of rice and wheat in the Indo-Gangetic plains of India.

Agriculture faces a significant problem as a result of the decline in per capita freshwater supply that has been observed over the past couple of decades. Better water management strategies are required in order to make agriculture water secure, environmentally sustainable, and economically attractive. The goal of this study was to develop water productivity scenarios for rice and wheat crops in India's Indo-Gangetic Plain (IGP) by analysing agricultural production from the perspective of water consumption. To pinpoint the areas that are hydrologically suitable for growing rice and wheat crops under RCP4.5 and RCP8.5 climate change scenarios, water footprints and water productivity have been mapped. The study found that changes in temperature trends under future climate scenarios are probably going to increase crop water requirements, leading to greater water footprints for IGP regions. Water risk hotspots have been identified in the IGP for the rice- and wheat-growing states of Uttar Pradesh, Punjab, and Haryana. In order to achieve food security in a sustainable way, attention should be given to increase the water productivity of both crops.

Simulation of Responsive Envelopes in Current and Future Climate Scenarios: A New Interactive Computational Platform for Energy Analyses

Despite the strong interest concerning the responsive façades, today there are still few built examples and few tools to assess their benefits due to the complex description of the phenomenon. Energy simulations should consider the interactions between a time-varying environment and an environment-dependent envelope, increasing the intricacy of the problem; moreover, these strong environment–envelope interlinkages increase the importance of the location and climate scenarios considered. The aim of this study is to provide a tool to easily model these phenomena in different geographical and climate contexts. For this purpose, an innovative interactive computational platform (ICP) was developed based on EnergyPlus as a simulation engine, Python as a simulation manager, and Grasshopper as a user interface. Thanks to a single user-friendly environment, the users can simply select the climate scenario, the location, the responsive technology, and its main properties to set and run the dynamic energy simulation. After an overview of the current state of the art, this study provides a description of the structure and workflow adopted for developing this platform and details regarding its functioning and input management. Finally, the platform was tested to run an evolutionary optimization of an electrochromic window control strategy in different climate scenarios.

On the issue of forecasting crop yields for the purposes of adaptive landscape agriculture

Long-term monitoring (1998–2023) of the yield of clover-timothy grass stands of the first year of use was carried out in order to find patterns of influence of landscape environmental conditions on it in various agroclimatic conditions. The research was conducted within the moraine hill located at the Gubino VNIIMZ agro-testing site in the Tver region. Soil-forming rocks are two-membered deposits consisting of an upper layer composed of relatively light rocks, underlain by moraine bouldery loam. The grass stands were exploited without fertilizers in a single-cut mode on a field divided into 120 plots. Using regression analysis, the influence of landscape and soil environment factors was determined: relief, physical and agrochemical properties of soils on grass yield, as well as the dependence of the degree of this impact on climatic conditions. It was found that the yield of perennial grasses is most strongly influenced by various fractions of the granulometric composition of soils – from stones to dust (up to 16 % of its variability) and the altitude of the location (up to 38 %), since the thermal and water-air characteristics of soils and vegetation largely depend on them. Terrain characteristics such as steepness and curvature of the surface have a minor impact on grass yield (up to 12 %). The degree of influence of agro-landscape environmental factors on the growth of grasses is largely regulated by fluctuations in weather conditions. “Climate scenarios” of a specific factor – sets of weather parameters under which its effect on the production process of a crop is manifested – in the years of sowing and mowing, as a rule, do not differ fundamentally. Knowing the nature of the influence of climatic factors allows us to more accurately predict crop yields within an agricultural landscape and, thus, optimize the location of crops on the territory of a particular farm.

Optimal Sizing, Energy Balance, Load Management and Performance Analysis of a Hybrid Renewable Energy System

This work utilizes the particle swarm optimization (PSO) for optimal sizing of a solar–wind–battery hybrid renewable energy system (HRES) for a rural community in Rivers State, Nigeria (Okorobo-Ile Town). The objective is to minimize the total economic cost (TEC), the total annual system cost (TAC) and the levelized cost of energy (LCOE). A two-step approach is used. The algorithm first determines the optimal number of solar panels and wind turbines. Based on the results obtained in the first step, the optimal number of batteries and inverters is computed. The overall results obtained are then compared with results from the Non-dominant Sorting Genetic Algorithm II (NGSA-II), hybrid genetic algorithm–particle swarm optimization (GA-PSO) and the proprietary derivative-free optimization algorithm. An energy management system monitors the energy balance and ensures that the load management is adequate using the battery state of charge as a control strategy. Results obtained showed that the optimal configuration consists of solar panels (151), wind turbine (3), inverter (122) and batteries (31). This results in a minimized TEC, TAC and LCOE of USD 469,200, USD 297,100 and 0.007/kWh, respectively. The optimal configuration when simulated under various climatic scenarios was able to meet the energy needs of the community irrespective of ambient conditions.

Prediction of Potential Suitable Distribution Areas for Northeastern China Salamander (Hynobius leechii) in Northeastern China.

The Northeastern China Salamander (Hynobius leechii) is classified as a rare, nationally protected Class II wild animal in China. Its population is declining, and its habitat is deteriorating. This study aimed to predict the distribution of suitable habitats for the Northeastern China Salamander under both current and future climate scenarios, utilizing the MaxEnt model optimized through ENMeval parameters. Species distribution data were collected from field surveys, existing literature, amphibian records in China, and the Global Biodiversity Information Network. A total of 97 records were compiled, with duplicate records within the ENMTools grid unit removed, ensuring that only one record existed within every 5 km. Ultimately, 58 distinct distribution points for the Northeastern China Salamander were identified. The R software package 'ENMeval 2.0' was employed to optimize the feature complexity (FC) and regularization multiplier (RM), and the optimized model was applied to assess the suitable distribution regions for the Northeastern China Salamander under present and future climate conditions. The findings indicated that rainfall and temperature are the primary environmental factors influencing Hynobius. Currently, the suitable habitat for the Northeastern China Salamander constitutes 6.6% of the total area of Northeastern China. Projections for the periods of 2050 and 2070 suggest that suitable habitats for the Northeastern China Salamander will continue to expand towards higher latitudes across three climate scenarios. While this study focuses solely on climate change factors and acknowledges certain limitations, it serves as a reliable reference and provides essential information for the distribution and conservation of the Northeastern China Salamander.

Boron to salinity ratios in the Fram Strait entering the Central Arctic: The role of sea ice formation and future predictions

The Arctic Ocean's sea ice loss dynamically impacts carbon uptake potential, as assessed through measured carbonate parameters, such as total alkalinity. In the open ocean, boron (B) is the third largest contributor to alkalinity via borate and is usually accounted for through the conservative boron to salinity ratio (B/S), and not directly measured. Here, we present findings on non-conservative boron dynamics, that results in significant B/S deviations, observed in ice melt zone waters, snow, slush, brine, and annual sea ice (n = 169) in the Fram Strait entering the Central Arctic. These samples were collected during the onset of the melt season on the 2023 ARTofMELT expedition, covering a wide practical salinity range (2–59). Barring snow, the average B/S ratio across the study was 0.1321 ± 0.0032 mg kg−1 ‰−1, similar to the mean B/S ratio measured amongst several polar water masses near Iceland, as well as the accepted B/S for other ocean regions. Results indicate minor deviations from accepted B/S ratios (indicating conservative behavior) across the sample practical salinity range and reflect an uncertainty in the borate contribution to total alkalinity of less than 2.9 μmol kg−1 at in-situ temperatures. B fractionation appears to occur during sea ice formation, causing greater B in the sea ice reservoir whereas brine, slush, lead, and under-ice water reservoirs are depleted in B. As such, under-ice and lead, brine, and slush samples all had measured B/S ratios (0.1305 ± 0.0011, 0.1305 ± 0.0018, and 0.1304 ± 0.0017 mg kg−1 ‰−1, respectively) lower than the established ratio whereas the average sea ice B/S ratio (0.1331 ± 0.0035 mg kg−1 ‰−1) was closest to accepted values (0.1336 ± 0.0005 mg kg−1 ‰−1). Arctic open ocean samples also had a lower B/S ratio (0.1304 ± 0.0014 mg kg−1 ‰−1). Our findings, together with a previous Arctic B ice study, suggest that B (probably in the form of B(OH)4−) is incorporated into authigenic CaCO3 minerals, replacing CO32− within the mineral lattice during sea ice formation. This process consequentially lowers the B/S ratio in the open Arctic Ocean, compared to the established global ocean ratio. Nevertheless, the incorporation of B into the sea ice reservoir does not fully account for the deficit of B in the Arctic Ocean samples, suggesting further accounting of B Arctic pathways is necessary. In future climate scenarios involving increased sea ice melt, the transition from multiyear to annual sea ice, permafrost thaw, and increased riverine discharge, the behavior of B in the Arctic Ocean is expected to become more dynamic.

Future Occurrence of Climate-Induced Extreme Heat Events in Museum Galleries: A Modeling Study under Two 21st Century Climate Scenarios at V&A South Kensington

ABSTRACT Museums, including the Victoria & Albert Museum (V&A), are committed to achieving ambitious sustainability goals, focusing on adapting their buildings and operations to adapt to climate change. This paper supports this ambition by developing a method to model internal gallery conditions under future climate projections, using a subset of environmental data from 2015 to 2023 from the V&A South Kensington galleries. The linear regression model, built on this data, predicts scenarios based on Representative Concentration Pathways (RCPs), specifically RCP2.6 and RCP8.5. Preliminary findings indicate little change in gallery closure frequencies in an RCP2.6 scenario compared to the current 0–10 closures per year. Conversely, the RCP8.5 scenario projects an almost tenfold increase in closure days due to high temperatures. This approach, implementable in the R programming language, provides a valuable tool for museums to inform and achieve their sustainability action plans amidst the challenges posed by climate change.

Resilient Coastal Protection Infrastructures: Probabilistic Sensitivity Analysis of Wave Overtopping Using Gaussian Process Surrogate Models

This paper presents a novel mathematical framework for assessing and predicting the resilience of critical coastal infrastructures against wave overtopping hazards and extreme climatic events. A probabilistic sensitivity analysis model is developed to evaluate the relative influence of hydrodynamic, geomorphological, and structural factors contributing to wave overtopping dynamics. Additionally, a stochastic Gaussian process (GP) model is introduced to predict the mean overtopping discharge from coastal defences. Both the sensitivity analysis and the predictive models are validated using a large homogeneous dataset comprising 163 laboratory and field-scale tests. Statistical evaluations demonstrate the superior performance of the GPs in identifying key parameters driving wave overtopping and predicting mean discharge rates, outperforming existing regression-based formulae. The proposed model offers a robust predictive tool for assessing the performance of critical coastal protection infrastructures under various climate scenarios.

Soil Organic Carbon May Decline Under Climate Change: A Case Study in Mexican Forests

Soil organic carbon is essential for ecosystem health, influencing water retention, soil fertility and biodiversity. However, climate change and deforestation are reducing SOC globally. This study models and projects changes in the SOC of Mexican forest soils under different climate scenarios. Over 100 models were developed relating SOC to the Lang index (precipitation and temperature), altitude, slope, bulk density, texture and soil depth. The results indicate that SOC can be effectively modelled to assess scenarios for decision making. The highest SOC levels were found in tropical rainforests and mesophyll forests and the lowest in broadleaved forests of the Sonoran plain. Climate change is projected to reduce SOC in forest ecosystems by up to 11%, especially in temperate forests. Conversely, mesophyll forests are expected to experience a slight increase in SOC of 3% due to rising temperatures and changing precipitation patterns. This decline could lead to increased HGH and reduced carbon storage capacity. This study highlights the need for sustainable management practices and multidisciplinary research to mitigate these impacts and emphasises the importance of comprehensive strategies for long-term environmental sustainability.

Global change drives potential niche contraction and range shift of globally threatened African vulture

Human-induced global change poses an increasingly severe threat to biodiversity, with species having limited population sizes being particularly vulnerable. Mapping and modeling the distribution ranges of such species, along with detecting potential range shifts and contractions at both local and regional scales, are essential for developing effective conservation plans. Ruppell's vulture Gyps rueppelli, an ecologically important bird species native to Africa, is experiencing a rapid decline in its range. The purpose of this study is to map and model potential regional spatio-temporal distribution of Ruppell's vulture in Africa, alongside detecting the possibility of the species' range shifts and contractions. A total of 804 rarefied localities were identified where the Ruppell's vulture was the dominant bird species. This study employed the Maximum Entropy (MaxEnt) algorithm to perform species distribution modeling for the Ruppell's vulture. The modeling considered current climate conditions (1970s-2000s) as a baseline, along with two future climate change scenarios (Shared Socioeconomic Pathways: SSPs 245 and 585) for two future time periods (2050s and 2070s). The model's performance was evaluated by optimizing settings and examining the Area under the Receiver Operating Characteristic Curve (AUC-ROC). Among the 13 bioclimatic and anthropogenic variables included in the model, four (isothermality, cropland expansion, anthropogenic biomes, and urban expansion (in order of importance)) emerged as the most influential drivers of Ruppell's vulture regional distribution. All considered Species distribution models (SDMs) achieved high predictive performance, with AUC-ROC values exceeding 0.9.The model predicted a total of approximately 19,453 ha of suitable habitat for Ruppell's vultures in Africa, with East Africa identified as the most prominent region under the current climate scenario. Isothermality (38.8%) was the primary factor influencing Ruppell's vulture distribution, followed by agricultural expansion (29.9%) and anthropogenic biomes (7.2%) in the face of global change. The results reveal considerable future habitat loss (up to 61%) for Ruppell's vultures in the study area, alongside an eastward range shift (longitudinal axis) by the 2050s under projected climate change scenarios. These imply that Ruppell's vultures face imminent population decline and range shift due to significant habitat loss and climate change. Hence, prioritizing the development and implementation of a coordinated conservation program that incorporates captive breeding and assisted migration is critical to save this vulture species in its native African range.