Search Results (47,473)

The health and productivity of rabbits are highly sensitive to the environmental conditions within the rabbit house, particularly to fluctuations and deviations in temperature, relative humidity, and carbon dioxide (CO₂) concentration. However, owing to the thermal inertia and residual evaporation effects inherent in ventilation and cooling systems, environmental changes often exhibit delayed responses, rendering real-time control inadequate. Accurate prediction of key environmental parameters is indispensable for formulating effective environmental control strategies, as it enables consideration of their future dynamics and thereby enhances the rationality of regulation in rabbit farming. Existing prediction models often exhibit unsatisfactory accuracy and weak generalization, which restricts the incorporation of prediction into effective environmental control strategies. To address these limitations, summer indoor and outdoor environmental data were collected from rabbit houses in Nanping, Fujian; Jiyuan, Henan; and Qingyang, Gansu, China—three climatically distinct regions—forming three datasets. Based on these datasets, a multi-parameter time-series prediction model, Patch and Cross-Attention Enhanced Transformer for Rabbit House Prediction (PatchCrossFormer-RHP), is introduced, integrating patching and attention mechanisms. The model partitions the sequences of rabbit house temperature, relative humidity, and CO₂ concentration into patches and incorporates auxiliary parameters, such as indoor air velocity and outdoor temperature and humidity, to enhance feature representation. Furthermore, it applies cross-attention with differentiated encoding to disentangle multi-parameter relationships and improve predictive performance. This study used the Fujian dataset as the primary benchmark. On this dataset, PatchCrossFormer-RHP achieved root mean square error (RMSE) values of 0.290°C, 1.554%, and 38.837 ppm for rabbit house temperature, humidity, and CO₂ concentration, respectively, with corresponding R² values of 0.963, 0.956, and 0.838, consistently outperforming RNN, GRU, and LSTM. Transfer experiments with single- and multi-source pretraining followed by fine-tuning on Fujian demonstrated that strong cross-regional generalization can be achieved with only limited target-domain data. Full article

Within the context of global climate change, the hydrological and sediment load dynamics in the Huai River Basin are expected to continue evolving due to intensified human activities and environmental changes. Effective river management requires a clear understanding of the magnitude, causes, and characteristics of these changes, coupled with insight into the dynamic response processes of the river channel. This study applied a suite of statistical methods, including the Mann–Kendall test, Sen’s slope estimator, Pettitt’s test, double mass curve, and morphological analysis, to examine trends in streamflow and sediment load at two hydrological stations in the mid-Huai River from 1982 to 2016, and to assess channel evolution between Wujiadu and Xiaoliuxiang. The results indicate that: (1) both hydrological stations exhibited no significant decrease in annual streamflow, but a significant reduction in sediment load, with a change point detected in 1991 at Wujiadu Station; (2) compared to 1982–1990, the mean streamflow and sediment load decreased by 23% and 50% during 1991–2016, with a significant shift in the streamflow-sediment relationship; (3) while temperature and evapotranspiration increased significantly, precipitation remained relatively stable, indicating that climate change had a minor effect on hydrological elements, and sediment load reduction was primarily driven by large-scale ecological restoration and engineering activities; and (4) differential channel adjustments were observed in response to reduced sediment supply and human activities, modulated by local boundary conditions. Erosion occurred in the WJD section, resulting in a transformation from a U-shape to a V-shape cross-section, whereas the XLX section remained stable with a local adverse gradient. This study reveals the complex mechanisms of hydro-sedimentary and channel evolution under human dominance, offering scientific support for the sustainable management of the Huai River basin and similar regulated rivers. Full article

The hyper-diverse Atlantic Rainforest on the eastern coast of South America comprises deciduous, semideciduous, and evergreen forest formations. How these formations, both as communities and through their individual species, are responding to climate change remains elusive. Using habitat suitability modeling, we examine the effects of climate change on the distribution of the Atlantic Rainforest assessed both at the species level and the formation level. Additionally, we investigated whether mismatches between species- and formation-level trends are linked to the climatic affinities of species at the formations where they occur. We predicted a decrease in habitat suitability for all deciduous, semideciduous, and evergreen formations, based on individual species models, up to 2100. However, when considering species together as formations, we predicted expansions of deciduous and semideciduous formations and contractions of evergreen formations for the same period. The divergence between the synchronous and individual suitability models for deciduous and semideciduous formations suggests that climate-tolerant species will likely expand their range, replacing those with narrower climate tolerances. This shift may alter the structure and composition of these communities as currently known. Our findings provide valuable insights that can inform strategies for conserving the Atlantic Rainforest, including the development of new regulatory measures, the establishment of protected areas, and the formulation of effective forest management policies. Full article

The Pantanal, considered the world’s largest tropical wetland, is increasingly threatened by intensifying droughts driven by climate variability and climate change. Using Multi-Source Weather data (MSWX), and bias-corrected multi-model means from five Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations for the years 1980–2100, we assessed historical and future drought conditions under SSP2-4.5 and SSP5-8.5 scenarios for the Pantanal. Drought conditions were identified through the Standardised Precipitation Index (SPI) and the Standardised Precipitation–Evapotranspiration Index (SPEI) across multiple timescales, and with different reference periods. A historical analysis revealed a significant drying trend, culminating in the extreme droughts of 2019/2020 and 2023/24. Future projections indicate a dual pressure of declining precipitation and rising temperatures, intensifying the severity of dry conditions. By the late 21st century, SSP5-8.5 shows persistent, severe multi-year droughts, while SSP2-4.5 projects more variable but still intensifying dry spells. The SPEI highlights stronger drying than the SPI, underscoring the growing role of evaporative demand, which was confirmed through risk ratios for drought occurrence across temperature anomaly bins. These results offer multi-scalar insights into drought dynamics across the Pantanal wetland, with critical implications for biodiversity, water resources, and wildfire risk. Thus, they emphasise the urgency of adaptive management strategies to preserve ecosystem integrity under a warmer, drier future climate. Full article

The vegetation of settlements can be particularly important for ecology and cityscapes and also plays a role in shaping and structuring the fabric of the settlement. However, there are very few settlements where the nature of woody vegetation is a defining characteristic of the settlement image. The vitality and health of the vegetation of a settlement can depend on the extent of development, increasing urbanization and the influencing effects of climate change. We monitored the changes in the vegetation of our study area, Balatonalmádi-Káptalanfüred, Hungary, going back 300 years by analyzing military and historical maps and satellite images, using the NDVI vegetation index of the last 20 years, as well as by field visits, tree examinations based on visual surveys and a plant population survey at 5 sampling points. Our results show that due to the increase in construction, the historical map shows a significant decrease in green space, and the satellite images show a dramatic decrease in the vitality of the remaining green spaces. In addition, field visits have also revealed serious plant health problems, which may lead to a relatively rapid decline of the dominant oak population. The research shows that as the upper canopy level decreases, the second canopy level becomes dominant. In order to preserve the strong, distinctive oak character of the settlement, we make proposals to mitigate the destruction of the current woody vegetation and, in the long term, to replace the stands with climate-resilient species. Full article

By 2050, the global population will be predominantly living in urban areas, and climate change mitigation planning will be crucial for addressing the climate emergency. Local governments are well-positioned to lead in adopting effective climate mitigation strategies. This systematic literature review examines the barriers, enablers, and systems that local governments will need to consider when implementing climate mitigation and strategies. A search across Scopus, Web of Science, and ProQuest databases yielded 411 results, from which 28 articles were selected for detailed analysis. Using Covidence and NVivo 14 software, the study employed a combination of deductive and inductive coding to identify key themes. The study identified themes specific to enablers, such as technology, collaboration, leadership, and management culture, as well as barrier themes, including short-term thinking, uncertainty avoidance, lack of knowledge among decision-makers, resource shortages, and organizational challenges. The findings underscore the importance of addressing organizational issues and allocating appropriate resources to bolster local-level systems change in support of climate change mitigation efforts. Full article

Storm surges are the leading marine disaster in China’s coastal cities, with their impacts exacerbated by climate change and rapid urbanization. Despite their significance, most existing studies focus on a single scale, neglecting the complex, multi-scale nature of urban resilience and the interrelated governance strategies needed to address storm surge risks. This study introduces a dual-scale resilience indicator system—macro (prefecture-level cities) and micro (coastal buffer grids)—within the “exposure–sensitivity–adaptation” framework, utilizing multi-source data for a comprehensive assessment. This research also explores the impact mechanisms of storm surges on urban areas and proposes zonal governance strategies. Findings indicate that resilience varies spatially in Chinese coastal cities, with a pattern of “high resilience in the north, low resilience in the south, and a mix in the center.” At the macro scale, key limitations include policy implementation, infrastructure capacity, and social vulnerability. At the micro scale, factors such as inadequate green space, increased impervious surfaces, limited shelter access, and low utility network density lead to the emergence of “low-resilience units” in ecologically sensitive and mixed coastal zones. The study further reveals the synergies between resilience drivers across scales, emphasizing the need for integrated cross-scale governance. This research advances resilience theory by expanding spatial scales and refining indicator systems, while proposing a zonal governance framework tailored to resilience gradation. It offers a quantitative basis and practical strategies for fostering “safe cities” and advancing “adaptive spatial planning” in the context of sustainable development. Full article

Insects are facing challenges with climate change, especially in tropical regions where small variations in temperature can affect their survival and behavior. The insect cuticle is a barrier against water loss and a source of signals for chemical communication triggered mainly by cuticular hydrocarbons. Knowing that tolerance in social wasps to temperature variations mainly depends on changes in the chemical composition of the cuticle, the objective was to evaluate how high temperatures affect the cuticular hydrocarbon composition of the social wasp Polybia rejecta. The wasps were exposed to a temperature of 40 °C for 1 h, 3 h, and 6 h following analyses of the cuticular hydrocarbons by GC-MS. The results revealed five long-chain hydrocarbons and one fatty alcohol. The relative percentages to each class of compounds indicated alkanes as the principal component in all samples. Tricosane was only identified after the third hour of exposure, increasing in the sixth hour, suggesting a possible chemical communication mechanism to alert critical situations between individuals. These results open up new avenues of research into insect communication in response to environmental stress. Full article

The understanding of coastal ecosystems regarding variability and resilience under climatic and anthropogenic forcing is reliant upon long-term ecological records. We examined the Helgoland Roads time series (1968–2017), which includes temperature, salinity, nutrients (nitrate, phosphate), and biological parameters (diatoms and Acartia spp.). We applied autocorrelation, multi-taper spectral analysis, and wavelet and cross-wavelet transforms to identify dominant temporal patterns and scale-dependent interactions. Sea surface temperature shows consistent long-term warming, and subdecadal (2–3-year) and decadal (7–8-year) oscillations reflect coherent patterns with the North Atlantic Oscillation and Arctic Oscillation. Salinity varied in anti-phase to Elbe River discharge at 6–7-year scales, reflecting control of seasonal, riverine freshwater, and salinity scenarios. Nutrients, as declining long-term trends (particularly phosphate), are associated with seasonal to multi-year variability linked to episodic discharge events. Biological parameters had strong annual periodicities reflective of bloom cycles but also variability above the annual limit. Diatoms responded to climatic, nutrient, and biological responses at the 3–5-year scale associated with this ecological context, particularly nitrate and phosphate; Acartia (spp.) respond to temperature, salinity, and resource availability (diatoms), reflecting climate/nutrient/trophic linkages. This study indicates that Helgoland Roads is represented as a multi-scale, non-stationary system, in which climate variability, riverine input, and ecological linkages are cascaded down to physical and chemical processes that structure biological communities. Spectral methods reveal scale-dependent synchrony and highlight the risks of trophic mismatch under climate change, emphasizing the importance of sustained high-frequency monitoring. Full article

Rising atmospheric CO₂ is expected to fertilize forest photosynthesis; yet, ecosystem-scale observations often reveal muted responses, creating a critical knowledge gap in global climate projections. In this review, we explore this paradox by moving beyond the traditional ‘CO₂ fertilization’ paradigm. We propose an integrated framework that positions elevated CO₂ as a complex modulator whose net effect is determined by a hierarchy of cross-scale constraints. At the plant level, photosynthetic acclimation acts as a universal first brake on the initial biochemical potential. At the ecosystem level, nutrient availability—primarily nitrogen in temperate/boreal systems and phosphorus in the tropics—emerges as the dominant bottleneck limiting long-term productivity gains. Furthermore, interactions with the water cycle, such as increased water-use efficiency, create state-dependent dynamic responses. By synthesizing evidence from pivotal Free-Air CO₂ Enrichment (FACE) experiments, we systematically evaluate these constraining factors. We conclude that accurately predicting the future of the forest carbon sink necessitates a paradigm shift: from single-factor analysis to multi-stressor approaches, and from ecosystem-scale observations to an integrated understanding that links these phenomena to their underlying molecular and genetic mechanisms. This review provides a roadmap for future research and informs more realistic strategies for forest management and climate mitigation in a high-CO₂ world. Full article

The Mediterranean coast is known for its great tourist attractions, concentration of population, and economic activities. Specifically, in the autonomous regions like Catalonia and Valencia, more than half of the population lives in coastal counties, and the population during the summer months increases due to the influx of tourists. Furthermore, in this stretch of coast, there are some areas of natural interest such as the Delta del Ebro or the Albufera, which are two of the most important wetland areas in the Mediterranean. However, according to studies by Day Today, the retreat of the coastline has increased in recent years, and this influences management of coastal territory both directly and indirectly, mostly harming all sectors with low levels, creating spaces with significant problems. It is for this reason that reporting on climate change and the impact on the coasts is assuming an important role in society, because they are essential tools for planning and management costs. In this thesis, the ground that would be affected by a +1 m, +2 m, and +3 m increase in average sea level, as simulated by the existing flood simulator, has been quantified. And a methodology has been developed for determining the vulnerability of the land based on flooding provided by terrain elevations, and each area studied was evaluated with different degrees of vulnerability: very high, high, moderate, or low. Finally, a first estimate has been made of economic loss that could involve a meter rise in the average sea level for Catalan beaches, and major damage to natural parks, urban areas, and major infrastructure has been identified. This study shows that there are nine areas with high vulnerability due to the low heights of their territory, and the majority of the flooded land is concentrated in the Ebro Delta and the Albufera, which jointly dominate the totals across scenarios. Full article

In Korea, apple (Malus domestica) is one of the major fruit crops. The area occupied by apple orchards has exhibited a consistent upward trend, increasing from 26,398 hectares in 2003 to 33,313 hectares in 2024, and production reached 460,088 tons in 2024. However, stable apple production is currently threatened by global challenges such as climate change and the decline in rural labor, which hinders timely and efficient orchard management. Under these circumstances, developing automated and data-driven technologies capable of rapidly predicting and responding to apple growth conditions is essential to enhancing management efficiency and ensuring consistent fruit quality and yield stability. In this study, unmanned aerial vehicle (UAV)-based multispectral imagery was acquired and used to analyze time series data. Vegetation indices (VIs) derived from this imagery were then applied to build models predicting fruit diameter and length, which reflect apple size. A total of nine VIs were calculated from the acquired data and utilized as input variables for model development. Based on these variables, four machine learning models—Gaussian process regression (GPR), the K-Nearest Neighbors (KNNs), Random Forest Regression (RFR), and Extreme Gradient Boosting (XGB)—were developed to predict the fruit diameter and length. Both RFR and XGB showed tendencies of overfitting, and although the KNNs demonstrated relatively stable performance (diameter: R² ≥ 0.82, RMSE ≤ 7.61 mm, RE ≤ 12.53%; length: R² ≥ 0.76, RMSE ≤ 8.85 mm, RE ≤ 15.08%), this model failed to follow the prediction line consistently. In contrast, GPR maintained stable performance in both the validation and calibration stages (diameter: R² ≥ 0.79, RMSE ≤ 8.23 mm, RE ≤ 13.56%; length: R² ≥ 0.72, RMSE ≤ 9.48 mm, RE ≤ 16.16%) and followed the prediction line relatively well, indicating that it is the most suitable model for predicting apple size. These results demonstrate that UAV-based multispectral imagery, combined with machine learning techniques, is an effective tool for predicting the size of apples, and it is expected to contribute to orchard management at different growth stages and improve apple productivity in the future. Full article

Climate change is impacting atmospheric patterns and therefore wave conditions, with ports being among the most affected infrastructures, making it crucial to ensure their operability under changing climatic conditions. Most scientific studies on climate change focus on coastal erosion and flooding, whereas research on its impact on port operability remains relatively scarce. This challenge could be tackled with the emergence of Artificial Intelligence (AI), where alternative modeling approaches can be developed. Thus, a novel AI-based model specifically designed for studying port agitation is introduced herein. By integrating a hybrid deep learning approach, combining Feedforward Neural Networks (FFNNs) to model wave climate and Convolutional Neural Networks (CNNs) for port image analysis, port agitation has been successfully predicted compared to linear wave propagation models. This marks the first instance of utilizing image processing tools to analyze port agitation, resulting in a model with a remarkably low error rate, while offering a significant reduction in computational time compared to traditional wave propagation models, reducing computational time by a factor of four to ten. The accuracy of the proposed model has been investigated and validated for the Port of Valencia, located in the Spanish section of the Mediterranean Sea. Full article

This study, based on global AERONET observation data from 2023, employs a synergistic inversion algorithm that integrates aerosol optical, microphysical, and chemical properties to retrieve the global distribution of aerosol parameters. We find that the global annual mean aerosol optical depth (AOD), fine-mode AOD (AOD_f), coarse-mode AOD (AOD_c), absorbing aerosol optical depth (AAOD), single scattering albedo (SSA) are 0.20, 0.15, 0.04, 0.024, and 0.87, respectively. From the perspective of spatial distribution, in densely populated urban areas, AOD is mainly determined by AOD_f, while in the areas dominated by natural sources, AOD_c contributes more. Combined with the optical and microphysical properties, fine-mode aerosols dominate optical contributions, whereas coarse-mode aerosols dominate volume contributions. In terms of chemical components, fine-mode aerosols at most global sites are primarily carbonaceous. The mass concentrations of black carbon (BC) exceed 10 mg m⁻² in parts of South Asia, Southeast Asia, and the Arabian Peninsula, while the mass fraction of brown carbon (BrC) accounts for more than 16% in regions such as the Sahara, Western Africa, and the North Atlantic Ocean reference areas. The dust (DU) dominates in coarse mode, with the annual mean DU fraction reaching 86.07% in the Sahara. In coastal and humid regions, the sea salt (SS) and water content (AW_c) contribute significantly to the aerosol mass, with fractions reaching 13.13% and 34.39%. The comparison of aerosol properties in the hemispheres reveals that the aerosol loading in the Northern Hemisphere caused by human activities is higher than in the Southern Hemisphere, and the absorption properties are also stronger. We also find that the uneven distribution of global observation sites leads to a significant underestimation of aerosol absorption and coarse-mode features in global mean values, highlighting the adverse impact of observational imbalance on the assessment of global aerosol properties. By combining analyses of aerosol optical, microphysical, and chemical properties, our study offers a quantitative foundation for understanding the spatiotemporal distribution of global aerosols and their emission contributions, providing valuable insights for climate change assessment and air quality research. Full article

Human-sourced emissions of carbon dioxide (CO₂) into the Earth’s atmosphere have been implicated in contemporary global warming, based mainly on computer modeling. Growing empirical evidence reviewed here supports the alternative hypothesis that global climate change is governed primarily by a natural climate cycle, the Antarctic Oscillation. This powerful pressure-wind-temperature cycle is energized in the Southern Ocean and teleconnects worldwide to cause global multidecadal warm periods like the present, each followed historically by a multidecadal cold period, which now appears imminent. The Antarctic Oscillation is modulated on a thousand-year schedule to create longer climate cycles, including the Medieval Warm Period and Little Ice Age, which are coupled with the rise and fall, respectively, of human civilizations. Future projection of these ancient climate rhythms enables long-term empirical climate forecasting. Although human-sourced CO₂ emissions play little role in climate change, they pose an existential threat to global biodiversity. Past mass extinctions were caused by natural CO₂ surges that acidified the ocean, killed oxygen-producing plankton, and induced global suffocation. Current human-sourced CO₂ emissions are comparable in volume but hundreds of thousands of times faster. Diverse evidence suggests that the consequent ocean acidification is destroying contemporary marine phytoplankton, corals, and calcifying algae. The resulting global oxygen deprivation could smother higher life forms, including people, by 2100 unless net human-induced CO₂ emissions into the atmosphere are ended urgently. Full article