Search Results (8,824)

Sicyos angulatus L. is a rapidly spreading invasive alien vine that threatens natural and agricultural ecosystems globally. We collected occurrence data from 4886 sites and applied the maximum entropy (MaxEnt) model to assess current and future habitat suitability for S. angulatus. Future climate conditions were represented by low and high greenhouse gas concentrations under representative concentration pathways (i.e., RCP2.6 and RCP8.5, respectively). The MaxEnt model accurately predicted the distribution of S. angulatus, and the area under the receiver operating characteristic curve in the receiver operating characteristic test reached 0.921. Among the 19 climatic variables investigated, the best predictors for the distribution of S. angulatus were the precipitation in the driest month (with a contribution of 37.4%), annual precipitation (26.8%), average annual temperature (18.1%), and temperature seasonality (14.9%). Currently, the most suitable areas cover the central and eastern United States, parts of southern Europe, most Japanese islands, the majority of the Korean Peninsula, and eastern China, with a total area of 180.3 × 10⁴ km² (1.2% of the Earth’s land area). During the 2050s and 2090s under RCP2.6 and RCP8.5, the most suitable regions worldwide are projected to expand by factors of 1.0 and 2.2, respectively. In particular, suitable areas might expand to higher-latitude regions and encompass previously unsuitable areas, such as Liaoning Province in Northeast China. These findings may aid in the surveillance and management of S. angulatus’ invasion globally. Full article

Integration of IoT and predictive modeling is critical for optimizing microclimate management in urban-agglomeration vertical farming. In this study, we present a hybrid digital twin approach that combines a physical microclimate model with a distributed IoT monitoring system to simulate and control the phytotron environment. A set of heat- and mass-balance equations governing the dynamics of temperature, humidity, and transpiration was implemented and parameterized using a genetic algorithm (GA)—an evolutionary optimization method—with real-time data collected over three intervals (72 h, 90 h, and 110 h) from LoRaWAN sensors (temperature, humidity, CO₂) and Wi-Fi-connected power meters managed by Home Assistant. The optimized model achieved mean temperature deviations ≤ 0.1 °C, relative humidity errors ≤ 2%, and overall energy consumption accuracy of 99.5% compared to measured values. The digital twin reliably tracked daily climate fluctuations and system energy use, confirming the accuracy of the hybrid approach. These results demonstrate that the proposed framework effectively integrates theoretical models with IoT-derived data to deliver precise environmental control and energy-use optimization in vertical farming, while also laying the groundwork for scalable digital twins in controlled-environment agriculture. Full article

The high-elevation ecosystems of the Tibetan Plateau provide crucial ecosystem services including watershed protection and water provision for downstream human and wildlife communities. Thus, understanding the relationship between soil properties and vegetation under different management regimes is important as a warming climate alters these systems. This study assessed vegetation cover, quantified the distribution of soil nutrients, and examined the relationships among soil chemical properties and plant cover in the high-elevation shrublands (3300 to 3700 m) in the Qilian Mountains on the northeastern Tibetan Plateau of China. These vegetation surveys and soil sample collections were conducted on 15 shrubland plots at different soil depths and soil chemical properties were investigated at each elevation. The content of soil organic matter (SOM), total nitrogen (TN), available phosphorus (AP), and available potassium (AK) fluctuated along the elevational gradient, while soil pH was close to neutral (pH 7.4). At our sites, SOM and TN contents generally increased with elevation, and AK was positively correlated with Salix plant cover. Using PCA, we determined that PC1 captured 43% of the total variance, and SOM and TN were the top contributing features. As climate in the region warms and precipitation becomes more variable, understanding the current soil–vegetation equilibria and how vegetation may migrate in future years is important to predicting changes in this region, especially at high elevations. From a managerial perspective, our goal was to provide additional information for restoring and managing subalpine and alpine shrubland vegetation in the Qilian Mountains to ensure the future sustainable use of these systems. Full article

Heat-related morbidity and mortality are increasing due to climate change, emphasizing the need to identify vulnerable areas and people exposed to extreme temperatures. To improve heat stress impact assessment, we developed a replicable machine learning model that integrates remote sensing, ground station, and geospatial data to estimate daily air temperature at a spatial resolution of 100 m × 100 m across the region of Tuscany, Italy. Using a two-stage approach, we first imputed missing land surface temperature data from MODIS using gradient-boosted trees and spatio-temporal predictors. Then, we modeled daily maximum and minimum air temperatures by incorporating monitoring station observations, satellite-derived data (MODIS, Landsat 8), topography, land cover, meteorological variables (ERA5-land), and vegetation indices (NDVI). The model achieved high predictive accuracy, with R² values of 0.95 for Tmax and 0.92 for Tmin, and root mean square errors (RMSE) of 1.95 °C and 1.96 °C, respectively. It effectively captured both temporal (R²: 0.95; 0.94) and spatial (R²: 0.92; 0.72) temperature variations, allowing for the creation of high-resolution maps. These results highlight the potential of integrating Earth Observation and machine learning to generate high-resolution temperature maps, offering valuable insights for urban planning, climate adaptation, and epidemiological studies on heat-related health effects. Full article

Phenology prediction is critical for optimizing the timing of rice crop management operations such as fertilization and irrigation, particularly in the face of increasing climate variability. This study aimed to estimate three key developmental stages in the temperate irrigated rice systems of Australia: panicle initiation (PI), flowering, and harvest maturity. Extensive and diverse field observations ($n\approx 302$) were collected over four consecutive seasons (2022–2025) from the rice-growing regions of the Murrumbidgee and Murray Valleys in southern New South Wales, encompassing six varieties and three sowing methods. The extent of data available allowed a number of traditional and emerging machine learning (ML) models to be directly compared to determine the most robust strategies to predict Australian rice crop phenology. Among all models, Tabular Prior-data Fitted Network (TabPFN), a pre-trained transformer model trained on large synthetic datasets, achieved the highest precision for PI and flowering predictions, with root mean square errors (RMSEs) of 4.9 and 6.5 days, respectively. Meanwhile, long short-term memory (LSTM) excelled in predicting harvest maturity with an RMSE of 5.9 days. Notably, TabPFN achieved strong results without the need for hyperparameter tuning, consistently outperforming other ML approaches. Across all stages, models that integrated remote sensing (RS) and weather variables consistently outperformed those relying on single-source input. These findings underscore the value of hybrid data fusion and modern time series modeling techniques for accurate and scalable phenology prediction, ultimately enabling more informed and adaptive agronomic decision-making. Full article

Riverbank erosion poses a significant threat to livelihoods and infrastructure in the Vietnamese Mekong Delta (VMD), necessitating innovative and sustainable solutions. This study explores the use of old tyres as a material for embankment construction to stabilize riverbanks, combining physical reinforcement with bioengineering techniques. A pilot project was conducted in Dinh My commune, An Giang Province, where an embankment was constructed using old tyres, geotextile, riprap, and vegetation. Field measurements using the Leica TS02 Plus Total Station and Finite Element Method (FEM) modeling were employed to assess the embankment’s performance. Results indicate that the embankment effectively stabilized the riverbank, with a maximum displacement of 18 mm observed after one year. The FEM predictions closely aligned with the measured data, achieving an accuracy of 68% or higher, validating the model’s accuracy. The integration of vegetation further enhanced stability, demonstrating the potential of this approach as a sustainable and cost-effective solution for riverbank protection. This study highlights the dual benefits of erosion control and waste management, offering a replicable strategy for addressing riverbank erosion across deltaic and lowland regions. The pilot offers a scalable model for climate-resilient infrastructure in deltaic regions globally, linking erosion control with circular economy strategies. Full article

Leaf phenology is a crucial functional trait in temperate forest trees that integrates environmental signals and reflects species’ adaptive capacity to stress. This study examined how moderate drought and elevated phosphorus availability, alone and in combination, affect the spring and autumn phenology of juvenile Fagus sylvatica and Quercus petraea saplings from two climatically distinct Croatian provenances. In a common garden experiment, saplings were subjected to four treatments involving drought and phosphorus addition. Phenological stages were scored using standardized ordinal scales across two growing seasons. Results revealed that phosphorus consistently advanced autumn leaf senescence in both species, independent of drought, while drought effects were species- and provenance-specific. Spring phenology was more sensitive to drought: beech from the drier provenance advanced budburst, suggesting an escape strategy, whereas oak delayed leaf-out under the same conditions. Notably, combined drought and phosphorus treatments often neutralized individual effects, indicating physiological compensation. Provenance-level differences highlighted contrasting strategies—phenotypic plasticity versus stress tolerance—under multi-stressor conditions. These findings underscore the dominant role of phosphorus in regulating phenology and the complex, non-additive nature of drought–nutrient interactions, emphasizing the need for integrative approaches in predicting phenological responses under climate change. Full article

The formation, development, and spatial distribution of CUHIs are influenced by urban spatial heterogeneity, yet the scale and seasonal dependencies of the effects of urban morphology modulation on CUHIs have not been fully explored, needing further study. Based on multi-source data for the Yangtze-Huaihe River Valley (YHRV), this study employs the XGBoost model to systematically investigate the effects of two-dimensional (2D)/three-dimensional (3D) urban morphological indicators on CUHIs and their inherent scale–seasonal dependencies. Results show significant provincial heterogeneity in YHRV’s CUHIs: Shanghai exhibits the highest CUHI intensity (CUHII) across all seasons, with a peak of 1.55 °C in winter, followed by Zhejiang and Jiangsu. Seasonally, winter CUHII averages 0.6–0.8 °C (the highest), followed by autumn, while spring and summer have lower values. The effect of the modulation of urban morphology on CUHIs exhibits distinct spatiotemporal dependence: in winter and autumn, CUHII is mainly dominated by the percentage of landscape (PLAND) and largest patch index (LPI) at the 4 km buffer scale (correlation coefficients r = 0.475 and 0.406 for winter); in spring and summer, the 2 km buffer scale shows a more balanced regulatory role of multiple urban morphological indicators. Notably, 2D indicators of urban morphology are consistently more influential in regulating CUHIs than 3D indicators. The Hefei station case effectively validates the model’s sensitivity to changes in urban morphology. This study provides a quantitative basis for season–scale collaborative regulation of urban thermal environments in the YHRV. Future research will integrate climatic factors into XGBoost via screening, reconstruction, and interaction quantification to enhance its predictability for transient heat island processes. Full article

Chinese forest ecosystems are key carbon sinks that significantly contribute to lowering carbon emissions. Accurate Net Ecosystem Productivity (NEP) estimations are essential for evaluating their carbon sequestration capabilities and overall health. This study employed the Physiological Principles Predicting Growth-Satellites (3-PGS) and soil heterotrophic respiration models to simulate China’s forest carbon sinks and sources distribution from 2013 to 2023. Then, climatic factors influencing NEP changes were examined through the application of a geographical detector model. The net carbon sequestered was 1.71 ± 0.09 PgC with an annual average of 0.156 ± 0.0071 PgC, signifying a substantial carbon sink in China’s forest. The annual NEP was highest in evergreen broadleaf forests (352.12 gC m⁻²) and lowest in deciduous needleleaf forests (148.31 gC m⁻²). NEP in China’s forests increased by a rate of 1.67 gC m⁻² annually, with most regions exhibiting a 275.32 gC m⁻² annual carbon sink. The geographical detector model analysis showed that solar radiation, precipitation, and vapor pressure deficit were the main drivers of NEP change, while temperature and frost days had a secondary influence. Furthermore, the interaction between solar radiation and temperature variables showed the greatest impact. This study can enhance the understanding of carbon sink and source distribution in China, serve as a reference for regional carbon cycle research, and provide key insights for policymakers in developing effective climate strategies. Full article

Achaea lienardi is a polyphagous moth occurring in sub-Saharan Africa. It is a fruit-sucking moth, causing secondary damage to fruit such as citrus and peaches, while the larval stage can cause significant tree defoliation, including in several indigenous trees, wattle, Eucalyptus, and castor oil plants, amongst others. In February and March of 2025, a massive outbreak of the caterpillars was observed in the Hluhluwe-iMfolozi Park in South Africa, feeding primarily on Tamboti trees (Spirostachys africana). Satellite imagery from the previous five years was examined, but no similar large defoliation events were observed during this period. Climate data for the last five years (September 2019–March 2025) were collated and examined to determine the conditions supporting the outbreak. Above average winter rainfall, early spring rains, sustained rains, and high humidity in January and February, with warm nighttime temperatures, likely acted in concert to create favourable conditions for the caterpillar outbreak. This outbreak coincided with historic outbreaks of the African armyworm (Spodoptera exempta) in the summer rainfall areas of South Africa where precipitation, temperature, solar radiation, and humidity were found to be critical factors affecting armyworm outbreaks. Further research is required to determine specific criteria to enable predictions of future outbreaks. Full article

The convergence of genomic selection and artificial intelligence (AI) is redefining precision breeding in dairy cattle, enabling earlier, more accurate, and multi-trait selection for health, fertility, climate resilience, and economic efficiency. This review critically examines how advanced genomic tools—such as genome-wide association studies (GWAS), genomic breeding values (GEBVs), machine learning (ML), and deep learning (DL) models to accelerate genetic gain for complex, low heritability traits. Key applications include improved resistance to mastitis and metabolic diseases, enhanced thermotolerance, reduced enteric methane emissions, and increased milk yield. We discuss emerging computational frameworks that combine sensor-derived phenotypes, omics datasets, and environmental data to support data-driven selection decisions. Furthermore, we address implementation challenges related to data integration, model interpretability, ethical considerations, and access in low-resource settings. By synthesizing interdisciplinary advances, this review provides a roadmap for developing AI-augmented genomic selection pipelines that support sustainable, climate-smart, and economically viable dairy systems. Full article

Given the significant implications of indoor air pollution for physical and mental health, well-being and productivity, indoor air quality (IAQ) is of critical importance. CO₂ is a prevalent indoor air contaminant and represents a key determinant for IAQ characterization. This study collected sensed air pollution and climatic data from a hospital environment in Greece and employed Long Short-Term Memory (LSTM) neural network variants with progressively increased architectural complexity to predict indoor CO₂ concentration across future horizons ranging from 15 min up to 180 min. Among the examined variants, the attention-based LSTM exhibited the most consistent performance across the forecasting horizons. Incorporating additional predictors reflecting climatic conditions, air pollution and occupancy status within the hospital settings, the multivariate attention-based LSTM further enhanced its predictive performance with an MAE of 8.9 ppm, 16.7 ppm, 31.2 ppm, 38.9 and 39.5 ppm for 15 min, 30 min, 60 min, 120 min, and 180 min ahead, respectively. Full article

Deep reinforcement learning (DRL)-based HVAC control has shown clear advantages over rule-based and model predictive methods. However, most prior studies remain limited to HVAC-only optimization or simple coordination with operable windows. Such approaches do not adequately address buildings with fixed glazing systems—a common feature in high-rise offices—where the lack of operable windows restricts adaptive envelope interaction. To address this gap, this study proposes a multi-zone control framework that integrates HVAC systems with electrochromic windows (ECWs). The framework leverages the Q-value Mixing (QMIX) algorithm to dynamically coordinate ECW transmittance with HVAC setpoints, aiming to enhance energy efficiency and thermal comfort, particularly in high-consumption buildings such as offices. Its performance is evaluated using EnergyPlus simulations. The results show that the proposed approach reduces HVAC energy use by 19.8% compared to the DQN-based HVAC-only control and by 40.28% relative to conventional rule-based control (RBC). In comparison with leading multi-agent deep reinforcement learning (MADRL) algorithms, including MADQN, VDN, and MAPPO, the framework reduces HVAC energy consumption by 1–5% and maintains a thermal comfort violation rate (TCVR) of less than 1% with an average temperature variation of 0.35 °C. Moreover, the model demonstrates strong generalizability, achieving 16.58–58.12% energy savings across six distinct climatic regions—ranging from tropical (Singapore) to temperate (Beijing)—with up to 48.2% savings observed in Chengdu. Our framework indicates the potential of coordinating HVAC systems with ECWs in simulation, while also identifying limitations that need to be addressed for real-world deployment. Full article

The rapid global expansion of hydropower poses questions about the resilience and sustainability of riparian vegetation, especially in the context of ongoing climate change. Satellite remote sensing provides a valuable means for monitoring long-term and spatially continuous changes in vegetation, offering insights into how dams influence RV dynamics worldwide. Here, we integrated satellite-derived environmental indicators, including Normalized Difference Vegetation Index (NDVI), to quantify and compare riparian vegetation trends upstream and downstream of dams globally. By applying paired linear regression analyses to pre- and post-construction NDVI time series, we identified dams associated with significant RV degradation following impoundment. Furthermore, we employed Gradient Boosting Regression Models (GBRM), calibrated using current observational data and driven by CMIP6 climate projections, to forecast global riparian vegetation trends through the year 2100 under various climate scenarios. Our analysis reveals that, although widespread vegetation degradation was not evident up to 2017—and many regions showed slight improvements—future projections under higher-emission pathways (SSP3-7.0 and SSP5-8.5) indicate substantial RV declines after 2040, particularly in high-latitude forests, grasslands, and arid regions. Conversely, tropical and subtropical riparian forests are predicted to maintain stable or increasing NDVI under moderate emission scenarios (SSP1-2.6). These results highlight the potential for adaptive dam development strategies supported by continued satellite-based monitoring to help reduce climate-related risks to riparian vegetation in regions. Full article

Per the United Nations, enhancing climate literacy can play an essential role in advancing climate mitigation, adaptation, and promoting sustainable human behaviors. Yet, there is a lack of empirical research explicitly studying the effects of climate solutions focused education. Here, we studied the effects of a climate solutions focused course—the University of California Bending the Curve (BtC) course on: (1) climate change beliefs, (2) personal pro-environmental actions, and (3) psychological health, using baseline and post-course surveys. A total of 374 youth (median age 21 ± 1.7 years, 63% female) participated in the study, and data analysis focused on statistically comparing pre- versus post-course survey-based data. We observed that the BtC course enhanced climate change beliefs. Specifically, at post-relative to pre-course, we observed significantly increased belief that global warming will impact individuals personally as well as impact our future generations; it tripled the number of students who believe that humans can and will act to reduce global warming; it significantly increased the number of individuals who believe in a scientific basis for climate change. Notably, climate solutions education also enhanced belief in the efficacy of personal climate action and increased agreement amongst youth that many of their friends also share the same views on global warming. With regard to personal pro-environmental actions, the course significantly improved self-reported actions, including waste reduction, making food choices with reduced emissions, and purchase of carbon offsets. These actions reduced the carbon footprint per student at post- vs. pre-course by a significant 0.3 ± 0.1 CO₂ tons/year, which is equivalent to the CO₂ absorbed by about 15 trees per year. While psychological health outcomes did not show any significant post- vs. pre-course change, we found that enhanced personal pro-environmental actions as well as enhanced psychological health were predicted by course-related strengthening of climate change beliefs. Overall, our findings provide evidence that solutions-based climate education can be an important strategy to enhance individual climate change awareness as well as personal pro-environmental actions that lead to significant individual carbon footprint reduction, with potential for widespread scale-up. Full article