Search Results (285)

Corydalis yanhusuo W. T. Wang, commonly known as Yanhusuo, is an important and rare medicinal plant resource in China. Its habitat integrity is facing severe challenges due to climate change and human activities. Establishing an integrative quality zoning system for this species is of significant practical importance for resource conservation and adaptive management. This study integrates multiple data sources, including 121 valid distribution points, 37 environmental factors, future climate scenarios (SSP126 and SSP585 pathways for the 2050s and 2090s), and measured content of tetrahydropalmatine (THP) from 22 sampling sites. A predictive framework for habitat suitability and spatial distribution of effective components was constructed using a multi-model coupling approach (MaxEnt, ArcGIS spatial analysis, and co-kriging method). The results indicate that the MaxEnt model exhibits high prediction accuracy (AUC > 0.9), with the dominant environmental factors being the precipitation of the wettest quarter (404.8~654.5 mm) and the annual average temperature (11.8~17.4 °C). Under current climatic conditions, areas of high suitability are concentrated in parts of Central and Eastern China, including the Sichuan Basin, the middle–lower Yangtze plains, and coastal areas of Shandong and Liaoning. In future climate scenarios, the center of suitable areas is predicted to shift northwestward. The content of THP is significantly correlated with the mean diurnal temperature range, temperature seasonality, and the mean temperature of the wettest quarter (p < 0.01). A comprehensive assessment identifies the Yangtze River Delta region, Central China, and parts of the Loess Plateau as the optimal integrative quality zones. This research provides a scientific basis and decision-making support for the sustainable utilization of C. yanhusuo and other rare medicinal plants in China. Full article

In recent years, early olive fruit drop has been observed in the northern Mediterranean regions, causing significant economic losses, although the exact cause remains unknown. Recent studies have identified several possible causes; however, our understanding of how olive trees respond to these environmental stresses remains limited. This study includes an analysis of selected meteorological and flowering data for Olea europaea L. “Istrska belica” to evaluate the use of a chilling and forcing model for a better understanding of flowering time dynamics under a changing climate. The flowering process is influenced by high diurnal temperature ranges (DTRs) during the pre-flowering period, resulting in earlier flowering. Despite annual fluctuations due to various climatic factors, an increase in DTRs has been observed in recent decades, although the mechanisms by which olive trees respond to high DTRs remain unclear. The chilling requirements are still well met in the region (1500 ± 250 chilling units), although their total has declined over the years. According to the Chilling Hours Model, chilling units—referred to as chilling hours—represent the number of hours with temperatures between 0 and 7.2 °C, accumulated throughout the winter season. Growing degree hours (GDHs) are strongly correlated with the onset of flowering. These results suggest that global warming is already affecting the synchrony between olive tree phenology and environmental conditions in the northern Mediterranean and may be one of the reason for the green drop. Full article

Evapotranspiration (ET) is an important part of agricultural water consumption, yet little is known about nocturnal evapotranspiration (ETN) patterns. An eddy covariance system was used to observe ET over five consecutive years (2020–2024) during the growing season in a dry farming area of the Loess Plateau. Daytime and nocturnal evapotranspiration were partitioned using the photosynthetically active radiation threshold to reveal the changing characteristics of ETN at multiple time scales and its control variables. The results showed the following: (1) In contrast to the non-significant trend in ETN on the diurnal and daily scales, monthly ETN dynamics exhibited two peak fluctuations during the growing season. (2) The contribution of ETN to ET exhibited seasonal characteristics, being relatively low in summer, with interannual variations ranging from 10.9% to 14.3% and an annual average of 12.8%. (3) The half-hourly ETN, determined by machine learning methods, was driven by a combination of factors. The main driving factors were the difference between surface temperature and air temperature (Ts-Ta) and net radiation (Rn), which have almost equivalent contributions. Regression analysis results suggested that Ta was the main factor influencing ETN/ET at the monthly scale. This study focuses on the nighttime water loss process in dry farming fields in Northwest China, and the results provide a basis for rational allocation and efficient utilization of agricultural water resources in arid regions. Full article

Radon’s radioactive decay is the main natural source of small air ions near the ground. Its exhalation from soil is affected by meteorological factors, while aerosol pollution reduces air ion concentrations through ion-particle attachment. This study aimed to analyze correlations between radon, ions, and air pollution under varying conditions and to assess potential health impacts. Measurements were taken at two sites: in early autumn at a suburban part of Belgrade with relatively clean air, and in late autumn in central Belgrade under polluted conditions, with low temperatures and high humidity. Parameters measured included radon, small air ions, particle size distribution, PM mass concentration, temperature, humidity, and pressure. Results showed lower radon concentrations in late autumn due to high soil moisture and absence of nocturnal inversions. Radon and air ion concentrations exhibited a strong positive correlation for both polarities under suburban conditions, whereas measurements in the urban setting revealed a weak negative correlation, despite radon concentrations in soil gas being approximately equal at both sites. Small ion levels were also reduced, mainly due to suppressed radon exhalation and increased aerosol concentrations, especially ultrafine particles. A strong negative correlation (r < −0.5) was found between small air ion concentrations and particle number concentrations in the 20–300 nm range, while larger particles (300–1000 nm and >1 µm) showed weak or no correlation due to their lower and more stable concentrations. In contrast, early autumn measurements showed a diurnal cycle of radon, characterized by nighttime maxima and daytime minima, unlike the consistently low values observed in late autumn. Full article

Phytophthora cinnamomi is a globally distributed plant-pathogenic oomycete that threatens economically important crops, including Lauraceae, Bromeliaceae, Fabaceae, and Solanaceae. Utilizing species occurrence records and 35 environmental variables (|R| < 0.8), we employed the MaxEnt model and ArcGIS spatial analysis to systematically predict the potential geographical distribution of P. cinnamomi under current (1970–2000) and future (2030S, 2050S, 2070S, 2090S) climate scenarios across three Shared Socioeconomic Pathways (SSPs). The results indicate that currently suitable habitats cover the majority of China’s provinces (>50% of their areas), with only sporadic low-suitability zones in Qinghai, Tibet, and Xinjiang. The most influential environmental variables were the mean diurnal temperature range, mean temperature of the warmest quarter, annual precipitation, precipitation of the driest month, and elevation. Under future climate scenarios, new suitable habitats emerged in high-latitude regions, while the highly suitable area expanded significantly, with the distribution centroid shifting northeastward. This study employs predictive modeling to elucidate the future distribution patterns of P. cinnamomi in China, providing a theoretical foundation for establishing a regional-scale disease early warning system and formulating ecological management strategies. Full article

Understanding optical turbulence within the atmospheric boundary layer (ABL) is essential for refining atmospheric motion analyses, enhancing numerical weather prediction models, and improving light propagation assessments. This study develops an optical turbulence model for the boundary layer over the South China Sea (SCS) by integrating multiple observational and reanalysis datasets, including ERA5 data from the European Center for Medium-Range Weather Forecasts (ECMWF), radiosonde observations, coherent Doppler wind lidar (CDWL), and ultrasonic anemometer (CSAT3) measurements. Utilizing Monin–Obukhov Similarity Theory (MOST) as the theoretical foundation, the model’s performance is evaluated by comparing its outputs with the observed diurnal cycle of near-surface optical turbulence. Error analysis indicates a root mean square error (RMSE) of less than 1 and a correlation coefficient exceeding 0.6, validating the model’s predictive capability. Moreover, this study demonstrates the feasibility of employing ERA5-derived temperature and pressure profiles as alternative inputs for optical turbulence modeling while leveraging CDWL’s high-resolution observational capacity for all-weather turbulence characterization. A comprehensive statistical analysis of the atmospheric refractive index structure constant ($C_n^2$) from November 2019 to September 2020 highlights its critical implications for optoelectronic system optimization and astronomical observatory site selection in the SCS region. Full article

Effectively managing carbon monoxide (CO) pollution in complex industrial cities like Jubail remains challenging due to the diversity of emission sources and local environmental dynamics. This study analyzes spatiotemporal CO patterns and builds accurate predictive models using five years (2018–2022) of data from ten monitoring stations, combined with meteorological variables. Exploratory analysis revealed distinct diurnal and moderate weekly CO cycles, with prevailing northwesterly winds shaping dispersion. Spatial correlation of CO was low (average 0.14), suggesting strong local sources, unlike temperature (0.92) and wind (0.5–0.6), which showed higher spatial coherence. Seasonal Trend decomposition (STL) confirmed stronger seasonality in meteorological factors than in CO levels. Low wind speeds were associated with elevated CO concentrations. Key predictive features, such as 3-h rolling mean and median values of CO, dominated feature importance. Spatiotemporal analysis highlighted persistent hotspots in industrial areas and unexpectedly high levels in some residential zones. A range of models was tested, with ensemble methods (Extreme Gradient Boosting (XGBoost) and Categorical Boosting (CatBoost)) achieving the best performance ($R^2>0.95$) and XGBoost producing the lowest Root Mean Squared Error (RMSE) of 0.0371 ppm. This work enhances understanding of CO dynamics in complex urban–industrial areas, providing accurate predictive models ($R^2>0.95$) and highlighting the importance of local sources and temporal patterns for improving air quality forecasts. Full article

Understanding the potential distribution patterns and habitat suitability of threatened species under climate change scenarios is essential for conservation efforts. This study aimed to assess the current and future distribution patterns of the endangered Cycas taiwaniana in China using the MaxEnt model under two contrasting climate change scenarios: SSP1-2.6 (low emissions) and SSP3-7.0 (high emissions), projected for the 2050s and 2070s periods. The model identified key bioclimatic variables influencing habitat suitability, including Annual Mean Temperature, Mean Diurnal Range, and Temperature Seasonality. Under current climate conditions, the species’ most suitable habitats are primarily located in southern coastal regions, with Hainan Island showing exceptional suitability. However, future projections under the moderate emission (SSP1-2.6) scenario suggest a significant shrinking of suitable habitat areas, particularly a 27.5% decline in excellent and a 35% decrease in good categories by the 2070s. In contrast, under the high-emission scenario (SSP3-7.0), while an initial decline in suitable habitats is projected, the model predicts an unexpected expansion of highly suitable areas by 2070, particularly in Guangxi, Guangdong, and Fujian coastal regions. The results highlight the vulnerability of C. taiwaniana to climate change and underscore the importance of developing adaptive conservation strategies to mitigate potential habitat loss. The findings also emphasize the need for further research on species-specific responses to climate change and the development of proactive measures to safeguard the future distribution of this threatened species. Full article

Urban green spaces, closely tied to local climates, significantly affect human comfort levels, yet existing assessment methods vary in applicability across different contexts and regions. Here, we determined the applicability of two commonly used indices to evaluate human comfort in urban green space types in Hohhot City in China, which is in an arid and semi-arid area. We established sites in four different urban green space types (S1–S4) and a control area (CK) through field-based assessment, and collected meteorological data over 10 days in each season from 2020 to 2021. Specifically, air temperature, relative humidity, and average wind speed were observed from 7:00 to 19:00. Air temperature was highest in summer and lowest in winter. Throughout the day, air temperature first increased and then decreased, with the maximum temperature occurring later in winter than in other seasons. Relative humidity showed an opposite diurnal trend to temperature, and there were no significant differences between urban green space types and CK. The average wind speed of CK was significantly higher than that of the urban green space types. HCI_Lu classifies thermal comfort levels across urban green space types and seasons into four distinct categories as uncomfortable, comfortable to less comfortable, less comfortable, and extremely uncomfortable. HCI_CMA further stratifies thermal conditions at urban green space types by season into cool and refreshing, most comfortable, most comfortable to slightly cool, cold, and uncomfortable. The HCI_Lu ranged from 2.3 to 25.1, and tended to first decrease and then increase on a daily basis. Conversely, HCI_CMA fluctuated throughout the day and ranged from 18.6 to 78.0. According to HCI_Lu, the urban green space types were comfortable for 45% of the observation time, and were comfortable for a greater proportion of time compared to if the comfort was calculated using HCI_CMA. HCI_CMA was strongly correlated with air temperature and average wind speed. According to receiver operating characteristic (ROC) curve analysis, the area under the curve (AUC) for HCI_CMA was 0.59–0.91, and was higher than that of HCI_Lu in each season, indicating greater suitability for the study site. Full article

The Quercus genus is found across a broad latitudinal range, and its spread in heterogeneous ecosystems is influenced by environmental, genetic, and anthropogenic factors. However, Mediterranean oak ecosystems, in particular, have been significantly impacted by climate-driven shifts. These shifts reshape the composition and spatial configuration of a great number of species. Here, this study evaluates the impact of climate change on the habitat suitability of Valonia oak (Quercus ithaburensis subsp. macrolepis (Kotschy) Hedge & Yalt.) and particularly focuses on understanding whether its population is native or was introduced to the Karagüney Mountains, Türkiye. Using ecological niche modeling with MaxEnt and climate data from CHELSA-TraCE21k (a 1 km climate time series), we built 120 models to analyze the habitat suitability of Valonia oak across different climatic periods from the Last Glacial Maximum (LGM) (21 ka BP) to the present. The results indicate that habitat suitability is primarily influenced by temperature- and precipitation-related variables. In fact, temperature fluctuations clearly affect the target species of this study. The most significant factors are the mean diurnal temperature range (bio2; 33.1%), precipitation in the wettest month (bio13; 19%), and mean annual temperature (bio1; 16.7%). Paleoclimatic predictions show that suitable habitats contracted during the early Holocene but expanded afterward, with current distributions aligning more closely with the natural range. In other words, it can be stated that Valonia oak’s habitat suitability has gradually improved from the LGM to the present, with both the total and natural ranges expanding over time. The results indicate that the species has demonstrated long-term stability, resilience, and adaptability to climate change, making it a potential alternative species for future climate scenarios. In addition, the data support the hypothesis that the species’ population in the Karagüney Mountains is relict, but was previously unrecognized as native. This study improves our knowledge about the distribution and environmental preferences of Valonia oak, which is important for underpinning its conservation strategies. Full article

Anoplophora horsfieldii Hope, a potential pest of the Cerambycidae family, is widely distributed throughout China, where it can cause damage to various living tree species. It has emerged as a critical invasive organism threatening China’s agricultural and forestry production as well as ecological security. This study comprehensively analyzed the key environmental factors influencing the geographical distribution of A. horsfieldii and its spatiotemporal dynamics by integrating multi-source environmental data and employing ecological niche modeling. Model validation demonstrated high reliability and accuracy of our predictions, with an area under the receiver operating characteristic curve (AUC) value of 0.933, Kappa coefficient of 0.704, and true skill statistic (TSS) reaching 0.960. Our analysis identified four dominant environmental factors governing the distribution of A. horsfieldii: mean diurnal range (Bio2), temperature annual range (Bio7), precipitation of driest quarter (Bio17), and precipitation of coldest quarter (Bio19). Under current climatic conditions, the total potential suitable distribution area for A. horsfieldii was estimated at 212.394 × 10⁴ km², primarily located in central, southern, eastern, southwestern, and northwestern China. Future projections under three climate scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) suggest significant reductions in highly and moderately suitable habitats, while low-suitability areas may expand into central, eastern, and southwestern regions, with Chongqing, Henan, and Anhui potentially becoming new suitable habitats. Concurrently, the centroid coordinates of suitable habitats exhibited a directional shift toward Guangdong Province, with the overall distribution pattern demonstrating a spatial transition characterized by movement from inland to coastal areas and from higher to lower latitudes. This study provides scientific theoretical support for forestry authorities in controlling the spread of A. horsfieldii, while establishing a solid foundation for future ecological conservation and biosecurity strategies. The findings offer both theoretical insights and practical guidance for pest management and ecosystem protection. Full article

Global warming, driven by increased greenhouse gas emissions, is a critical global concern. However, long-term trends in emissions remain poorly understood due to limited year-round data. The automated chamber method was used for continuous monitoring of soil N₂O fluxes in a mixed forest in Northeast China’s Changbai Mountains, analyzing monthly diurnal patterns and their relationships with soil temperature (Ts) and soil volumetric water content (VWC). The results revealed significant diurnal and seasonal variations, with peak emissions at 11:00 during the growing season (May–October) and elevated nighttime fluxes in winter (March, April, November, and December). The optimal sampling time was 14:00, closely reflecting daily mean fluxes. Soil Ts and VWC were key drivers, with seasonal variability in their effects: N₂O fluxes showed no significant relationship with Ts in January but strong correlations in February and March. The growing season Q₁₀ values ranged from 0.4 to 7.2 (mean = 2.5), indicating high-temperature sensitivity. Soil VWC effects were complex, with moderate VWC promoting denitrification and excessive VWC suppressing microbial activity. These findings provide critical insights for optimizing N₂O monitoring and improving emission estimates. Full article

The apparent respiratory quotient (ARQ) of tree stems, defined as the ratio of net stem CO₂ efflux (E_{S_CO2}) to net stem O₂ influx (E_{S_O2}), offers insights into the balance between local respiratory CO₂ production and CO₂ transported via the xylem. Traditional static chamber methods for measuring ARQ can introduce artifacts and obscure natural diurnal variations. Here, we employed an open flow-through stem chamber with ambient air coupled with cavity ring-down spectrometry, which uses the molecular properties of CO₂ and O₂ molecules to continuously measure E_{S_CO2}, E_{S_O2}, and ARQ, at the base of a California cherry tree (Prunus ilicifolia) during the 2024 growing season. Measurements across three stem chambers over 3–11-day periods revealed strong correlations between E_{S_CO2} and E_{S_O2} and mean ARQ values ranging from 1.3 to 2.9, far exceeding previous reports. Two distinct diurnal ARQ patterns were observed: daytime suppression with nighttime recovery, and a morning peak followed by gradual decline. Partitioning E_{S_CO2} into local respiration and xylem-transported CO₂ indicated that the latter can dominate when ARQ exceeds 2.0. Furthermore, transported CO₂ exhibited a higher temperature sensitivity than local respiration, with both processes showing declining temperature sensitivity above 20 °C. These findings underscore the need to differentiate stem CO₂ flux components to improve our understanding of whole-tree carbon cycling. Full article

Plateau pika (Ochotona curzoniae, hereafter pika) is a key species in the alpine grasslands on the Qinghai-Tibetan Plateau (QTP). They are susceptible to the influence of external disturbance and may present great variation, which is important to evaluate their ecological role in alpine grasslands. However, our knowledge regarding their interannual variation and the influencing mechanism is still limited due to the lack of long-term observation of pika density. This study aimed to investigate the spatiotemporal variations in pika and the associated key influencing factors by aerial photographing at 181 sites in Gannan Tibetan Autonomous Prefecture in 2016, 2019, and 2022. Our findings showed that: (1) pika primarily distributed in the central and northeastern Maqu County and the southwestern part of Luqu County, and their average density was in a range of 9.87 ha⁻¹ to 14.43 ha⁻¹ from 2016 to 2022; (2) high pika density were found in 1.22 to 3.61 °C for annual mean temperature, 12.86 to 15.06 °C for diurnal temperature range, 3400 to 3800 m for DEM and less than 3° for slope; and (3) pika density showed varied response to interannual changes in mean diurnal range, annual precipitation and precipitation of the driest month in different years. Our results concluded that pika density showed significant spatiotemporal variations, and climate and terrain variables dominantly affected pika density. Given the great interannual fluctuation of climate variables and different responses of pika density to these variables, our results suggested that long-term monitoring of pika is crucial to reveal their real distribution, response mechanism to habitat environment, and role in alpine grasslands. Moreover, unmanned aerial vehicles are cost-effective tools for the long-term monitoring of pika. Full article

This study’s field observations of Light Source Efficiency (LSE) in the Taklamakan Desert have unveiled significant daily average variations across different wavelengths, with LSE values ranging from 0.827 at 9.1 μm to a peak of 0.969 at 12.1 μm, and notably, a substantial daily variation (DV) of Δε = 0.080 in the 14.3 μm band. These findings underscore the necessity for wavelength-specific analysis in LSE research, which is crucial for enhancing the precision of remote sensing applications and climate models. This study’s high-temporal-resolution FTIR field observations systematically reveal the diurnal dynamics of infrared surface emissivity in the desert for the first time, challenging existing satellite-based inversion products and highlighting the limitations of traditional temperature–emissivity separation algorithms in arid regions. The diurnal fluctuations are governed by three primary mechanisms: the amplification of lattice vibrations in quartz minerals under high daytime temperatures, changes in the surface topography due to thermal expansion and contraction, and nocturnal radiative cooling effects. The lack of a significant correlation between environmental parameters and the emissivity change rate suggests that microclimate factors play a dominant indirect regulatory role. Model comparisons indicate that sinusoidal functions outperform polynomial fits across most wavelengths, especially at 12.1 μm, confirming the significant influence of diurnal forcing. The high sensitivity of the 14.3 μm band makes it an ideal indicator for monitoring desert surface–atmosphere interactions. This study provides three key insights for remote sensing applications: the development of dynamic emissivity correction schemes, the prioritization of the long-wave infrared band for surface temperature inversion in arid regions, and the integration of ground-based observations with geostationary high-spectral data to construct spatiotemporally continuous emissivity models. Future research should focus on multi-angle observation experiments and the exploration of machine learning’s potential in cross-scale emissivity modeling. Full article