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Summary: In theory, there is a trade‐off between hydraulic efficiency and safety. However, the strength and direction of this trade‐off at the leaf level are not consistent across studies, and habitat climate may impact this trade‐off.We compiled a leaf hydraulic efficiency and safety dataset for 362 species from 81 sites world‐wide, with 280 paired observations of both traits, and tested whether climate was associated with departure from the proposed trade‐off.The leaf hydraulic efficiency–safety trade‐off was weak (R2 = 0.144) at the global scale. Mean annual precipitation and solar radiation (SR) modified the trade‐off. Species from dry and high SR habitats (e.g. desert and tropical savanna) were generally located above the trade‐off line, indicating that these species tended to have higher leaf hydraulic safety and efficiency than species from wet habitats with low SR (e.g. subtropical monsoon forest and montane rainforest), which were located below the trade‐off line. Leaves with high vein density, dry leaf mass per area, and osmotic regulation enhanced safety without compromising hydraulic efficiency.Variation in the hydraulic efficiency–safety trade‐off at the leaf level likely facilitates plant survival in specific habitats and allows for a more nuanced view of leaf hydraulic adaption strategies at the global scale. [ABSTRACT FROM AUTHOR]

Pinus densiflora is a pine species native to the Korean peninsula, and seed orchards have supplied material needed for afforestation in South Korea. Climate variables affecting seed production have not been identified. The purpose of this study was to determine climate variables that influence annual seed production of two seed orchards using multiple linear regression (MLR), elastic net regression (ENR) and partial least square regression (PLSR) models. The PLSR model included 12 climatic variables from 2003 to 2020 and explained 74.3% of the total variation in seed production. It showed better predictive performance (R2 = 0.662) than the EN (0.516) and the MLR (0.366) models. Among the 12 climatic variables, July temperature two years prior to seed production and July precipitation after one year had the strongest influence on seed production. The time periods indicated by the two variables corresponded to pollen cone initiation and female gametophyte development. The results will be helpful for developing seed collection plans, selecting new orchard sites with favorable climatic conditions, and investigating the relationships between seed production and climatic factors in related pine species. [ABSTRACT FROM AUTHOR]

Understanding the influence of climatic factors on vegetation dynamics and cumulative effects is critical for global sustainable development. However, the response of vegetation to climate and the underlying mechanisms in different climatic zones remains unclear. In this study, we analyzed the response of vegetation gross primary productivity (GPP) to climatic factors and the cumulative effects across various vegetation types and climatic zones, utilizing data on precipitation (Pr), temperature (Ta), and the standardized precipitation evapotranspiration index (SPEI). The results showed that: (1) GPP showed significant differences among the seven climatic zones, with the highest value observed in zone VII, reaching 1860.07 gC·m− 2, and the lowest in zone I, at 126.03 gC·m− 2. (2) GPP was significantly and positively correlated with temperature in climatic zones I, IV, V, and VI and with precipitation in climatic zones I, II, and IV. Additionally, a significant positive correlated was found between SPEI and GPP in climatic zones I, II, and IV. (3) Drought exerted a cumulative effect on GPP in 45.10% of the regions within China, with an average cumulative duration of 5 months. These effects persisted for 6–8 months in zones I, II, and VII, and for 2–4 months in zones III, IV and VI. Among different vegetation types, forests experienced longest cumulative effect time of 6 months, followed by grasslands (5 months), croplands (4 months), and shrublands (4 months). The cumulative time scale decreased with increasing annual SPEI. The varying responses and accumulation of GPP to drought among different vegetation types in various climatic zones underscore the complexity of vegetation-climate interactions the response and accumulation of GPP to drought. [ABSTRACT FROM AUTHOR]

Objective: This study investigates the key factors contributing to the dengue outbreak in Oman. Methods: Data on climate (e.g., temperature, humidity, wind pace), population traits (e.g., populace density), and vector dynamics (e.g., mosquito density) within the Seeb district of Oman from 2022 to 2023 were gathered. The partial least squares structural equation modeling (PLS-SEM) was performed to study which variables affect dengue outbreaks. Results: The results indicate that climatic factors significantly affect the dengue vector (β = -0.361, p < 0.001) but do not directly impact the dengue outbreak. Population characteristics, however, have a more substantial impact on dengue transmission, with a total effect (β = 0.231, p = 0.002) being relatively higher than that of the vector itself (total effect: β = 0.116, p < 0.001). Conclusions: Even with ongoing vector intervention efforts, the study underscores the need to include innovative public health interventions when considering environmental and demographic factors. More advantageous surveillance and focused interventions in excessive-threat regions are essential to mitigate the effect of dengue in Oman. [ABSTRACT FROM AUTHOR]

Adequate access to nutritious food is essential for human health and well-being. However, attaining food security has become a grave concern due to increasing population and climate change vulnerabilities. Recently, several studies around the globe have indicated that climate change is likely to impact food security. Studies also show that some non-climatic factors are crucial in determining food security. The present study, therefore, investigates the nexus between food security and non-climatic and climatic factors in the context of India. This paper has considered both climatic factors (temperature, precipitation, and carbon emissions) and non-climatic factors (land and electricity used in the agriculture sector) as explanatory variables, whereas the crop production index is considered a proxy for food security. The "autoregressive distributive lag (ARDL)" model has been used to assess the variables' short- and long-run association by considering the time range from 1990 to 2020. The outcome of the estimation indicates that precipitation, carbon emissions, land, and electricity significantly impact food security in both the short and long run. However, the impact of temperature on food security is insignificantly negative in the short run and positive in the long run. This study thus suggests that the government should develop and implement comprehensive policies to promote sustainable agriculture practices to improve the country's food security level. [ABSTRACT FROM AUTHOR]

Climate change has emerged as a major concern at both regional and global scales in recent decades. Northwestern Algeria is particularly vulnerable as a semi-arid zone, where changes in climate and land cover (LC) will have a significant impact on vegetation in the long term. This study analyses, through a multidimensional approach, the influence of climate change and LC on vegetation dynamics. Hierarchical partitioning (HP) analysis was conducted to determine the most influential climatic variables (precipitation, temperature) on the dynamics of NDVI. The results show that the annual NDVI shows a fluctuating spatial trend between decrease and increase in different regions. Trends in seasonal NDVI are spatially varied and less uniform. Variations in precipitation are stable, while temperatures show clear and consistent significant increases across the region, with a general tendency to increase (p<0.01) in spring and summer. In mountainous areas, NDVI shows an increasing trend both annually and seasonally. The correlation (r²) between NDVI, temperature and precipitation (0.75–1.0) over the different seasons reveals significant seasonal and regional variability. LC transition patterns also influence spatio-temporal trends in vegetation cover. They reveal that the rate of change of NDVI varies between LC types and regions, with resilience in forests and grasslands. These variations have significant implications for vegetation dynamics, as observed by NDVI. [ABSTRACT FROM AUTHOR]

Background: Congenital hypothyroidism (CH) is one of the most prevalent preventable cause of mental retardation and intellectual disability in newborns. Genetic and environmental factors have been related to congenital hypothyroidism. Hence, this study was conducted to illustrate the incidence and spatial distribution of the CH using the dataset from the national newborn CH screening information system during 2011-2020in counties affiliated to the Kohgiluyeh and Boyer Ahmad University of Medical Sciences. Methods: This was a cross-sectional study. All newborns that were screened for CH in 3–5 days of age in the health centers of Yasuj University of Medical Sciences were enrolled the study since 2011–2020. Meteorological data including information on temperature, humidity, precipitation in a 10-year period (2011–2020) was taken from the Meteorological Organization. The required information layers include slope, slope direction, height, vegetation and political divisions of the province, were purchased from the organizations that made the layers. The data related to the incidence of congenital hypothyroidism was collected from the 10-year reports of the provincial health center. Addresses of patients were transferred to Arc GIS 10.5 software and entered in urban and rural point's layer to prepare and draw disease distribution maps in the province's newborns. The influence of geographical factors on the occurrence of disease was done by SPSS 23 software using multivariate binary logistic regression. P-value < 0.05 was assumed as significant level. Results: Out of 142,674 screened newborns for CH, 827 had positive screening, including 441 boys (53.3%) and 386 girls (46.7%). Total incidence per 1000 live birth was 5.79. Based on multivariate logistic regression, slope (sig = 0.0001, Exp(B) = 0.91, CI95% Exp(B) = 0.87–0.95), thin forest (sig = 0.001, Exp(B) = 0.2, CI95% Exp(B) = 0.07–0.53), water area (sig = 0.0001, Exp(B) = 8.91, CI95% Exp(B) = 3.35–23.69), were predicting factors of CH. Conclusion: Given that, the incidence of CH in the Kohgiluyeh and Boyar Ahmad province is higher than the average of country (Iran), and the geographical factors of land slope and type of vegetation were related to it that cannot be changed, it is very important to carry out necessary nutritional interventions during pregnancy to prevent newborns from contracting this disease. [ABSTRACT FROM AUTHOR]

ABSTRACT Batocera horsfieldi is a major wood‐boring pest in China's timber and economic forests, belonging to the order Coleoptera and the family Cerambycidae. This study headspace solid‐phase microextraction and gas chromatography–mass spectrometry (GC–MS) were used to analyse the volatile components of B. horsfieldi supplementary feeding host plants, and fingerprint profiles were established. The results revealed that the five plants contained 21, 18, 22, 16 and 17 volatile compounds, mostly terpenes, aldehydes and alcohols, along with trace amounts of furans, ketones and ethers. The terpene compounds were mainly monoterpenes and sesquiterpenes. The results of hierarchical cluster analysis (HCA) and principal component analysis (PCA) were consistent. Five common peaks were identified in these host plants, with (E)‐2‐hexenal, caryophyllene, (E)‐cinnamaldehyde, nonanal, and 3,7‐dimethyl‐1,6‐octadien‐3‐ol being the shared volatile components among the five plants. The mantel test, correlation analysis and grey relational analysis indicated that temperature and precipitation are important factors affecting the content of volatile components. These analytical results provide a basis for using plant volatiles as attractants to control B. horsfieldi. [ABSTRACT FROM AUTHOR]

This study presents an innovative investigation into the spatiotemporal dynamics of vegetation growth and its response to both individual and composite climatic factors. The Normalized Difference Vegetation Index (NDVI), derived from SPOT satellite remote sensing data, was employed as a proxy for vegetation growth. Multiple analytical methods, including the coefficient of variation, Mann–Kendall trend analysis, and Hurst index, were applied to characterize the spatiotemporal patterns of the NDVI in Sichuan Province from 2000 to 2020. The Standardized Precipitation Evapotranspiration Index (SPEI) was calculated using monthly precipitation and temperature data from 45 meteorological stations to examine the influence of composite climatic factors on vegetation growth, while the time lag effects between the NDVI and various climatic variables were also explored. Our findings unveil three key insights: (1) Vegetation coverage in Sichuan Province exhibited an overall increasing trend, with the highest NDVI values in summer and the lowest in winter. Significant NDVI fluctuations were observed in spring in the western Sichuan plateau and in winter in northern, eastern, and southern Sichuan. (2) A significant upward trend in the NDVI was detected across Sichuan Province, except for Chengdu Plain, where a downward trend prevailed outside the summer season. (3) On shorter time scales, the NDVI was positively correlated with precipitation, temperature, and the SPEI, with a one-month lag. The response of the NDVI to sunlight duration showed a two-month lag, with the weakest correlation and a five-month lag in western Sichuan. This research advances our understanding of the complex interactions between vegetation dynamics and climatic factors in Sichuan Province and provides valuable insights for predicting future vegetation growth trends. [ABSTRACT FROM AUTHOR]

Forest above-ground carbon stock (AGCS) is one of the primary ecological evaluation indicators, so it is crucial to estimate the AGCS accurately. In this research, we added the climatic and topographic factors to the estimation process by a remote sensing approach to explore their impact and to achieve more precise estimations. We hope to develop a more accurate estimation method for AGCS based on remote sensing data and climate data. The random forest (RF) method has good robustness and wide applicability. Therefore, we modeled and predicted the AGCS by RF based on sixty field sample plots of Pinus densata pure forests in southwest China and the factors extracted from Landsat 8 OLI images (source I), Sentinel-2A images (source II), and combined Landsat 8 OLI and Sentinel-2A images (source III). We added the topographic and climatic factors to establish the AGCS estimation model and compared the results. The topographic factors contain elevation, slope, and aspect. Climatic factors contain mean annual temperature, annual precipitation, annual potential evapotranspiration, and monthly mean potential evapotranspiration. It was found that the R2 and RMSE of the model based on source III were better than the R2 and RMSE of the models based on source I and source II. Compared to the models based on source I and source II, the model based on source III improved R2 by up to 0.08, reduced RMSE by up to 2.88 t/ha, and improved P by up to 4.29%. Among the models without adding factors, the model based on source III worked the best, with an R2 of 0.87, an RMSE of 10.81 t/ha, an rRMSE of 23.19%, and a P of 79.71%. Among the models that added topographic factors, the model based on source III worked best after adding elevation, with an R2 of 0.89, an RMSE of 10.01 t/ha, an rRMSE of 21.47%, and a P of 82.17%. Among the models that added climatic factors, the model that added the annual precipitation factor had the best modeling result, with an R2 of 0.90, an RMSE of 9.53 t/ha, an rRMSE of 20.59%, and a P of 83.00%. The prediction result exhibited that the AGCS of the Pinus densata forest in 2021 was 9,737,487.52 t. The combination of Landsat 8 OLI and Sentinel-2A could improve the prediction accuracy of the AGCS. The addition of annual precipitation can effectively improve the accuracy of AGCS estimation. Higher resolution of climate data is needed to enhance the modeling in future work. [ABSTRACT FROM AUTHOR]

Increased wildfire activity is the most significant natural disturbance affecting forest ecosystems as it has a strong impact on their natural recovery. This study aimed to investigate how burn severity (BS) levels and climate factors, including land surface temperature (LST) and precipitation variability (Pr), affect forest recovery in the Middle Volga region of the Russian Federation. It provides a comprehensive analysis of post-fire forest recovery using Landsat time-series data from 2000 to 2023. The analysis utilized the LandTrendr algorithm in the Google Earth Engine (GEE) cloud computing platform to examine Normalized Burn Ratio (NBR) spectral metrics and to quantify the forest recovery at low, moderate, and high burn severity (BS) levels. To evaluate the spatio-temporal trends of the recovery, the Mann–Kendall statistical test and Theil–Sen's slope estimator were utilized. The results suggest that post-fire spectral recovery is significantly influenced by the degree of the BS in affected areas. The higher the class of BS, the faster and more extensive the reforestation of the area occurs. About 91% (40,446 ha) of the first 5-year forest recovery after the wildfire belonged to the BS classes of moderate and high severity. A regression model indicated that land surface temperature (LST) plays a more critical role in post-fire recovery compared to precipitation variability (Pr), accounting for approximately 65% of the variance in recovery outcomes. [ABSTRACT FROM AUTHOR]

Lake ice phenology directly reflects local climate changes, serving as a key indicator of climate change. In today's rapidly evolving climate, utilizing advanced remote sensing techniques to quickly extract long-term lake ice phenology features and studying their correlation with other climate factors is crucial. This study focuses on lakes in Xinjiang, China, with a mountainous area greater than 100 km2, including Sayram Lake, Ayahkum Lake, Achihkul Lake, Jingyu Lake, and Ahsaykan Lake. The Bayesian ensemble change detection algorithm was employed to extract lake ice phenology information, and the Mann–Kendall (MK) non-parametric test was used to analyze trends. The visual interpretation method was used to interpret the spatial evolution characteristics of lake ice, and the Pearson correlation coefficient was used to explore the driving factors of lake ice phenology. Results indicate the following: (1) Jingyu Lake exhibited the most significant delay in both freezing and complete freezing days, while Ayahkum Lake showed the most stable pattern. Ahsaykan Lake demonstrated the least delay in both starting and complete melting days. (2) Sayram Lake's ice evolution was unstable, with wind causing variability in the locations where freezing begins and melting spreading from the west shore. Ayahkum Lake, Ahsaykan Lake, and Jingyu Lake exhibited similar seasonal variations, while Achihkul Lake's ice spatial changes spread from the east to the center during freezing and from the center to the shore during melting. (3) The study found that the freeze–thaw process is influenced by a combination of factors including lake area, precipitation, wind speed, and temperature. [ABSTRACT FROM AUTHOR]

Recurring use of chemical fertilizers (CFs) in agriculture has resulted the remarkable improvement in crop productivity but their ruinous effects on environment have made a serious issue. Biological entities (e.g., several microorganisms) showing fertilizer‐like activities have gained attention in this regard. Several soil resident microorganisms interact strongly with neighboring plants and promote the growth and development of those plants through various means. In exchange of this, microbes utilize different compounds released from plant roots for their own nutrition. This mutualistic mode of interrelation predominantly relies on the transmission of signals from microbes to plants and vice versa. However, climatic factors (e.g., CO2 level, temperature, and water availability) are also important for this association. These bacterial strains are literally known as plant growth promoting rhizobacteria (PGPR) which facilitate plant growth through nitrogen fixation, mineral solubilization, phytostimulation, stress resistance, etc. Responding to the external environmental stimuli, they often modulate the expression of genes responsible for the transport of nutrients. Reduction of the use of CFs through the application of PGPR strains in the cultivation of some economically important plants has been reported by several authors. Significant yield improvement compared to the control groups was found in all experimental studies. Commercial development of the PGPR inoculants with remarkable biostimulating activities and their successive application should be expanded through collaborative association with different sectors after the removal of existing lacunae. Reading more than 100 articles on various aspects of rhizobacteria, the plan of writing this article has been executed. In this review, we have discussed about the colonization and potency of PGPR strains and how their well‐planned application in agriculture could evidently reinforce the global economy. The main structure of this text is designed as an outline from the development of interrelation between plants and PGPR to the commercialization of PGPR based on their potential role in the field of agriculture. [ABSTRACT FROM AUTHOR]

Copyright of Arab International Journal of Digital Art & Designs is the property of Arab Institution of Knowledge Management and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The coupled processes of ecosystem carbon and water cycles are usually evaluated using the water use efficiency (WUE), and improving WUE is crucial for maintaining the sustainability of ecosystems. However, it remains unclear whether the WUE in different ecosystem responds synchronously to the synergistic effect of the same climate factors at daily and monthly scales. Therefore, we employed a machine learning-driven factor analysis method and a geographic detector model, and we quantitatively evaluated the individual effects and the synergistic effect of climate factors on the daily mean water use efficiency (WUED) and monthly mean water use efficiency (WUEM) in different ecosystems in China. Our results showed that (1) among the 10 carbon flux monitoring sites in China, WUED and WUEM exhibited the highest positive correlations with the near-surface air humidity and the highest negative correlation with solar radiation. The correlation between WUEM and climate factors was generally greater than that between WUED and climate factors. (2) There were significant differences in the order of importance and degree of impact of the same climate factors on WUED and WUEM in the different ecosystems. Among the 10 carbon flux monitoring sites in China, the near-surface air humidity imposed the greatest influence on the WUED and WUEM changes, followed by the near-surface water vapor pressure. (3) There were significant differences in the synergistic effects of the same climate factors on WUED and WUEM in the different ecosystems. Among the 10 carbon flux monitoring sites in China, the WUED variability was most sensitive to the synergistic effect of solar radiation and photosynthetically active radiation, while the WUEM variability was most sensitive to the synergistic effect of the near-surface air humidity and soil moisture. The research results indicated that synchronous responses of the WUE in very few ecosystems to the same climate factors and their synergistic effect occurred at daily and monthly scales. This finding enhances the understanding of sustainable water resource use and the impact of climate change on water use efficiency, providing crucial insights for improving climate-adaptive ecosystem management and sustainable water resource utilization across different ecosystems. [ABSTRACT FROM AUTHOR]

Energy generation and economic development are closely linked, with energy playing a pivotal role in wealth creation. However, the finite nature of fossil fuel resources and associated environmental challenges has emphasized the need for sustainable energy alternatives based on renewable sources. Among these alternatives, photovoltaic (PV) systems hold significant potential for fostering a sustainable energy system. Despite ongoing material research, achieving a major breakthrough in enhancing the conversion efficiency of commercial PV modules remains a challenge. To optimize PV system yield, installation must consider geographic location and design factors. PV modules often yield different results compared to manufacturer specifications due to factors such as high temperatures, dust, and arid to semiarid climates. Therefore, it is crucial to conduct outdoor testing and characterization of PV modules tailored to specific locations, such as Jordan, to maximize system performance. This research aims to examine the impact of ambient temperature, dust accumulation, and solar irradiance intensity on PV system performance in Jordan, providing valuable insights through monitoring power output variations. The findings will contribute to improving PV system efficiency in Jordan's unique climate and aid manufacturers in developing innovative PV applications. The collected data includes solar radiation, temperature, voltage, current, and output power. The results confirm that the Azraq site is a better location when compared to Mafraq based on the possibility of generated more output power using a PV system. [ABSTRACT FROM AUTHOR]

Plant aboveground biomass reflects the resilience of ecosystem productivity to different environmental factors and plays an important role in biodiversity and ecosystem carbon cycles. Although many studies have investigated the relationship between aboveground biomass and species diversity, stand structural diversity is an important factor affecting ecosystem function. The mechanism of how climate and soil factors affect aboveground biomass through species diversity and structural diversity remains unclear. Here, combing data from 8 locations of Abies fargesii var. faxoniana primary forest, a structural equation model was used to study the effects of climate factors, soil factors, species diversity, and structural diversity on aboveground biomass. Our results demonstrated that climate factors affect aboveground biomass mainly through the indirect effect of species diversity. Species diversity indirectly affects aboveground biomass through its effects on structural diversity. Both direct effects of soil factors and indirect effects through structural diversity can significantly affect aboveground biomass. Therefore, maintaining higher diversity of stand structure and soil nutrients can improve aboveground biomass, and thus better improve ecosystem functions such as carbon storage. [ABSTRACT FROM AUTHOR]

Copyright of Pearl River is the property of Pearl River Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As a vital indicator for measuring the coupled carbon–water cycle of an ecosystem, water use efficiency (WUE) can also reflect the adaptive capacity of plants in different ecosystems. Located in Southwest China, the Li River Basin has a representative karst landform, and the uneven rainfall in the region leads to severe water shortage. In this study, we analyzed the spatial–temporal transformation characteristics of the WUE of the basin and its relationship with different influencing factors from 2001 to 2020 based on a correlation analysis and trend analysis. The main conclusions are as follows: (1) The average value of WUE in the Li River Basin was 1.8251 gC· mm−1·m−2, and it kept decreasing at a rate of 0.0072 gC· mm−1·m−2·a−1 in the past 20 years. With respect to the spatial distribution of the multi-year average of WUE, it exhibits a gradual increasing trend from west to east. (2) Between gross primary productivity (GPP) and evapotranspiration (ET), it was found that ET was the primary influencing factor of WUE. Precipitation was positively correlated with WUE in the Li River Basin, accounting for 67.22% of the total area of the basin. The air temperature was negatively correlated with WUE, and the area was negatively correlated with WUE, accounting for 92.67% of the basin area. (3) The normalized difference vegetation index (NDVI) and leaf area index (LAI) were negatively correlated with WUE, and the proportions of negatively correlated areas to the total area of the basin were similar; both were between 60 and 70%. The growth of vegetation inhibited the increase in WUE in the basin to a certain extent. Regarding Vapor Pressure Deficit (VPD), the proportions of positive and negative correlation areas with WUE were similar, accounting for 49.58% and 50.42%, respectively. (4) The occurrence of drought events and the enhancement in its degree led to a continuous increase in WUE in the basin; for different land cover types, the correlation of the standardized precipitation evapotranspiration index (SPEI) was in the following order from strongest to weakest: grassland > cropland > forest > shrubland. [ABSTRACT FROM AUTHOR]

Abstract Background Congenital hypothyroidism (CH) is one of the most prevalent preventable cause of mental retardation and intellectual disability in newborns. Genetic and environmental factors have been related to congenital hypothyroidism. Hence, this study was conducted to illustrate the incidence and spatial distribution of the CH using the dataset from the national newborn CH screening information system during 2011-2020in counties affiliated to the Kohgiluyeh and Boyer Ahmad University of Medical Sciences. Methods This was a cross-sectional study. All newborns that were screened for CH in 3–5 days of age in the health centers of Yasuj University of Medical Sciences were enrolled the study since 2011–2020. Meteorological data including information on temperature, humidity, precipitation in a 10-year period (2011–2020) was taken from the Meteorological Organization. The required information layers include slope, slope direction, height, vegetation and political divisions of the province, were purchased from the organizations that made the layers. The data related to the incidence of congenital hypothyroidism was collected from the 10-year reports of the provincial health center. Addresses of patients were transferred to Arc GIS 10.5 software and entered in urban and rural point’s layer to prepare and draw disease distribution maps in the province’s newborns. The influence of geographical factors on the occurrence of disease was done by SPSS 23 software using multivariate binary logistic regression. P-value

Base flow is an important part of runoff, so it is important to choose a reasonable base flow segmentation method to estimate base flow, understand the changing characteristics of base flow and base flow index, and identify the driving factors of base flow and base flow index change for the protection and management of river ecology. Based on the daily runoff data and meteorological data of 64 hydrological stations in the middle and lower reaches of the Yangtze River, the base flow segmentation results of three digital filtering methods (Lyne-Hollick method, Chapman-Maxwell method, and Eckhardt Method) and smoothing minimum method were compared, and the driving factors of base flow and base flow index changes were analyzed from the aspects of climate and underlying surface factors. The results show that the Chapman-Maxwell filtering method is more suitable for base flow segmentation in the middle and lower reaches of the Yangtze River. The base flow of the main stream in the middle and lower reaches of the Yangtze River is much larger than that of tributaries. The base flow is greater in wet seasons than in dry seasons, while the base flow index is not significantly different between the main stream and tributaries, and the base flow index is significantly greater in dry seasons than in wet seasons. Climate and underlying surface are the driving factors of base flow changes between wet and dry seasons. Specifically, climate is the dominant factor in the reduction of base flow in wet and dry seasons, while the underlying surface is the dominant factor in the increase of base flow in dry seasons.

Rompin has played an active role in Malaysia's rice production for several decades. However, it also faces a threat, particularly from the hemipteran insect pests. There is limited documentation on the abundance of these insects and their relationship with climatic factors. Therefore, this study aimed to determine the abundance of hemipterans in different growth stages and to understand the effects of climatic factors on their population in rice fields. The study was conducted in farmers' rice fields in Rompin, Pahang, Malaysia, during the main and off-season rice planting. Sweep nets, yellow sticky traps, and yellow pan traps were utilized in each plot. Trapped insects were identified based on the rice planting seasons and growth stages. The trends of climatic factors such as rainfall, temperature, light intensity, and wind speed were analysed and correlated with insect abundance. A total of 23,023 individuals belonging to eight families and eleven species of the hemipteran order were recorded. Nine of these species were pests, namely Leptocorisa oratorius, Recilia dorsalis, Nephotettix virescens, N. nigropictus, Amaurochrous dubius, Nilaparvata lugens, Sogatella furcifera, Nezara viridula, and Scotinophara coarctata. The remaining two species were predators, Cyrtorhinus lividipennis and Polytoxus fuscoviftatus. R. dorsalis, N. lugens, S. furcifera, and A. dubius were found in significantly higher numbers and exceptionally high during rice's late vegetative and early reproductive stages. The maximum temperature significantly increased during the main season, while wind speed increased dramatically during the off-season. Wind speed significantly correlated with N. virescens, S. furcifera, L. oratorius, and A. dubius. On the other hand, rainfall exhibited a significant correlation with L. oratorius. Various pest species and their high populations in this study pose a significant threat to plants due to their preferences for different plant parts and strata.

Drought, as one of the main natural hazards faced by human beings, results in grassland degradation, crop yield reduction, forest activity weakening, and even forest mortality. Utilizing drought indices to identify drought is pivotal for improving ecological protection and hazard mitigation. The temperature vegetation drought index (TVDI) is a remotely sensed index that has been widely applied in monitoring drought and assessing its severity. In this study, the TVDI was utilized to investigate drought across different vegetation types from 2001 to 2020 in Jilin Province, China, and to demonstrate the applicability of the TVDI in this region. Our results showed that the drought degree of different vegetation types showed distinct differences. The forest area experienced lower incidences of drought, whereas the grassland and cropland areas exhibited higher incidences of drought. The variation trends of drought were different in Jilin Province from 2001 to 2020. The increasing trends of drought were mainly distributed in the grassland and cropland, while declining trends of drought were mainly distributed in the forest. The impacts of climatic factors on the TVDI differed among different vegetation types. Evapotranspiration (ET) was the main influencing factor for the TVDI in the forest area, and air temperature played a dominant role in the TVDI in the grassland and cropland areas. The cropland biomass was more susceptible to drought stress than that in grassland and forest areas. This study will serve as a valuable reference for assessing the applicability of approaches to identifying regional drought events. [ABSTRACT FROM AUTHOR]

As global warming intensifies, both the frequency and magnitude of extreme precipitation events have undergone significant changes, leading to a widespread manifestation of nonstationary characteristics in extreme precipitation sequences. Regarding the current research status in the Yangtze River Basin (YRB), there is a deficiency in comprehensive investigations into the connection between extreme precipitation and climate change. This paper aims to identify the primary climate factors affecting extreme precipitation in the YRB and use them for dynamic assessment of future rainstorm risks. It thoroughly examines the correlation between 11 climate factors and extreme rainfall across different temporal scales to identify potential climate drivers for nonstationary models. Subsequently, ten nonstationary generalized extreme value (GEV) models were constructed, and the optimal covariates for 190 stations were determined within a Bayesian framework. Finally, a complete forecasting process was established, and the extreme rainfall in the YRB under nonstationary conditions was assessed. The results indicate that, on average, 144 stations exhibited nonstationary characteristics across various event scales, accounting for an average of 75.8%. The extreme rainfall in the YRB is influenced by multiple factors, with no single factor demonstrating absolute dominance across different time scales. Neglecting the nonstationarity of extreme precipitation will inevitably lead to misconceptions about extreme rainfall. Furthermore, the spatial distribution of the maximum scenarios of extreme rainfall under nonstationary conditions is significantly different from that under stationary conditions. [ABSTRACT FROM AUTHOR]

Abstract Background Malaria remains an important public health problem, particularly in sub-Saharan Africa. In Rwanda, where malaria ranks among the leading causes of mortality and morbidity, disease transmission is influenced by climatic factors. However, there is a paucity of studies investigating the link between climate change and malaria dynamics, which hinders the development of effective national malaria response strategies. Addressing this critical gap, this study analyses how climatic factors influence malaria transmission across Rwanda, thereby informing tailored interventions and enhancing disease management frameworks. Methods The study analysed the potential impact of temperature and cumulative rainfall on malaria incidence in Rwanda from 2012 to 2021 using meteorological data from the Rwanda Meteorological Agency and malaria case records from the Rwanda Health Management and Information System. The analysis was performed in two stages. First, district-specific generalized linear models with a quasi-Poisson distribution were applied, which were enhanced by distributed lag non-linear models to explore non-linear and lagged effects. Second, random effects multivariate meta-analysis was employed to pool the estimates and to refine them through best linear unbiased predictions. Results A 1-month lag with specific temperature and rainfall thresholds influenced malaria incidence across Rwanda. Average temperature of 18.5 °C was associated with higher malaria risk, while temperature above 23.9 °C reduced the risk. Rainfall demonstrated a dual effect on malaria risk: conditions of low (below 73 mm per month) and high (above 223 mm per month) precipitation correlated with lower risk, while moderate rainfall (87 to 223 mm per month) correlated with higher risk. Seasonal patterns showed increased malaria risk during the major rainy season, while the short dry season presented lower risk. Conclusion The study underscores the influence of temperature and rainfall on malaria transmission in Rwanda and calls for tailored interventions that are specific to location and season. The findings are crucial for informing policy that enhance preparedness and contribute to malaria elimination efforts. Future research should explore additional ecological and socioeconomic factors and their differential contribution to malaria transmission.

This study investigated the effects of climatic factors on radial growth of Picea schrenkiana var. tianschanica at various altitudes in Central Tianshan Mountains. Tree-ring core samples were collected at three altitudinal levels (1900, 2 200, 2 500 m) within the practice forest of Xinjiang Agricultural University. Dendrochronological techniques, including temperature, precipitation, the standardized precipitation evapotranspiration index, and the Palmer drought severity index, were employed to analyze meteorological data from 1955 to 2022. The results indicate that at low altitudes, tree-ring width is positively correlated with previous June and current June-August temperatures, previous August precipitation, and the current June-September Palmer drought severity index (P<0.05). At mid-altitudes, tree ring width was positively correlated with the previous June temperature, August precipitation, and previous June-August standardized precipitation evapotranspiration index (P<0.05). At high altitudes, tree-ring width was negatively correlated with the current January temperature and previous November to the current March standardized precipitation evapotranspi- ration index (P<0.05), but positively correlated with the current June-July precipitation (P<0.05). Therefore, in the forest, P. schrenkiana var. tianschanica growth is closely linked to the environmental moisture conditions, which vary with altitude. Precipi- tation is pivotal for radial growth of P. schrenkiana var. tianschanica at low altitudes, whereas temperature and precipitation jointly affected growth at mid-altitudes, with temperature exerting a greater impact than precipitation on radial growth at higher altitudes. [ABSTRACT FROM AUTHOR]

Specimen data play a crucial role in geographical distribution research. In this study, the collection information of liverwort specimens in China was compiled and analyzed to investigate the history, current status, and limitations of liverwort research in China. By utilizing the latest systematic research findings and corresponding environmental data, a niche model was developed to offer theoretical support for exploring the potential geographical distribution and diversity of liverwort resources. A total of 55,427 liverwort specimens were collected in China, resulting in the recording of 1212 species belonging to 169 genera and 63 families. However, there are imbalances in the distributions of liverwort data among different groups, collection units, and geographical areas, with families such as Lejeuneaceae, Porellaceae, and Plagiochilaceae having the highest number of specimens. Similarly, genera such as Porella, Frullania, and Horikawaella were well represented. Remarkably, 125 species had specimen counts exceeding 100. Unfortunately, approximately 51.77% of the species had fewer than 10 recorded specimens. There were four obvious peaks in the collection years of the bryophyte specimens in China, among which the largest collection occurred from 2010 to 2023. Notably, the number of specimens collected at different stages closely aligned with the history of taxonomic research on liverworts in China. The results of the integrity of the liverwort collection indicate that there is insufficient representation of some families and genera, with a concentration of common and widely distributed large families and genera. Tropical and subtropical humid areas are key regions for liverwort diversity, with water and temperature being the primary environmental factors influencing their geographical distribution. The specific temporal and spatial data of species recorded from plant specimens will enhance the study of species diversity, comprehensive protection, and sustainable utilization. Additionally, these data will contribute to the investigation of large-scale biodiversity distribution patterns and the impact of global change on diversity. [ABSTRACT FROM AUTHOR]

Background: Malaria remains an important public health problem, particularly in sub-Saharan Africa. In Rwanda, where malaria ranks among the leading causes of mortality and morbidity, disease transmission is influenced by climatic factors. However, there is a paucity of studies investigating the link between climate change and malaria dynamics, which hinders the development of effective national malaria response strategies. Addressing this critical gap, this study analyses how climatic factors influence malaria transmission across Rwanda, thereby informing tailored interventions and enhancing disease management frameworks. Methods: The study analysed the potential impact of temperature and cumulative rainfall on malaria incidence in Rwanda from 2012 to 2021 using meteorological data from the Rwanda Meteorological Agency and malaria case records from the Rwanda Health Management and Information System. The analysis was performed in two stages. First, district-specific generalized linear models with a quasi-Poisson distribution were applied, which were enhanced by distributed lag non-linear models to explore non-linear and lagged effects. Second, random effects multivariate meta-analysis was employed to pool the estimates and to refine them through best linear unbiased predictions. Results: A 1-month lag with specific temperature and rainfall thresholds influenced malaria incidence across Rwanda. Average temperature of 18.5 °C was associated with higher malaria risk, while temperature above 23.9 °C reduced the risk. Rainfall demonstrated a dual effect on malaria risk: conditions of low (below 73 mm per month) and high (above 223 mm per month) precipitation correlated with lower risk, while moderate rainfall (87 to 223 mm per month) correlated with higher risk. Seasonal patterns showed increased malaria risk during the major rainy season, while the short dry season presented lower risk. Conclusion: The study underscores the influence of temperature and rainfall on malaria transmission in Rwanda and calls for tailored interventions that are specific to location and season. The findings are crucial for informing policy that enhance preparedness and contribute to malaria elimination efforts. Future research should explore additional ecological and socioeconomic factors and their differential contribution to malaria transmission. [ABSTRACT FROM AUTHOR]

As a key component of the ecosystem, soil seed banks (SSBs) play a vital role in the evolution and renewal of plant communities. Although the pattern and mechanisms of influence of SSBs along the altitudinal gradient have been reported, most studies have focused on forest, grassland and alpine meadow ecosystems. The pattern and factors of SSBs across the altitudinal gradient in sedge peatlands remain largely unknown. Through vegetation surveys and seed germination experiments, we studied the changes in aboveground vegetation and SSBs in sedge peatlands at altitudes ranging from 300 m to 1300 m in the Changbai Mountains, China, and discussed the direct and indirect effects of climatic factors, soil properties and aboveground vegetation on SSBs. The results showed that the richness and density of the SSBs of sedge peatlands decreased with the altitude. Similarly, aboveground vegetation richness and density declined with altitude. A Spearman correlation analysis showed that SSB richness and density were mainly correlated with mean annual temperature, soil total phosphorus and ammonia nitrogen and the plant composition and richness of aboveground vegetation. A structural equation model analysis showed that climatic factors and aboveground vegetation directly affected seed bank richness, while soil properties indirectly affected it by directly affecting aboveground vegetation. Climatic factors, soil properties and aboveground vegetation directly affected SSB density, and soil properties indirectly affected it by directly affecting aboveground vegetation. This finding enhances our understanding of the altitude patterns of the SSBs in sedge peatlands and the response to future climate and environmental changes. [ABSTRACT FROM AUTHOR]

Stingless bees are essential for the pollination of cultivated and native plant species. Anthropogenic activities in natural ecosystems trigger changes in the microclimate, impacting the flight activity of bees. Here, we examine the effects of some abiotic factors on the flight activity of some stingless bees. We installed four colonies of each species (Plebeia droryana, Scaptotrigona bipunctata and Melipona quadrisfasciata) in three distinct areas (predominantly urbanized area, mosaic of pastures with agricultural cultivation and mosaic of natural vegetation with fragmented landscapes) represented by different uses and land cover (L.U.L.C.). All observations were in the morning during three time slots. For each area, we recorded daily abiotic factors and measured the flight activity of bees entering and leaving the colony. P. droryana and S. bipunctata had similar flight activities, with less activity in anthropized areas. M. quadrifasciata showed the lowest activity among species in the three areas. The flight activity of S. bipunctata showed significant differences. Abiotic factors had significant differences between areas, light intensity had the greatest influence on flight activity among the three species. Our study indicates that stingless bees exhibit a pattern of behavior. However, the abiotic characteristics of a given ecosystem can affect the flight activity of each species based on its normative behavior. This is the first study to evaluate abiotic effects on the flight activity of P. droryana, S. bipunctata and M. quadrifasciata in southern Brazil. The results obtained here can contribute to the establishment of a conservation plan for stingless bee species and prevent future extinctions in a scenario of environmental changes. [ABSTRACT FROM AUTHOR]

The chickpea plays a significant role in global agriculture and occupies an increasing share in the human diet. The main aim of the research was to develop a model for the prediction of two chickpea productivity traits in the available dataset. Genomic data for accessions were encoded in Artificial Image Objects, and a model for the thousand-seed weight (TSW) and number of seeds per plant (SNpP) prediction was constructed using a Convolutional Neural Network, dictionary learning and sparse coding for feature extraction, and extreme gradient boosting for regression. The model was capable of predicting both traits with an acceptable accuracy of 84–85%. The most important factors for model solution were identified using the dense regression attention maps method. The SNPs important for the SNpP and TSW traits were found in 34 and 49 genes, respectively. Genomic prediction with a constructed model can help breeding programs harness genotypic and phenotypic diversity to more effectively produce varieties with a desired phenotype. [ABSTRACT FROM AUTHOR]

Specific leaf area (SLA) and leaf dry matter content (LDMC) are key leaf functional traits often used to reflect plant resource utilization strategies and predict plant responses to environmental changes. In general, grassland plants at different elevations exhibit varying survival strategies. However, it remains unclear how grassland plants adapt to changes in elevation and their driving factors. To address this issue, we utilized SLA and LDMC data of grassland plants from 223 study sites at different elevations in China, along with climate and soil data, to investigate variations in resource utilization strategies of grassland plants along different elevational gradients and their dominant influencing factors employing linear mixed-effects models, variance partitioning method, piecewise Structural Equation Modeling, etc. The results show that with increasing elevation, SLA significantly decreases, and LDMC significantly increases (P < 0.001). This indicates different resource utilization strategies of grassland plants across elevation gradients, transitioning from a "faster investment-return" at lower elevations to a "slower investment-return" at higher elevations. Across different elevation gradients, climatic factors are the main factors affecting grassland plant resource utilization strategies, with soil nutrient factors also playing a nonnegligible coordinating role. Among these, mean annual precipitation and hottest month mean temperature are key climatic factors influencing SLA of grassland plants, explaining 28.94% and 23.88% of SLA variation, respectively. The key factors affecting LDMC of grassland plants are mainly hottest month mean temperature and soil phosphorus content, with relative importance of 24.24% and 20.27%, respectively. Additionally, the direct effect of elevation on grassland plant resource utilization strategies is greater than its indirect effect (through influencing climatic and soil nutrient factors). These findings emphasize the substantive impact of elevation on grassland plant resource utilization strategies and have important ecological value for grassland management and protection under global change. [ABSTRACT FROM AUTHOR]

Rubber plantations are an important component of tropical forest ecosystems and are emerging as crucial contributors to carbon sequestration in the tropics. However, ecosystem respiration (RECO), which constitutes an essential constraint on the carbon fixation capacity of rubber plantations, and its driving mechanisms remains unclear. Therefore, this study developed a data-driven semi-empirical model to simulate rubber plantations RECO utilising eddy covariance flux measurements and was upscaled to Hainan Island using remote sensing images and climatic data. Numerical simulations experiments analysed direct and indirect of climatic factors impacts on rubber plantations RECO. The results showed that the model accurately captured RECO trends and seasonality (R2 = 0.87, RMSE = 1.27 g C m−2 d−1); in the past 19 years, RECO showed a noticeable increase, particularly in the late rainy season and the seasonality of RECO has shown a delayed pattern. The RECO in the central region (52% area) exhibited multimodal enhancement while northern and southern regions (37% area) showed oscillations or decreases. Temporally, RECO is higher in the rainy season compared to the dry season, and spatially, RECO is higher in the southern region than in the northern region. Among the climatic factors, water conditions (rainfall and air humidity) have been emerged as dominant factors (5.38%) influencing RECO, surpassing temperature (3.96%) and radiation (3.81%). In addition, climatic factors make a positive overall contribution during the dry season but perform oppositely in the rainy season. This study offers theoretical and technical insights into high carbon sink management in rubber plantations and carbon sequestration in tropical forests ecosystems. [ABSTRACT FROM AUTHOR]

Introduction: As an exceptional geographical entity, the vegetation of the Qinghai-Tibetan Plateau (QTP) exhibits high sensitivity to climate change. The Baima Snow Mountain National Nature Reserve (BNNR) is located in the southeastern sector of the QTP, serving as a transition area from sub-tropical evergreen broadleaf forest to high-mountain vegetation. However, there has been limited exploration into predicting the temporal and spatial variability of vegetation cover using anti-interference methods to address outliers in longterm historical data. Additionally, the correlation between these variables and environmental factors in natural forests with complex terrain has rarely been analyzed. Methods: This study has developed an advanced approach based on TS (Theil-Sen slope estimator) MK (Mann-Kendall test)-FVC (fractional vegetation cover) to accurately evaluate and predict the time and spatial shifts in FVC within the BNNR, utilizing the GEE (Google Earth Engine). The satellite data utilized in this paper consisted of Landsat images spanning from 1986 to2020. By integrating TS and MK methodologies to monitor and assess the FVC trend, the Hurst index was employed to forecast FVC. Furthermore, the association between FVC and topographic factors was evaluated, the partial correlation between FVC and climatic influences was analyzed at the pixel level (30×30m). Results and discussion: Here are the results of this research: (1) Overall, the FVC of the BNNR exhibits a growth trend, with the mean FVC value increasing from 59.40% in 1986 to 68.67% in 2020. (2) The results based on the TS-MK algorithm showed that the percentage of the area of the study area with an increasing and decreasing trend was 59.03% (significant increase of 28.04%) and 22.13% (significant decrease of 6.42%), respectively. The coupling of the Hurst exponent with the Theil-Sen slope estimator suggests that the majority of regions within the BNNR are projected to sustain an upward trend in FVC in the future. (3) Overlaying the outcomes of TS-MK with the terrain factors revealed that the FVC changes were notably influenced by elevation. The partial correlation analysis between climate factors and vegetation changes indicated that temperature exerts a significant influence on vegetation cover, demonstrating a high spatial correlation. [ABSTRACT FROM AUTHOR]

Severe fever with thrombocytopenia syndrome virus (SFTSV) is a tick-borne virus recognized by the World Health Organization as an emerging infectious disease of growing concern. Utilizing phylodynamic and phylogeographic methods, we have reconstructed the origin and transmission patterns of SFTSV lineages and the roles demographic, ecological, and climatic factors have played in shaping its emergence and spread throughout Asia. Environmental changes and fluctuations in tick populations, exacerbated by the widespread use of pesticides, have contributed significantly to its geographic expansion. The increased adaptability of Lineage L2 strains to the Haemaphysalis longicornis vector has facilitated the dispersal of SFTSV through Southeast Asia. Increased surveillance and proactive measures are needed to prevent further spread to Australia, Indonesia, and North America. [ABSTRACT FROM AUTHOR]

In order to investigate the impact of climatic factors on the quality of Siraitia grosvenorii fruits pollinated in different seasons and the underlying molecular mechanism involved, the differences in climate factors, quality traits, and gene expression between fruits of the main cultivar S. grosvenorii “Qingpiguo” pollinated in summer and autumn were statistically analyzed by monitoring climate factors at different developmental stages, measuring morphological changes, detecting mogroside metabolism, and analyzing gene expression using qRT-PCR. The results were as follows: (1)Compared to summer-pollinated fruits, the average temperature and effective accumulated temperature of autumn-pollinated fruits decreased significantly after 35 d. Additionally, the temperature difference between day and night increased significantly before 65 d. However, this difference was still less than that of the average temperature and effective accumulated temperature. The light intensity and air humidity remained similar. (2)The transverse diameters, longitudinal diameters, and single fruit weights of autumn-pollinated fruits increased compared to those of summer-pollinated fruits, however, these differences were not statistically significant. (3)Mogroside V and 11-O-mogroside V in autumn-pollinated fruits accumulated slowly from 55 d with a delay of about 10 d, moreover, the content of both compounds in ripe fruits decreased by 40.66% and 46.07%, respectively. (4)In autumn, the number and extent of up-regulated mogroside V synthetase genes were relatively lower, and their co-expression consistency was poorer than in summer. Furthermore, the glucosyltransferase gene SgUGT94-289-3 which was responsible for the final step in mogroside V biosynthesis, exhibited down-regulation at all time points of 55 d. In summary, the shape and size of S. grosvenorii fruits pollinated in different seasons were not significantly affected by climatic factors; however, the content of mogroside V was significantly influenced by the temperature, which potentially influences these variations in mogroside V by regulating both the co-expression consistency and expression level of mogroside V synthetase genes. The results of this study provide the theoretical reference for high-quality cultivation and genetic breeding of S. grosvenorii. [ABSTRACT FROM AUTHOR]

The stand basal area, closely related to age, site quality, and stand density, is an important factor for predicting forest growth and yield. The accurate estimation of site quality is especially a key component in the stand basal area model. We utilized sample plots with Picea asperata Mast. as the dominant species in the multi-period National Forest Inventory (NFI) dataset to establish a site index (SI) model including climate effects through the difference form of theoretical growth equations and mixed-effects models. We combined the SI calculated from the SI model, stand age, and stand density index to construct a basal area growth model for Picea asperata Mast. stands. The results show that the Korf model is the best SI base model for Picea asperata Mast. The mean temperatures in summer and winter precipitation were used as the fixed parameters to construct a nonlinear model. Ultimately, elevation, origin, and region, as random effects, were incorporated into the mixed-effects model. The coefficients (R2) of determination of the base model, the nonlinear model including climate, and the nonlinear mixed-effects model are 0.869, 0.899, and 0.921, with root-mean-square errors (RMSEs) of 1.320, 1.315, and 1.301, respectively. Among the basal area models, the Richards model has higher precision. And the basal area model including an SI incorporating climatic factors had a higher determination coefficient (R2) of 0.918 than that of the model including an SI without considering climatic effects. The mixed-effects model incorporating climatic and topographic factors shows a better fitting performance of SI, resulting in a higher precision of the basal area model. This indicates that in the development of forest growth models, both biophysical and climatic factors should be comprehensively considered. [ABSTRACT FROM AUTHOR]

This research addresses the knowledge gap regarding glacier surging detection (GSD) and their potential downstream implications for Glacier Lake Outburst Floods (GLOFs) risk. This study investigates GSD across the Karakoram region, Pakistan, focusing on 30 glaciers from 2013 to 2023. The increasing occurrence of irregular and rapid glacial surges poses significant threats to downstream communities, the potential formation of ice dam lakes, and subsequent outburst floods. High-resolution satellite imagery, DEM, temperature, and precipitation data quantified glacier velocity rate (GVR) using feature-tracking, surge detection, and correlations. Findings indicate substantial movement ≥42 m/y during 2015–19 and 2021–22, peaking at 56.98 m/y in 2018–19; with significant Z-Scores, surges were recorded in KG-01, KG-06, KG-07, KG-19, KG-29, and KG-30. The region witnessed significant elevation changes, with a positive change at the terminus of KG-01, KG-06, KG-07, and KG-29 and a negative at the glacier’s accumulation zones. Furthermore, climatic factors, including rising average temperatures (17°C to 20°C) and precipitation (14 to 30 mm/y), significantly influenced glacier velocity, affecting glaciers by 20-43% and 38-59%, respectively. These findings underscore the pivotal role of surge velocities in shaping glacier outlets and influencing lake drainage, potentially leading to GLOFs.

The Region of Central Macedonia (RCM) in Northern Greece recorded the highest number of human West Nile virus (WNV) infections in Greece, despite considerable local mosquito control actions. We examined spatial patterns and associations of mosquito levels, infected mosquito levels, and WNV human cases (WNVhc) across the municipalities of this region over the period 2010–2023 and linked it with climatic characteristics. We combined novel entomological and available epidemiological and climate data for the RCM, aggregated at the municipality level and used Local and Global Moran's I index to assess spatial associations of mosquito levels, infected mosquito levels, and WNVhc. We identified areas with strong interdependencies between adjacent municipalities in the Western part of the region. Furthermore, we employed a Generalized Linear Mixed Model to first, identify the factors driving the observed levels of mosquitoes, infected mosquitoes and WNVhc and second, estimate the influence of climatic features on the observed levels. This modeling approach indicates a strong dependence of the mosquito levels on the temperatures in winter and spring and the total precipitation in early spring, while virus circulation relies on the temperatures of late spring and summer. Our findings highlight the significant influence of climatic factors on mosquito populations (∼60 % explained variance) and the incidence of WNV human cases (∼40 % explained variance), while the unexplained ∼40 % of the variance suggests that targeted interventions and enhanced surveillance in identified hot-spots can enhance public health response.

Porang (Amorphophallus muelleri Blume) is native to the forests of Timor-Leste, with cultivation starting in 2017. This study examined the morphological characteristics and nutrient content of porang from six gardens across Timor-Leste, assessing relationships with climatic factors. Various morphological traits and climate variables, including elevation, were measured. Nutrient analysis of porang tubers utilized chromatographic and mass spectrometric techniques, while principal component analysis helped identify links between morphology, nutrient content, and abiotic factors. Despite some qualitative similarities, such as colour and leaf patterns, significant differences were noted in measurable traits. Findings indicated that environmental conditions significantly influenced porang growth. For instance, higher air humidity in the Bobonaro District positively affected plant height and canopy width. In contrast, elevated temperatures and soil conductivity in the Liquiça District influenced leaf venation. The Aileu District exhibited the highest bulbil abundance, while larger tubers were found in the Ermera District, which is at a higher elevation. The Manufahi District had rich organic carbon content, and porang in Manatuto, with higher soil pH, produced more bulbils. However, drought conditions in Manatuto led to moisture loss and partial tuber decay. The highest glucomannan concentration was found in tubers from Bobonaro (10.25%), followed by Liquiça (9.87%) and Ermera (9.81%). Overall, environmental factors like elevation, soil temperature, and humidity significantly impacted porang characteristics and yield.

Climate can shape plant genetic diversity and genetic structure, and genetic diversity and genetic structure can reflect the adaptation of plants to climate change. We used rbcl and trnL-trnF sequences to analyze the genetic diversity and genetic structure of C. squarrosa under the influence of different environmental factors in Inner Mongolia grassland. The results showed that the genetic diversity of this species was low. (The trnL-trnF sequences have higher genetic diversity than rbcl sequences.) C. squarrosa had low genetic diversity compared to other prairie plants, but had a more pronounced genetic structure. The haplotype network diagram of the combined sequences could be divided into two categories, and the results of the NJ, MP, and ML trees also showed that the haplotypes were divided into two branches. The results of genetic structure analysis showed that that the populations located in the desert steppe fall into exactly one cluster, and the populations located in the typical steppe fall into exactly another cluster. The neutrality tests were all negative and the mismatch distribution also showed a single peak across the population, suggesting that C. squarrosa had undergone population expansion and was well adapted to the local environment. The results of the mantel test showed that climate had a greater influence on the genetic distance of C. squarrosa, with annual precipitation having a higher influence than mean annual temperature. This study provided basic genetic information on the genetic structure of C. squarrosa and contributes to the study of genetic adaptation mechanisms in grassland plants.

The study of spatiotemporal variation and driving forces of the normalized difference vegetation index (NDVI) is conducive to regional ecosystem protection and natural resource management. Based on the 1982–2022 GIMMS NDVI data and 26 influencing variables, by using the Theil-Sen median slope analysis, Mann-Kendall (M − K) test method and GeoDetector model, we analyzed the spatial and temporal characteristics of vegetation cover and the driving factors of its spatial differentiation in the northern foothills of the Yinshan Mountains in Inner Mongolia. The NDVI showed a significantly increasing trend during 1982–2022, with a growth rate of 0.0091 per decade. It is further predicted that future change in NDVI will continue the 1982–2022 trend, and sustainable improvement will dominate in the future; however, 17.69 % of vegetation will degrade, that is, NDVI will degrade instead of improvement. The spatial distribution of the NDVI in the northern foothills of the study area was generally characterized by high in the east and low in the west. Annual precipitation (Pre), evapotranspiration (Evp), relative humidity (Rhu) and sunshine hours (Ssd) had >70 % explanatory power (73.5, 79.9, 79.0, and 74.9 %, respectively). The explanatory power of edaphic factors was >30 %, whereas anthropogenic and topographic factors had little influence on the spatial variation of NDVI, with an explanatory power of

Study region: The Amur River Basin (ARB), a transboundary river basin comprising four sub-regions: the Northeast Plain of China, Mongolian Plateau, Siberian Federal region and Far East Federal region. Study focus: This research presents a comprehensive analysis on the lake dynamics in both lake area and number across the entire ARB from 1999 to 2021, using multi-source remote sensing datasets. The driving mechanisms are analyzed, considering the gradients in climatic conditions and varying levels of anthropogenic activities in different sub-regions. New hydrological insights for the region: The study found that both the total area and number of lakes in ARB decreased before 2009, whereafter increased significantly until 2021. Specifically, lakes in the Mongolian Plateau shrank seriously (-46.6 km2 yr−1), while those in the Northeast Plain of China, Siberian Federal region, and Far Eastern Federal region all showed overall expanding trends. Lake changes in the entire ARB were predominantly influenced by precipitation (R2:0.54,) and soil moisture (R2:0.55). Increasing irrigation and grazing were the main forces in the Mongolian Plateau, while hydro-climatic factors played more important roles in Northeast Plain, Siberian Federal region, and Far Eastern Federal region. This study provides a comprehensive cross-regional analysis of lake area changes and driving forces, which is of great value in ensuring lake ecological safety in ARB and other regions with densely distributed lakes.

Leaf anatomical traits are susceptible to environmental changes and can reflect plant adaptation strategies to the environment. Populus euphratica plays a key role in maintaining ecosystem processes and functions in arid zones, but the variations in leaf anatomical traits of Populus euphratica and their drivers at large scales remain unclear. Here, we investigated 10 leaf anatomical traits of Populus euphratica from 12 sampling sites in the arid zone of China, and explored how they were affected by geographic, climatic, and soil factors. Our results showed that these traits differed significantly between sites (P < 0.05), which was mainly determined by climate and soil factors that together explained 41.4 % of the trait variation. The impact of climate factors is the most critical, as they can directly influence the variation in leaf anatomical traits and indirectly affect trait variation by influencing soil factors. Among them, the max temperature of the warmest month (BIO5), soil available phosphorus (SAP) and soil moisture content (SM) had the most critical effect. Besides, temperature was the major influencing factor for the ratio of spongy tissue thickness to leaf thickness (SR), palisade tissue thickness (PT), the ratio of palisade tissue thickness to spongy tissue thickness (PSR), and the ratio of palisade tissue thickness to leaf thickness (CTR), while soil nutrient was the major influencing factor for leaf thickness (LT), lower epidermis thickness (LET), and spongy tissue thickness (ST). Our results provide important insights into the response of dryland forests to climate change, and suggest that climate warming and declining soil quality may have a stronger effect on Populus euphratica.