Search Results (107)

Vegetation dynamics are complexly influenced by multiple factors such as climate, human activities, and topography. In recent years, the frequency, intensity, and diversity of human activities have increased, placing substantial pressure on the growth of vegetation. Arid and semi-arid regions are particularly sensitive to climate change, and climate change and large-scale ecological restoration have led to significant changes in the dynamic of dryland vegetation. However, few studies have explored the nonlinear relationships between these factors and vegetation dynamic. In this study, we integrated trend analysis (using the Mann–Kendall test and Theil–Sen estimation) and machine learning algorithms (XGBoost-SHAP model) based on long time-series remote sensing data from 2001 to 2020 to quantify the nonlinear response patterns and threshold effects of bioclimatic variables, topographic features, soil attributes, and anthropogenic factors on vegetation dynamic. The results revealed the following key findings: (1) The kNDVI in the study area showed an overall significant increasing trend (p < 0.01) during the observation period, of which 26.7% of the area showed a significant increase. (2) The water content index (Bio 23, 19.6%), the change in land use (15.2%), multi-year average precipitation (pre, 15.0%), population density (13.2%), and rainfall seasonality (Bio 15, 10.9%) were the key factors driving the dynamic change of vegetation, with the combined contribution of natural factors amounting to 64.3%. (3) Among the topographic factors, altitude had a more significant effect on vegetation dynamics, with higher altitude regions less likely to experience vegetation greening. Both natural and anthropogenic factors exhibited nonlinear responses and interactive effects, contributing to the observed dynamic trends. This study provides valuable insights into the driving mechanisms behind the condition of vegetation in arid and semi-arid regions of China and, by extension, in other arid regions globally. Full article

Solanum aculeatissimum is a herbaceous to semi-woody perennial plant native to the Brazilian ecosystem. It has naturalized extensively in southwestern China, posing significant threats to local biodiversity. This study systematically screened and integrated 100 distribution records from authoritative databases, including the Chinese Virtual Plant Specimen Database, the Global Biodiversity Information Facility, and Chinese Natural Museums. Additionally, 23 environmental variables were incorporated, comprising 19 bioclimatic factors from the World Climate Dataset, 3 topographic indicators, and the Human Footprint Index. The objectives of this research are as follows: (1) to simulate the plant’s current and future distribution (2050s/2070s) under CMIP6 scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5); (2) to quantify changes in the distribution range; and (3) to determine the migration trajectory using MaxEnt 3.4.4 software. The findings reveal that human pressure (contributing 79.7%) and isothermality (bioclimatic factor 3: 10.1%) are the primary driving forces shaping its distribution. The core suitable habitats are predominantly concentrated in the provinces of Yunnan, Guizhou, and Sichuan. By 2070, the distribution center shifts northeastward to Qujing City. Under the SSP5-8.5 scenario, the invasion front extends into southern Tibet, while retreat occurs in the lowlands of Honghe Prefecture. This study underscores the synergistic effects of socioeconomic development pathways and bioclimatic thresholds on invasive species’ biogeographical patterns, providing a robust predictive framework for adaptive management strategies. Full article

This study investigates the variations in the Universal Thermal Climate Index (UTCI) calculated based on meteorological data from six synoptic stations across the Central Region of Poland from 2001 to 2024, focusing on spatial and temporal trends in thermal stress. The average annual UTCI was found to be 7.7 °C, indicating “slight cold stress,” with regional differences. Higher values were recorded in the west and northwest compared to lower values in the southeast. Maximum UTCI values associated with “very strong heat stress” exceeded 40.0 °C, while minimum values denoting “extreme cold stress” occurred in eastern cities more often. A linear trend analysis revealed a general increase in UTCI values across all stations, varying from 0.6 °C to 1.8 °C per decade, and showed distinct positive trends for heat stress categories, particularly “strong heat stress.” In contrast, a decrease in “strong cold stress” was observed. Favorable bioclimatic conditions, defined as “comfort”, predominated during most months, especially from April to October, while extreme thermal conditions were infrequently recorded. This research shows significant changes in thermal comfort and stress patterns, highlighting regional disparities and the implications for public health and urban planning in response to evolving bioclimatic conditions. Full article

Exploring the relationship between the magnitude and temporal trend of the Biodiversity Intactness Index (BII) is critical to assessing current and future changes in biodiversity intactness. However, investigations into the relationship between BII magnitude and trends worldwide have been limited. Using annual BII time series data from 2000 to 2020, we assess the global spatial patterns of BII magnitude and trend, as well as their relationship. Our findings reveal four key insights: First, the global mean BII magnitude exhibits pronounced latitudinal and climatic heterogeneity, with higher values observed in less human-impacted regions. Second, biodiversity trends display contrasting trajectories between areas of differing baseline intactness—regions with initially low biodiversity (BII < 0.50) show recovery potential (−0.007 ± 0.021 decade⁻¹), while high-biodiversity areas (BII > 0.90) face accelerated declines (0.002 ± 0.012 decade⁻¹). Third, continental and climatic disparities are striking: Europe and temperate zones demonstrate stabilizing trends, whereas tropical and polar regions experience marked deterioration. Fourth, climate variables, particularly precipitation seasonality (BIO15) and mean temperature of the coldest quarter (BIO11), show strong negative correlations with the BII trend, indicating climate-linked declines while exhibiting minimal influence on baseline BII magnitude. This study has the potential to help develop more efficient sustainable practices and behaviors to mitigate biodiversity disparities and achieve sustainable development goals. Full article

Viticulture represents an important agricultural sector in Oltenia, which is one of the Romanian regions most affected by temperature increases. The main purpose of the present study was to analyze the changes in climate suitability for grapevine and wine production against this climate context in the region. Two specific bioclimatic indices were applied, namely the bioclimatic index and the oenoclimate aptitude index, both reflecting the cumulated influence of temperature, actual sunshine duration, and precipitation amounts on the grapevine during the growing season (1 April–30 September). The indices were calculated as average values for the period 1961–2020. In order to emphasize potential shifts in suitability, the mean, maximum, and minimum values were calculated for two distinct periods, 1961–1990 and 1991–2020. The results of the analysis underlined three distinct suitability changes: the area suitable for quality red wines shifting northwards (on average, about 30′ of latitude or 55.5 km), including the eastern part of the Getic Subcarpathians, which is not currently part of any winegrowing region; the emerging new areas suitable for quality white wine (the western part of the Subcarpathians); and a potentially overly hot climate developing in Southern Oltenia where grapevine varieties are currently grown. Thus, the development of adequate adaptation strategies for viticulture to climate change in the region should be considered in the near future. Full article

Climate conditions in combination with the concentration of pollutants increase the human health stress and exacerbate systemic diseases. The city of Rhodes is a desirable tourist destination that is located in a sensitive climate region of the southeastern Aegean Sea in the Mediterranean region. In this work, hourly recordings from a mobile air quality monitoring system, which is located in an urban area of Rhodes city, are employed in order to measure the concentration of regulated pollutants (${\mathrm{S}\mathrm{O}}_2,{\mathrm{N}\mathrm{O}}_2,{\mathrm{O}}_3,{\mathrm{P}\mathrm{M}}_{10} \mathrm{a}\mathrm{n}\mathrm{d} {\mathrm{P}\mathrm{M}}_{2.5})$ and meteorological factors (pressure, temperature, and relative humidity). The air quality health index (AQHI) and the discomfort index (DI) are calculated to study the impact of air quality and meteorological conditions on human health. The analysis is conducted during a hot summer period, from 29 June to 14 July 2024. During the second half of the studied period, a heatwave episode occurred that affected the bioclimatic conditions over the city. The results show that despite the fact that the concentration of pollutants is lower than the pollutant thresholds (according to Directive 2008/50/EC), the AQHI and DI conditions degrade significantly over the heatwave days. In particular, the AQHI is classified in the “Moderate” class, and the DI indicates that most of the population suffers discomfort. The AQHI and DI simultaneously increase during the days of the heat episode, showing a possible negative synergy for the health risk. Finally, both the day maximum and night minimum temperature are increased (about 0.8 and 0.6 °C, respectively) during the heatwave days as compared to the whole studied period. Full article

Thornthwaite’s water balance approach serves as a fundamental tool for assessing hydrological dynamics, particularly in regions vulnerable to aridity and water stress. This study evaluates the performance of gridded datasets in estimating Thornthwaite’s water balance attributes in Greece, leveraging climatic averages of the period 1960–1997. Ground station data from 91 meteorological sites and gridded data from the Climate Research Unit (CRU) of the University of East Anglia were utilized to assess key water balance components. The results indicate that while gridded datasets offer an alternative for regions with limited ground data, local calibration is required due to notable discrepancies. More specifically, it was found that gridded data tended to underestimate precipitation, with estimates approximately 25% lower compared to ground station data. The potential evapotranspiration (PET) estimates using gridded data were more accurate, with underestimation on the order of 10%. Moreover, the gridded data produced overestimations for all of the water balance key components including soil moisture (St), monthly changes in soil moisture (ΔSt), and actual evapotranspiration (AE) compared to the ground station data. The water surplus (S) estimates showed a significant dispersion of values when using the gridded data, particularly in regions characterized by more arid conditions. In addition, the application of gridded data led to a great increase in the aridity index (AI) values, altering the desertification classification of sites from semi-arid to sub-humid or humid categories. These findings underscore the importance of careful consideration when utilizing gridded datasets for hydrological and bioclimatic assessments, particularly in Mediterranean climate regions characterized by a complex topography and temporal climatic variability. Full article

(1) Background: The variability and trend in harvest dates of table and Pisco grapes have been scarcely studied. This can be closely influenced by bioclimatic indices since they account for the interactions between climatic factors and vine phenology. Understanding the environmental factors influencing harvest timing has become increasingly critical to perform specific viticultural practices. (2) Methods: The aim of this research was to evaluate the influence of bioclimatic indices on variability and trend of harvest date from the 2002–2003 to 2017–2018 seasons in Flame Seedless, Thompson Seedless, Muscat of Alexandria, and Moscatel Rosada growing in Northern Chile. (3) Results: The harvest date of Flame Seedless advanced significantly with an increasing Growing Season Temperature (GST) (from 1 October to 31 December), while Thompson Seedless showed a significant advancement in harvest date with rising the Maximum Springtime Temperature Summation SON_max (from 1 September to 30 November) values. Similarly, the harvest date of Muscat of Alexandria was significantly earlier with higher Heliothermal Index (HI) (from 1 July to 31 January and from 1 August to 30 April) values, whereas Moscatel Rosada exhibited a significant advancement in harvest date as the GST (from 1 July to 31 December and from 1 July to 31 January) increased. The trend in the harvest date of Thompson Seedless was statistically significant, reaching a coefficient of determination of 0.42. (4) Conclusions: Understanding the influence of bioclimatic indices on harvest date in long-term periods is critical in the context of climatic variability since producers can make more informed decisions to optimize grape quality and maintain sustainability in production systems. Full article

Climate change is driving the restructuring of global biological communities. As a species sensitive to climate change, studying the response of small rodents to climate change is helpful to indirectly understand the changes in ecology and biodiversity in a certain region. Here, we use the MaxEnt (maximum entropy) model to predict the distribution patterns, main influencing factors, and range changes of various small rodents in the Ordos desert steppe in China under different climate change scenarios in the future (2050s: average for 2041–2060). The results show that when the parameters are FC = LQHPT, and RM = 4, the MaxEnt model is optimal and AUC = 0.833. We found that NDVI (normalized difference vegetation index), Bio 12 (annual precipitation), and TOC (total organic carbon) are important driving factors affecting the suitability of the small rodent habitat distribution in the region. At the same time, the main influencing factors were also different for different rodent species. We selected 4 dominant species for analysis and found that, under the situation of future climate warming, the high-suitability habitat area of Allactaga sibirica and Phodopus roborovskii will decrease, while that of Meriones meridianus and Meriones unguiculatus will increase. Our research results suggest that local governments should take early preventive measures, strengthen species protection, and respond to ecological challenges brought about by climate change promptly. Full article

In Portugal, maize is a major crop, occupying about 40% of the cereals area. The present study aimed to assess future bioclimatic conditions that could affect maize production in Portugal. For this purpose, a set of indicators was selected including dry spells (DSs) and the aridity index (AI). Two additional indicators were included, one related to the soil water reservoir available for maize (RAW) and the other related to the maize thermal unit (MTU), which were designed to assess the suitability of land for growing different varieties of maize. The analysis focused on historical (1971–2000) and future (2011–2070; 2041–2070; 2071–2100) climate scenarios (RCP4.5 and RCP8.5) using a four-member ensemble of global climate models. The results for the more distant and severe scenario suggest that there will be an overall increasing tendency in the AI, i.e., higher aridity, namely in the southern part of Portugal compared to the north (0.65 vs. 0.45). The soils in the south are characterized by a lower average RAW (<35 mm) than in the north (>50 mm), which leads to a lower irrigation frequency requirement in the north. As a result of the increased MTU, maize production will shift, allowing for varieties with higher thermal requirements and the conversion of areas traditionally used for silage maize to grain maize production areas. Adaptation measures to improve the climate resilience of maize are discussed. Full article

This article explores the impacts of climate change on the rural and natural landscapes in the region of Eastern Macedonia and Thrace, northeastern Greece. The spatial distributions of the bioclimatic de Martonne Index and the phytoclimatic Emberger Index were calculated at a very high resolution (~500 m) for present conditions (1970–2000), two future time periods (2030–2060; 2070–2100), and two greenhouse gas concentration scenarios (RCP4.5; RCP8.5). The results show significant bioclimatic changes, especially in the Rhodope Mountain range and along almost the whole length of the Greek–Bulgarian border, where forests of high ecosystem value are located, together with the rural areas along the Evros river valley, as well as in the coastal zone of the Aegean Sea. The article describes the processes of bioclimatic changes that can significantly modify the study area’s landscapes. The study area reveals a shift toward xerothermic environments over time, with significant bioclimatic changes projected under the extreme RCP8.5 scenario. By 2100, de Martonne projections indicate that around 40% of agricultural areas in the eastern, southern, and western regions will face Mediterranean and semi-humid conditions, requiring supplemental irrigation for sustainability. The Emberger Index predicts that approximately 42% of natural and agricultural landscapes will experience sub-humid conditions with mild or cool winters. In comparison, 5% will face drier humid/sub-humid, warm winter conditions. These foreseen futures propose initial interpretations for key landscape conservation, natural capital, and ecosystem services management. Full article

Milk yield and its composition show individual variation due to the effects of the environment. Previous studies suggest that meteorological variables exert negative effects on milk yield and composition, especially during summer. This study aimed to examine the effects of meteorological variables on bulk milk composition in the Sardinian sheep production system. In this work, a total of 218,170 records belonging to 4562 dairy sheep farms were merged with the meteorological data provided by 60 meteorological stations located on Sardinia Island (Italy). Milk composition in the late spring and summer recorded during a 5-year period was used to evaluate the impact of climate exposure on bulk milk traits. The milk quality was analyzed using a linear mixed model that included the fixed effects of the year of sampling, the flock size, the temperature humidity index (THI) and the random effect of the flock. The variability of milk composition explained by flock and management ranged from 30 to 64%. The flock size exerted a significant effect on milk composition: large flocks characterized by advanced management and feeding techniques resulted in higher milk quality (e.g., higher protein and fat, lower lactose) compared to traditionally managed small flocks. The impact of THI on milk composition was statistically significant across different milk quality traits (p < 0.001); the effect of thermal stress varied according to the month of lactation. For instance, milk fat content in May increased by +0.4% for THI > 76. In June, no relevant differences were observed, whereas a decrease in fat percentage was observed in July as THI values increased (up to −0.5% for THI > 76). While somatic cell counts remained relatively stable across different conditions, total bacterial count showed greater seasonal variability, peaking during warmer periods. In addition, using factor analysis, we developed a multivariate meteorological index (MMI), which explained 51% of the variance of the original meteorological data. MMI was highly correlated with THI (r = 0.75). The same linear mixed model applied for modeling THI was used to assess the effect of MMI on milk traits. Fat, protein fractions and lactose showed significant variation across MMI classes (p-value < 0.001) in the same direction as those based on THI. Overall, our findings underscore the impact of both flock size and environmental conditions on milk quality, with heat stress and traditional versus modern management practices leading to measurable differences in milk traits. Full article

The management of thermal environments in animal production facilities presents significant challenges, requiring continuous adjustments to meet animals’ physiological needs. This study evaluated the effects of green roofs on the thermal environment and comfort indices in small-scale poultry house prototypes, comparing facilities with and without green roof installations. The research tested various roof types (ceramic, fiber cement, and metal) combined with emerald grass (Zoysia japonica) green roof systems. Parameters measured included air temperature, relative humidity, internal roof surface temperature, Temperature and Humidity Index (THI), Black Globe Humidity Index (BGHI), Human Comfort Index (HCI), and Thermal Radiation Load (TRL) under both open and closed conditions. Results showed that green roofs reduced indoor air temperature by up to 2.4 °C in open prototypes and 10.6 °C in closed prototypes during peak heat periods. In combinations using green roofs with fiber cement tiles, internal roof surface temperature decreased by 24.0 °C in open prototypes and 27.0 °C in closed configurations. The implementation of green roofs resulted in THI reductions of 2.3 and 8.1 units in open and closed prototypes, respectively, BGHI decreases of 2.8 and 11.3 units, and TRL reductions of 21.0 W/m² and 74.0 W/m². HCI measurements confirmed improved thermal comfort conditions with green roof installations in both settings. This study concludes that green roofs effectively enhance the thermal environment by reducing bioclimatic indices during hot periods while maintaining stable conditions during cooler weather, thereby improving overall thermal comfort in animal facilities. Full article

The study explores the integration of thermo and ombroclimatic bioindicators into sustainable agricultural practices, focusing on olive production in the Mediterranean. Given the increasing challenges posed by climate change, the study highlights the critical role of bioindicators in optimizing crop resilience and yield. Using data from the CHELSA model and long-term olive production records, the research identifies the impact of key climatic factors, such as temperature and precipitation, on olive yield variability. Principal component analysis (PCA) revealed that geographical location, precipitation, and thermal extremes significantly influence productivity. Furthermore, a multiple linear regression model confirmed the ombroclimatic index (Io) as a strong predictor of yield, emphasizing the necessity of balanced climatic conditions. The findings advocate for the integration of bioclimatic principles into agricultural training programs, promoting a holistic approach to crop management. Ultimately, the study underscores the need for informed decision-making and adaptive practices to ensure sustainable agricultural production under varying climatic conditions. This research provides a framework for future studies aiming to enhance crop resilience and address climate challenges, suggesting a pathway towards an innovative, sustainable agricultural model. Full article

Studying the current range of species presence is crucial for ecologists and related scientists to understand potential habitats and the influence of environmental factors on species distribution. In this study, we used species distribution modeling (SDM) to look into where the yellow-bellied gecko, also known as the northern house gecko (Hemidactylus flaviviridis Rüppell, 1835), lives in Iran. We achieved this by combining four machine learning algorithms: Random Forest (RF), the Support Vector Machine (SVM), Maximum Entropy (Maxent), and the Generalized Linear Model (GLM). We utilized 19 historical bioclimatic variables, the Digital Elevation Model (DEM), slope, aspect, and the Normalized Difference Vegetation Index (NDVI). After calculating their correlations, we selected variables for modeling with a variance inflation factor (VIF) of less than 10. The findings indicate that the variables “Precipitation of the Coldest Quarter” (BIO19) and “Mean Temperature of Wettest Quarter” (BIO8) have the most significant influence on the species’ distribution. The gecko primarily inhabits low elevations and slopes, particularly those below 400 m above sea level with slopes less than 8 degrees, primarily in southern Iran. Additionally, we found that the NDVI had a minimal impact on the distribution of the species. Therefore, we identify the provinces of Khuzestan, Bushehr, Hormozgan, and Fars, along with parts of the coastal strip of Sistan and Baluchistan, as suitable areas for the current presence of this species. Full article