Seasonal Temperature Variations Research Articles

Impact of Oceanographic Variability on Chlorophyll-a Concentration and Sea Surface Temperature in North Maluku Waters and its Influence on Fish Abundance

The waters of North Maluku are rich in fisheries resources, which are important for the local economy and marine biodiversity. The variability of oceanographic conditions, such as chlorophyll-a and sea surface temperature, plays a significant role in determining fish abundance in this region. This study analyzes the seasonal variations of chlorophyll-a and sea surface temperature during 2021, as well as their impact on the productivity of the waters. Satellite imagery data of chlorophyll-a and sea surface temperature were analyzed using spatial interpolation. The results show seasonal fluctuations in chlorophyll-a concentration and sea surface temperature, where higher chlorophyll-a concentrations support primary productivity and fish abundance during mid-year. The highest chlorophyll-a concentration occurred in June (0.212 mg/m³) and the lowest in February (0.103 mg/m³). The negative phase of the Indian Ocean Dipole (IOD) reduces upwelling intensity, decreases nutrient availability, and impacts the decline in primary productivity. The highest sea surface temperature was recorded in November (30.99°C) and the lowest in August (29.87°C). These oceanographic variations affect the availability of phytoplankton, which is crucial in the marine food chain, and have implications for sustainable fisheries management in North Maluku. The results of this study can also serve as a reference for fish distribution or abundance modeling, such as with the maximum entropy method, to better understand the factors influencing fish distribution patterns in the region.

Poromechanical analysis of regional frozen ground responses under heat exchanger operations

The permafrost of the northern hemisphere is under threat of degradation due to increasing global temperatures resulting from climate change. Thawing permafrost exhibits lower strength and increased permeability, leading to soil instability and subsidence beneath structures. To mitigate the effects of permafrost thaw, heat extraction is utilized to cool the frozen soil bodies. Ground heat exchangers (GHE) are geo-structures that exchange heat with the ground and can be used for heat extraction. In this work, Thermo-Hydro-Mechanical modeling is employed to simulate the state of a saturated soil body during freezing and thawing in Umiujaq, Quebec, Canada. An initially frozen soil body is analyzed under seasonal temperature variations and GHE operations. The pore pressure development and ground deformation are well quantified. It is shown that GHE operations can effectively mitigate the risks associated with talik formation, with group-GHE formations being more effective in this regard. Additionally, it is demonstrated that group-GHE operations are effective in mitigating excessive thawing settlement. Furthermore, the shortcomings of single-GHE operations, such as limited radius of effect and differential settlement, can be improved by group-GHE formations through a confinement effect.

Enhancing Data Collection Time Intervals and Modeling the Structural Behavior of Bridges in Response to Temperature Variations

The impact of temperature on bridges represents one of the main long-term challenges of structural health monitoring (SHM). Temperature is an environmental variable that changes both throughout the day and between different seasons, and its variations can induce thermal loads on bridges, potentially resulting in considerable displacements and deformations. Therefore, it is essential to obtain current data on the impact of daily and seasonal temperature variations on bridge displacements. Unfortunately, the maintenance costs associated with using precise estimates of thermal loads in a bridge design are quite high. The introduction of more accessible structural monitoring services is imperative to increase the number of observed structures. Viable solutions to make SHM more efficient include minimizing the costs of equipment, sensors, data loggers, data transmission systems, or monitoring data processing software. This research aims to improve the time intervals for collecting data on external temperature variations measured on a bridge structure through a sensor-based detection system and the integration of results into a regression analysis model. The paper aims to determine the appropriate interval for capturing and transmitting the structural response influenced by temperature variations over a year and to develop a behavioral mathematical model for the concrete structural components of a monitored bridge. The structural behavior was modeled using the statistical software TableCurve 2D, v.5.01. The results indicate that extending the data collection periods from 15 min to 4 h, in a static regime, maintains the accuracy of the regression model; instead, the effects of this integration are a significant reduction in the costs of data collection, transmission, and processing. The practical implications of this study consist of improving the monitoring of the structural behavior of bridges and the prediction under thermal stress, aiding in the design of more resilient structures, and enabling the implementation of efficient maintenance strategies.

Body Temperature Regulation in Domestic Dogs After Agility Trials: The Effects of Season, Training, Body Characteristics, Age, and Genetics.

An animal's body mass is said to be indirectly related to its rate of heat loss; that is, smaller animals with higher surface area to volume tend to lose heat faster than larger animals. Thus, thermoregulation should be related to body size, however, generalizable patterns are still unclear. Domestic dogs are a diverse species of endothermic mammals, including a 44-fold difference in body size. Previous work in sedentary dogs has determined that body size and other morphological variables tend to predict the thermoregulation of exercising pet dogs. Here, we aimed to address three questions: (1) whether thermoregulatory differences in domestic dogs across seasons are dictated strictly by external environmental temperatures or if individual thermal acclimation is affected by seasonal temperature variation, even indoors; (2) whether athleticism (or training experience) affects or changes thermoregulation in dogs, as it does in humans; and (3) whether thermoregulation in domestic dogs has a genetic basis. We obtained tympanic membrane (Tear) temperatures and thermal images to measure the rate of temperature change in the eyes, mouth, and nose of athletic dogs following an indoor agility trial. Additionally, we used image analysis to determine body morphology differences. We found body mass to play a strong role in thermoregulation in winter trials (Tmouth p = 0.017, Tnose p = 0.052) but a less determinate role in summer trials. We found distinct differences in thermoregulation patterns between winter and summer. Particularly, coat morphology and length may play different roles in thermoregulation across seasons. Additionally, we found that rates of mouth temperature change differ by an interaction between environmental temperature and training experience (p = 0.044), suggesting seasonal thermoregulation patterns in dogs depend on relative athleticism. Lastly, we found important genetic predictors of temperature change rate, such as GORAB and IGF1, as well as others that exert influence over body size, mitochondrial function, or coat characteristics. These genetic markers indicate markers similar to our whole-animal physiological results. Overall, our data suggest that domestic dogs demonstrate thermal acclimation across seasons, that athleticism changes thermoregulatory patterns in domestic dogs, and that body size-related genes are associated with thermoregulation in dogs.

Relationships Between Soil Respiration and Crop Productivity in Different Crop Fields

ABSTRACT Investigating the seasonal variations in soil respiration in different crop fields and their relationships with crop productivity is crucial to understanding the key biotic controls of soil respiration. A field experiment was performed during the 2020‒2021 winter wheat‒soybean, canola‒maize and broad bean‒sweet potato growing seasons. The seasonal variations in soil respiration, soil temperature and moisture were measured. The variables that were associated with crop productivity were also determined. The results showed that crop types significantly (p < .05) affected the mean seasonal soil respiration. Most variables that were associated with crop productivity exhibited obvious seasonal variation patterns. The soil temperature, moisture and crop productivity comprehensively influenced the soil respiration, and models based on these potential controlling factors explained 45.7%‒59.2% (R 2 = 0.457‒0.592) of the variation in soil respiration in the different crop rotation fields. A model based on the mean seasonal soil temperature, moisture, leaf area index, and root carbon content explained 68.3% (R 2 = 0.683) of the variation in the mean seasonal soil respiration across the different crop fields. We demonstrated the potential of effectively characterizing the variations in soil respiration in agroecosystems using temperature, moisture and crop productivity.

The JAGUAR-DAS whole neutral atmosphere reanalysis: JAWARA

Using the Japanese Atmospheric General circulation model for the Upper Atmosphere Research-Data Assimilation System (JAGUAR-DAS), a whole neutral atmosphere reanalysis dataset (JAWARA) over about 19 years from September 2004 to December 2023 is produced. JAWARA is the first long-period reanalysis covering the height region from the surface to the lower thermosphere (~ 110 km). This wide height coverage is a notable advantage over other operational reanalysis datasets, which cover up to the middle mesosphere. Key dynamical characteristics are compared between JAWARA and two satellite observations and three other operational reanalysis datasets in their covered height regions. The seasonal variations of zonal mean temperature and zonal wind are similar between JAWARA and the datasets used for comparison. The climatologies of zonal mean temperature, zonal wind, residual-mean circulation, and E-P flux in the meridional cross section are also broadly consistent with other reanalysis datasets. The analyzed residual-mean vertical flow in the northern high latitudes in the middle atmosphere exhibits the well-known patterns of upwelling in summer and downwelling in winter. JAWARA also shows a prominent feature of strong downward propagating anomalies from the lower thermosphere to the upper stratosphere after sudden stratospheric warmings. This analysis takes full advantage of the JAWARA data, which cannot be made using satellite observations and other reanalysis datasets. This reanalysis product is expected to contribute broadly to future research on the characteristics of observed mesospheric phenomena, thermosphere–ionosphere coupling, space weather, and improvement of middle atmospheric meteorological systems, including their interannual and decadal scale variability.

Pilot-Scale Investigation of Passive Methane Oxidation System Materials Performance under Seasonal Temperature Variations

Pilot-Scale Investigation of Passive Methane Oxidation System Materials Performance under Seasonal Temperature Variations

Modeling of periodic input Ornstein-Uhlenbeck temperature-tick-borne disease transmission coupling mechanism under climate change.

Given the rapid increase in climate change, investigating the impact of climate change on the transmission mechanism of tick-borne diseases is imperative. In order to fully capture the influence of the seasonal variation of temperature, environmental disturbances and the co-feeding transmission on the spread of tick-borne diseases, we propose a novel stochastic dynamical model that couples the mean-reverting Ornstein-Uhlenbeck temperature equation with periodic input to the tick-borne disease model. Through theoretical analysis, we derive sufficient conditions for the extinction of tick populations and the eradication of tick-borne diseases, as well as the stochastic persistence conditions of the system. In numerical simulations, we find that the periodic Ornstein-Uhlenbeck temperature equation can effectively fit the actual temperature data in low, medium, and high latitude regions of China. In risk assessment, we find that at the spatial perspective, low-latitude areas have a higher risk of tick-borne diseases, requiring enhanced control measures; from a temporal perspective, compared to the past, the current stage presents a greater risk of tick-borne diseases when preventive measures are not implemented. Additionally, we observe that larger noise of environment for tick populations favors the extinction of tick populations, while smaller temperature fluctuations, noise on infected hosts and ticks, as well as higher temperature regression rate, are more likely to lead to the extinction of tick-borne diseases. These findings provide crucial insights into understanding the impact of climate change on the transmission mechanism of tick-borne diseases.

State of knowledge for thermohaline conditions on the shelf of West Kamchatka

All available data on water temperature and salinity for the shelf of West Kamchatka are analyzed and all scientific publications and the most important unpublished thesises concerning this area are summarized. Current ideas about the water masses distribution and seasonal and long-term variability of water temperature and salinity on the shelf are discussed. There is noted that the existing estimates of seasonal and long-term variability of water temperature require significant additions for recent times (last 20 years) and certain spatial areas. Insufficient understanding is revealed for spatial-temporal dynamics of salinity in the mixing zone between the river and sea waters that requires special research.

Assessing Temperature Change Impact in the Wake of Ongoing Land Use Change: A Case Study at Lake Dianshan

Climate change exerts both direct and indirect influences on the eutrophication of surface water ecosystems in various ways. This study aimed to evaluate the impact of temperature fluctuations on trophic levels through various interdisciplinary coupling analysis methods after land use change, which including water and sediment sample analysis, hydraulic model, remote sensing, and historic data analysis. For the historical analysis, six satellite images of Lake Dianshan were examined to assess algal bloom occurrences and the coverage of Eichhornia crassipes from 2013 to 2023. The correlation between trophic indicators and temperature was analyzed using statistical methods. For the monthly analysis, a total of 27 sediment samples and 54 water samples collected from Lake Dianshan were assessed to determine how seasonal temperature variations influence eutrophication status. The trophic indicators have higher concentration at inlet sampling sites compared to outlet sites, which validated the potential external pollution source. The trophic level of Lake Dianshan is influenced not only by climate change but also significantly by urban human activities. The management of eutrophication has substantially improved the water quality of Lake Dianshan over the past few decades. Furthermore, increasing temperatures demonstrate a positive correlation with the proliferation of cyanobacteria, particularly in urban areas.

The Impact of Seasonal Variations in Rainfall and Temperature on the Performance of Wastewater Treatment Plant in the Context of Environmental Protection of Lake Como, a Tourist Region in Italy

This study investigates the impact of extreme weather conditions on the performance of a wastewater treatment plant (WWTP) in Como, Italy, in terms of influent (inf) and effluent (eff) quality parameters. During winter (October–December), the average temperature was 8.76 °C ± 11.43 °C, with 7.01 mm rainfall, while summer (May–September) averaged 23.24 °C ± 6.2 °C with 5.2 mm rainfall. Despite seasonal variations, the pH levels remained consistent. Phosphorus removal by the WWTP was efficient, with the winter influent averaging 4.16 ± 5.53 mg/L, the winter effluent averaging 0.33 ± 1.06 mg/L, the summer influent averaging 3.53 ± 2.9 mg/L, and the summer effluent averaging 0.31 ± 0.75 mg/L. The COD and BOD5 levels showed seasonal trends, with a higher winter-influent COD (450.43 ± 560.56 mg/L) than in summer (410.96 ± 302 mg/L). These higher winter values of effluent may be due to lower biological activity at cooler temperatures, affecting the efficiency of organic matter breakdown and treatment. The winter influent BOD averaged 249.57 ± 220.42 mg/L, with the winter effluent being 2.95 ± 2.04 mg/L, while the summer influent BOD was 214.44 ± 345.5 mg/L and the summer effluent was 3.01 ± 7.5 mg/L. The TSSs and Total-N showed similar seasonal patterns, with there being slight decreases in the TSSs removal efficiency during warmer months. Although microplastic pollution was not directly analyzed in this study, wastewater treatment plants play a crucial role in mitigating microplastic contamination. Despite rainfall influencing the phosphorus and organic load concentrations, the studied plant maintained over 90% pollutant removal efficiency, demonstrating resilience and compliance with regulatory standards. The WWTP’s consistent COD and BOD5 reductions highlight its robust performance amid climate variations

Spatial and seasonal variations in physico-chemical parameters and the water quality index at the Okhuaihe River in Ikpe Community, Edo State, Nigeria

The scarcity of clean water in rural Nigerian communities leads to dependence on streams and rivers which are often polluted by domestic and industrial activities distorting the quality of water. Freshwater ecosystems, which are important for global biodiversity, are constantly burdened with threats from environmental changes as well as human misuse. This study was aimed at investigating the spatial and temporal variations of physico-chemical parameters and the water quality index (WQI) of the Okhuaihe River in Ikpe, Edo state, Nigeria, used for different domestic activities. The study assessed parameters like air and water temperature, pH, electrical conductivity, flow rate, total dissolved solids, phosphate, chloride, iron, zinc, and manganese at four stations over a six-month period. Samples such as air and water temperature and pH were collected and measured in situ, while others were collected using polyethylene bottles and a 250 ml amber bottle for biochemical oxygen demand (BOD5). Results indicated that while some physico-chemical parameters like the width, the flow rate, and phosphate showed considerable differences, most did not differ significantly. Temporally, there were significant seasonal variations in air temperature, electrical conductivity, total dissolved solids, chloride, iron, zinc, and manganese. The WQI calculations showed that the water quality at all stations was within safe limits for human consumption and supported aquatic life. Despite some fluctuations due to seasonal changes and anthropogenic activities, the overall quality remained suitable for domestic use. This underscores the importance of constant monitoring and management of water resources to allay pollution and ensure sustainable water supply in rural communities.

Simulation and Prediction of Thermokarst Lake Surface Temperature Changes on the Qinghai–Tibet Plateau

Thermokarst lakes are shallow bodies of freshwater that develop in permafrost regions, and they are an essential focus of international permafrost research. However, research regarding the mechanisms driving temperature fluctuations in thermokarst lakes and the factors that influence these changes is limited. We aimed to analyze seasonal variations in the surface water temperature, clarify historical trends in the phenological characteristics of lake ice, and predict future temperature changes in surface water of the thermokarst lakes using the air2water model. The results indicated that in comparison with air temperature, the thermokarst lake’s surface water temperature showed a certain lag and significantly higher values in the warm season. The warming rate of the thermokarst lake’s average surface water temperature based on historical data from 1957 to 2022 was 0.21 °C per decade, with a notably higher rate in August (0.42 °C per decade) than in other months. Furthermore, the ice-covered period steadily decreased by 2.12 d per decade. Based on the Coupled Model Intercomparison Project 6 projections, by 2100, the surface water temperatures of thermokarst lakes during the warm season are projected to increase by 0.38, 0.46, and 2.82 °C (under scenarios SSP126, SSP245, and SSP585), respectively. Compared with typical tectonic lakes on the Qinghai–Tibet Plateau, thermokarst lakes have higher average surface water temperatures during ice-free periods, and they exhibit a higher warming rate (0.21 °C per decade). These results elucidate the response mechanisms of thermokarst lakes’ surface water temperature and the phenological characteristics of lake ice in response to climate change.

An Electro‐Driven Dynamic and Multicolored Radiative Thermal Regulation Material for All‐Year‐Round Building Energy Saving

AbstractRational dynamic thermal regulation of both solar and thermal regions is of significant importance for building energy saving. In this work, an electro‐driven dynamic radiative thermal regulation material (EDRTRM) capable of switching the thermal regulation capacity between the heating, white cooling, and multicolored cooling (blue, green, and yellow) modes is designed. These multi‐modes with high thermal regulation power and color variation are achieved through a synergistic material combination involving an electrochromism of Prussian blue (PB) and the electrodeposition of copper (Cu) with a polyformaldehyde (POM)‐based mid‐infrared (MIR) emitter. Optical characterization confirmed the superior spectral performance of materials, with a remarkable modulation capability of power density up to 659 W m−2, indicating an exceptional heating and cooling performance, which is evidenced by a temperature modulation difference of up to 11 °C. Notably, the EDRTRM under a multi‐colored cooling mode also achieves a temperature reduction of ≈ 4–6 °C. The EnergyPlus simulations demonstrate that the EDRTRM can save a huge amount of energy consumption of 16.56 MJ m−2 in cities with seasonal temperature variations such as Beijing. This dual‐functional design not only maximizes thermal regulation capabilities but also satisfies aesthetic requirements, showcasing its potential for widespread practical application.

Evaluation of effects on a viaduct affected by a snowmelt-induced landslide using AI-Driven Pattern Recognition

Over recent years, the worldwide increase in infrastructure failures due to natural hazards such as landslides and earthquakes has raised significant concern. Aggravated by the effects of climate change, this issue has had a considerable impact, particularly in Italy. This study focuses on the "Villa Ilii" Viaduct on the A24 highway to analyze landslide-structure interactions, emphasizing the significant deformation risks. The methodology includes comprehensive geological, morphometric, and geophysical analyses, with findings showing cumulative deformation rates up to +2.5 cm/year at critical points. Field surveys and high-resolution SAR data analysis have identified deformation patterns linked to seasonal snow melt and temperature variations, adding a quantitative dimension to the hazard assessment. A novel component of this study is the application of Gradient Boosting Machines (GBM) for automated pattern recognition within SAR data processed using the Differential Interferometry SAR (DInSAR) technique. By leveraging datasets on snow melting patterns and cumulative radar target deformation, GBMs uncover intricate, non-linear relationships between snowmelt dynamics and ground deformation. This integration of GBMs and DInSAR data advances landslide hazard assessment by providing a robust 87% accuracy predictive model, effectively capturing environmental triggers that impact infrastructure stability. These findings contribute to more effective hazard management strategies and inform resilient infrastructure design, highlighting the study's significance in addressing climate-driven geohazards.

Coldness or Darkness? Which Places Greater Stress on the Thyroid? Seasonal Changes in Thyroid-Stimulating Hormone and Thyroid Hormones.

Background: Although seasonal changes were suggested to be among the many factors that affect thyroid functions, this issue is still controversial. In this study, we aimed to investigate the possible relationship between seasonal changes and thyroid function. Methods: We retrospectively scanned all thyroid-stimulating hormone (TSH), free triiodothyronine (FT3), and free thyroxine (FT4) values checked in our hospital between 2019 and 2023. Using the big data approach, we examined the relationship between TSH and thyroid hormones and monthly and seasonal recorded climatic changes, particularly the duration of daylight and temperature. Results: A total of 195,534 serum samples were analyzed for TSH, 195,491 for FT3, and 195,487 for FT4. There were significant differences in the values of TSH, FT3, and FT4 between months (p = 0.001 for TSH, p < 0.001 for FT3 and FT4). The months with the highest levels of TSH, FT3, and FT4 values were January, December, and June, while the months with the lowest levels were July, May, and March-April, respectively. The differences between the maximum and minimum median values were 14.5% for TSH, 4.9% for FT3, and 5.7% for FT4. From January to August, as the temperature rose, there was a decrease in TSH values. Between September and December, as the temperature decreased, an increase in TSH was observed. Conclusions: This study revealed that TSH, FT3, and FT4 show seasonal variability and change in temperature is an important factor that plays role in this variability. It would be appropriate to take these changes into consideration when interpreting thyroid function tests.

Seasonal and Temporal Ensemble Models for Accurate Near-Surface Air Temperature Estimation.

The near-surface air temperature (NSAT) is crucial for understanding thermal and urban environments. Traditional estimation methods using general remote sensing images often focus on the types of spatial data or machine learning models used, neglecting the importance of seasonal and temporal variations, limiting their accuracy. This study introduces a novel ensemble model that incorporates both seasonal and temporal information integrated with satellite-derived land surface temperature (LST) data to enhance NSAT estimation, along with a rigorous feature importance analysis to identify the most impactful parameters. Data from 2022, collected from 147 South Korean weather stations, were used to develop and evaluate the models. Thirteen initial variables, including the LST and other auxiliary data, were considered. Random forest regression was employed to build separate models for each season. This novel approach of separating data by season allowed optimized feature selection tailored to each season, improving the model efficiency and capturing finer seasonal and daily temperature variations. These seasonal models were then combined to form an ensemble model. The seasonal models demonstrated varying accuracy, with the R2 values indicating a strong correlation between the predicted and actual NSAT, particularly high in spring and fall and lower in summer and winter. The ensemble model showed improved performance, achieving an MAE of 0.534, an RMSE of 0.391, an R2 of 0.996, and a cross-validated R2 of 0.968. These findings highlight the effectiveness of incorporating seasonal and temporal information into NSAT estimation models, offering significant improvements over traditional approaches. The developed models support precise temperature monitoring and forecasting, aiding environmental and urban management.

Satellite observations indicate slower recovery of woody components compared to upper-canopy and leaves in tropical rainforests after drought

The 2015–2016 El Niño-induced drought caused biomass loss in global tropical forests, yet the recovery duration of different vegetation components (woody components, upper canopies, and leaves) remains unknown. Here, we use satellite remote sensing data of vegetation optical depth and leaf area index, with varying sensitivity to different vegetation components, to examine vegetation recovery during the drought event. We find that the woody component had the slowest recovery compared to the upper canopy and leaves, and displayed greater spatial variability between continents. Key factors influencing woody recovery include drought severity, moisture-related climatic conditions (i.e., vapor pressure deficit, precipitation, and soil moisture), and seasonal variations in temperature and precipitation. Our study highlights the importance of different vegetation components for maintaining ecosystem balance under drought disturbances and indicates the need for further research to explore recovery mechanisms and the long-term impacts of drought on forest dynamics.

The Future Migration Direction of Deer and Japanese Yew Is Consistent Under Climate Change

Climate change is becoming an important driver of biodiversity loss by altering the habitat, distribution and interspecific relationships of species. Japanese yew (Taxus cuspidata) is a first class protected plant in China, which has important ecological significance and occupies a certain position in the feeding habit of wapiti (Cervus elaphus) and Siberian roe deer (Capreolus pygargus). Due to human and animal damage, the number of Japanese yew has gradually decreased. Therefore, understanding the potential distribution of Japanese yew and the suitable areas for deer to browse on it under climate change will help to further protect these three species in Northeast China, especially migrate to more suitable areas in different scenarios in the future. From July 2021 to July 2024, we collected the information of species distribution and the variables associated with the species’ ecological limits in Muling National Nature Reserve to cross-reflect the current and future distribution and feeding area of the two species to assess each other’s impacts with Maximum entropy model (MaxEnt). The results showed that under the SSP2-4.5 and SSP5-8.5 scenarios, feeding pressure, driest quarter precipitation (BIO17) and seasonal temperature variation coefficient (BIO4) were the main variables affecting the distribution of Japanese yew, and the driest quarter precipitation (BIO17) and annual precipitation (BIO12) were the main variables affecting wapiti and Siberian roe deer foraging them. Under SSP2-4.5 and SSP5-8.5 scenarios, the suitable area of Japanese yew and the feeding area of the two species of deer gradually decreased from 2041 to 2100. Compared with wapiti, Siberian roe deer has a greater impact on the distribution range of Japanese yew, and the suitable feeding area is wider. It is expected that the potential centroid of Japanese yew, wapiti and Siberian roe deer will migrate to higher latitudes in the future. These findings provide a scientific basis for the reserve to develop relevant measures and plans and effectively protect the three species.

Multi-dimensional analysis of land use/land cover and urbanization on seasonal variation of land surface temperature in İzmir, Türkiye

Multi-dimensional analysis of land use/land cover and urbanization on seasonal variation of land surface temperature in İzmir, Türkiye