Distribution Area Research Articles

Research on the Spatial Distribution Characteristics and Influencing Factors of Educational Facilities Based on POI Data: A Case Study of the Guangdong–Hong Kong–Macao Greater Bay Area

This study aims to provide a precise assessment of the distribution of educational facilities within the Guangdong–Hong Kong–Macao Greater Bay Area, serving as a crucial foundation for managing educational resource allocation and enhancing the quality of educational services. Utilizing a kernel density analysis, global autocorrelation analysis, and geographic detectors, this research systematically analyzes the spatial distribution characteristics and influencing factors of educational facilities in the area. The findings reveal significant geographical disparities in facility distribution with dense clusters in urban centers such as Guangzhou and Shenzhen, and less dense distributions in peripheral areas like Zhongshan and Macau. These facilities exhibit a multi-center cluster pattern with strong spatial autocorrelation, mainly influenced by the population size and economic and urban development levels. The results provide actionable insights for refining educational planning and resource allocation, contributing to the enhancement of educational quality across diverse urban landscapes.

From barley straw biomass to N/S co-doped as electrode material for high-performance supercapacitor applications

An innovative and cost-efficient methodology is proposed for developing porous carbon materials from barley straw biomass waste using thiourea as dopant and FeCl3 as activator. By varying precursor ratio and activation, porous carbon frameworks (BCF-Ts) with tailored porosity and N/S-doping were synthesized. BCF-Ts showed remarkable pore size distribution and high surface area responsible for enhanced electrochemical performance by enabling efficient ion transmission. The optimized N/S-doped activated carbon (BCF-T) exhibited high specific surface area of 938.7 m2 g−1. As supercapacitor electrode, BCF-T displayed exceptional performance including high specific capacitance of 496 F g−1, power density of 500.95 Wkg-1, and energy density of 42.02 Whkg−1 in KOH electrolyte. Moreover, BCF-T showed impressive cyclic stability, retaining 90.13 % capacitance after 2000 cycles. The tailored porosity, heteroatom doping, large surface area, and proper graphitization of BCF-T facilitated rapid ion diffusion, sufficient charge storage, and pseudocapacitance. This work provides valuable guidance on synthesizing high-performance porous carbon materials from biomass waste for supercapacitor applications.

The buckwheat-derived hard carbon as an anode material for sodium-ion energy storage system

Biomass-derived hard carbon (HC) has attracted much attention as a promising electrode material for sodium-ion batteries (SIBs) due to its unique properties such as high porosity, large surface area and adjustable pore size distribution. This work presents the results of electrochemical studies of HC derived from buckwheat seeds. The HCs were preoxidized at 300 °C and synthesized using a pyrolysis method in the range 600–1400 °C. It was found that the electrochemical characteristics of the HC anodes are strongly dependent on the morphology and structure. Furthermore, the positive effect of preoxidation on the porosity of HC was confirmed. Although all buckwheat-derived HC-based anodes showed stable cyclability over 100 cycles with CE of approximately 97–98 %, the preoxidized HCs pyrolyzed at 1400 °C achieved the highest reversible capacity of 330.23 mAh g−1 with excellent stability. Additional study using “single particle” measurement of HC1400 and HCox1400 showed that the preoxidation may significantly contribute to the improvement of the mass transfer.

Current and future invasion risk of tomato red spider mite under climate change.

Tomato red spider mite Tetranychus evansi Baker and Pritchard (Acari: Tetranychidae) is a phytophagous pest that causes severe damage to Solanaceous plants worldwide, resulting in significant economic losses. In this study, the maximum entropy model was used to predict the potential current (1970-2000) and future (2021-2060) global distribution of the species based on its past occurrence records and high-resolution environmental data. The results showed that the mean values of the area under the curve were all >0.96, indicating that the model performed well. The three bioclimatic variables with the highest contributions were the coldest quarterly mean temperature (bio11), coldest monthly minimum temperature (bio6), and annual precipitation (bio12). A wide range of suitable areas was found across continents except Antarctica, both currently and in the future, with a much larger distribution area in South America, Africa, and Oceania (Australia), dominated by moderately and low suitable areas. A comparison of current and future suitable areas reveals a general trend of north expansion and increasing expansion over time. This study provides information for the prevention and management of this pest mite in the future.

Caustic area biases and how to deal with them

Quadruply imaged strongly lensed point sources (quads) are routinely used for measurements of the expansion rate of the Universe with time delays. It has recently been suggested that any quad lens is subject to a Malmquist-like bias that causes the inferred area enclosed within the tangential caustic to be systematically underestimated, and that such a bias might translate into a corresponding bias on the expansion parameter. In this work we extended that analysis by also considering the effect of Eddington bias. We find that the sign and amplitude of the combined bias depend on the functional form of the caustic area distribution of the lens population and on the noise associated with the caustic area estimation process. Based on simulations, we estimate that the corresponding impact on $H_0$ is of the order of a percent or smaller. If the likelihood of the lensing data is known, then the bias can be accounted for when modelling the lens population. However, ignoring the criteria used to select a quad might lead to a bias at the lens modelling stage that causes the inferred caustic area to be overestimated. Such a bias disappears for lens models that are well constrained by the data.

SALINE MAPS OF SOME SOILS OF MUTHANNA GOVERNORATE

performed based on data collected from 15 soil samples from 0 to 20 cm deep. We used salinity of the irrigated soils of Al-Muthana governorate in southern Iraq. These maps were 20% of the world's land. This study was made in order to map the spatial distribution of soil or anthropogenic processes, which is certainly an environmental problem that affects more than environment and agricultural production. The main causes of this salinization come from natural It is important to study the problem of salinization, soil salinity has adverse effects both on the ordinary kriging (OK) to analyze the spatial variability of soil salinity, The results confirmed that the use of saline maps was supportive in giving a clear vision of the of 12.11 dS m-1, for developing probability maps to determine the distribution of risk areas. showed the different classes of salinity with the highest value of 23.17 dS m-1 and lowest value map predicted by the electrical conductivity (EC) values using the ordinary kriging (OK) method while indicator kriging (IK) was used to analyze salinity versus threshold values . The salinity salinity of the soils for the study area

Removal of a model reactive azo dye from aqueous solution by a bioadsorbent in batch and fixed-bed column modes: Application of the developed technology to a textile wastewater

Sugarcane bagasse (SB) was used to produce a new bioadsorbent (STEA), drawing on circular economy concepts. STEA was synthesized using a two-step one-pot reaction, employing epichlorohydrin and triethylamine in the presence of N,N-dimethylformamide, without the use of a petroleum-based catalyst. The structure and surface of STEA were characterized by elemental C, H, N, and Cl analysis, X-ray diffraction, infrared spectroscopy, 13C solid-state nuclear magnetic resonance spectroscopy, thermogravimetric analysis, specific surface area and pore size distribution determination, and point of zero charge measurement. Batch adsorption and desorption tests were performed with the model dye Remazol Golden Yellow (RGY) RNL, a reactive anionic azo dye widely used in textile industry, to evaluate the potential reuse and application of STEA in a fixed-bed column for wastewater treatment. For batch adsorption, the best dose and agitation speed were 0.2 g L−1 and 50 rpm, respectively. STEA effectively removed RGY over a wide range of pH (2.00–10.00). The equilibrium time, maximum adsorption capacity (Qmax), and desorption efficiency (Edes) were 720 min, 369 mg g−1 (0.71 mmol g−1), and 49.5 %, respectively. The fixed-bed column fed with a spiked aqueous RGY solution could be operated for 415 min, with Qmax of 422 mg g−1 (0.81 mmol g−1) and Edes of 58.9 %. Batch and continuous experiments using real textile industry wastewater containing reactive azo dyes showed high color removal efficiency by STEA, with no interference of other compounds present in wastewater on adsorption of the reactive azo dyes (overshooting effect). The technology was validated in a relevant environment and achieved technology readiness level 5, showing potential to be upscaled. Therefore, STEA proved to be an efficient bio-based technology for application in tertiary treatment of real textile plant wastewater to remove reactive anionic azo dyes.

Wetting and aggregation mechanisms of coal molecules via XTG and SDBS based on molecular simulation

To study the interaction process of sodium dodecyl benzene sulfonate (SDBS) and xanthan gum (XTG) molecules in a composite dust suppressant and its influence mechanisms on the wettability and agglomeration characteristics of bituminous coal dust, Material Studio software was used to establish water–XTG binary system models, water–SDBS–XTG ternary system models, and water–SDBS–XTG–coal quaternary system models. Then, molecular dynamics simulations were performed through angular distribution, relative concentration distribution, mean squared displacement (MSD), and interaction energy to study the interaction mechanism between SDBS and XTG and the change in wettability of coal dust. Results showed that the configuration in the binary system changed after stabilization, and SDBS transformed from a disordered state into a directional arrangement with the head group oriented toward the liquid phase and the tail group oriented toward the gas phase. In the ternary system, the head group of SDBS interacted with the XTG molecules, and the XTG wound into a helical structure, reducing the viscosity of the solution. With an increase in the number of SDBS molecules, the angle of the head group of SDBS and the tail chain of SDBS increased to 113.95°. The number of H bonds in the system increased from 1676 to 1697. In the quaternary system, the rate of change of the diffusion coefficient continuously increases. The absolute value of the interaction energy increased from 5920.48 kJ/mol to 9295.35 kJ/mol. The higher the concentration of SDBS, the better the adsorption effect, and the more stable the molecular configuration. The number of H bonds and the distribution area of the overlapping region between water and coal gradually increased, indicating that the larger the area where water molecules could penetrate into the coal molecules, the better the wetting effect on coal. This study reveals the intermolecular interaction process and the mechanisms of coal dust wetting and agglomeration in a composite dust suppressant. It provides theoretical support for the application of this composite dust suppressant.

MaxEnt-Based Potential Distribution Mapping and Range Shift under Future Climatic Scenarios for an Alpine Bamboo Thamnocalamus spathiflorus in Northwestern Himalayas

Thamnocalamus spathiflorus is a shrubby woody bamboo invigorating at the alpine and sub-alpine region of the northwestern Himalayas. The present investigation was conducted to map the potential distribution of Th. spathiflorus in the western Himalayas for current and future climate scenario using Ecological Niche Modelling (ENM). In total, 125 geo-coordinates were collected for the species presence from Himachal Pradesh (HP) and Uttarakhand (UK) states of India and modelled to predict the current distribution using the Maximum Entropy (MaxEnt) model, along with 13 bioclimatic variables selected after multi-collinearity test. Model output was supported with a significant value of the Area Under the “Receiver Operating Characteristics” Curve (AUC = 0.975 ± 0.019), and other confusion matrix-derived accuracy measures. The variables, namely precipitation seasonality (Bio 15), precipitation (Prec), annual temperature range (Bio 7), and altitude (Alt) showed highest level of percentage contribution (72.2%) and permutation importance (60.9%) in predicting the habitat suitability of Th. spathiflorus. The actual (1 km2 buffer zone) and predicted estimates of species cover were ~136 km2 and ~982 km2, respectively. The predicted range was extended from Chamba (HP) in the north to Pithoragarh (UK) in southeast, which further protracted to Nepal. Furthermore, the distribution modelling under future climate change scenarios (RCP 8.5) for year 2050 and 2070 showed an eastern centroidal shift with slight decline of the species area by ~16 km2 and ~46 km2, respectively. This investigation employed the Model for Interdisciplinary Research on Climate (MIROC6)–shared socio-economics pathways (SSP245) for cross-validation purposes. The model was used to determine the habitat suitability and potential distribution of Th. spathiflorus in relation to the current distribution and RCP 8.5 future scenarios for the years 2021–2040 and 2061–2080, respectively. It showed a significant decline in the distribution area of the species between year 2030 and 2070. Overall, this is the pioneer study revealing the eco-distribution prediction modelling of this important high-altitude bamboo species.

Land-cover change in Cuba and implications for the area of distribution of a specialist's host-plant.

Changes in land cover directly affect biodiversity. Here, we assessed land-cover change in Cuba in the past 35 years and analyzed how this change may affect the distribution of Omphalea plants and Urania boisduvalii moths. We analyzed the vegetation cover of the Cuban archipelago for 1985 and 2020. We used Google Earth Engine to classify two satellite image compositions into seven cover types: forest and shrubs, mangrove, soil without vegetation cover, wetlands, pine forest, agriculture, and water bodies. We considered four different areas for quantifications of land-cover change: (1) Cuban archipelago, (2) protected areas, (3) areas of potential distribution of Omphalea, and (4) areas of potential distribution of the plant within the protected areas. We found that "forest and shrubs", which is cover type in which Omphalea populations have been reported, has increased significantly in Cuba in the past 35 years, and that most of the gained forest and shrub areas were agricultural land in the past. This same pattern was observed in the areas of potential distribution of Omphalea; whereas almost all cover types were mostly stable inside the protected areas. The transformation of agricultural areas into forest and shrubs could represent an interesting opportunity for biodiversity conservation in Cuba. Other detailed studies about biodiversity composition in areas of forest and shrubs gain would greatly benefit our understanding of the value of such areas for conservation.

Habitat quality or quantity? Niche marginality across 21 plants and animals suggests differential responses between highland and lowland species to past climatic changes

Climatic changes can affect species distributions, population abundance, and evolution. Such organismal responses could be determined by the amount and quality of available habitats, which can vary independently. In this study, we assessed changes in habitat quantity and quality independently to generate explicit predictions of the species' responses to climatic changes between Last Glacial Maximum (LGM) and present day. We built ecological niche models for genetic groups within 21 reptile, mammal, and plant taxa from the Baja California peninsula inhabiting lowland or highland environments. Significant niche divergence was detected for all clades within species, along with significant differences in the niche breadth and area of distribution between northern and southern clades. We quantified habitat quantity from the distribution models, and most clades showed a reduction in distribution area towards LGM. Further, niche marginality (used as a measure of habitat quality) was higher during LGM for most clades, except for northern highland species. Our results suggest that changes in habitat quantity and quality can affect organismal responses independently. This allows the prediction of genomic signatures associated with changes in effective population size and selection pressure that could be explicitly tested from our models.

Comparing machine learning algorithms to predict vegetation fire detections in Pakistan

Vegetation fires have major impacts on the ecosystem and present a significant threat to human life. Vegetation fires consists of forest fires, cropland fires, and other vegetation fires in this study. Currently, there is a limited amount of research on the long-term prediction of vegetation fires in Pakistan. The exact effect of every factor on the frequency of vegetation fires remains unclear when using standard analysis. This research utilized the high proficiency of machine learning algorithms to combine data from several sources, including the MODIS Global Fire Atlas dataset, topographic, climatic conditions, and different vegetation types acquired between 2001 and 2022. We tested many algorithms and ultimately chose four models for formal data processing. Their selection was based on their performance metrics, such as accuracy, computational efficiency, and preliminary test results. The model’s logistic regression, a random forest, a support vector machine, and an eXtreme Gradient Boosting were used to identify and select the nine key factors of forest and cropland fires and, in the case of other vegetation, seven key factors that cause a fire in Pakistan. The findings indicated that the vegetation fire prediction models achieved prediction accuracies ranging from 78.7 to 87.5% for forest fires, 70.4 to 84.0% for cropland fires, and 66.6 to 83.1% for other vegetation. Additionally, the area under the curve (AUC) values ranged from 83.6 to 93.4% in forest fires, 72.6 to 90.6% in cropland fires, and 74.2 to 90.7% in other vegetation. The random forest model had the highest accuracy rate of 87.5% in forest fires, 84.0% in cropland fires, and 83.1% in other vegetation and also the highest AUC value of 93.4% in forest fires, 90.6% in cropland fires, and 90.7% in other vegetation, proving to be the most optimal performance model. The models provided predictive insights into specific conditions and regional susceptibilities to fire occurrences, adding significant value beyond the initial MODIS detection data. The maps generated to analyze Pakistan’s vegetation fire risk showed the geographical distribution of areas with high, moderate, and low vegetation fire risks, highlighting predictive risk assessments rather than historical fire detections.

Analysis of urban network operation in presence of renewable sources for decarbonization of energy system

In order to decarbonize the energy system, it is necessary to change all aspects of the production, transport and consumption of electricity chain. The European Union (EU) aims to become the first climate-neutral continent by 2050. To deliver on this ambition, decarbonising the energy sector is crucial because the production and use of energy accounts for more than 75 % of the EU's greenhouse gas emissions (EEA, 2021).The connection of renewable sources near the places of consumption can lead to flexibility in changing the operating diagram, the operator having the possibility to keep certain distribution cables disconnected, which diminishes the reactive power circulating through the distribution network. The article analyzed two types of distribution areas that show that in the area where consumption is high, it is easier to implement measures to transform the area into positive energy. The concept The concept of Ecs (“energy communities”) provides a great boost for overcoming resistance to infrastructure development, increasing acceptance and penetration of distributed energy resources (DERs), encouraging private investments, and establishing public-private partnerships.

Collaborative design method for multi-impeller turbomachinery based on variable sectional-area distribution

By establishing the characterization model of variable sectional area distributions, a cascade design method of torque converter based on variable sectional area distribution is proposed. Additionally, the corresponding collaborative design method for multi-impeller turbomachinery is proposed and further studied. The design cases of the impellers with different sectional area distributions are constructed through the response surface method, and the corresponding hydrodynamic characteristics are further compared and explored. However, the design of different impeller sectional area distributions for the optimal performance of the whole turbomachinery requires the collaborative design of each component’s distribution results rather than a simple combination of such optimal impeller’s area distribution result. Thus a collaborative design strategy is proposed, which can improve the pump’s capacity performance by 8.83%, the maximum torque ratio performance can also increased by 6.33%, which proves that such a collaborative design method is an effective optimization design approach to improve the performance of the torque converter without altering the overall dimension. The experimental results of the optimal prototype show that the torque ratio reaches 5.33% higher than the original value, which indicates that such a collaborative optimization method for multi-impeller turbomachinery is effective and reliable.

The effect of Inc parameter on VMAT radiotherapy plans quality for rectal cancer using Monaco TPS.

To investigate the effect of the Increment of gantry angle (Inc) parameter setting of the Monaco Treatment planning system (Monaco TPS) on the dosimetry and quality parameters of the volumetric modulated arc therapy (VMAT) program for rectal cancer. A retrospective analysis was conducted on 50 patients with rectal cancer who underwent intensity modulated radiation therapy using the Monaco TPS system from 2020 to 2021. Under the same optimization function configuration and other parameter settings, the Inc parameters in the VMAT radiotherapy plan were set to 10°, 20°, 30°, and 40°. The dose-volume histogram (DVH) was used to evaluate the dose distribution of the target area and the radiation dose of the organs at risk (OAR). The differences in the dosimetry of the planning target volume (PTV) and OAR, as well as the gamma pass rate (GPR) were compared. In terms of target dose, D98, Dmin, HI, and conformity index (CI) of Inc10 group was significantly lower than those of Inc20, 30, and 40 groups (P<0.05), and D2 of Inc10 group was significantly higher than that of Inc20 group (P=0.009). We also found CI of Inc20 and 30 were significantly better than that of Inc40 (both P<0.05). In terms of OAR dose, the study found that the Dmean, Dmin, V50%, V45%, and V40% for the bladder of the Inc10 group were lower than those of the other groups (all P<0.05), the Dmean for femoral head of the Inc20 group was lower than that of the Inc30 group (P<0.05), and Inc20 showed a better protective effect on the femoral head. The MUs tend to decrease as the Inc parameter setting is increased. The monitor unit (MU) in Inc10 group were significantly higher than those in Inc20, Inc30, and Inc40 groups, and the MU of Inc20 group was significantly higher than that of Inc40 group (both P<0.05). We found that for the 3%/3mm and 2%/2mm standards, the GPRs of each plan were>90%, which met clinical requirements. Different settings of Inc parameters have varying degrees of impact on target dose, OAR dose, and machine MU. It is important for doctors to choose different Inc parameters according to different clinical needs.

AI-assisted deep learning segmentation and quantitative analysis of X-ray microtomography data from biomass ashes

X-ray microtomography is a non-destructive method that allows for detailed three-dimensional visualisation of the internal microstructure of materials. In the context of using phosphorus-rich residual streams in combustion for further ash recycling, physical properties of ash particles can play a crucial role in ensuring effective nutrient return and sustainable practices. In previous work, parameters such as surface area, porosity, and pore size distribution, were determined for ash particles. However, the image analysis involved binary segmentation followed by time-consuming manual corrections. The current work presents a method to implement deep learning segmentation and an approach for quantitative analysis of morphology, porosity, and internal microstructure. Deep learning segmentation was applied to microtomography data. The model, with U-Net architecture, was trained using manual input and algorithm prediction.•The trained and validated deep learning model could accurately segment material (ash) and air (pores and background) for these heterogeneous particles.•Quantitative analysis was performed for the segmented data on porosity, open pore volume, pore size distribution, sphericity, particle wall thickness and specific surface area.•Material features with similar intensities but different patterns, intensity variations in the background and artefacts could not be separated by manual segmentation – this challenge was resolved using the deep learning approach.

The Influence of Climate Change on the Distribution of Hibiscus mutabilis in China: MaxEnt Model-Based Prediction.

Our study utilized 374 geographical distribution records of H. mutabilis and 19 bioclimatic factors, employing the MaxEnt model and the Geographic Information System (ArcGIS). The key environmental variables influencing the suitable distribution areas of H. mutabilis were analyzed through the comprehensive contribution rate, permutation importance, and Pearson correlation coefficient. Based on this analysis, the contemporary and future suitable distribution areas and their extents were predicted. The results indicate that the key limiting factor affecting the suitable distribution areas of H. mutabilis is the precipitation of the driest month (bio14), with secondary factors being annual precipitation (bio12), annual mean temperature (bio1), and annual temperature range (bio7). Under contemporary climate conditions, the total suitable area for H. mutabilis is approximately 2,076,600 km2, primarily concentrated in the tropical and subtropical regions of southeastern China. Under low-to-medium-emission scenarios (SSP1-2.6, SSP2-4.5), the total suitable area of H. mutabilis shows a trend of first decreasing and then increasing compared to the current scenario. In contrast, under high-emission scenarios (SSP5-8.5), it exhibits a trend of first increasing and then decreasing. The spatial pattern changes indicate that the retention rate of suitable areas for H. mutabilis ranges from 95.28% to 99.28%, with the distribution centers primarily located in Hunan and Guizhou provinces, showing an overall migration trend towards the west and north. These findings suggest that H. mutabilis possesses a certain level of adaptability to climate change. However, it is crucial to consider regional drought and sudden drought events in practical cultivation and introduction processes. The results of our study provide a scientific basis for the rational cultivation management, conservation, and utilization of germplasm resources of H. mutabilis.

Distribution of the black grouse, capercaillie, and hazel grouse (Tetraonidae) at the northern edge of their species range in the Nenets Autonomous Area

This paper presents data on the distribution of the black grouse Lyrurus tetrix (L., 1758), capercaillie Tetrao urogallus (L., 1758), and hazel grouse Tetrastes bonasia (L., 1758) in the Nenets Autonomous Area (the northeast of the European Russian Federation). The study has revealed trends indicating an expansion of the distribution range of these species to the north. According to our observations, black grouse, capercaillie, and hazel grouse in the Malozemelskaya Tundra are located further north than in the published data – about 40 km for black grouse, 20 km for capercaillie, and 20 km for hazel grouse. In the Malozemelskaya Tundra, the northernmost black grouse and capercaillie were found in spruce forest islands, while the hazel grouse inhabited riverine floodplain forests. In the Bolshezemelskaya Tundra, the capercaillie was the only grouse bird found further north compared to the published data, in the extremely northern islands of the forest within the Shapkina River valley. In the Bolshezemelskaya Tundra, new registrations of the grouse (200 km) and ruffed grouse (100 km) were noted to the east of the known data, indirectly indicating an expansion of the distribution area of these birds to the north in the regions adjacent to the Nenets Autonomous Area. It was noted that the trends of expansion of the range of distribution of black grouse to the north are unidirectional with positive trends in air temperature in the Nenets Autonomous Area and with the processes of gradual overgrowth of woody vegetation in treeless areas located between the forest tundra and northern spruce forest islands.

Genetic diversity of the critically endangered Blue‐crowned laughingthrush (Garrulax courtoisi)

AbstractTo evaluate the genetic quality and provide available management strategies for Blue‐crowned laughingthrush (BCLT), fifteen polymorphic microsatellite loci were developed and applied. The genetic diversity of wild individuals was indicated to be higher than the two captive populations. The average number of alleles (5.50 ± 0.317), the number of effective alleles (3.417 ± 0.222), observed heterozygosity (0.828 ± 0.04), and genetic differentiation index (0.028 ± 0.007) of 64 wild individuals showed high genetic diversity despite drastic bottleneck and low genetic differentiation. The number of effective migrants (22.737 ± 8.318) indicated the intriguing wintering grounds may be surrounded by the breeding sites where the syncheimadia occurred in Wuyuan. Efficient conservation, winter flocking, and cooperative breeding may facilitate gene exchange and inclusive fitness. We recommend that monitoring concentrated distribution areas for BCLT should be strengthened, and geographical barriers, interference types, and the inner mechanism of distribution patterns should be further explored.

Possible Changes in Red Pine (Pinus brutia Ten.) Distribution Areas in Kastamonu due to Global Climate Change

In this study, it was aimed to determine the current status of red pine (Pinus brutia Ten.) distribution areas in Kastamonu Forestry Regional Directorate, which is the Forestry Regional Directorate with the highest production in Türkiye, and the possible changes in suitable distribution areas due to global climate change. In the study, in addition to the current suitable distribution areas of red pine (Pinus brutia Ten.), suitable distribution areas in 2040, 2070 and 2100 according to SSP 126, SSP 370 and SSP 585 scenarios were determined. The results of the study show that there may be a loss of more than 15% (approximately 114,5 km2) in the suitable distribution areas of red pine populations in Kastamonu until 2100 due to the effects of climate change. It does not seem possible for the species to adapt to these changes without human intervention. Therefore, it is recommended that necessary adjustments should be made in forest management plans taking into account the results of the study.