Climatic Conditions Research Articles

Some first observations on ant-nest morphology and micromorphology, the effects of wildfires, and their implications for the understanding of archaeological features

Ants are among the soil mesofauna that cause significant bioturbation at the location of their nests. They can have significant impact on the preservation of soil features and on post-depositional artefact distribution. Moreover, there is discussion on the natural or anthropogenic nature of so-called ‘pit-hearth’ features dating to the Mesolithic. Such features are common in parts of the Netherlands, NW Belgium, and NW Germany, and form an important body of evidence in the study of hunter-gatherer landscape use. However, it has been hypothesized that these features represent ant nests burnt due to climate-related wildfires, instead of anthropogenic pit hearths, and are therefore of little archaeological value, other than as a potential proxy for climate conditions. Considering the lack of direct evidence on underground characteristics of ant nests, we investigated the characteristics of two wood ant nests: Oone abandoned and one accidently burnt in a wildfire c. 10 years earlier. We trenched through the ant nest remains and used micromorphology to study the characteristics of the ant-influenced soil profiles. The surface domes of the ant nests had disappeared, leaving behind a bowl-shaped depression. In the burnt ant nest, a thin band of charred litter-like organic material was all that was left over from the burned superstructure. These depressions seem not to reach deeper than the top of cemented podzol Bhs-horizons. Surrounding and underneath the depression, extensive networks of tunnels and chambers were found in the B- and C-horizons. If archaeological artefacts had been present on the site, they would have become buried and moved to the top of the B-horizon. Any soil features would have been destroyed. Moreover, the extensive tunnel and chamber network extending from the depression would potentially affect the botanical record and the OSL signal of the deposits.Comparison of our results with Mesolithic pit-hearth features shows substantial differences between the two types of remains. Ant nest depressions do not extend into (cemented) podzol B horizons, whereas Mesolithic pit hearths typically have their base in the C horizons. The extensive tunnelling in B- and C-horizons of large ant nests has not been observed in Mesolithic pit-hearth features. On the other hand, the large amount of charred humus and charcoal fragments in Mesolithic pit hearths are lacking in the burned ant nest we studied. We therefore conclude that Mesolithic pit-hearth features are not the result of the burning down of ant nests, but should be regarded as anthropogenic features.

Machine Learning Approaches for Short-Term Photovoltaic Power Forecasting

A photovoltaic (PV) power forecasting prediction is a crucial stage to utilize the stability, quality, and management of a hybrid power grid due to its dependency on weather conditions. In this paper, a short-term PV forecasting prediction model based on actual operational data collected from the PV experimental prototype installed at the engineering college of Misan University in Iraq is designed using various machine learning techniques. The collected data are initially classified into three diverse groups of atmosphere conditions—sunny, cloudy, and rainy meteorological cases—for various seasons. The data are taken for 3 min intervals to monitor the swift variations in PV power generation caused by atmospheric changes such as cloud movement or sudden changes in sunlight intensity. Then, an artificial neural network (ANN) technique is used based on the gray wolf optimization (GWO) and genetic algorithm (GA) as learning methods to enhance the prediction of PV energy by optimizing the number of hidden layers and neurons of the ANN model. The Python approach is used to design the forecasting prediction models based on four fitness functions: R2, MAE, RMSE, and MSE. The results suggest that the ANN model based on the GA algorithm accommodates the most accurate PV generation pattern in three different climatic condition tests, outperforming the conventional ANN and GWO-ANN forecasting models, as evidenced by the highest Pearson correlation coefficient values of 0.9574, 0.9347, and 0.8965 under sunny, cloudy, and rainy conditions, respectively.

A new transferable deep learning approach for crop mapping

ABSTRACT Accurate crop mapping provides important information for government decision-making and agricultural management. Recent advancements in deep learning have significantly enhanced the capabilities of remote sensing-based crop mapping. In this study, we developed a new deep learning approach, DSH, which comprises three modules, DeepLabV3+ (D), channel self-attention (S), and histogram matching (H). The DeepLabV3+ module learns the spatial distribution of crops in the spatial and spectral dimensions. The channel self-attention module was used to enhance the weights of the important features. The histogram-matching module addresses the domain gap between the training and testing images and enhances the transferability of the DSH. In addition, the input data of the DSH are monthly synthesized Sentinel-2 data, thus relaxing the data collection requirements. The performance of the DSH was evaluated and benchmarked against HRNetV2, DeepLabV3+, and random forest (RF) at eight sites in the U.S. China, and France with different farming structures, climate conditions, and landscape complexities. Temporal transfer revealed that the classification performance of DSH improved as the growth season progressed, peaked in the peak growth season (August), and then declined at each study site. The DSH model trained during the peak growth season demonstrated superior temporal transferability. Spatial transfer experiments demonstrated that transferring DSH to sites with similar planting structures achieved higher precision than full spatial transfers. Spatiotemporal transfer experiments conducted at eight sites over three years (2020–2022) demonstrated an average overall accuracy (OA) of 88.1% for DSH, highlighting the importance of similar planting structures for effective spatiotemporal transfer. DSH outperformed HRNetV2, DeepLabV3+, and RF in OA, producer’s accuracy, and user’s accuracy at all eight study sites. Notably, DSH exhibited comparable performance during both the peak and full growth seasons, with an average OA of 93.9%. The classification accuracy of test images after histogram matching remained stable from 2020 to 2022, whereas the accuracy of raw test images fluctuated annually. This indicates that histogram matching is effective in improving the transferability of DSH. Embedding the channel self-attention module into the position of the high-level features had a more significant impact on improving DSH’s performance than other configurations. Visualizing features from the channel self-attention and high-level layers revealed that integrating the channel self-attention module with DeepLabV3+‘s high-level features significantly improved crop mapping by enhancing relevant semantic information. Overall, DSH exhibits robust spatiotemporal generalizability and requires minimal input data, making it suitable for large-scale crop mapping.

Trends in Greenhouse Production Technology with Special Reference to Protected Cultivation of Horticultural Crops

Greenhouse is an important application scenario of modern agriculture. With the improvement of people's life quality, the demand for vegetables, fruits, flowers and has also increased, and the amount of greenhouses has developed rapidly. Greenhouse adopts artificial technical means to change natural light and temperature conditions, and to create environmental factors to optimize the growth of animals and plants, so that greenhouse crops can grow throughout the year and the weather. Compared with traditional planting methods, the greenhouse is more automated, intelligent, and mechanized. Greenhouse farming (Protected cultivation) refers to the practice of growing crops in an enclosed or partially enclosed environment, such as greenhouses, shade houses, or high tunnels. The primary purpose of protected cultivation is to create a controlled microclimate that shields the crops from adverse weather conditions, pests, and diseases, while optimizing environmental factors. It involves the use of structures, materials, and technologies that provide a protective barrier and allow for manipulation of temperature, humidity, light, and other environmental variables. The benefits of greenhouses are high production per unit area because the genetic potential of the crop can be completely exploited, vegetables and nurseries can be grown off-season that obtain high market prices, good quality products and it is easy to protect the crops from pests, diseases and extreme climatic conditions. Greenhouse farming is a highly intensive industry that needs considerable labour and capital inputs. Because of this, all factors important for a successful enterprisers should be carefully considered by potential growers.

Analysis and Prediction of Bird Migration Using RFA and GBR

The project "Analysis and Prediction of Bird Migration Using RFA and GBR" utilizes an extensive eBird dataset with approximately 100,000 observations to analyze and forecast bird migration patterns. The primary goals involved data collection, preprocessing, and feature extraction, followed by the progression of predictive models Gradient Boosting in use and Random Forest algorithms. Data preprocessing encompassed cleaning, normalization, and encoding to ensure the dataset’s quality and relevance. Through rigorous training and validation, our models demonstrated high accuracy and precision. The resulting analysis uncovered notable migration patterns and seasonal trends that correspond to known bird behaviors. These findings highlight the capabilities of artificial intelligence in deepening our knowledge of bird migration and contributing to effective conservation efforts. It also highlights the value of incorporating additional environmental data to further refine predictive models, offering a robust framework for analyzing and predicting migration patterns and other ecological phenomena. Furthermore, the project demonstrated the critical role of machine learning in environmental science, demonstrating how sophisticated algorithms can uncover complex ecological connections previously challenging to identify. The findings from This project could aid policymakers and conservationists in developing targeted conservation efforts, ensuring better protection of migratory bird species amidst changing climatic conditions. Future work could expand on this foundation by integrating real-time data streams and leveraging more sophisticated ensemble methods to enhance the migration forecasts.

Methods of determining thresholds of climate variables in building thermal design zoning in China

Climate variable thresholds (CVTs) are essential for building thermal design zoning (BTZ). However, the CVTs in the current BTZ are no longer suitable for developing the current building design. Therefore, the calculation method of bidirectional optimization zone index division standard is established: Forward optimization to obtain the outdoor threshold value that meets the indoor temperature calculation; Reverse optimization to obtain the distribution trend of the combined form of the envelope structure under the known indoor calculated temperature and outdoor climate conditions. According to the new calculation method, the outdoor heating threshold temperatures (OHTT) are adjusted from the original 5 °C–10 °C. In addition, CVTs are updated for significant subregions, such as: 1) the CVT for the hot summer and cold winter and hot summer and warm winter zone is calculated as 10 °C; 2) the CVT for severe cold (SCZ) and cold zones (CZ) is updated to −8 °C. These results will support the development of a BTZ and provide a scientific basis for reforming the heating method in China.

Mangrove dynamics driven by Late Holocene drought at Laguna Bacalar, Mexico

Laguna Bacalar, one of Mexico's most significant near-coast lakes, remains underexplored in the context of its Late Holocene paleoecology. A 2,40 m long core was retrieved in northern Laguna Bacalar. Palynological and geochemical proxies were isolated and analyzed. Our findings reveal the intricate paleoecological dynamics and hydrodynamic shifts of this vital area in southeastern Mexico. Climate and ecological changes delineate three pivotal phases during the Late Holocene at Laguna Bacalar: (i) a period between 3600 and 3250 cal BP mirroring current climatic conditions with seasonal dry tropical forest and mangroves as the dominant vegetation and evidence of human activities indicated by the presence of fossil maize pollen, (ii) a relatively arid phase from 3250 to 2600 cal BP, influenced by heightened ENSO-driven precipitation variability and sea level rise favoring an estuarine environment at Laguna Bacalar, and (iii) a period between 2600 and 2200 cal BP, exhibiting hydrological conditions akin to contemporary conditions with wetland forest mangrove vegetation. These data ultimately underscore the region's susceptibility to regional climatic fluctuations and ecological resilience, providing a case study from which to predict future conditions and establish modern conservation policy for Laguna Bacalar and similar environments.

Deep desert aquifers as an archive for Mid- to Late Pleistocene hydroclimate: An example from the southeastern Mediterranean

Many efforts have been made to illuminate the nature of past hydroclimates in semi-arid and arid regions, where current and future shifts in water availability have enormous consequences on human subsistence. Deep desert aquifers, where groundwater is stored for prolonged periods, might serve as a direct record of major paleo-recharge events. To date, groundwater-based paleoclimate reconstructions have mainly focused on a relatively narrow timescale (up to ∼40 kyr), limited by the relatively short half-life of the widely used radiocarbon (5.73 kyr). Here we demonstrate the usage of deep regional aquifers in the arid southeastern Mediterranean as a hydroclimate archive for earlier Mid-to-Late Pleistocene epochs. State-of-the-art dating tools, primarily the 81Kr radioisotope (t1/2 = 229 kyr), were combined with other atmosphere-derived tracers to illuminate the impact of four distinguishable wetter episodes over the past 400 kyr, with differences in climatic conditions and paleo-recharge locations. Variations in stable water isotope composition suggest moisture transport from more proximal (Mediterranean) and distal (Atlantic) sources to different parts of the region at distinct times. Large variability in the computed noble gas-based recharge temperature (NGT), ranging ~15–30 °C, cannot be explained by climate variations solely, and points to different recharge pathways, including geothermal heating in the deep unsaturated zone and recharge from high-elevation (colder) regions. The obtained groundwater record complements and enhances the interpretation of other terrestrial archives in the arid region, including a contribution of valuable information regarding the moisture source origin as reflected in the deuterium-excess values, which is unattainable from the common practice analysis of calcitic cave deposits. We conclude that similar applications in other deep (hundred-m-order) regional groundwater systems (e.g., the Sahara desert aquifers) can significantly advance our understanding of long-term (up to 1 Myr) paleo-hydroclimate in arid regions, including places where no terrestrial remnants, such as cave, lake, and spring sediments, are available.

Path to Net Zero: Understanding the Building Energy Efficiency in Different Climates across Various Building Types

This study analyses the determinants of building energy efficiency in different climate zones and user types. The energy consumption of buildings in different climate zones can be affected by well-known determinants in different ways. So do the buildings with different user types. The primary aim of this study is to investigate how building energy efficiency is determined in five major climate zones and four main property types. This study uses the global building data (Points Achieved dataset) from the Leadership in Energy and Environmental Design (LEED) rating system 2010 to conduct three cross-sectional tests with logit regression models. The results confirm that the determinants of building energy efficiency are the location of the building, adoption of Building Energy Codes (BECs), climate zones, building types, regional economic development level (namely Gross National Income—GNI, Purchasing Power Parity—PPP) and population density. However, the impact of the determinants varies considerably in different climate zones or for different building types. This is the first empirical study exploring building energy efficiency and how it is determined in different climate conditions and user types. The findings are helpful for the stakeholders, such as policymakers, developers, and local authorities, when they hope to implement measures to improve building energy efficiency and the policy/regulation to boost it. Each building requires specific measures that suit its different climate zones or building types to enhance energy efficiency.

The influence of environment on invasive Carpobrotus sp. populations across genetic clusters

The study aims to explore the natural variation in the metabolome of different populations of the invasive plant Carpobrotus from different genetic clusters and geographical origins to enhance our comprehension of its involvement in the adaptation process and phenotypic diversity. The metabolomic profile of shoots was analysed in four populations from two different genetic clusters (Cluster A: Cádiz and A Lanzada; Cluster B: La Marina and Samil) and two different biogeographical regions in Spain (Atlantic: Samil and A Lanzada; Mediterranean: Cádiz and La Marina), collected in the field and subsequently grown in the greenhouse. In addition, climatic, and physiological parameters were analysed. The Mediterranean populations (Cádiz and La Marina) showed lower initial weight and length measurements in morphological parameters than the Atlantic populations. On the contrary, only root parameters showed significant differences in growth parameters among populations. The analysis of ion levels revealed a consistent pattern of higher concentrations in shoots compared to roots, with significant differences among populations, particularly in sodium (Na+) and chlorides (Cl−) levels. Regarding metabolomic analysis, clear correlations between the metabolome, genetic and climatic conditions of Carpobrotus sp.pl populations are described. Pairwise comparisons using t-tests and Principal Component Analysis (PCA) indicated that the differences in metabolomic profile between the Samil and La Marina populations, which correspond to the same genetic cluster (cluster B), were smaller than in the rest of the comparisons indicating that populations from the same genetic cluster were more similar metabolically than those from the same climatic region. The study identified key metabolites representative of each cluster, with significant differences in amino acids, organic acids, and sugars contributing to the variation among populations. Pathway analysis highlighted the impact of climatic conditions on metabolic pathways, particularly in populations from Cluster A. In conclusion, the different populations were more similar according to the genetic cluster than to the climatic region of origin when studied at the metabolomic level. Consequently, the metabolites more representative of each cluster were also identified.

Propolis: a natural compound with potential as an adjuvant in cancer therapy - a review of signaling pathways.

Propolis is a natural product used in cancer treatment, which is produced by bees via different sources. The chemical composition of Propolis is determined based on the climatic and geographical conditions, as well as harvesting time and method. This compound has been the subject of numerous investigational endeavors due to its expansive therapeutic capacity which includes antibacterial, anti-fungal, anti-inflammatory, anti-oxidant, anti-viral, and anti-cancer effects. The growing incidence rate of different cancers necessitates the need for developing novel preventive and therapeutic strategies. Chemotherapy, radiotherapy, and stem cell therapy have proved effective in cancer treatment, regardless of the adverse events associated with these modalities. Clinical application of natural compounds such as Propolis may confer promise as an adjuvant therapeutic intervention, particularly in certain subpopulations of patients that develop adverse events associated with anticancer regimens. The diverse biologically active compounds of propolis are believed to confer anti-cancer potential by modulation of critical signaling cascades such as caffeic acid phenethyl ester, Galangin, Artepillin C, Chrysin, Quercetin, Caffeic acid, Nymphaeols A and C, Frondoside A, Genistein, p-coumaric acid, and Propolin C. This review article aims to deliver a mechanistic account of anti-cancer effects of propolis and its components. Propolis can prevent angiogenesis by downregulating pathways involving Jun-N terminal kinase, ERK1/2, Akt and NF-ƘB, while counteracting metastatic progression of cancer by inhibiting Wtn2 and FAK, and MAPK and PI3K/AKT signaling pathways. Moreover, propolis or its main components show regulatory effects on cyclin D, CDK2/4/6, and their inhibitors. Additionally, propolis-induced up-regulation of p21 and p27 may result in cell cycle arrest at G2/M or G0/G1. The broad anti-apoptotic effects of propolis are mediated through upregulation of TRAIL, Bax, p53, and downregulation of the ERK1/2 signaling pathway. Considering the growing body of evidence regarding different anti-cancers effects of propolis and its active components, this natural compound could be considered an effective adjuvant therapy aimed at reducing related side effects associated with chemotherapy and radiotherapy.

Assessing the efficiency of using precision farming technology and remote monitoring of weather conditions in the activities of agricultural enterprises

The object of this study was the technology of precision agriculture and remote monitoring of weather conditions. The task to evaluate the effectiveness of using precision farming and precision irrigation technologies in the activities of agricultural enterprises under different conditions, in particular, different climatic and weather conditions, has been considered. To solve the task, a hardware-software system for smart irrigation and remote monitoring of weather conditions in the activities of agricultural enterprises during the cultivation of agricultural crops was designed and described. Results of the system's performance were analyzed in the activities of the Ukrainian agricultural company, which grew potatoes of various varieties in the Kyiv oblast (Ukraine) from 2021 to 2023. The results show that the average yield of potatoes of different varieties without irrigation for three years of observation was 29.74 t/ha, with irrigation – 48.99 t/ha, and with smart irrigation – 55.26 t/ha. At the same time, in the latter case, water, human, and financial resources were saved. The increase in yield with smart irrigation compared to yield with conventional irrigation over the three years of observation was on average 12.8 %. According to the results of the implementation of the hardware-software system for smart irrigation and remote monitoring of weather conditions in Ukraine, the effect of the possible implementation of this system by agricultural companies in the Republic of Kazakhstan was analyzed. The forecast of the average yield of potatoes for the period from 2024 to 2026 was built based on the model of linear weighted moving average, taking into account corrections in the case of using smart irrigation. Data on potato yield from 1990 to 2023 were chosen as the basis. The use of smart irrigation according to the described technology could increase the yield of potatoes of various varieties on average from 31.71 t/ha to 35.78 t/ha in comparison with the forecast values of yield without irrigation at the level of 19.25 t/ha. This confirms the need to apply the transfer of precision farming technologies to increase the yield of agricultural crops, in particular potatoes, and the productivity of agricultural companies

Investigating perforated egg crates as dynamic shading systems for open-plan offices in tropical climates

This study explores the use of perforated egg crates as a dynamic shading system to improve daylight quality by achieving better uniformity in open-plan offices. Glazed curtain walls, although a popular choice in modern open-plan offices, can cause problems especially in tropical climates, such as glare and heat particularly near the windows. As egg crates (EC) generally perform better as shading devices than plain horizontal or vertical systems, this study simulated three different combinations of perforated, solid, overlapping and non-overlapping egg crate models for an existing open-plan office building in Indonesia. Daylight measurements of the existing horizontal shading device without any egg crate (NO-EC) were first recorded. Subsequent simulations of non-overlapping perforated (EC-NP) egg crate models with different blade widths and blade rotation angles in accordance with the sun elevation angles on specified working hours were performed. Similar simulations were then performed for two combinations: other combinations of overlapping solid (EC-OS) and overlapping perforated (EC-OP) egg crate models. Finally, a full-scale site mock-up of the EC-NP and NO-EC were tested under real-life climatic conditions. Results show that EC-NP provides better uniformity than the other two combinations, which is within the range of 0.59–0.99. This is due to the deeper penetration of daylight achieved by the overlapping placement of the horizontal blades in front of vertical blades, while keeping the building envelope shaded.

How human activities affect and reduce ecological sensitivity under climate change: Case study of the Yangtze River Economic Belt, China

Ecological sensitivity (ES) shows the adaptability of ecosystem. As anthropogenic activities and climatic change intensify, they affect ES individually and interactively. Thus, clarifying the effects of anthropogenic activities and climatic change on ES, and grading them to improve them, is beneficial for improving the ecological resilience and sustainable development of the region. However, few studies focus on the effects of climate-anthropogenic interactions on ES. According to data from 130 cities in the Yangtze River Economic Belt (YREB), this study constructs index systems of ES, anthropogenic activities and climate change. And response surface methodology (RSM) is utilized to analyze the effects of factors acting alone and interacting on ES, as well as the ecological improvement paths under different climatic condition. The results manifest that: (1) From years 2001–2021, the ES of the YREB fluctuates in time, increasing by 2.2%, due to declining biological abundance and water network density. Spatially, it displays a spatial distribution of “high in the north and low in the south.” In 2021, the percentages of cities in the YREB with relatively high and high sensitivity reach nearly 50%. (2) Among the climatic and anthropogenic factors, the proportions of cities where urbanization, advanced industrial structure, precipitation and temperature factors individually significantly affected ES is 67.7%, 63.1%, 56.2% and 54.6%, respectively, suggesting that all these factors have a significant impact on ES in most of the YREB. The proportion of negative interaction effects in the significant interaction between anthropogenic and climatic factors is 78.6%, indicating that most of the climatic factors have an inverse role in the anthropogenic effect on ES. (3) Improvement paths for areas with high sensitivity (27 cities) are found by RSM. A total of 24 cities can reduce ES by harmonizing anthropogenic factors under different climatic conditions. Meanwhile, 17 cities should take targeted measures in the light of climate change. This study proposes relevant recommendations for zoning governance, which is expected to provide enlightening opinions for the field of regional ecological governance. Meanwhile, it provides ideas for the in-depth application of RSM in the ecological field.

Bayesian integrated species distribution models for hierarchical resource selection by a soaring bird

Migratory birds exhibit seasonal geographic range (hereafter, range) dynamics during the annual cycle. Few studies have examined how migratory birds select their habitats for range occupancy at the species level and space use at the individual level simultaneously. We hypothesized that environmental variables directly related to fitness components would affect the range occupancy probabilities of migrants, whereas environment variables related to movements and flights would affect the space use intensities of migrants. We built Bayesian integrated species distribution models (ISDMs) to evaluate the effects of climate conditions, wind conditions, and landcover compositions on the seasonal range dynamics of American white pelicans Pelecanus erythrorhynchos (hereafter, pelican) during summer and winter. The ISDMs estimated the summer range occupancy probabilities of pelicans with Breeding Bird Survey data, winter range occupancy probabilities with Christmas Bird Count data, and summer and winter space-use intensity rates with eBird data jointly. We evaluated the predictive performance of ISDMs using independent datasets of pelican GPS locations. Integrated species distribution models outperformed the occupancy-only models in the predictive performance of occupancy probabilities. Climate conditions had opposite effects on the range occupancy probabilities between the breeding and non-breeding grounds, whereas landcovers had relatively consistent effects on range occupancy probabilities between the seasons.

Morphological type and taxonomic diversity of arbuscular mycorrhizal fungi along an altitudinal gradient at Mount Ibuki, Japan

ABSTRACT Arbuscular mycorrhiza (AM) associations are among the most widespread interactions between plants and fungi. However, little is known about the AM distribution along altitudinal gradients in forest ecosystems, where the gradients are accompanied by shifts in climatic conditions and soil properties as well as the identity of host plant species. This study aimed to clarify AM fungal associations along an altitudinal gradient. Therefore, we determined the colonization rate and community composition of AM fungi at different elevations on Mount Ibuki, Japan. Root and soil samples were collected from Cryptomeria japonica at altitudes of 200, 500, and 800 m, Acer shirasawanum at an altitude of 1100 m, and Chrysanthemum japonicum at the summit of 1350 m. To evaluate AM fungal colonization, 15 root samples from each altitude were examined microscopically. These samples were identified by targeting V4 and V5 regions of a partial small subunit rRNA gene using Sanger sequencing. AM fungal colonization rates were significantly and positively correlated with altitude. The Arum and Paris types were observed in all plant species examined at each altitude. In total, 101 molecular operational taxonomic units belonging to two genera were identified: Glomus and Diversispora. No significant clustering among elevations was observed in the AM fungal community composition. Among environmental variables, soil temperature, and pH significantly affected AM fungal structure patterns. These results suggest that as altitude increases, plants require more tight mycorrhizal associations, which are selectively filtered by the surrounding environment.

Land suitability and human adaptation: River Siang from Sangam to Pongging, Arunachal Pradesh, India

Land suitability in mountainous regions profoundly influences human adaptation strategies, particularly in environments shaped by morphometric characteristics. This study focuses on the Siang river valley from Sangam to Pongging, Arunachal Pradesh, India, employing Land Suitability Analysis (LSA) to assess terrain viability for settlement, and orchard cultivation. Key determinants such as topographic features, drainage morphometry, and infrastructural accessibility were analyzed to develop thematic maps delineating optimal zones for different land uses. The research underscores how geomorphic attributes and accessibility influence settlement patterns and orchard suitability, with lower elevations proving favorable for settlements and orchards due to accessible topography and climatic conditions. By integrating spatial analysis techniques and assessing variables like elevation, slope, and proximity to infrastructure by euclidian distance and pareto principle (80/20 rule), this research provides a comprehensive framework for informed land use planning. Strategic planning recommendations emphasize sustainable practices and community engagement to balance development needs with environmental preservation in Arunachal Pradesh's rugged terrain. It advocates for collaborative efforts between stakeholders to optimize land utilization and foster resilient development amidst challenging environmental conditions. This initial attempt aims to raise awareness among land users, planners, research workers, and administrators to ensure proper and effective land management.

Spatio-temporal variability of soil water content across plot scales within a vineyard

Understanding spatio-temporal variability of soil water content (SWC) at different scales is of great importance for designing monitoring networks and assimilating observed data in hydrological modeling. The objective of the study was to examine spatio-temporal variability of SWC for different soil layers at different extent scales within an agricultural field. Spatio-temporal variability of SWC and the relationship between variability and mean were investigated at three extent scales for different soil layers of 0–100 cm in different years within an intensively irrigated vineyard. Nested sampling grids were used, and at each scale, there was a total of 16 points distributed regularly at the grids. The distance between any two adjacent sampling points was 25-m, 50-m, and 75-m for the three scales respectively. Overall, there was no consistent pattern regarding the change of mean, standard deviation (SD), and coefficient of variation (CV) with scales during the three years. For the surface soil layer, mean SWC was similar between scales, while SD was closely related to soil clay content at the sampling locations of each scale. For subsurface soils, the overall magnitude of variability of SWC was greater, whereas the difference in the variability between scales was lower than that of the soil texture of the corresponding soil layer. The proportional decrease of CV with increasing mean was found to be largely consistent between different years for all soil layers excluding 20–40 cm, but variable between scales and soil layers. For subsurface soil layers, the CV vs. mean relationship differed between relatively wet and dry years, indicating that factors controlling SWC dynamics for the layers below the surface layer were different depending on climatic conditions. Variance partitioning analysis showed that spatial variance accounted for at least 57 % of total variance for all cases, and the temporal variance at different scales fluctuated within a narrow range for subsurface soils for the three years. The results could benefit soil water content monitoring network design, calibration of soil moisture related parameters and assimilation of observed soil moisture data into field soil water modeling.

Fine‐scale alpine plant community assembly: Relative roles of environmental sorting, dispersal processes and species interactions

Fine‐scale alpine plant community assembly: Relative roles of environmental sorting, dispersal processes and species interactions

The potential for climate change and the resulting urban heat island effect in cities to assist the northerly migration of tree species

ABSTRACT Anthropogenic climate warming increased by approximately 1.35°C above pre-industrial levels (1850–1900) by 2021, and if it continues to increase at the current rate, such warming is expected to increase by a further 1.5°C between 2030 and 2052. As a result, many cities around the world are increasingly promoting and encouraging tree-planting initiatives to sequester carbon and mitigate this climate change. The changing climate is expected to shift the geographical distribution of tree species worldwide, and the phrase that such species need to “adapt, move or die” is gaining increased recognition. Thus, many cities have begun planting tree species from more southerly latitudes in their urban parks, streets and other municipal landscapes, particularly as many native tree species are increasingly struggling to adapt to these changes when growing in the city. The urban heat island effect in cities means that they are already ahead of their rural surroundings in terms of climate warming, and therefore they potentially offer suitable climatic conditions for tree species from more southerly latitudes to thrive. In addition, as trees can help to cool their environment, planting them in cities can slow the rate of warming, which in turn provides a better environment for native species to survive for decades to reach reproductive maturity, thus assisting regenerative expansion. This article considers the potential of tree planting in urban areas to assist the poleward migration of tree species in temperate regions. It specifically considers how the concept of the “urban heat island” that has begun to emerge in cities will provide opportunities for the establishment, growth and expansion of tree species from more southerly latitudes, as well as influencing the proposals of the recently introduced Local Nature Recovery Strategies (LNRS).