Monthly Temperature Research Articles

Climate Variability and the Resilience of Smallholder Tea Production in the East Usambara Mountain Farming System, Tanzania

The complexities of climate change are well-known, and it has immediate and long-term consequences on the environment and natural resources. This global concern adversely affects sustainable development sectors and economic systems including the agricultural sector. The tea sector is among the largest export crops contributing to earnings and revenues. Its production has been declining, and the reasons behind this decline remain unclear. This study proposed to explore the influences of climate variability on the current productivity trend of smallholder tea farmers in Tanzania. The study involves quantitative data analysis, collected from 1990 to 2020 including monthly precipitation, temperature statistics, and Greenleaf production gathered as secondary data from the Tea Research Institute of Tanzania and the Tea Board of Tanzania. The study found that rainfall and temperature change significantly influence smallholder farmers' tea production in the East Usambara Mountains. The optimal climate elements (temperature and precipitation) positively improve smallholder farmers' tea production. In contrast, extreme climate variability triggers negative effects on tea production when precautions and adaptation measures are not considered. To summarize, it is recommended that farmers engage in different programs and training sessions to enhance their perception and adaptability to climate change.

A Dynamically Consistent ENsemble of Temperature at the Earth surface since 1850 from the DCENT dataset

Accurate historical records of Earth’s surface temperatures are central to climate research and policy development. Widely-used estimates based on instrumental measurements from land and sea are, however, not fully consistent at either global or regional scales. To address these challenges, we develop the Dynamically Consistent ENsemble of Temperature (DCENT), a 200-member ensemble of monthly surface temperature anomalies relative to the 1982–2014 climatology. Each DCENT member starts from 1850 and has a 5° × 5° resolution. DCENT leverages several updated or recently-developed approaches of data homogenization and bias adjustments: an optimized pairwise homogenization algorithm for identifying breakpoints in land surface air temperature records, a physics-informed inter-comparison method to adjust systematic offsets in sea-surface temperatures recorded by ships, and a coupled energy balance model to homogenize continental and marine records. Each approach was published individually, and this paper describes a combined approach and its application in developing a gridded analysis. A notable difference of DCENT relative to existing temperature estimates is a cooler baseline for 1850–1900 that implies greater historical warming.

Relationship of litterfall anomalies with climatic anomalies in a mangrove swamp of the Yucatan Peninsula, Mexico.

Among the set of phenological traits featuring mangrove ecosystems, litterfall production stands out with marked intra-annual and longer-term variation. Furthermore, mangrove forests resilience is one of the most important ecological attribute, reconciling the juxtaposed terrestrial and marine environment such transitional systems occupy. However, world's mangroves are nowadays facing recurrent climatic events, reflected in anomalies depicted by major drivers, including temperature and precipitation. This physical-environmental setting may either constrain or favor overall forest productivity. A combination of time series analysis (spectral density and cross-correlation techniques) and statistical model fitting (General additive model) was implemented to explore trends in total litterfall of a well-developed mangrove forest in southeastern Gulf of Mexico (Celestun Lagoon, SE Mexico) and potential association with the varying behavior of temperature (°C) and precipitation (mm month-1), highlighting their anomalies. The results are consistent with a synchronous response between litterfall production and climatic variables (mean monthly temperature and total monthly precipitation). Concurrent peak litterfall production in Celestun lagoon with high temperatures and precipitation occurred during June and October, featuring a two-month time lag for the response time. More than half of the litterfall anomalies (53.5%) could be reflecting either multiple sources of climatic anomalies (maximum, minimum, and monthly average temperature and monthly total precipitation) or single point events (cyclone landfall). This relationship dynamics showed an interannual persistence (1999-2010). The structure portrayed by the litterfall time-series was not unequivocally related to climatic anomalies. Arguably, climatic anomalies behave with different intensities and even may exhibit complex interactions among them. The study of anomalies provides a baseline for a better grasp of: i) mangrove anomalies responses and ii) their vulnerability to these extremes.

Artificial Neural Networks for Drought Forecasting in the Central Region of the State of Zacatecas, Mexico

Drought is, among natural hazards, one of the most harmful to humanity. The forecasting of droughts is essential to reduce their impact on the economy, agriculture, tourism and water resource systems. In this study, drought forecast in the central region of the state of Zacatecas, a semi-arid region of Mexico, is explored by means of artificial neural networks (ANNs), forecasting numerical values of three drought indices—the standardized precipitation index (SPI), the standardized precipitation and evapotranspiration index (SPEI) and the reconnaissance drought index (RDI)—in an effort to establish the most suitable index for drought forecasting with ANNs in semi-arid regions. Records of 52 years of monthly precipitation and temperature were used. The indices were calculated in three different time scales: 3, 6 and 12 months. The analyzed models showed great capacity to forecast the values of the three drought indices, and it was found that for the trial set, the RDI was the drought index that was best fitted by the models, with the evaluation metrics R2 (determination coefficient), RMSE (root mean square error), MAE (mean absolute error) and MBE (Mean Bias Error) showing ranges of 0.834–0.988, 0.099–0.402, 0.072–0.343 and 0.017–0.095, respectively. For the validation set, the evaluation metrics were slightly better.

Predicting Forest Fire Area Growth Rate Using an Ensemble Algorithm

Due to its unique geographical and climatic conditions, the Liangshan Prefecture region is highly prone to large fires. There is an urgent need to study the growth rate of fire-burned areas to fill the research gap in this region. To address this issue, this study uses the Grey Wolf Optimizer (GWO) algorithm to optimize the hyperparameters in the eXtreme Gradient Boosting (XGBoost) model, constructing a GWO-XGBoost model. Finally, the optimized ensemble model (GWO-XGBoost) is used to create a fire growth rate warning map for the Liangshan Prefecture in Sichuan Province, China, filling the research gap in forest fire studies in this area. This study comprehensively selects factors such as monthly climate, monthly vegetation, terrain, and socio–economic aspects and incorporates monthly reanalysis data from forest fire assessment systems in Canada, the United States, and Australia as features to construct the forest fire dataset. After collinearity tests to filter redundant features and Pearson correlation analysis to explore features related to the burned area growth rate, the Synthetic Minority Oversampling Technique (SMOTE) is used to oversample the positive class samples. The GWO algorithm is used to optimize the hyperparameters in the XGBoost model, constructing the GWO-XGBoost model, which is then compared with XGBoost, Random Forest (RF), and Logistic Regression (LR) models. Model evaluation results showed that the GWO-XGBoost model, with an AUC value of 0.8927, is the best-performing model. Using the SHapley Additive exPlanations (SHAP) value analysis method to quantify the contribution of each influencing factor indicates that the Ignition Component (IC) value from the United States National Fire Danger Rating System contributes the most, followed by the average monthly temperature and the population density. The growth rate warning map results indicate that the southern part of the study area is the key fire prevention area.

Influence of meteorological variables on the development in plantation of Tachigali vulgaris L.G. Silva & H.C. Lima (tachi-branco)

The objective of this work was to evaluate the influence of meteorological variables on height and diameter increment in a Tachigali vulgaris plantation fertilized with NPK. The meteorological data were recorded by the agroclimatological station of Embrapa Amazônia Oriental, located in Igarapé-Açu, in the period from March 2016 to June 2018. The monitoring of the increment rate in height and diameter of the plants was done. The data collected were: monthly temperature, average maximum temperature, average minimum temperature andprecipitation. The maximum quarterly periodic increment in height occurred in the June-September 2017 quarter, while the highest diameter increment occurred in the June-September 2016 quarter. Principal component analysis showed that the first three factors together explained 94% of the total variance of the species’ growth, where the unfolding analysis of the first two principal components showed that the variables that contributed most to explain the species’ stand development correlated with the climate measures observed in this study, were mean and maximum temperatures and precipitation, followed by height, diameter and minimum temperature. It was concluded that the weather variables positively influenced the increment in height and diameter of plants Tachi, with precipitation being the variable with the greatest contribution to predicting tree growth.

The Potential Habitat Response of Cyclobalanopsis gilva to Climate Change.

Cyclobalanopsis gilva, a valuable timber species in China, holds significant importance for understanding the constraints imposed by climate change on the dynamic geographic distribution of tree species. This study utilized the MaxEnt maximum entropy model to reconstruct the migratory dynamics of C. gilva geographical distribution since the Last Glacial Maximum. The objective was to comprehend the restrictive mechanisms of environmental factors on its potential geographical distribution, aiming to provide insights for mid-to-long-term afforestation planning of C. gilva. The optimized MaxEnt model exhibited a significantly high predictive accuracy, with an average AUC value of 0.949 ± 0.004 for the modern suitable habitat model of C. gilva. The total suitable habitat area for C. gilva in contemporary times was 143.05 × 104 km2, with a highly suitable habitat area of 3.14 × 104 km2. The contemporary suitable habitat was primarily located in the southeastern regions of China, while the highly suitable habitat was concentrated in eastern Fujian and central-eastern Taiwan. Bioclimatic variables such as mean diurnal range (Bio2), min temperature of coldest month (Bio6), precipitation of driest quarter (Bio17), and precipitation of driest month (Bio14) predominantly influenced the modern geographic distribution pattern of C. gilva, with temperature factors playing a leading role. With global climate warming, there is a risk of fragmentation or even loss of suitable habitat for C. gilva by 2050 and 2090. Therefore, the findings of this study can significantly contribute to initiating a habitat conservation campaign for this species.

Unveiling Wheat’s Future Amidst Climate Change in the Central Ethiopia Region

Quantifying how climatic change affects wheat production, and accurately predicting its potential distributions in the face of future climate, are highly important for ensuring food security in Ethiopia. This study leverages advanced machine learning algorithms including Random Forest, Maxent, Boosted Regression Tree, and Generalised Linear Model alongside an ensemble approach to accurately predict shifts in wheat habitat suitability in the Central Ethiopia Region over the upcoming decades. An extensive dataset consisting of 19 bioclimatic variables (Bio1–Bio19), elevation, solar radiation, and topographic positioning index was refined by excluding collinear predictors to increase model accuracy. The analysis revealed that the precipitation of the wettest month, minimum temperature of the coldest month, temperature seasonality, and precipitation of the coldest quarter are the most influential factors, which collectively account for a significant proportion of habitat suitability changes. The future projections revealed that up to 100% of the regions currently classified as moderately or highly suitable for wheat could become unsuitable by 2050, 2070, and 2090, illustrating a dramatic potential decline in wheat production. Generally, the future of wheat cultivation will depend heavily on developing varieties that can thrive under altered conditions; thus, immediate and informed action is needed to safeguard the food security of the region.

The seasonal distribution of immune thrombotic thrombocytopenic purpura is influenced by geography: Epidemiologic findings from a multi-center analysis of 719 disease episodes.

Prior studies have suggested that immune thrombotic thrombocytopenic purpura (iTTP) may display seasonal variation; however, methodologic limitations and sample sizes have diminished the ability to perform a rigorous assessment. This 5-year retrospective study assessed the epidemiology of iTTP and determined whether it displays a seasonal pattern. Patients with both initial and relapsed iTTP (defined as a disintegrin and metalloprotease with thrombospondin type motifs 13activity <10%) from 24 tertiary centers in Australia, Canada, France, Greece, Italy, Spain, and the US were included. Seasons were defined as: Northern Hemisphere-winter (December-February); spring (March-May); summer (June-August); autumn (September-November) and Southern Hemisphere-winter (June-August); spring (September-November); summer (December-February); autumn (March-May). Additional outcomes included the mean temperature in months with and without an iTTP episode at each site. A total of 583 patients experienced 719 iTTP episodes. The observed proportion of iTTP episodes during the winter was significantly greater than expected if equally distributed across seasons (28.5%, 205/719, 25.3%-31.9%; p = .03). Distance from the equator and mean temperature deviation both positively correlated with the proportion of iTTP episodes during winter. Acute iTTP episodes were associated with the winter season and colder temperatures, with a second peak during summer. Occurrence during winter was most pronounced at sites further from the equator and/or with greater annual temperature deviations. Understanding the etiologies underlying seasonal patterns of disease may assist in discovery and development of future preventative therapies and inform models for resource utilization.

Addressing the Urgent Need for Direct Climate Cooling: Rationale and Options

Abstract Emissions reduction and removal are not proceeding at a pace that will limit global average warming to less than the Paris Agreement targets of 1.5 °C or 2.0 °C. Accelerating global warming is indicated by record high 2023-24 monthly temperatures and annual 2023 global mean surface temperatures around 1.5 °C above pre-industrial levels. Only direct climate cooling has the potential to avert continued temperature rise in the near term and moderate at least some projected climate change disruption including extreme weather, sea level rise, loss of sea ice, glacier and permafrost melting, and coral reef die-off. Strategically deployed at scale, starting in the near term, several cooling measures have the potential to reduce or reverse global warming. Others can exert local or regional cooling influences. The world needs an approach to climate change that extends beyond sole reliance on emission reductions and removal. We propose a) researching, field testing and deploying one or more large-scale cooling influence(s) perhaps initially in polar regions and applying local and regional cooling measures that also support adaptation, b) accelerating emissions reductions with an early prioritization of short-lived climate drivers, and c) deploying large scale carbon removal to draw down legacy greenhouse gas. The authors make no attempt to determine what measures or mix of measures is optimal. That will depend on modeling and experimentation. Only by including properly researched emergency cooling “tourniquets,” in the near-term to our “bleeding” Earth can we slow and then reverse ongoing and increasingly severe climate change in the 21st Century.

Tracking the Dynamics and Uncertainties of Soil Organic Carbon in Agricultural Soils Based on a Novel Robust Meta-Model Framework Using Multisource Data

Monitoring and estimating spatially resolved changes in soil organic carbon (SOC) stocks are necessary for supporting national and international policies aimed at assisting land degradation neutrality and climate change mitigation, improving soil fertility and food production, maintaining water quality, and enhancing renewable energy and ecosystem services. In this work, we report on the development and application of a data-driven, quantile regression machine learning model to estimate and predict annual SOC stocks at plow depth under the variability of climate. The model enables the analysis of SOC content levels and respective probabilities of their occurrence as a function of exogenous parameters such as monthly temperature and precipitation and endogenous, decision-dependent parameters, which can be altered by land use practices. The estimated quantiles and their trends indicate the uncertainty ranges and the respective likelihoods of plausible SOC content. The model can be used as a reduced-form scenario generator of stochastic SOC scenarios. It can be integrated as a submodel in Integrated Assessment models with detailed land use sectors such as GLOBIOM to analyze costs and find optimal land management practices to sequester SOC and fulfill food–water–energy–-environmental NEXUS security goals.

Modeling the richness and spatial distribution of the wild relatives of Iranian pears (Pyrus L.) for conservation management

The preservation of the genetic resources of crop wild relatives (CWRs) is crucial for food production systems and is considered a vital measure for global agricultural health and food security. The identification of potential areas where CWRs can thrive is one of the first steps towards their conservation. In this study, we used a maximum entropy model (MaxEnt) to determine the habitat suitability of seven wild relatives of pears (Pyrus L.) for the first time. We aimed to identify high-priority areas for conservation and determine the hotspots for rich biodiversity in Iran. The study showed excellent predictive performance for all species studied (AUC value ≥ 90). The soil depth, solar radiation, minimum temperature of the coldest month (Bio6), and precipitation of the wettest quarter (Bio16) were the main environmental factors that influenced the habitat suitability of all seven species, according to permutation importance. The projected maps revealed that P. elaeagnifolia had the largest suitable habitat area, while P. glabra had the lowest. The results also showed that less than 5% of the suitable habitats for these seven species were in protected areas. This research highlights the need for national preservation policies and the development of cultivation and rehabilitation strategies for these threatened species.

Factors associated with the spatiotemporal distribution of dog rabies in Tunisia.

Despite continuous efforts of veterinary services to control rabies in dogs since 1982, rabies remains a cause of death in Tunisia, with more than five reported human cases in 2022. As little is known on the determinants of transmission of rabies in dogs, better understand which factors contribute to its spatial heterogeneity in Tunisia is critical for developing bespoke mitigation activities. In this context, we developed Bayesian Poisson mixed-effect spatio-temporal model upon all cases of rabid dogs reported in each delegation during the period from 2019 to 2021. The best fitting model highlighted the association between the risk of rabies and the mean average monthly temperature, the density of markets and the density of dogs in delegations. Interestingly, no relationship was found between intensity of vaccination in dogs and the risk of rabies. Our results provided insights into the spatio-temporal dynamics of dog rabies transmission and highlighted specific geographic locations where the risk of infection was high despite correction for associated explanatory variables. Such an improved understanding represent key information to design bespoke, cost-efficient, rabies prevention and control strategies to support veterinary services activities and policymaking.

The health benefits of reducing micro-heat islands: A 22-year analysis of the impact of urban temperature reduction on heat-related illnesses in California's major cities

This study investigates the relationship between temporal changes in temperatures characterizing local urban heat islands (UHIs) and heat-related illnesses (HRIs) in seven major cities of California. UHIs, which are a phenomenon that arises in the presence of impervious surfaces or the lack of green spaces exacerbate the effects of extreme heat events, can be measured longitudinally using satellite products. The two objectives of this study were: (1) to identify temperature trends in local temperatures to characterize UHIs across zip code tabulation areas (ZCTAs) in the seven observed cities over a 22-year period and (2) to use propensity score and inverse probability weighting to achieve exchangeability between different types of ZCTAs and assess the difference in hospital admissions recorded as HRIs attributable to temporal changes in UHIs. We use monthly land surface temperature data derived from MODIS Terra imagery from the summer months (June–September) from 2000 to 2022. We categorized ZCTAs (into three groups) based on their monthly land surface temperature trends. Of the 216 ZCTAs included in this study, the summertime land surface temperature trends of 43 decreased, while 161 remained unchanged, and 12 increased. Los Angeles had the greatest number of decreased ZCTAs, San Diego and San Jose had the highest number of increased ZCTAs. To analyze the number of monthly HRI attributable to changes in UHI, we used inverse probability of treatment weighting to analyze the difference in HRI between the years of 2006 and 2017 which were two major extreme heat events over the entire State. We observed an average reduction of 3.2 (95 % CI: 0.5; 5.9) HRIs per month and per ZCTAs in decreased neighborhoods as compared to unchanged. This study emphasizes the importance of urban climate adaptation strategies to mitigate the intensity and prevalence of UHIs to reduce health risks related to heat.

Predicting potential habitat distribution of the invasive species Rhynchophorus ferrugineus Olivier in China based on MaxEnt modelling technique and future climate change.

Changes in the distribution of species due to global climate change have a critically significant impact on the increase in the spread of invasive species. An in-depth study of the distribution patterns of invasive species and the factors influencing them can help to better predict and combat invasive alien species. Rhynchophorus ferrugineus Olivier is an invasive species that primarily harms plants of Trachycarpus H. Wendl. The pest invades trees in three main ways: by laying eggs and incubating them in the crown of the plant, on roots at the surface and at the base of the trunk or petiole. Most of the plants in the genus Trachycarpus are taller, and the damage is concentrated in the middle and upper parts of the plant, making control more difficult. In this paper, we combine 19 bioclimatic variables based on the MaxEnt model to project the current and future distributions of R. ferrugineus under three typical emission scenarios (2.6 W m-2 (SSP1-2.6), 4.5 W m-2 (SSP2-4.5) and 8.5 W m-2 (SSP5-8.5)) in the 2050s and 2090s. Among the 19 bioclimatic variables, five variables were screened out by contribution rates, namely annual mean temperature (BIO 1), precipitation of driest quarter (BIO 17), minimum temperature of coldest month (BIO 6), mean diurnal range (BIO 2) and precipitation of wettest quarter (BIO 16). These five variables are key environmental variables that influence habitat suitability for R. ferrugineus and are representative in reflecting its potential habitat. The results showed that R. ferrugineus is now widely distributed in the southeastern coastal area of China (high suitability zone), concentrating in the provinces of Hainan, Guangdong, Fujian, Guangxi and Taiwan. In the future, the area of high and low suitability zones will increase and the area of medium suitability zones will decrease. The area of low suitability zone will still be in the largest proportion. This study aims to provide a theoretical reference for the future control of R. ferrugineus from the perspective of geographic distribution.

Modeling the current and future habitat suitability of Neltuma pallida in the dry forest of northern Peru under climate change scenarios to 2100.

The development of anthropic activities and climate change effects impact worldwide species' ecosystems and habitats. Habitats' adequate prediction can be an important tool to assess current and future trends. In addition, it allows strategies development for their conservation. The Neltuma pallida of the forest region in northern Peru, although very significant, has experienced a decline in recent years. The objective of this research is to evaluate the current and future distribution and conservation status of N. pallida in the Peruvian dry forest under climate change (Location: Republic of Peru). A total of 132 forest presence records and 10 variables (bioclimatic, topographic, and soil) were processed and selected to obtain the current and future distribution for 2100, using Google Earth Engine (GEE), RStudio, and MaxEnt. The area under the curve values fell within the range of 0.93-0.95, demonstrating a strong predictive capability for both present and future potential habitats. The findings indicated that the likely range of habitats for N. pallida was shaped by factors such as the average temperature of wettest quarter, maximum temperature of warmest month, elevation, rainfall, and precipitation of driest month. The main suitable areas were in the central regions of the geographical departments of Tumbes, Piura, and Lambayeque, as well as in the northern part of La Libertad. It is critical to determine the habitat suitability of plant species for conservation managers since this information stimulates the development of policies that favor sustainable use programs. In addition, these results can contribute significantly to identify new areas for designing strategies for populations conserving and recovering with an ecological restoration approach.

Phleum pratense-pollen adaptive variations and pollen microbiome investigation under different climatic regions and prospects of allergenicity.

Phleum pratense is an allergenic grass that pollinates in spring in Pakistan. Databases Allergenonline.org and Allergen.org record ten P. pratense allergens and their isoforms. Phl P 1, Phlp 5, and Phl p 11 are major P. pratense-pollen allergens with demonstrated basophil activity and skin test reactivity. Little is known about P. pratense pollen adaptive variations in different climatic regions and pollen-associated microbial diversity. In this study, we collected P. pratense-pollen and soils in the spring season 2022. Samples were collected from three climatic regions in Pakistan (R1, R2 and R3) with differences in mean monthly air temperature, mean monthly precipitation and elevation. The morphology of pollen was observed by light microscopy, scanning electron microscopy (SEM), biochemical fingerprint analysis, and composition of pollen were investigated by fourier-transform infrared spectroscopy (FTIR). The pollen-associated bacterial populations were identified through a Biolog GEN III microplate system. The pollen water-soluble proteins were isolated and stabilized in phosphate buffer saline (PBS) and tested for allergenicity responses through dot blots and western blots analysis. The morphology study found difference in pollen biochemical composition. Biolog identified Brevibacterium epidermidis and Pantoea agglomerans from P. pratense pollen. Protein extract quantification and sodium dodecyl sulfate-poly acrylamide gel electrophoresis (SDS-PAGE) gel found decreased protein expression in R1 region pollen compared to R2 and R3 region pollen. Allergenicity studies found differential expression of beta-expansin and profilin allergens in pollen obtained from the three regions. Beta-expansin and profilin were suppressed in R1 pollen and expressed in compared to R2 and R3 pollen. This is the first study to identify B. epidermidis and P. agglomerans growth on P. pratense pollen. Variable allergen expression in P. pratense pollen has also been observed in different regions. Soil pH, an increase in mean monthly temperature and a decrease in mean monthly precipitation correlated with pollen biochemical composition, and reduced beta-expansin and profilin expression involved in pollen growth and development. The findings of this research are unique, which enhances basic knowledge and understanding of P. pratense-pollen associated microbiota and climate change impacts on the pollen allergen expression.

Влияние абиотических факторов весенне-летнего периода на формирование хозяйственно ценных признаков у сортов черешни (Сerasus avium L.) в условиях юга России

Abstraсt. This paper shows a variation in the yield of 11 studied sweet cherry varieties from a minimum value of 5 kg/tree in 2020 (Dar izobilya variety) to a maximum of 65.0 kg per tree in 2021 (Kavkazskaya variety), which was more dependent on the weather conditions of the growing season. In this regard, the aim of the research is to study the biological features of the formation of indicators of yield and quality of fruits of Russian and introduced varieties of sweet cherries, and related weather and climatic factors. By means of statistical evaluation, the coefficient of variation was established, which was high for all varieties – from 63.1 % to 97.3 %, which indicates strong fluctuations in yield during the study period (2019-2023). The indicator "fruit weight" is a varietal feature, therefore it does not vary much by year of observation within each variety. Thus, the coefficient of variation in the studied varieties ranged from 2.7 % (Sashenka variety) to 15.8 % (Kavkazskaya variety). By varieties, this indicator varied quite strongly from the lowest value in 2022 of 4.3 g (Kavkazskaya variety) to the highest 10.3 g in 2022 (Krupnoplodnaya variety). According to the results of the study, the varieties that are least affected by climatic factors were identified, which had an average yield of 31.5-35.2 kg per tree over 5 years of study, subject to the almost complete absence of fruits in 2020 these include – Alaya, Volshebnitsa and Dar Isobiliay. A correlation has been established between the studied breeding-valuable indicators and various abiotic factors influencing their formation. The yield index had a high correlation with the average annual air temperature R2 = 0.60 and the amount of precipitation in April – R 2 = 0.60. The fruit weight indicator was most dependent on the average monthly temperature in June, in this case the correlation coefficient was – R 2 = 0.80, as well as the amount of precipitation for the year – R 2 = 0.54. Keywords: variety, sweet cherry, abiotic factors, yield, productivity, fruit weight. Financing source: The research was carried out within the State Task of Ministry of Science and Higher Education of the Russian Federation for the FSBSI NCFSCHVW.

Trend analysis of air temperature in a megacity between two continents: the synoptic weather station in İstanbul Atatürk Airport

İstanbul is the largest city located in the Mediterranean Basin and has a medium to high risk of climate change and future climate risks. Changes in temperature and other weather variables have had significant impacts on İstanbul. In this context, there is a need for studies on the issues of climate monitoring and climate change vulnerability to reduce the adverse impacts. The aim of this study is to investigate the temperature trends of synoptic weather station in İstanbul Atatürk Airport between 1973 and 2023 to have a general idea about how the temperature has changed over the last half-century and to establish statistically whether a trend is significant or not. The values of minimum (Tmin), maximum (Tmax) and mean (Tmean) temperature related parameters were estimated. Annual, monthly and seasonal temperature trends are also analyzed. The findings of this study indicate a significant (p < 0.001) rise in the mean air temperature (Tmean) of İstanbul over the past 51 years (1973–2023), with an annual warming trend of 0.06 °C. The strongest increasing trend in seasonal mean air temperatures has been observed in the summer season, with an increase of 0.08 °C per year. The trend analysis also shows a statistically non-significant increase in yearly average minimum temperature (Tmin) between 1973 and 2023, with a rate of 0.04 °C per year. However, the annual maximum temperature (Tmax) has shown no changes.

Genetic differentiation across a steep and narrow environmental gradient: Quantitative genetic and genomic insights into Lake Superior populations of Quercus rubra.

Adaptive differentiation of traits and underlying loci can occur at a small geographical scale if natural selection is stronger than countervailing gene flow and drift. We investigated this hypothesis using coupled quantitative genetic and genomic approaches for a wind-pollinated tree species, Quercus rubra, along the steep, narrow gradient of the Lake Superior coast that encompasses four USDA Hardiness Zones within 100 km. For the quantitative genetic component of this study, we examined phenotypic differentiation among eight populations in a common garden, measuring seed mass, germination, height, stem diameter, leaf number, specific leaf area and survival. For the genomic component, we quantified genetic differentiation for 26 populations from the same region using RAD-seq. Because hybridisation with Quercus ellipsoidalis occurs in other parts of the species' range, we included two populations of this congener for comparison. In the common garden study, we found a strong signal of population differentiation that was significantly associated with at least one climate factor for nine of 10 measured traits. In contrast, we found no evidence of genomic differentiation among populations based on FST or any other measures. However, both distance-based and genotype-environment association analyses identified loci showing the signature of selection, with one locus in common across five analyses. This locus was associated with the minimum temperature of the coldest month, a factor that defines the climate zones and was also significant in the common garden analyses. In addition, we documented introgression from Q. ellipsoidalis into Q. rubra, with rates of introgression correlated with the climate gradient. In sum, this study reveals signatures of selection at the quantitative trait and genomic level consistent with climate adaptation, a pattern that is more often documented at a much broader geographical scale, especially in long-lived wind-pollinated species.