Monthly Temperature Research Articles

Results of long-term studies of the annual growth of mosses of the genus <i>Sphagnum</i> L. In the central taiga of Karelia

The studies were carried out on the territory of the Kindasovo forest-bog research station of the Karelian Research Center, Russian Academy of Sciences, located in the middle taiga subzone (61°48′ N, 33°35′ E). The experiments were carried out in the Nenazvannoe natural mesoeutrophic bog of the aapa type in seven permanent bog areas located in different ecological and coenotic conditions. The paper presents long-term results (from 10 to 38 years) of studies of the dynamics of annual growth of 11 widespread hygro- and hydrophilic species of Sphagnum mosses. Based on fluctuations in the average annual growth data during the years of study, the largest increases were observed for Sphagnum riparium (140 mm) and S. obtusum (93 mm), growing in heavily watered bog areas and not withstanding drying out; medium growths of S. fallax (65 mm), S. subsecundum (57 mm), S. majus (42 mm), S. balticum (36 mm), and S. warnstorfii (28 mm) are found in low-flow watered bogs, hollows, and on carpets; the lowest growth was shown for S. divinum (17 mm), S. centrale (15 mm), S. papillosum (12 mm), and S. fuscum (8 mm), growing on ridges, low hummocks, and carpets under conditions of sharply variable conditions hydration. A correlation analysis to study the influence of precipitation and average temperature on the annual growth of Sphagnum mosses did not show any significant dependence of moss growth on temperature during the growing season. For S. fuscum, S. obtusum, and S. warnstorfii, negative dependences on the temperature of individual months are observed in different months of the growing season. Precipitation during the growing season is the main factor determining the dependence of the annual growth of Sphagnum mosses. A significant dependence of annual growth on precipitation during the growing season was shown for species S. obtusum, S. papillosum, S. centrale, S. fallax, and S. subsecundum, while the significance was weak for S. divinum, S. fuscum, and S. majus.

Alien aquatic plants in Poland: Temporal and spatial distribution patterns and the effects of climate change

Distribution of alien aquatic plants in European freshwater ecosystems is still not fully recognized. Little is also known about the impact of climatic factors on the expansion of particular neophyte species in freshwater ecosystems. In this article, we present the first comprehensive assessment of the occurrence of non-native aquatic vascular plants in Poland (CE Europe), with a particular focus on: (1) inventory of the species, (2) temporal and spatial changes in their distribution, and (3) the climate impact on species occurrence and diversity. For this purpose, we collected both published and unpublished data on alien macrophytes occurrence in Poland covering past 70 years. In order to investigate the mechanism of alien aquatic plant expansion, climate data from 24 meteorological stations were analyzed. Redundancy analysis (RDA) was used to analyze the relationship between species composition and microclimate variables. In addition, Generalized Additive Model (GAM) was used to examine the response curves of neophyte species to temperature parameters.In total, we found records of 15 neophytes of alien aquatic plant taxa in Poland, including 14 species and collective taxon of Nymphaea cultivars. The most frequent was Elodea canadensis (over 25,000 records). Excluding this species, we counted more than 300 records of the occurrence of alien aquatic plants, of which three (E. nuttallii, Azolla filiculoides and Lemna turionifera) were the most abundant. Ten species occurred only in isolated sites. In addition, analyses showed a substantial increase in the number of the aquatic neophyte sites in the last 20 years that could be linked with the raised average monthly and yearly air temperatures in Poland.Our results showed a strong relationship between raised average temperatures in Poland and the spread of some species, e.g. Azolla filiculoides, while other species such as Elodea nuttallii and Lemna turionifera expanded irrespectively of this factor. The occurrence of some species (e.g. Vallisneria spiralis, Hygrophila polysperma) was exclusively associated to high thermally altered or thermally contaminated waters, and thus was limited to only a small part of the country. Our results confirm the major role of elevated temperatures (mean annual temperatures and minimum monthly temperatures) and thermally polluted freshwater ecosystems in the distribution of alien aquatic plants.

Spontaneous Wing Tip Edema in Captive Birds of Prey: Review of 41 Cases in the United Kingdom (2004-2022).

There is limited literature regarding wing tip edema (WTE) in raptors, and much of our current understanding of the condition is based on anecdotal reports. The aims of this retrospective study were to describe the clinical features of WTE in birds of prey, to identify prognostic factors for return to flight and patient survival following diagnosis, and to develop and assess the clinical significance of a novel WTE grading system. Between 2004 and 2022, 41 cases of WTE were identified in 39 captive birds. No cases were found in wild birds. Harris's hawks (Parabuteo unicinctus), lanner falcons (Falco biarmicus), and peregrine falcons (Falco peregrinus) had the highest frequencies of WTE, and all cases presented between October and May. Increasing days of air frost per month and colder median monthly temperatures were significant risk factors for the development of WTE. Of the cases where patient outcomes were known, 23/31 (74.2%) cases returned to normal flight and 29/34 (85.3%) cases survived. End-stage disease, represented by primary flight feather loss and metacarpal ischemic (dry) gangrene, and enalapril use were associated with poor patient outcomes. Presentation within 24 hours of disease onset, isoxsuprine use, and physiotherapy were associated with improved patient outcomes. This study showed that WTE is an infrequently encountered but clinically significant condition in captive raptors and is associated with an overall high morbidity and moderate mortality risk.

Venturing Into the Unknown: The Importance of Variable Selection When Modelling Alien Species Under Non‐Analogue Climatic Conditions

ABSTRACTSpecies distribution models (SDMs) are widely used to address species' responses to bioclimatic conditions in the fields of ecology, biogeography and conservation. Among studies that have addressed reasons for model prediction variability, the impact of climatic variable selection has received limited attention and is rarely assessed in sensitivity analyses. Here, we tested the assumption that this aspect of model design is a major source of uncertainty, especially when projections are made to non‐analogue climates. As a study system, we used 142 alien plant species introduced to the sub‐Antarctic islands. Based on global occurrence data, we fitted SDMs as functions of seven bioclimatic variable sets that only differed in the identity of two temperature variables. Moreover, we calculated the overlap between the island's climatic conditions and the niches the species have realised outside of the islands. Despite comparable internal evaluation metrics, projections of these models were in sharp contrast with each other, with some models predicting the sub‐Antarctic islands' climate to be almost ubiquitously suitable to most species and others unsuitable to almost all species. In particular, the mean temperature of the warmest month led to strong underpredictions of the SDMs, while its replacement by the mean temperature of the coldest month led to massive overpredictions. Partitioning the variance in projections demonstrated that predictor identity was its most important source, followed by island and species identity. The size of area projected to be suitable was also related to the overlap in predictor values realised in the global range of species (outside of the islands) and on the islands. Our findings emphasise the importance of bioclimatic variable selection in SDMs, especially when making projections to non‐analogue climates. Such extrapolations are often required, especially when using SDMs to assess invasion risk under both current and future climates.

Can Expert Prediction Markets Forecast Climate-Related Risks?

Abstract Prediction markets use a betting-like mechanism to aggregate information from disparate sources to produce probability forecasts that represent the collective view of participants. They have similarities with financial markets but are designed specifically to discover information rather than transfer assets or risks. Contracts that pay out a fixed amount if a well-defined event occurs are traded in these markets and, under certain conditions, the price at which these event-contracts trade can be interpreted as a probability estimate that the event will occur. This article examines the performance of 24 prediction markets for climate-related variables that have been run over the past 5 years. The predicted variables included the monthly temperature and rainfall for the United Kingdom, an index of El Niño–Southern Oscillation, Atlantic hurricane activity, and U.K. wheat yield. The markets had horizons of 2–12 months. Invitations to participate in the markets were extended to individuals and teams with relevant expertise. Participants did not have to pay to take part but did receive cash rewards based on their performance. Trades were made through an automated market maker overcoming the problems of low activity that have affected previous prediction markets for specialized topics. The predictions of the markets were consistent with good reliability, given the resolving power afforded by the sample size. Whether this level of reliability would persist for longer, multiyear, horizons relevant to climate change cannot be known without running markets on such time scales, something that we strongly advocate.

Modeling spatiotemporal distribution of yellow rust wheat pathogen using machine learning algorithms: Insights from environmental assessment

The yellow rust pathogen (Puccinia striiformis Westend) poses a significant threat to wheat production in the world, necessitating a comprehensive understanding of its spatiotemporal distribution and the influence of climatic factors. In this study, we employed an ensemble of four prominent machine learning algorithms to assess the impact of various environmental and remote sensing variables on the spread of yellow rust at a national scale. Our analysis incorporated 55 climatic parameters, including monthly temperature, precipitation, solar radiation, and wind speed. The results demonstrated that the RF algorithm yielded robust predictions, with a Receiver Operator Characteristic (ROC) of 0.916 and True Skill Statistic (TSS) of 0.748. Furthermore, the study identified key influencing variables for wheat disease modeling, such as annual precipitation, temperature seasonality, and isothermality. Projections based on the model indicate a potential decrease in disease spread by 2050 in specific regions. The findings underscore the efficacy of ensemble modeling in predicting the spatiotemporal distribution of yellow rust on a large scale, offering valuable insights for the development of robust agricultural management strategies in the face of evolving climate conditions.

Modeling of ecological niches of Barbary Partridge (Alectoris barbara) under conditions of bioclimatic variability in the Fes-Meknes region (Morocco)

Abstract The Barbary Partridge (Alectoris barbara) is a bird species belonging to the Phasianidae family and is endemic to North Africa, including Morocco. Effective conservation of this species requires a thorough understanding and accurate assessment of the environmental factors that influence reproductive parameters. In this study, we used the species distribution models (SDM) to study the geographical distribution of the Barbary Partridge in Morocco. These models are tools that allow us to study the effects of climate change on the spatial distribution of species, they are based on the Maxent (maximum entropy) algorithm. These models are based on environmental and biological data and can predict the probability of a species’ occurrence in a given geographic area. They can also predict how climatic and environmental conditions will change over time and how these changes will affect the species’ distribution. The results obtained revealed that the environmental factors that have a significant influence on the distribution area of Barbary Partridge under Moroccan conditions are: annual precipitation (Bio12) with a contribution rate of 23.3%; precipitation of the driest quarter (Bio17) at 19.9%; altitude at 9.9%; minimum temperature of the coldest month (Bio 6) at 7.6%; precipitation of the driest month (Bio14) at 7.2%; and mean temperature of the wettest quarter (Bio8) at 4.4%. This information can be used to assist in long-term conservation planning by identifying areas that may become more or less suitable for the species.

Comparison of GARP and MaxEnt in Modeling Current and Future Geographic Distribution of Ceracris nigricornis Walker (Acrididae, Orthoptera) in China.

Ceracris nigricornis Walker is an insect of the Acrididae, which can harm bamboo, rice, corn, sorghum and other crops, and can cause serious economic losses. In this study, based on 234 occurrence sites of C. nigricornis obtained from the Global Biodiversity Information Facility and literature, and data of three future climate scenarios presented by CMIP6, two niche models (GARP, MaxEnt) were used to predict the suitable area of C. nigricornis in China. The result shows that the main environmental factors affecting the distribution of C. nigricornis are min temperature of coldest month (bio6), mean temperature of coldest quarter (bio11), precipitation of driest month (bio14) and precipitation of wettest quarter (bio16). From the result of MaxEnt model, it can be seen that the suitable area of C. nigricornis in China is 128.91 × 104 km2 under current scenario. It will decrease by 3.19% in the 2050s and then increase by 12.04% in the 2090s under the SSP1-2.6 scenario, increase by 5.79% in the 2050s and then decrease by 7.53% in the 2090s under the SSP2-4.5 scenario, and increase by 33.03% in 2050s and then decrease by 23.31% in the 2090s under SSP5-8.5 scenario. From the result of GARP model, it can be seen that the suitable area of C. nigricornis in China is 166.09 × 104 km2 under current scenario. It will increase by 8.41% in 2050s and then continue to increase by 6.11% in 2090s under SSP1-2.6 scenario, increase by 23.84% in the 2050s and then decrease by 0.88% in the 2090s under the SSP2-4.5 scenario, and increase by 34.37% in 2050s and then decrease by 1.75% in 2090s under SSP5-8.5 scenario. The boundaries of suitable areas will expand to the north and southwest of China under future climate change scenarios, specially in Sichuan, Chongqing and Yunnan. Local forestry authorities should strengthen the monitoring of bamboo forests to prevent the damage caused by the introduction of C. nigricornis.

Predicting the Potential Distribution of Rare and Endangered Emmenopterys henryi in China Under ClimateChange.

Climate change has a pivotal impact on the potential distribution of endangered and relic tree species. Probably due to unrepresentative sampling and single algorithm, at present, there are different views on the potential range of the endangered tree Emmenopterys henryi, endemic to China. Here, we first collated 612 occurrence records and 22 environmental variables including climate, topography, and soil. Combined the Biomod2 with MaxEnt, we then predicted its past, current, and future potential suitable area in China, and determined the key factors influencing its distribution. The ensemble model results showed that the main environmental variables affecting this species were the minimum temperature of the coldest month (BIO6), precipitation of warmest quarter (BIO18), and temperature seasonality (BIO4). Its current potential distribution area was 176.53 × 104 km2, mainly concentrated in eastern, central, and southwestern China. Collectively, the suitable area of E. henryi would averagely decrease by 3.90% in all 16 future scenarios, with its centroid largely migrating northeastward. Our findings indicate that the endangered E. henryi covered 18 provinces in China, having a larger area than known. Moreover, climate change may have an adverse effect on its potential distribution. In addition, the ensemble model can produce more effective prediction outcomes than MaxEnt for such endemic tree species with large environmental range. We recommend increasing sample representativeness by analyzing the completeness properties of sample coverage, and simultaneously selecting appropriate algorithms to ensure the reliability of distribution prediction for endangered and relict tree species.

Seasonal variation in newborn hip dysplasia: Unraveling the impact of weather on hip development

BackgroundBabies born in winter have greater incidence of developmental dysplasia of the hip (DDH) and related surgeries. How weather conditions impact hip development and how screening program reacts weather issue remain unknown. This study tests a hypothesis that winter born babies have more newborn acetabular dysplasia and laxity that is responsible of later DDH. MethodsRetrospective data from newborns who had hip ultrasonography in the first 3 days of life were analyzed. The Graf type IIc, III, IV (shallow acetabulum) and type D (laxity) were classified as abnormal. The association and risks of an abnormal hip were analyzed with gender, gestational age, fetal presentation, parity and external temperature of birth month and the last 3 months before birth using the Pearson chi-square test and logistic regression. ResultsA total of 10962 newborns participated in hip ultrasound exams voluntarily in nurseries from 2016 to 2022. Distribution of babies with Graf type I, IIa, IIc, D, III/IV hips were 88.8%, 10%, 0.5%, 0.6%, and 0.1%, respectively. Female was the most significant factor for congenital shallow acetabulum (3.8x) and hip laxity (4x) compared to male (p <0.001). Preterm babies had a borderline lower risk of abnormal hips (0.4x, p = 0.05). Winter season is not associated with newborn abnormal hips (p = 0.36, statistical power = 80%), but a positive correlation was noted between external temperature and incidence of abnormal hips (r = 0.62, p = 0.03). Cold weather does not have a direct internal effect in acetabular dysplasia or hip laxity at birth. ConclusionsBabies who were born in winter were not associated with acetabular dysplasia and hip laxity at birth but had greater risks of late-diagnosed DDH and surgeries. The postnatal effects from weather should be addressed by a public awareness campaign, and hip screening may not be limited on the neonatal stage. Level of evidencelevel III, diagnostic

Antecedent Effect Models as an Exploratory Tool to Link Climate Drivers to Herbaceous Perennial Population Dynamics Data

ABSTRACTUnderstanding mechanisms and predicting natural population responses to climate is a key goal of Ecology. However, studies explicitly linking climate to population dynamics remain limited. Antecedent effect models are a set of statistical tools that capitalize on the evidence provided by climate and population data to select time windows correlated with a response (e.g., survival, reproduction). Thus, these models can serve as both a predictive and exploratory tool. We compare the predictive performance of antecedent effect models against simpler models and showcase their exploratory analysis potential by selecting a case study with high predictive power. We fit three antecedent effect models: (1) weighted mean models (WMM), which weigh the importance of monthly anomalies based on a Gaussian curve, (2) stochastic antecedent models (SAM), which weigh the importance of monthly anomalies using a Dirichlet process, and (3) regularized regressions using the Finnish horseshoe model (FHM), which estimate a separate effect size for each monthly anomaly. We compare these approaches to a linear model using a yearly climatic predictor and a null model with no predictors. We use demographic data from 77 natural populations of 34 plant species ranging between seven and 36 years in length. We then fit models to the asymptotic population growth rate (λ) and its underlying vital rates: survival, development, and reproduction. We find that models including climate do not consistently outperform null models. We hypothesize that the effect of yearly climate is too complex, weak, and confounded by other factors to be easily predicted using monthly precipitation and temperature data. On the other hand, in our case study, antecedent effect models show biologically sensible correlations between two precipitation anomalies and multiple vital rates. We conclude that, in temporal datasets with limited sample sizes, antecedent effect models are better suited as exploratory tools for hypothesis generation.

Mapping Species Distributions of Latoia consocia Walker under Climate Change Using Current Geographical Presence Data and MAXENT (CMIP 6).

Latoia consocia Walker is an important phytophagous pest that has rapidly spread across North China in recent years, posing a severe threat to related plants. To study the impact of climatic conditions on its distribution and to predict its distribution under current and future climate conditions, the MaxEnt niche model and ArcGIS 10.8 software were used. The results showed that the MaxEnt model performs well in predicting the distribution of L. consocia, with an AUC value of 0.913. The annual precipitation (Bio12), the precipitation of the driest month (Bio14), the temperature annual range (Bio7), and the minimum temperature of the coldest month (Bio6) are key environmental factors affecting the potential distribution of L. consocia. Under current climate conditions, L. consocia has a highly suitable growth area of 2243 km2 in China, among which Taiwan has the largest high-suitable area with a total area of 1450 km2. With climate warming, the potential habitat area for L. consocia shows an overall decreasing trend in future. This work provides a scientific basis for research on pest control and ecological protection. A "graded response" detection and early warning system, as well as prevention and control strategies, can be developed for potentially suitable areas to effectively address this pest challenge.

LAND SURFACE TEMPERATURE FORECASTING IN THE CONTEXT OF THE DEVELOPMENT OF SUSTAINABLE CITIES AND COMMUNITIES

The article examines the aspects of land surface temperature forecasting for effective planning and development of sustainable cities and communities. The relevance of the research lies in the need to develop effective approaches to the analysis and forecasting of climate data, for the timely determination of existing problems and ways to solve them, in accordance with civilizational challenges. The trends of changes in the land surface temperature from the middle of the 20th to the beginning of the 21st century were analyzed, using the example of five megacities located in different regions: Tokyo (Japan), Lagos (Nigeria), Berlin (Germany), Singapore (Singapore) and Belo Horizonte (Brazil). The results of the analysis of changes in average monthly and average annual temperatures are presented. The factors affecting the formation of the temperature regime of each of the cities are determined. The role of urbanization as a key factor in the development of the city is described, the main challenges caused by it are considered. An overview of megacities from the point of view of sustainable development was carried out. Prospects for urban development and measures aimed at reducing the urban heat island effect are considered. The role of modern technologies, in particular machine learning for predicting the land surface temperature, is described. The expediency of land surface temperature forecasting, in order to implement strategies for mitigating negative consequences and achieving the goals of sustainable development, is substantiated. The conducted research makes it possible to be convinced of the need for a responsible approach to the design and development of sustainable cities and communities. The temperature of the earth’s surface is one of the key indicators that allows you to monitor the main trends of urban life and can be an indicator of the effectiveness of strategies used to increase comfort and sustainable development.

Response of black pine (Pinus nigra) in southwestern Anatolia to climate change

Global climate change is one of the most critical challenges facing natural ecosystems. As sedentary organisms, plant communities are especially vulnerable, with climate change significantly impacting their development, productivity, and distribution. This study focuses on modelling and mapping the potential distribution of black pine (Pinus nigra) within the Antalya Regional Forest Directorate under the SSP1 2.6 climate scenario for the years 2040, 2060, 2080, and 2100. The MaxEnt modelling method, combined with the HadGEM3-GC31-LL climate model from WorldClim, was used to assess the impact of climate change on black pine. The current distribution model results showed “good” predictive performance, with AUC values of 0.855 for training data and 0.851 for testing data. Key variables influencing the model included precipitation during the wettest quarter, annual precipitation, maximum temperature of the warmest month, and elevation. Projections indicate that the black pine’s distribution will shrink by 2040, become increasingly fragmented by 2060, and decline further by 2080, with near-total disappearance by 2100. In conclusion, this study highlights the urgent need for adaptive management strategies to address the effects of climate change on black pine forests.

Forecasting Appropriate Habitats for Rare and Endangered Indocalamus Species in China in Response to Climate Change

China’s rare and endangered bamboo species belonging to the Indocalamus genus, specifically Indocalamus decorus Q. H. Dai, Indocalamus hirsutissimus Z. P. Wang &amp; P. X. Zhang, and Indocalamus pedalis (Keng) P. C. Keng, possess notable value in biodiversity conservation and have significant potential for use in landscape design. Using an enhanced MaxEnt model, this study forecasted shifts in the species’ potential range under four separate climate scenarios (SSP1-RCP2.6, SSP2-RCP4.5, SSP3-RCP7.0, and SSP5-RCP8.5), considering both the historical period (1970–2000, referred to as “the current period”) and upcoming timeframes (2021–2040, 2041–2060, 2061–2080, and 2081–2100). The analysis disclosed that the present total potential habitat area for these species is approximately 251.79 × 104 km2, with high, medium, and low suitability areas occupying 0.15 × 104 km2, 125.39 × 104 km2, and 126.26 × 104 km2, respectively. The minimum temperature of the coldest month emerged as the critical determinant of their potential habitat distribution. Expected climate changes are likely to increase the suitable habitat for these species, although areas with low suitability might slightly diminish, with Guizhou and Chongqing showing the least impact. The distribution centers of suitable habitats for the three plant species consistently exhibit a westward shift under various climate scenarios. These results contribute valuable insights for the spatial distribution, continuous monitoring, sustainable management, and ex situ conservation in response to climate change.

Потенциальный биоклиматический ареал Ephedra distachya L.

The paper presents modern data on the distribution of Ephedra distachya L. an attempt was made to model the bioclimatic range of the species using the maximum entropy method and compare the obtained data with the range defined in the report «Flora of the USSR», taking into account additions from the monographs «Flora of the European Part of the USSR» and «Geography of Trees plants of the USSR». MaxEnt modeling of the potential range of E. distachya was carried out on the basis of the herbarium collections of Saratov State University (SARAT), the GBIF database and the Plantarium electronic atlas. A total of 6973 species location points were used. In accordance with the obtained bioclimatic model, in the modern climate the most favorable conditions for the existence of the species are on the coasts of the Black, Azov, Marmara, Mediterranean and Caspian Seas, in the territories of the former AzSSR, Ukrainian SSR and Georgian SSR. In the RSFSR (modern Russian Federation), the species is optimally located in the Volga, North Caucasus and Southern federal districts. A sharp narrowing of the potential range is observed in the Ural and Central Federal Districts of the Russian Federation, as well as in the territories of the former TurSSR and KazSSR. The accuracy of the model is confirmed by the high AUC (Area Under Curve) score, which is 0.933 for training data and 0.930 for test data. Climatic parameters have been established that influence the distribution of the species, in which its existence is optimal. The greatest contribution to the construction of the model after permutation was made by isothermality (32%), average annual temperature (23.1%), average monthly daily temperature amplitude (12.4%), as well as the maximum temperature of the warmest month (11.6%). A correlation analysis of the parameters that made the greatest contribution after permutation was carried out. When estimating the error using the jackknife method, a variable (average annual temperature) was obtained that contains the most information that is not in other variables; the exclusion of this variable leads to deterioration of the model.

Characteristics of air temperature on the southern and southeastern slopes of the Greater Caucasus in the modern period

In the paper, the characteristics of air temperature in the south part of the Greater Caucasus region and the effects of regional climate changes on this area in the modern period were studied. In the research, the air temperature observation data of 8 hydrometeorological stations operating in the region for the years 1961-2022 were used, and the modern classification of the average monthly, seasonal, annual and altitude time-space distributions of temperature was given.Based on the data of hydrometeorological stations in the south and southeast piedmonts of the Greater Caucasus Mountains, an electronic map was prepared for the distribution of average annual air temperature indicators on the territory, and for determining the indicators of areas without data. DEM (Digital Elevation Model) files obtained fromthe decoding of satellite images (satellite data) were used in the prepared map. The research shows that July and August are the hottest months when the temperature is 22.80C, while January is the coldest by temperature about 0.50C in this region. In this part of the Greater Caucasus province, in the cold months of the year, the average monthly temperature is indicating below 00C in the area located above the moderately mountainous part of the region. The dynamics of the average indicators of temperature of the multi-year period (1961-2022) are expressed by a positive trend. The average annual temperature distribution map of the territory shows that the air temperature is 15.00C in the low mountainous part of the region, while it decreases upwards and, according to the adiabatic law, equals 00C at altitudes above 3500 m. According to the climate studies conducted in the region, compared to the years 1981-2010, it was found that there was a temperature increase of 1.20C in the mountainous region in 2011-2022. Also, a comparative analysis of 10-year averages of air temperature trends, using the Holt- Winters model, shows that temperatures in 2100 are expected to increase by 3.00C compared to 2030. In the region, climate changes that worsen every year lead to water scarcity, desertification, drought, etc. and can expand the area of recurrence of environmental crises and destructive natural events. This research can be used in future climate change mitigation , climate atlases, infrastructure, agricultural and industrial projects in the region.

Assessment of Annual Litterfall of Woody Plant Community in Southern Thorn Forest, Tirunelveli, Peninsular India

The biological phenomenon, litterfall acts as a connection between tree canopy and substratum of the habitats, influencing the concentration of vital soil nutrients thereby contributing to the tree growth and forest productivity. Information on litter generation of tropical forests including tropical thorn forest is lacking. Therefore, the current study was conducted to find annual litterfall of tree community existing within a Reserve Forest in Tirunelveli, India. A field ecological study was carried out to find annual litter production of woody plant community. Litter traps were kept randomly across the forest to assess the litter production. The litter fallen in each trap was collected separately on monthly basis for one calendar year. The collected litter was separated in to four different classes viz., leaf, wood, amorphous and reproductive organs. The relationship of climatic variables with litterfall was estimated by Pearson’s simple correlation test. The annual litter generation of the study area was estimated at 8.058 tons ha-1 y-1. The amount of total fallen and four classes litter per month varied significantly. In addition, litterfall did not show any significant relationship with the mean monthly temperature (p = 0.128; r2= 0.216) and monthly rainfall (p = 0.817; r2 = 0.0056). The deciduous species accounted for 95% (3.449 tons ha-1) of total annual leaf litterfall. Among four litter classes, the leaf litter accounted for 45.05%. The quantity of annual litterfall recorded from present study area is comparable with other tropical dry forests. The present study concentrated on limited forest areas, further studies with larger study area are needed to quantify the actual annual litter generation of southern thorn forests flourishing in various districts of Tamil Nadu.

Tree-ring blue intensity measurements from treeline sites in the Ural Mountains exhibit a strong summer temperature signal

The high northern latitudes offer an ideal environment to analyze tree growth responses to unprecedented recent warming. In this study, for the first time, we explore the dendroclimatological potential of latewood blue intensity (LWBI) and delta blue intensity (DBI) at two Siberian larch (Larix sibirica Ledeb.) sites in the upper treeline ecotone of the Ural Mountains, northern Russia. To assess the climate signals encoded in LWBI and DBI, as well as tree-ring width (TRW) and maximum latewood density (MXD), we correlated these parameter-specific chronologies against monthly temperature means, precipitation totals, and the SPEI index. LWBI and BDI exhibit robust and stable positive correlations with summer temperature, higher than TRW but slightly lower than MXD at both sites, with marginal negative effects from precipitation and strong negative correlations with SPEI. As direct surrogates for MXD, LWBI and DBI from larch trees offer reasonable alternatives as proxies for temperatures in northern latitudes.

Global Distribution Prediction of Cyrtotrachelus buqueti Guer (Coleoptera: Curculionidae) Insights from the Optimised MaxEnt Model.

Cyrtotrachelus buqueti Guer is a major pest affecting bamboo forests economically, causing significant damage to bamboo forests in Sichuan Province, China. To understand how C. buqueti responds to future climate conditions, an optimized Maximum Entropy Model (Maxent) was used to simulate the potential global distribution patterns of C. buqueti under current climate conditions and three different future climate scenarios and to analyze the dominant factors influencing its distribution. The results indicate that Bio18 (precipitation of the warmest quarter), Bio04 (temperature seasonality), Bio06 (minimum temperature of the coldest month), and Bio02 (mean diurnal temperature range) are the main environmental factors affecting the distribution of this species. The global area of high-suitability habitats for C. buqueti is 9.00 × 104 km2, primarily distributed in China. Under three different future climate scenarios, there are varying degrees of expansion in both the total suitable habitat and the medium-suitability areas for C. buqueti. Under the SSP5-8.5 scenario, the medium-suitability area of the species increases the most, reaching 9.83 × 104 km2. Additionally, these findings can serve as a reference for developing and implementing control strategies, assisting relevant authorities in more effectively managing and controlling this pest, and mitigating its potential threats to bamboo forest ecosystems and economies.