Monthly Temperature Research Articles

Future shifts in climatic conditions promoting northward expansion of the Mediterranean climate in the circum-Mediterranean region.

In the upcoming decades, precipitation and temperature patterns are expected to shift in the Mediterranean basin due to global warming, potentially having an influence on the environment and the economy in the area. Using monthly precipitation and temperature data from 15 global climate models (GCMs) developed as part of the Coupled Model Intercomparison Project Phase 6 (CMIP6), the Mediterranean Climate Envelop (MCE), as defined by Daget's (1977) criteria, is projected under two climate change scenarios: SSP2-4.5 and SSP5-8.5, and for two future periods: 2050s and 2070s. According to the findings, the MCE is expected to expand by 3.51 and 4.93% in the 2050s under the SSP2-4.5 and SSP5-8.5 scenarios, respectively, in comparison to the current state. This expansion is expected to reach 5.28 and 9.87% for SSP2-4.5 and SSP5-8.5, respectively, in 2070s. For both situations and durations, MCE contraction would be minor, however, at less than 1%. More than 99% of the present MCE would stay stable proportionately. The northern Mediterranean region is mostly concerned by the MCE's expansion. The SSP2-4.5 scenario predicts that by the 2070s, expansion zones will occupy 674,183 km2, with 64% of the area located in Southern Europe and 36% in Western Asia. In SSP5-8.5 scenario, this area is expected to be significantly larger, estimated to be approximately 1,256,881 km2; 67% in Southern Europe and 33% in Western Asia.

Temperature and precipitation projection in the lower Mahanadi Basin through machine learning methods

This study examined climate change dynamics in the lower Mahanadi River basin by integrating observed and climate model data. Historical precipitation and temperature data (1979–2020) from the India Meteorological Department (IMD) and monthly climate model data from the CORDEX-SMHI-MIROC model via the Earth System Grid Federation (ESGF) are utilized. Four machine learning models (Fbprophet, Holt-Winters, LSTM RNN, and SARIMAX) are applied to forecast precipitation, Tmax, and Tmin, and are compared across different representative concentration pathway (RCP 2.6, 4.5, and 8.5) scenarios. Diverse trajectories emerge, highlighting potential shifts in precipitation and temperature dynamics over near, mid, and far-term intervals. Fbprophet and SARIMAX are identified as superior models through performance evaluation metrics (R2, RMSE, r, P-bias, and NSE). Spatial analysis using ArcGIS and IDW interpolation reveals spatial variations in climate projections, aiding in visualizing future climate trends within the Mahanadi Basin. This study acknowledges limitations such as historical data uncertainties, socio-economic indicators, and unpredictable RCP trajectories, introducing a novel method to integrate machine learning with climate model data for assessing reliability. It also explores anticipated shifts in monthly precipitation and temperature patterns, providing insights into future climate variations.

Habitat suitability and relative abundance of wild boars in the east‐central Tianshan Mountains, China

AbstractAs wild boar populations and their distribution ranges increase, human–wild boar conflicts have become increasingly prevalent in numerous regions across the globe. These conflicts have a profound impact on human livelihoods, resulting in significant economic losses. Understanding the habitat requirements and relative abundance of wild boars is crucial prior to implementing any conservation measures. However, studies on wild boar habitat and population in the central and eastern regions of the Tianshan Mountains in China are lacking. We assessed the activity patterns and relative abundance of wild boars in these areas and evaluated habitat suitability using a combination of camera trapping, line transects, species distribution modeling (maximum entropy model), and hierarchical abundance modeling (Bayesian N‐mixture model). We used 311 infrared cameras and 280 field‐based line transects to cover approximately 31,000 km² from September 2022 to May 2023 in the east‐central Tianshan Mountains. We used 240 wild boar distribution locations and 13 environmental predictors in the development of species distribution models. We also used species counts and associated environmental predictors in the N‐mixture model to estimate the relative abundance of wild boar. Wild boars were most active during crepuscular hours (1800), and relatively active in the diurnal period compared to the nocturnal period. The probability of wild boar occurrence increased with higher normalized difference vegetation index (NDVI), the minimum temperature of the coldest month, and annual temperatures below 39°C. Boars were most likely to be found in closed deciduous‐coniferous forests. The relative abundance of wild boars was positively affected by NDVI and negatively affected by the minimum temperature of the coldest month and temperature annual range. Based on our results, we suggest areas of management priority. In particular, extensive and intact habitat with substantial wild boar populations, such as the Banfanggou, the South Mountain of Urumqi, and the Hutubi, should be prioritized for long‐term wild boar population monitoring and management so the adverse impacts of increasing wild boar populations in the study region can be minimized.

Effects of ensemble-forecasted key environmental factors on the distribution, active constituents, and transcription regulation in Ligusticum chuanxiong Hort.

Ligusticum chuanxiong Hort., with over 2000 years of medicinal use and cultivation history, is extensively used in clinical settings for treating heart disease, headache, dysmenorrhea, and amenorrhea. Constructing the geographic distribution pattern of L. chuanxiong and identifying the environmental factors limiting its range, as well as clarifying the effects of key environmental factors on the content of major active constituents and transcription regulation, could provide a scientific foundation for the conservation and effective management of this valuable medicinal resource. The results reveal that the predominant environmental factors influencing the distribution were the minimum temperature of the coldest month (Bio6) and solar radiation (Srad), with cumulative account for 87.46% of the importance. Correlation analysis further reveals significant negative correlations between Bio6 and the content of major active constituents in L. chuanxiong, with Srad exhibiting a negative correlation with these constituents. The gene differential expression analysis indicated that the expression levels of some genes associated with growth and active constituent biosynthesis pathways, such as RPT2_13888, UVR8_16871, CLPB3_3155, and 4CLL5_116, varied significantly among locations influenced by differing key environmental factors. Consequently, alterations in the environment were found to influence the gene expression levels within these pathways, resulting in variations in the content of active constituents. These findings contribute to an enhanced understanding of how environmental factors impact the distribution and quality of medicinal plants and offer a theoretical reference for the introduction, cultivation, quality improvement, resource utilization and management of L. chuanxiong. © 2024 Society of Chemical Industry.

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.

Global and regional climate in 2023

Abstract2023 was the warmest year on record with a global mean temperature of 1.45 ± 0.12 degC above pre‐industrial levels, surpassing the previous record (from 2016) by 0.17 degC. The onset of El Niño in early July was accompanied by record‐breaking land and sea‐surface temperatures, with June to September all exceeding previous monthly temperature records, and July and August being the hottest months on record. Sea‐surface temperatures have attained record highs for all months since March 2023 and Antarctic sea ice reached record lows throughout the year.

Hybrid modeling approach for precise estimation of energy production and consumption based on temperature variations

This study introduces an advanced mathematical methodology for predicting energy generation and consumption based on temperature variations in regions with diverse climatic conditions and increasing energy demands. Using a comprehensive dataset of monthly energy production, consumption, and temperature readings spanning ten years (2010–2020), we applied polynomial, sinusoidal, and hybrid modeling techniques to capture the non-linear and cyclical relationships between temperature and energy metrics. The hybrid model, which combines sinusoidal and polynomial functions, achieved an accuracy of 79.15% in estimating energy consumption using temperature as a predictor variable. This model effectively captures the seasonal and non-linear consumption patterns, demonstrating a significant improvement over conventional models. In contrast, the polynomial model for energy production, while yielding partial accuracy (R² = 0.65), highlights the need for more advanced techniques to fully capture the temperature-dependent nature of energy production. The results indicate that temperature variations significantly affect energy consumption, with higher temperatures driving increased energy demand for cooling, while lower temperatures affect production efficiency, particularly in systems like hydropower. These findings underscore the necessity for integrating sophisticated models into energy planning to ensure resilience in energy systems amidst climate variability. The study offers critical insights for policymakers to optimize energy generation and distribution in response to changing climatic conditions.

ПОВТОРЯЕМОСТЬ АДВЕКТИВНО-РАДИАЦИОННЫХ ТУМАНОВ НАД АПШЕРОНСКИМ ПОЛУОСТРОВОМ (АЗЕРБАЙДЖАН)

The article examines the physical and meteorological characteristics of advective-radiation fogs formed on the Apsheron Peninsula and nearby islands, including the dependence of atmospheric elements that influence the formation of fogs on the meteorological visibility range, monthly and long-term fog trends. For this purpose, the initial data of aviation meteorological stations in the water area for 1999...2022 were used. The absence of a large morphometric unit on the Apsheron Peninsula and the archipelago, surroundings by the sea, and air masses affecting the area throughout the year determine its synoptic conditions. With the help of physical-meteorological and mathematical-statistical analyses, it has been established that the bulk of advective-radiation fogs formed in the Apsheron waters are repeated in march...april. With this type of fog, it has been established that the range of meteorological visibility varies depending on wind speed and sky conditions. During the period of advective-radiation fogs, south-eastern winds are most often observed in the water area. Analyzes show that an increase in average monthly and annual air temperatures on the peninsula has recently affected the frequency of advective-radiation fogs. Studying the spatiotemporal distributions of fogs can create conditions for the effective organization of aviation work.

Assessment of CMIP6 models and multi-model averaging for temperature and precipitation over Iran

In this study, the performances of 40 Coupled Model Intercomparison Project Phase 6 are evaluated against observational data at synoptic stations in Iran using various evaluation criteria. The results reveal diverse model accuracy across different climate conditions and criteria, emphasizing particularly notable disparities in the nonstationarity R criterion compared to others. Although according to the ranking of the raw and bias-corrected outputs of CMIP6 GCMs for Iran, the NorESM2-MM, AWI-ESM-1-1-LR, and MPI-ESM1-2-LR models are consistently among the top six ranked models for precipitation in both raw and corrected outputs. For temperature, MPI-ESM1-2-LR, TaiESM1, INM-CM4-8, and IITM-ESM are consistently among the top six models for both the raw and bias-corrected outputs of CMIP6 GCMs. The Bias correction methods, including quantile mapping and linear scaling, integrated with Bayesian model averaging, were applied. While quantile mapping demonstrates superior performance and less disparity than linear scaling, it proves ineffective for correcting biases at stations with bias nonstationarity over time. The RMSE for monthly precipitation ranges from almost 0 to 200 mm, with a large RMSE value related to the high precipitation stations, and the monthly temperature exhibits a range of 0 to 4 °C. The use of a multi-model ensemble improves accuracy compared to individual models, resulting in a reduction in the differences between the minimum and maximum RMSE values from 178.6 to 91.0. Additionally, the range for mean absolute error decreases from 126.9 to 93.3, and the difference in the correlation coefficient narrows from 0.9 to 0.42. Averaging models after bias correction prevents significant fluctuations while maintaining higher accuracy, in contrast to the second method, which involves bias-correcting models after averaging.

Weather events influence survival and recruitment of Coereba flaveola (Bananaquit) in the Caribbean

Abstract The West Indies is considered a biodiversity hotspot and a priority for ecological conservation efforts. Understanding how environmental conditions influence survival of resident avifauna is an important information need given the predicted increases of drought and frequency and intensity of severe storms in the region. Throughout much of the Caribbean, Coereba flaveola (Bananaquit) are widespread and abundant, traits that may facilitate understanding their response to environmental changes in the region. We used a 10-yr capture–mark–recapture data set to examine C. flaveola survival, recruitment, population growth, and age structure in context of monthly and seasonal precipitation and temperatures, drought conditions, and occurrence and intensity of storm events. Our models suggested wing length, occurrence of storms, and drought all influenced survival. Both the incidence of storms and drier than average conditions in the preceding wet season (April–June) decreased survival. Sex of bird and net hours influenced capture probability, but weather influences on recruitment were equivocal. During non-storm years, mean population growth was stable at 1.019 (95% CI: 0.962, 1.098), but dropped to 0.843 (95% CI: 0.795, 0.846) in storm years. Increasing frequency of storms, such as back-to-back years, would likely push the growth rate lower. A protracted pattern of increased storm frequency, especially if coupled with a subsequent drought during the wet season, may lead to localized extirpations or strongly reduced populations.

Solar and Wind Energy Feasibility for a Water Treatment and Recirculation System Intended for Carniculture: Comparative Study Between the Cities Of Monteiro and Sousa – Paraíba – Brazil

Objective: This article presents a comparative study of renewable solar and wind energy production capacity between Monteiro and Sousa cities, located in the semi-arid region of Paraíba state, Brazil, to analyze the feasibility of using solar panels and/or wind turbines for energy supply in a water recirculation and treatment model applied to carciniculture. Method: A hypothetical-deductive method was employed and a bibliographic character was adopted in the research, taking as basis technical-scientific work information. Additionally, the research also presents quantitative theory, since it makes use of quantifiable results presented from measurable data that need to be properly expressed. A composite system was proposed, initially consisting of six identical units exposed in Figure 1, disposed under a roof, over which photovoltaic plates are placed for the supply of electrical energy to the hydraulic pumps contained in the system. The modeling study was based on wind speed and solar irradiance data in the cities of Monteiro and Sousa, with coordinates latitude -37.1214299 and longitude -7.8983769, and latitude -38.2285597 and longitude -6.7671611. These information were extracted from the Terraclimate database, using scripts written in Java and Python languages for extraction and processing. To predict wind power energy, the average wind speed in meters per second was used for each month, from January 1st, 2010 to December 31st, 2020. For modeling of photovoltaic power prediction, the average short-wave irradiance (W/m²) per month was considered, during the same period. To establish the monthly mean temperature, the average temperatures in Celsius were considered for the period from January 1st, 2010 to December 31st, 2020. For verification of the sufficiency of wind and photovoltaic systems for energy production maintenance, the energy demand of the System of Carciniculture was compared with the energy production by each of the sources considered, and based on the analysis made, it was defined which is the best energy production systemto be used and the best city where the defined system will be installed. Results and conclusions: The results show that the use of solar energy is economical, reduces social impacts generating employment and elevates environmental awareness, and has low environmental impact. The study found that solar energy generation capacity is sufficient and most suitable to keep the shrimp farming system running throughout the year, with observed variations between 226.8 kWh/month and 299.7 kWh/month in Souza city, and between 232.2 kWh/month and 369.0 kWh/month in Monteiro city, quantities more than enough for energy supply of the shrimp culture system, which presents a demand of 207.4 kWh/month. Implications of the research: The study points out that renewable energy can be an alternative more economical than other methods of energy generation, which could be a positive response to environmental and economic problems affecting the northeastern region, especially along the São Francisco River transposition, where Monteiro and Souza cities are among the last to receive it. Originality/Value: The use of solar energy is a viable solution for providing energy to shrimp culture. Additionally, the study also points out that this method may be more economical than other methods of energy generation.

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 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.