Stability analysis of a metapopulation model for the dynamics of malaria's spread including climatic factors.

Eradicating malaria remains a big challenge for computer scientists, mathematicians, epidemiologists, entomologists, physicians and many others. Their approaches range from recovering patients to eradicating the disease. However, collaboration, not always efficient between all these scientists, leads to the implementation of incomplete prototypes or to an under-exploitation of their results. Environmental and climatic factors are part of these elements that are usually omitted by computer scientists and mathematicians in the modelling of the malaria spread dynamic. Tropical countries, most affected by the disease are also mostly underdeveloped or developing countries, and therefore, statistical data are often lacking or difficult to access. Populations are constantly in motion over ecosystems with different environmental and climatic conditions, from a region to another. In this paper, we analyse the global asymptotic stability at the disease-free equilibrium of a metapopulation model including climatic factors.

Exploring the Relationship between Wind Patterns and Hospital Admissions Due to Respiratory Symptoms in Children.

Respiratory disorders significantly impact adolescents' health, often resulting in hospital admissions. Meteorological elements such as wind patterns have emerged as potential contributors to respiratory symptoms. However, it remains uncertain whether fluctuations in wind characteristics over extended periods have a tangible impact on respiratory health, particularly in regions characterized by distinct annual wind patterns. Crete is situated in the central-eastern Mediterranean Sea and frequently faces southerly winds carrying Sahara Desert sand from Africa and northerly winds from the Aegean Sea. This retrospective study analyzes long-term wind direction data and their relationship to respiratory symptoms observed in children up to 14 years old admitted at the University Hospital of Heraklion between 2002 and 2010. Symptoms such as headache, dyspnea, dry cough, dizziness, tachypnea, throat ache, and earache were predominantly reported during the presence of southern winds. Fever, productive cough, and chest pain were more frequently reported during northern winds. Cough was the most common symptom regardless of the wind pattern. Southern winds were significantly associated with higher probabilities of productive or non-productive cough, headache, dyspnea, tachypnea, dizziness, earache, and throat ache. Northern winds were related to a higher incidence of productive cough. Rhinitis, asthma, allergies, pharyngitis, and sinusitis were related to southern winds, while bronchiolitis and pneumonia were associated with northern winds. These findings underscore the critical role of local climatic factors, emphasizing their potential impact on exacerbating respiratory conditions in children. Moreover, they point out the need for further research to elucidate the underlying mechanisms and develop targeted interventions for at-risk populations.

The Influence of Climatic Factors on the Provocation of Epileptic Seizures.

Background/Objectives: Recent studies provide the first indications of the impact of climate factors on human health, especially with individuals already grappling with internal and neurological conditions being particularly vulnerable. In the face of escalating climate change, our research delves into the specific influence of a spectrum of climatic factors and seasonal variations on the hospital admissions of patients receiving treatment for epileptic seizures at our clinic in Kaiserslautern. Methods: Our study encompassed data from 9366 epilepsy patients who were admitted to hospital due to epileptic seizures. We considered seven climate parameters that Germany's National Meteorological Service made available. We employed the Kruskal-Wallis test to examine the correlation between the frequency of admittance to our hospital in the mentioned patient group and seasons. Furthermore, we used conditional Poisson regression and distributed lag linear models (DLMs) to scrutinize the coherence of the frequency of patient admittance and the investigated climate parameters. The mentioned parameters were also analyzed in a subgroup analysis regarding the gender and age of patients and the classification of seizures according to ILAE 2017. Results: Our results demonstrate that climatic factors, such as precipitation and air pressure, can increase the frequency of hospital admissions for seizures in patients with general-onset epilepsy. In contrast, patients with focal seizures are less prone to climatic changes. Consequently, admittance to the hospital for seizures is less affected by climatic factors in the latter patient group. Conclusions: The present study demonstrated that climatic factors are possible trigger factors for the provocation of seizures, particularly in patients with generalized seizures. This was determined indirectly by analyzing the frequency of seizure-related emergency admissions and their relation to prevailing climate factors. Our study is consistent with other studies showing that climate factors, such as cerebral infarcts or cerebral hemorrhages, influence patients' health.

Spatial variations impact the soil fungal communities of Larix gmelinii forests in Northeast China.

Soil fungi play a critical role in the biogeochemical cycles of forest ecosystems. Larix gmelinii is a strong and important timber tree species, which forms close associations with a wide range of soil fungi. However, the temporal-spatial disparity effects on the assembly of soil fungal communities in L. gmelinii forests are poorly understood. To address these questions, a total of 120 samples, including 60 bulk soil and 60 root samples, were collected from Aershan and Genhe in July (summer) and October (autumn)2021. We obtained 7,788 operational taxonomic units (OTUs) after merging, filtering, and rarefying using high-throughput sequencing. The dominant phyla are Basidiomycota, Ascomycota, Mortierellomycota, and Mucoromycota. There were 13 dominant families, among which the families with average relative abundance more than 5% included Thelephoraceae, Mortierellaceae, Archaeorhizomycoaceae, and Inocybaceae. In the functional guilds, symbiotrophic fungi had a relative advantage in the identified functions, and the relative abundances of pathotrophic and saprotrophic fungi varied significantly between sites. There were 12 families differentially expressed across compartments, 10 families differentially expressed between seasons, and 69 families were differentially expressed between sites. The variation in alpha diversity in the bulk soil was greater than that in the rhizosphere soil. Among the three parts (compartment, season, and site), the site had a crucial effect on the beta diversity of the fungal community. Deterministic processes dominated fungal community assembly in Genhe, whereas stochastic processes dominated in Aershan. Soil physicochemical properties and climatic factors significantly affected fungal community structure, among which soil total nitrogen and pH had the greatest effect. This study highlights that spatial variations play a vital role in the structure and assembly of soil fungal communities in L. gmelinii forests, which is of great significance for us in maintaining the health of the forests.

Environmental impacts on intraspecific variation in Ambrosia artemisiifolia genome size in Slovakia, Central Europe.

The quantity of DNA in angiosperms exhibits variation attributed to many external influences, such as environmental factors, geographical features, or stress factors, which exert constant selection pressure on organisms. Since invasive species possess adaptive capabilities to acclimate to novel environmental conditions, ragweed (Ambrosia artemisiifolia L.) was chosen as a subject for investigating their influence on genome size variation. Slovakia has diverse climatic conditions, suitable for testing the hypothesis that air temperature and precipitation, the main limiting factors of ragweed occurrence, would also have an impact on its genome size. Our results using flow cytometry confirmed this hypothesis and also found a significant association with geographical features such as latitude, altitude, and longitude. We can conclude that plants growing in colder environments farther from oceanic influences exhibit smaller DNA amounts, while optimal growth conditions result in a greater variability in genome size, reflecting the diminished effect of selection pressure.

Forest Age Drives the Resource Utilization Indicators of Trees in Planted and Natural Forests in China.

Specific leaf area (SLA) and leaf dry matter content (LDMC) are key leaf functional traits commonly used to reflect tree resource utilization strategies and predict forest ecosystem responses to environmental changes. Previous research on tree resource utilization strategies (SLA and LDMC) primarily focused on the species level within limited spatial scales, making it crucial to quantify the spatial variability and driving factors of these strategies. Whether there are discrepancies in resource utilization strategies between trees in planted and natural forests, and the dominant factors and mechanisms influencing them, remain unclear. This study, based on field surveys and the literature from 2008 to 2020 covering 263 planted and 434 natural forests in China, using generalized additive models (GAMs) and structural equation models (SEMs), analyzes the spatial differences and dominant factors in tree resource utilization strategies between planted and natural forests. The results show that the SLA of planted forests is significantly higher than that of natural forests (p < 0.01), and LDMC is significantly lower (p < 0.0001), indicating a "faster investment-return" resource utilization strategy. As the mean annual high temperature (MAHT) and mean annual precipitation (MAP) steadily rise, trees have adapted their resource utilization strategies, transitioning from a "conservative" survival tactic to a "rapid investment-return" model. Compared to natural forests, planted forest trees exhibit stronger environmental plasticity and greater variability with forest age in their resource utilization strategies. Overall, forest age is the dominant factor influencing resource utilization strategies in both planted and natural forests, having a far greater direct impact than climatic factors (temperature, precipitation, and sunlight) and soil nutrient factors. Additionally, as forest age increases, both planted and natural forests show an increase in SLA and a decrease in LDMC, indicating a gradual shift towards more efficient resource utilization strategies.

The Estimation of Grassland Aboveground Biomass and Analysis of Its Response to Climatic Factors Using a Random Forest Algorithm in Xinjiang, China.

Aboveground biomass (AGB) is a key indicator of the physiological status and productivity of grasslands, and its accurate estimation is essential for understanding regional carbon cycles. In this study, we developed a suitable AGB model for grasslands in Xinjiang based on the random forest algorithm, using AGB observation data, remote sensing vegetation indices, and meteorological data. We estimated the grassland AGB from 2000 to 2022, analyzed its spatiotemporal changes, and explored its response to climatic factors. The results showed that (1) the model was reliable (R2 = 0.55, RMSE = 64.33 g·m-2) and accurately estimated the AGB of grassland in Xinjiang; (2) the spatial distribution of grassland AGB in Xinjiang showed high levels in the northwest and low values in the southeast. AGB showed a growing trend in most areas, with a share of 61.19%. Among these areas, lowland meadows showed the fastest growth, with an average annual increment of 0.65 g·m-2·a-1; and (3) Xinjiang's climate exhibited characteristics of warm humidification, and grassland AGB showed a higher correlation with precipitation than temperature. Developing remote sensing models based on random forest algorithms proves an effective approach for estimating AGB, providing fundamental data for maintaining the balance between grass and livestock and for the sustainable use and conservation of grassland resources in Xinjiang, China.

A MaxEnt Model of Citrus Black Fly Aleurocanthus woglumi Ashby (Hemiptera: Aleyrodidae) under Different Climate Change Scenarios.

The citrus blackfly (CBF), Aleurocanthus woglumi Ashby, is an exotic pest native to Southeast Asia that has spread rapidly to the world's main centers of citrus production, having been recently introduced to Brazil. In this study, a maximum entropy niche model (MaxEnt) was used to predict the potential worldwide distribution of CBF under current and future climate change scenarios for 2030 and 2050. These future scenarios came from the Coupled Model Intercomparison Project Phase 6 (CMIP6), SSP1-2.6, and SSP5-8.5. The MaxEnt model predicted the potential distribution of CBF with area under receiver operator curve (AUC) values of 0.953 and 0.930 in the initial and final models, respectively. The average temperature of the coldest quarter months, precipitation of the rainiest month, isothermality, and precipitation of the driest month were the strongest predictors of CBF distribution, with contributions of 36.7%, 14.7%, 13.2%, and 10.2%, respectively. The model based on the current time conditions predicted that suitable areas for the potential occurrence of CBF, including countries such as Brazil, China, the European Union, the USA, Egypt, Turkey, and Morocco, are located in tropical and subtropical regions. Models from SSP1-2.6 (2030 and 2050) and SSP5-8.5 (2030) predicted that suitable habitats for CBF are increasing dramatically worldwide under future climate change scenarios, particularly in areas located in the southern US, southern Europe, North Africa, South China, and part of Australia. On the other hand, the SSP5-8.5 model of 2050 indicated a great retraction of the areas suitable for CBF located in the tropical region, with an emphasis on countries such as Brazil, Colombia, Venezuela, and India. In general, the CMIP6 models predicted greater risks of invasion and dissemination of CBF until 2030 and 2050 in the southern regions of the USA, European Union, and China, which are some of the world's largest orange producers. Knowledge of the current situation and future propagation paths of the pest serve as tools to improve the strategic government policies employed in CBF's regulation, commercialization, inspection, combat, and phytosanitary management.

Parallel prediction of dengue cases with different risks in Mexico using an artificial neural network model considering meteorological data.

In 2022, Mexico registered an increase in dengue cases compared to the previous year. On the other hand, the amount of precipitation reported annually was slightly less than the previous year. Similarly, the minimum-mean-maximum temperatures recorded annually were below the previous year. In the literature, it is possible to find studies focused on the spread of dengue only for some specific regions of Mexico. However, given the increase in the number of cases during 2022 in regions not considered by previously published works, this study covers cases reported in all states of the country. On the other hand, determining a relationship between the dynamics of dengue cases and climatic factors through a computational model can provide relevant information on the transmission of the virus. A multiple-learning computational approach was developed to simulate the number of the different risks of dengue cases according to the classification reported per epidemiological week by considering climatic factors in Mexico. For the development of the model, the data were obtained from the reports published in the Epidemiological Panorama of Dengue in Mexico and in the National Meteorological Service. The classification of non-severe dengue, dengue with warning signs, and severe dengue were modeled in parallel through an artificial neural network model. Five variables were considered to train the model: the monthly average of the minimum, mean, and maximum temperatures, the precipitation, and the number of the epidemiological week. The selection of variables in this work is focused on the spread of the different risks of dengue once the mosquito begins transmitting the virus. Therefore, temperature and precipitation were chosen as climatic factors due to the close relationship between the density of adult mosquitoes and the incidence of the disease. The Levenberg-Marquardt algorithm was applied to fit the coefficients during the learning process. In the results, the ANN model simulated the classification of the different risks of dengue with the following precisions (R2): 0.9684, 0.9721, and 0.8001 for non-severe dengue, with alarm signs and severe, respectively. Applying a correlation matrix and a sensitivity analysis of the ANN model coefficients, both the average minimum temperature and precipitation were relevant to predict the number of dengue cases. Finally, the information discovered in this work can support the decision-making of the Ministry of Health to avoid a syndemic between the increase in dengue cases and other seasonal diseases.

Spatiotemporal patterns and driving factors of gross primary productivity over the Mongolian Plateau steppe in the past 20 years.

The Eurasian steppe is the largest temperate grassland in the world. The grassland of the Mongolian Plateau (MP) represents an important part of the Eurasian steppe with high climatic sensitivity. Gross primary productivity (GPP) is a key indicator of the grassland's production, status and dynamic on the MP. In this study, we calibrated and evaluated the grassland-specific light use efficiency model (GRASS-LUE) against the observed GPP collected from nine eddy covariance flux sites on the MP, and compared the performance with other four GPP products (MOD17, VPM, GLASS and GOSIF). GRASS-LUE with higher R2 (0.91) and lower root mean square error (RMSE = 0.99 gC m-2 day-1) showed a better performance compared to the four GPP products in terms of model accuracy and dynamic consistency, especially in typical and desert steppe. The parameters of the GRASS-LUE are more suitable for water-limited grassland could be the reason for its outstanding performance in typical and desert steppe. Mean grassland GPP derived from GRASS-LUE was higher in the east and lower in the west of the MP. Grassland GPP was on average 205 gC m-2 over the MP between 2001 and 2020 with mean annual total GPP of 322 TgC yr-1. 30 % of the MP steppe showed a significant GPP increase. Growing season precipitation is the main factor affecting GPP of the MP steppe across regions. Anthropogenic factors (livestock density and population density) had greater effect on GPP than growing season temperature in pastoral counties in IM that take grazing as one of main industries. These findings can inform the status and trend of the productivity of MP steppe and help government and scientific research institutions to understand the drivers for spatial pattern of grassland GPP on the MP.

Relative effects of climate factors and malaria control interventions on changes of parasitaemia risk in Burkina Faso from 2014 to 2017/2018.

BACKGROUND: In Burkina Faso, the prevalence of malaria has decreased over the past two decades, following the scale-up of control interventions. The successful development of malaria parasites depends on several climatic factors. Intervention gains may be reversed by changes in climatic factors. In this study, we investigated the role of malaria control interventions and climatic factors in influencing changes in the risk of malaria parasitaemia. METHODS: Bayesian logistic geostatistical models were fitted on Malaria Indicator Survey data from Burkina Faso obtained in 2014 and 2017/2018 to estimate the effects of malaria control interventions and climatic factors on the temporal changes of malaria parasite prevalence. Additionally, intervention effects were assessed at regional level, using a spatially varying coefficients model. RESULTS: Temperature showed a statistically important negative association with the geographic distribution of parasitaemia prevalence in both surveys; however, the effects of insecticide-treated nets (ITNs) use was negative and statistically important only in 2017/2018. Overall, the estimated number of infected children under the age of 5 years decreased from 704,202 in 2014 to 290,189 in 2017/2018. The use of ITNs was related to the decline at national and regional level, but coverage with artemisinin-based combination therapy only at regional level. CONCLUSION: Interventions contributed more than climatic factors to the observed change of parasitaemia risk in Burkina Faso during the period of 2014 to 2017/2018. Intervention effects varied in space. Longer time series analyses are warranted to determine the differential effect of a changing climate on malaria parasitaemia risk.

Response of fine particulate matter and ozone concentrations to meteorology and anthropogenic precursors over the "2+26" cities of northern China.

Analyzing the influencing factors of fine particulate matter and ozone formation and identifying the coupling relationship between the two are the basis for implementing the synergistic pollutants control. However, the current research on the synergistic relationship between the two still needs to be further explored. Using the Geodetector model, we analyzed the effects of meteorology and emissions on fine particulate matter and ozone concentrations over the "2 + 26" cities at multiple timescales, and also explored the coupling relationship between the two pollutants. Fine particulate matter concentrations showed overall decreasing trends on inter-season and inter-annual scale from 2015 to 2021, whereas ozone concentrations showed overall increasing trends. While ozone concentrations displayed an inverted U-shaped distribution from month to month, fine particulate matter concentrations displayed a U-shaped fluctuation. On inter-annual scale, climatic factors, with planet boundary layer height as the main determinant, have higher effects for both pollutants than human precursors. In summer and autumn, sunshine duration had the most influence on fine particulate matter, while planet boundary layer height was the greatest factor in winter. Fine particulate matter is the leading impacting factor on ozone concentrations in summer, and there were positive associations between them on both annual and seasonal scale. The impact of nitrogen oxides and volatile organic compounds for both pollutants concentrations varied significantly between seasons. The two pollutants concentration were enhanced by the interactions between the various components. On inter-annual scale, interactions between the planet boundary layer height and other factors dominated the concentrations of the two pollutants, whereas in summer, interactions between fine particulate matter and other factors dominated the concentrations of ozone. The study has implications for the treatment of atmospheric pollution in China and other nations and can serve as an important reference for the creation of integrated atmospheric pollution regulation policies over the "2 + 26" cities.

Interactive effects of grassland utilization and climatic factors govern the plant diversity-soil C relationship in steppe of North China.

The relationship between plant diversity and the ecosystem carbon pool is important for understanding the role of biodiversity in regulating ecosystem functions. However, it is not clear how the relationship between plant diversity and soil carbon content changes under different grassland use patterns. In a 3-year study from 2013 to 2015, we investigated plant diversity and soil total carbon (TC) content of grasslands in northern China under different grassland utilization methods (grazing, mowing, and enclosure) and climatic conditions. Shannon-Wiener and Species richness index of grassland were significantly decreased by grazing and mowing. Plant diversity was positively correlated with annual precipitation (AP) and negatively correlated with annual mean temperature (AMT). AP was the primary regulator of plant diversity. Grazing and mowing decreased TC levels in grasslands compared with enclosures, especially in topsoil (0-20 cm). The average TC content was decreased by 58 % and 36 % in the 0-10 cm soil layer, while it was decreased by 68 % and 39 % in 10-20 cm soil layer. TC was positively correlated with AP and negatively correlated with AMT. Principal component analysis (PCA) showed that plant diversity was positively correlated with soil TC, and the correlation decreased with an increase in the soil depth. Overall, this study provides a theoretical basis for predicting soil carbon storage in grasslands under human disturbances and climate change impacts.

Spatiotemporal investigation of near-surface CH4 and factors influencing CH4 over South, East, and Southeast Asia.

Methane (CH4) is the second most abundant greenhouse gas after CO2, which plays the most important role in global and regional climate change. To explore the long-term spatiotemporal variations of near-surface CH4, datasets were extracted from Greenhouse gases Observing SATellite (GOSAT), and the Copernicus Atmospheric Monitoring Service (CAMS) reanalyzed datasets from June 2009 to September 2020 over South, East, and Southeast Asia. The accuracy of near-surface CH4 from GOSAT and CAMS was verified against surface observatory stations available in the study region to confirm both dataset applicability and results showed significant correlations. Temporal plots revealed continuous inflation in the near-surface CH4 with a significant seasonal and monthly variation in the study region. To explore the factors affecting near-surface CH4 distribution, near-surface CH4 relationship with anthropogenic emission, NDVI data, wind speed, temperature, precipitation, soil moisture, and relative humidity were investigated. The results showed a significant contribution of anthropogenic emissions with near-surface CH4. Regression and correlation analysis showed a significant positive correlation between NDVI data and near-surface CH4 from GOSAT and CAMS, while a significant negative correlation was found between wind and near-surface CH4. In the case of temperature, soil moisture, and near-surface CH4 from GOSAT and CAMS over high CH4 regions of the study area showed a significant positive correlation. However significant negative correlations were found between precipitation and relative humidity with GOSAT and CAMS datasets over high CH4 regions in South, East, and Southeast Asia. Moreover, these climatic factors showed no significant correlation within the low near-surface CH4 areas in our study region. Our study results showed that anthropogenic emissions, NDVI data, wind speed, temperature, precipitation, soil moisture, and humidity could significantly affect the near-surface CH4 over South, East, and Southeast Asia.

The challenge of managing yellow rust (Puccinia striiformis f.sp. tritici) in winter wheat: how combined climate and pathogen stressors impact variability in genotype reactions.

Despite the ongoing evolution of wheat pathogens due to the selection pressures of agro-ecological conditions, many studies have often overlooked the combined impact of both biotic and abiotic factors on disease occurrence. From 2016 to 2023, a comprehensive screening of obligate pathogens, including B. graminis f. sp. tritici, P. graminis f. sp. tritici, P. triticina, and P. striiformis f. sp. tritici, was carried out. This screening was conducted on a phenotyping platform encompassing 2715 winter wheat genotypes and their wild relatives, both with and without resistant genes (Lr, Yr, and Sr) for rust diseases. The data were analyzed using PCAmix, best subsets regression, and linear regression modeling. The findings from this study reveal that the plant reactions to leaf and yellow rust infections is far from straightforward. It is heavily influenced not only by prevalent rust races and climatic factors that impact pathogen life cycles but also by variations in the susceptibility reactions of wheat genotypes to the broader agro-ecological conditions. We also observed a tendency for leaf rust and yellow rust to coexist within the same host plant, even though yellow rust is typically considered more aggressive. We reported for the first time genes related to yellow rust resistance breakdown in Serbia in 2023. Lastly, we underscored the importance of investigating resistance responses to rust diseases not exclusively through the interrelation between resistance genes and pathogen virulence, but also by considering how plants respond to the combined stresses of abiotic and biotic factors. Consequently, our study sets the groundwork for further research into how plants respond to multiple stressors and contributes for further investigations related with effective integrated rust management.

Spatiotemporal and Individual Patterns of Domestic Cat (Felis catus) Hunting Behaviour in France.

Domestic cats (Felis catus), one of the most popular pets, are widespread worldwide. This medium-sized carnivore has well-known negative effects on biodiversity, but there is still a need to better understand the approximate causes of their predation. Based on a citizen science project, we assessed the role of spatiotemporal (i.e., latitude, longitude, and seasons), climatic (i.e., rainfall), anthropogenic (i.e., human footprint, HFI), and individual (i.e., sex and age) variables on the number of preys returned home by cats in metropolitan France. Over the 5048 cats monitored between 2015 and 2022, prey from 12 different classes (n = 36,568) were returned home: 68% mammals, 21% birds, and 8% squamates. Shrews brought home by cats peaked during summer, while rodents were recorded during summer-autumn. Birds brought home by cats peaked in spring-summer and in autumn, and lizards peaked in spring and in late summer. Lower HFI was associated with more voles and mice brought home, and the opposite trend was observed for lizards and birds. Younger cats were more prone to bring home shrews, birds, and reptiles. Although environmental factors play a minor role in prey brought home by cats, some geographical characteristics of prey species distribution partly explains the hunting behaviour of cats.

Fine root decomposition in forest ecosystems: an ecological perspective.

Fine root decomposition is a physio-biochemical activity that is critical to the global carbon cycle (C) in forest ecosystems. It is crucial to investigate the mechanisms and factors that control fine root decomposition in forest ecosystems to understand their system-level carbon balance. This process can be influenced by several abiotic (e.g., mean annual temperature, mean annual precipitation, site elevation, stand age, salinity, soil pH) and biotic (e.g., microorganism, substrate quality) variables. Comparing decomposition rates within sites reveals positive impacts of nitrogen and phosphorus concentrations and negative effects of lignin concentration. Nevertheless, estimating the actual fine root breakdown is difficult due to inadequate methods, anthropogenic activities, and the impact of climate change. Herein, we propose that how fine root substrate and soil physiochemical characteristics interact with soil microorganisms to influence fine root decomposition. This review summarized the elements that influence this process, as well as the research methods used to investigate it. There is also need to study the influence of annual and seasonal changes affecting fine root decomposition. This cumulative evidence will provide information on temporal and spatial dynamics of forest ecosystems, and will determine how logging and reforestation affect fine root decomposition.

Differential effects of climatic and non-climatic factors on the distribution of vegetation phenology trends on the Tibetan plateau.

The study of vegetation phenology changes is important because it is a sensitive indicator of climate change, affecting the exchange of carbon, energy and water fluxes between the land and the atmosphere. Previous studies have focused on the effects of climatic factors among environmental factors on vegetation phenology, thus the effects of non-climatic factors among environmental factors have not been well quantified. This study endeavors to scrutinize the spatiotemporal inconsistency in the start-of-season (SOS) and the end-of-season (EOS) on the Tibetan Plateau (TP) and to quantify the effects of environmental factors on phenology. To this end, the Moderate-resolution Imaging Spectroradiomater (MODIS) Normalized Difference Vegetation Index (NDVI) data from 2001 to 2018 and four common used methods were employed to extract SOS and EOS, and the site data was used to select the most appropriate phenology results. The Geodetector model was used to assess and measure the explanatory power of different environmental factors. The research results indicate that temperature exerts a more substantial impact on phenology than precipitation on TP. non-climatic factors such as longitude, latitude, and elevation are more influential in determining the distribution of phenological trends than climatic factors. Among these non-climatic factors, latitude has the most prominent effect on the trends of SOS. Furthermore, non-climatic factors exhibit a stronger effect on SOS, whereas EOS is more susceptible to climatic factors and less influenced by non-climatic factors. These discoveries bear great significance in comprehending the intricate outcomes of regional changes on vegetation phenology and enhancing phenology models.

VS-Cambium-Developer: A New Predictive Model of Cambium Functioning under the Influence of Environmental Factors.

Climate changes influence seasonal tree-ring formation. The result is a specific cell structure dependent on internal processes and external environmental factors. One way to investigate and analyze these relationships is to apply diverse simulation models of tree-ring growth. Here, we have proposed a new version of the VS-Cambium-Developer model (VS-CD model), which simulates the cambial activity process in conifers. The VS-CD model does not require the manual year-to-year calibration of parameters over a long-term cell production reconstruction or forecast. Instead, it estimates cell production and simulates the dynamics of radial cell development within the growing seasons. Thus, a new software based on R programming technology, able to efficiently adapt to the VS model online platform, has been developed. The model was tested on indirect observations of the cambium functioning in Larix sibirica trees from southern Siberia, namely on the measured annual cell production from 1963 to 2011. The VS-CD model proves to simulate cell production accurately. The results highlighted the efficiency of the presented model and contributed to filling the gap in the simulations of cambial activity, which is critical to predicting the potential impacts of changing environmental conditions on tree growth.