Grazing alters the soil nematode communities in grasslands: A meta-analysis

Grazing causes great disturbances in grassland ecosystems and may change the abundance, diversity, and ecological function of soil biota. Because of their important role in nutrient cycling and as good environmental indicators, nematodes are very representative soil organisms. However, the mechanisms by which grazing intensity, livestock type, duration, and environmental factors (e.g., climate and edaphic factors) affect soil nematodes remain poorly understood. In this study, we collected 1964 paired observations all over the world from 53 studies to clarify the grazing response patterns of soil nematodes and their potential mechanisms. Overall, grazing significantly decreased the abundance of bacterial-feeding (BF) nematodes (-16.54%) and omnivorous-predatory (OP) nematodes (-36.81%), and decreased nematode community diversity indices (Shannon-Weiner index: -4.33%, evenness index: -9.22%, species richness: -5.35%), but had no effect on ecological indices under a global regional scale. The response of soil nematodes to grazing varied by grazing intensity, animals, and duration. Heavy grazing decreased OP nematode abundance, but had no effect on the abundance of other trophic groups, or on diversity or ecological indices. Grazing by small animals had stronger effects than that by large animals and mixed-size animals on BF, fungal-feeding (FF), plant-feeding (PF) and OP nematodes, the Shannon-Wiener index, and the species richness index. The abundance of FF and OP nematodes influenced significantly under short-term grazing. The evenness index decreased significantly under long-term grazing (>10 years). Climate and edaphic factors impacted the effects of grazing on nematode abundance, diversity, and ecological indices. When resources (i.e., rain, heat, and soil nutrients) were abundant, the negative effects of grazing on nematodes were reduced; under sufficiently abundant resources, grazing even had positive effects on soil nematode communities. Thus, the influence of grazing on soil nematode communities is resource-dependent. Our study provides decision makers with grazing strategies based on the resource abundance. Resource-poor areas should have less grazing, while resource-rich areas should have more grazing to conserve soil biodiversity and maintain soil health.

Climate change impacts on oyster aquaculture - Part I: Identification of key factors

Oysters are enriched with high-quality protein and are widely known for their exquisite taste. The production of oysters plays an important role in the local economies of coastal communities in many countries, including Atlantic Canada, because of their high economic value. However, because of the changing climatic conditions in recent years, oyster aquaculture faces potentially negative impacts, such as increasing water acidification, rising water temperatures, high salinity, invasive species, algal blooms, and other environmental factors. Although a few isolated effects of climate change on oyster aquaculture have been reported in recent years, it is not well understood how climate change will affect oyster aquaculture from a systematic perspective. In the first part of this study, we present a systematic review of the impacts of climate change and some key environmental factors affecting oyster production on a global scale. The study also identifies knowledge gaps and challenges. In addition, we present key research directions that will facilitate future investigations.

Decision tree ensemble with Bayesian optimization to predict the spatial dynamics of chlorophyll-a concentration: A case study in Bay of Bengal

An accurate prediction of the spatial distribution of phytoplankton biomass, as represented by Chlorophyll-a (CHL-a) concentrations, is important for assessing ecological conditions in the marine environment. This study developed a hyperparameter-optimized decision tree-based machine learning (ML) models to predict the geographical distribution of marine phytoplankton CHL-a in the Bay of Bengal. To predict CHL-a over a large spatial extent, satellite-derived remotely sensed data of ocean color features (CHL-a, colored dissolved organic matter, photosynthetically active radiation, particulate organic carbon) and climatic factors (nighttime sea surface temperature, surface absorbed longwave radiation, sea level pressure) from 2003 to 2022 are used to train and test the models. Results obtained from this study have shown the highest concentrations of CHL-a occurred near the Bay's coastal belts and river estuaries. Analysis revealed that aside from photosynthetically active radiation, organic components exhibited a stronger positive relationship with CHL-a than climatic features, which are correlated negatively. Results showed the chosen decision tree methods to all possess higher R2 and lower root mean square error (RMSE) errors. Furthermore, XGBoost outperforms all other models in predicting the geographic distribution of CHL-a. To assess the model efficacy on seasonal basis, a best performing XGBoost model was validated in the Bay of Bengal region which has shown a good performance in predicting the spatial distribution of Chl-a as well as the pixel values during the summer, winter and monsoon seasons. This study provides the best ML model to researchers for predicting CHL-a in the Bay of Bengal. Further it helps to improve our knowledge of CHL-a spatial dynamics and assist in monitoring marine resources in the Bay of Bengal. It worth noting that the water quality in the Indian Ocean is very dynamic in nature, therefore, additional efforts are needed to test the efficacy of this study model over different seasons and spatial gradients.

Leaf phenotypic variation and its response to environmental factors in natural populations of Eucommia ulmoides

BACKGROUND

Eucommia ulmoides leaves have high medicinal and economic value as a dual-purpose substance for medicine and food. Employing leaves from 13 natural populations of Eucommia ulmoides as research objects, this study reveals the variation patterns of intra-specific and inter-specific trait variation and explores the response of leaf characteristics to geographical and climatic changes, aiming to provide a scientific basis for the efficient utilization of leaf resources and the breeding of superior varieties.

RESULTS

Descriptive statistical analysis and nested analysis of variance showed significant differences in 11 leaf traits of Eucommia ulmoides inter-populations and intra-populations, with an average coefficient of variation of 17.45%. The coefficient of variation for average leaf phenotypic traits is 20.77%, and the leaf phenotypic variation is mainly from the variation intra-populations. Principal component analysis reveals that the cumulative contribution rate of the top three principal components which mainly contributed to the phenotypic variation of Eucommia ulmoides leaves reached 74.98%, which could be sorted into size traits (34.57%), color traits (25.82%) and shape traits (14.58%). In addition, correlation analysis expresses there is a specific co-variation pattern among leaf traits, with a strong connection between shape, size, and color traits. Geographic and climatic distances are significantly correlated, and mantel test and correlation analysis indicate that leaf traits of Eucommia ulmoides are mainly influenced by altitude. With the increase of altitude, the leaves become smaller. Partial correlation analysis shows that after controlling climate factors, the correlation between some characters and geographical factors disappears significantly. Temperature and precipitation have a great influence on the variation of leaf phenotypic traits, and the larger the leaves are in areas with high temperature and heavy rainfall.

CONCLUSIONS

These findings contribute to a further understanding of the leaf morphological characteristics of Eucommia ulmoides and the extent to which the environment influences leaf trait variation. They can provide a scientific basis for the protection and application of Eucommia ulmoides leaf resources in the future.

Spatio-temporal distribution and contributing factors of tegumentary and visceral leishmaniasis: A comparative study in Bahia, Brazil

Tegumentary (TL) and visceral (VL) leishmaniasis are neglected zoonotic diseases in Brazil, caused by different parasites and transmitted by various vector species. This study investigated and compared spatio-temporal patterns of TL and VL from 2007 to 2020 in the state of Bahia, Brazil, and their correlations with extrinsic factors. The results showed that the total number of cases of both TL and VL were decreasing. The number of municipalities with reported cases reduced for TL over time but remained almost unchanged for VL. There were few municipalities with reported both diseases. Statistical analysis showed that local TL incidence was associated positively with natural forest. Local VL incidence was associated positively with Cerrado (Brazilian savannah) vegetation. This study identified different patterns of occurrence of VL and TL and the risk areas that could be prioritized for epidemiological surveillance.

[Skin aging exposome]

The skin is a barrier organ and thus exposed to environmental factors from birth, which essentially determine skin aging. In order to describe and understand this complex process exactly, we applied the concept of the "exposome" to the environmentally induced skin aging process. In this review, we summarize current knowledge on the skin aging exposome. In this context, we characterize the most important exposomal factors, address their relative importance for skin aging and also the relevance of their mutual interactions. Finally, we discuss the clinical consequences resulting from this concept for an effective prevention of skin aging.

Spatio-temporal pattern and associate factors of intestinal infectious diseases in Zhejiang Province, China, 2008-2021: a Bayesian modeling study

BACKGROUND

Despite significant progress in sanitation status and public health awareness, intestinal infectious diseases (IID) have caused a serious disease burden in China. Little was known about the spatio-temporal pattern of IID at the county level in Zhejiang. Therefore, a spatio-temporal modelling study to identify high-risk regions of IID incidence and potential risk factors was conducted.

METHODS

Reported cases of notifiable IID from 2008 to 2021 were obtained from the China Information System for Disease Control and Prevention. Moran's I index and the local indicators of spatial association (LISA) were calculated using Geoda software to identify the spatial autocorrelation and high-risk areas of IID incidence. Bayesian hierarchical model was used to explore socioeconomic and climate factors affecting IID incidence inequities from spatial and temporal perspectives.

RESULTS

From 2008 to 2021, a total of 101 cholera, 55,298 bacterial dysentery, 131 amoebic dysentery, 5297 typhoid, 2102 paratyphoid, 27,947 HEV, 1,695,925 hand, foot and mouth disease (HFMD), and 1,505,797 other infectious diarrhea (OID) cases were reported in Zhejiang Province. The hot spots for bacterial dysentery, OID, and HEV incidence were found mainly in Hangzhou, while high-high cluster regions for incidence of enteric fever and HFMD were mainly located in Ningbo. The Bayesian model showed that Areas with a high proportion of males had a lower risk of BD and enteric fever. People under the age of 18 may have a higher risk of IID. High urbanization rate was a protective factor against HFMD (RR = 0.91, 95% CI: 0.88, 0.94), but was a risk factor for HEV (RR = 1.06, 95% CI: 1.01-1.10). BD risk (RR = 1.14, 95% CI: 1.10-1.18) and enteric fever risk (RR = 1.18, 95% CI:1.10-1.27) seemed higher in areas with high GDP per capita. The greater the population density, the higher the risk of BD (RR = 1.29, 95% CI: 1.23-1.36), enteric fever (RR = 1.12, 95% CI: 1.00-1.25), and HEV (RR = 1.15, 95% CI: 1.09-1.21). Among climate variables, higher temperature was associated with a higher risk of BD (RR = 1.32, 95% CI: 1.23-1.41), enteric fever (RR = 1.41, 95% CI: 1.33-1.50), and HFMD (RR = 1.22, 95% CI: 1.08-1.38), and with lower risk of HEV (RR = 0.83, 95% CI: 0.78-0.89). Precipitation was positively correlated with enteric fever (RR = 1.04, 95% CI: 1.00-1.08), HFMD (RR = 1.03, 95% CI: 1.00-1.06), and HEV (RR = 1.05, 95% CI: 1.03-1.08). Higher HFMD risk was also associated with increasing relative humidity (RR = 1.20, 95% CI: 1.16-1.24) and lower wind velocity (RR = 0.88, 95% CI: 0.84-0.92).

CONCLUSIONS

There was significant spatial clustering of IID incidence in Zhejiang Province from 2008 to 2021. Spatio-temporal patterns of IID risk could be largely explained by socioeconomic and meteorological factors. Preventive measures and enhanced monitoring should be taken in some high-risk counties in Hangzhou city and Ningbo city.

Spatiotemporal changes of ecological environment quality and climate drivers in Zoige Plateau

Ecological environment is the essential material basis of human survival and connects regional economy with socially sustainable development. However, climate changes characterized by global climate warming have caused a series of ecological environmental problems in recent years. Few studies have discussed various climate factors affecting the ecological environment, and the spatial non-stationary effects of different climate factors on the ecological environment are still unclear. Dynamically monitoring ecological environment changes in fragile areas and identifying its climate-driving mechanism are essential for ecological protection and environmental repair. Taking Zoige Plateau as a case, this paper simulated the eco-environmental quality during 1987-2020 using remote sensing data, utilized Geodetector method to identify the contributions of various climate drivers to ecological environment quality, and then adopted the Geographically Weighted Regression model to explore the spatial non-stationary impacts of climate factors on ecological environment quality. The results showed that the ecological quality in the middle regions of the Zoige Plateau was slightly better than in the surrounding marginal areas. For the whole area of Zoige Plateau, the average ecological environment quality index was 54.92, 53.99, 56.17, 57.88, 63.44, 56.93, 59.43, and 59.76 in 1987, 1992, 1997, 2001, 2006, 2013, 2016 and 2020, respectively, which indicated that eco-environmental quality witnessed several fluctuations during the study period but showed a generally increasing trend. Among five climate factors, the temperature was the dominant climate factor affecting the ecological environment quality (q value: 0.11-0.19), sunshine duration (0.03-0.17), wind speed (0.03-0.11), and precipitation (0.03-0.08) were the main climate drivers, while the explanatory power of relative humidity to ecological environment quality was relatively small. Such various climate factors impacting the ecological environment quality demonstrated distinct spatial non-stationary and the range of driving impact varied with time. Temperature, sunshine duration, wind speed, and relative humidity promoted ecological environment quality in most regions (regression coefficients > 0), while precipitation mainly had a negative inhibitory impact (regression coefficients < 0). Meanwhile, the greater impacts of these five climate factors were concentrated in high-elevation regions of the south and west or the northern areas. The appropriate enhancement of climate warming and air humidity was beneficial to the improvement of the ecological environment, but the excessive precipitation would result in landslides and exhibit inhibition of vegetation growth. Therefore, selecting cold-tolerant herbs and shrubs, and strengthening climate monitoring and early warning systems (such as drought and excessive precipitation) are essential for ecological restoration.

Uncovering the determinants of biodiversity hotspots in China: Evidence from the drivers of multiple diversity metrics on insect assemblages and implications for conservation

Understanding large-scale biodiversity patterns and underlying mechanisms during the formation process is essential for guiding conservation efforts. However, previous studies on the identification and formation mechanism of diversity hotspots in China were often limited to a single (alpha) diversity metric, while multiple (beta or zeta) diversity has rarely been used for exploring drivers and conservation actions. Here, a comprehensive species distribution dataset consisting of representative families of three insect orders was compiled to explore biodiversity hotspots based on different algorithms. Furthermore, to assess the effects of environmental factors on hotspots, we fitted generalized additive mixed-effects models (GAMMs) for species richness, generalized dissimilarity models (GDMs) and multi-site generalized dissimilarity modeling (MS-GDM) for the total beta and zeta diversity. Our results showed that biodiversity hotspots were mainly concentrated in central and southern China, especially in mountainous areas with complex topography, which indicated the insects' affinity to montane environments. Further analyses based on multiple models showed that water-energy factors exerted the strongest explanatory power for the insect assemblage diversity in hotspots of both alpha and beta (or zeta) levels. Additionally, anthropogenic factors also exerted a significant effect on hotspots, and this effect was higher for beta diversity than for alpha diversity. Overall, our study elucidates a comprehensive analysis of the identification and underlying mechanism of biodiversity hotspots in China. Despite several limitations, we still believe that our findings can provide some new insights for conservation efforts in Chinese hotspots.

Association of climate factors with dengue incidence in Bangladesh, Dhaka City: A count regression approach

Background

In Bangladesh, particularly in Dhaka city, dengue fever is a major factor in serious sickness and hospitalization. The weather influences the temporal and geographical spread of the vector-borne disease dengue in Dhaka. As a result, rainfall and ambient temperature are considered macro factors influencing dengue since they have a direct impact on Aedes aegypti population density, which changes seasonally dependent on these critical variables. This study aimed to clarify the relationship between climatic variables and the incidence of dengue disease.

Methods

A total of 2253 dengue and climate data were used for this study. Maximum and minimum temperature (°C), humidity (grams of water vapor per kilogram of air g.kg^-1), rainfall (mm), sunshine hour (in (average) hours per day), and wind speed (knots (kt)) in Dhaka were considered as the independent variables for this study which trigger the dengue incidence in Dhaka city, Bangladesh. Missing values were imputed using multiple imputation techniques. Descriptive and correlation analyses were performed for each variable and stationary tests were observed using Dicky Fuller test. However, initially, the Poisson model, zero-inflated regression model, and negative binomial model were fitted for this problem. Finally, the negative binomial model is considered the final model for this study based on minimum AIC values.

Results

The mean of maximum and minimum temperature, wind speed, sunshine hour, and rainfall showed some fluctuations over the years. However, a mean number of dengue cases reported a higher incidence in recent years. Maximum and minimum temperature, humidity, and wind speed were positively correlated with dengue cases. However, rainfall and sunshine hours were negatively associated with dengue cases. The findings showed that factors such as maximum temperature, minimum temperature, humidity, and windspeed are crucial in the transmission cycles of dengue disease. On the other hand, dengue cases decreased with higher levels of rainfall.

Conclusion

The findings of this study will be helpful for policymakers to develop a climate-based warning system in Bangladesh.

The driving mechanism of soil organic carbon biodegradability in the black soil region of Northeast China

The biodegradability of soil organic carbon (BSOC), defined as soil mineralization C per unit of soil organic carbon (SOC), is considered to be an important indicator of SOC stability and is closely related to the global C cycle. However, the magnitude and driving mechanism of BSOC in farmland remain largely unexplored, especially at the regional scale. Here, we conducted regional scale sampling to investigate latitude distribution pattern of BSOC and the relative contributions of biotic (soil micro-food web) and abiotic (climate and soil) drivers to BSOC in the black soil region of Northeast China. Results showed that BSOC declined with increasing latitude, which indicates that as the latitude increases, SOC becomes more stable in the black soil region of Northeast China. Over a range of latitude from 43°N to 49°N, BSOC was negatively correlated with soil micro-food web metrics of diversity (indicated by species richness), biomass and connectance, and soil factors of soil pH and clay content (CC), while it was positively correlated with climate factors of mean annual temperature (MAT), mean annual precipitation (MAP) and soil factor of soil bulk density (SBD). Among those predictors, soil micro-food web metrics were the most direct factors contributing to the variations of BSOC, which exerted the largest total effect on BSOC (-0.809). Collectively, our results provide convincing evidence that soil micro-food web metrics play a direct vital role in determining the distribution pattern of BSOC over a range of latitudes in the black soil region of Northeast China. This highlights the necessity of considering the role of soil organisms in regulating C dynamics in prediction of SOC mineralization and retention in the terrestrial ecosystem.

Effects of drought and climate factors on vegetation dynamics in Central Asia from 1982 to 2020

Ecological security and ecosystem stability in Central Asia depend heavily on the local vegetation. Vegetation dynamics and the response and hysteresis relationships to climate factors and drought on multiple scales over long time series in the region still need to be further explored. Using the net primary productivity (NPP) values as the vegetation change index of interest, in this study, we analyzed vegetation dynamics in Central Asia from 1982 to 2020 and assessed the responses and time lags of vegetation to climate factors and drought. The results showed that NPP gradually decreased from north to south and from east to west. Vegetation was distributed along both sides of the mountains. The temperatures rose from northeast to southwest, while precipitation gradually increased from southwest to northeast. The proportion of dry and wet years was as follows: normal (56.41%) > slightly dry (28.2%) > slightly humid (15.39%). Precipitation and drought conditions were positively correlated with NPP during the growing season, while temperature was negatively correlated with NPP. Increased spring temperature, precipitation, and drought conditions positively affected vegetation, while sustained summer temperature resulted in suppressed vegetation growth. Autumn vegetation was positively affected by temperature and drought, and precipitation was negatively correlated with autumn vegetation. Increasing winter temperatures promoted vegetation growth. The time lag between NPP and temperature gradually increased from northeast to southwest, and the time lag between NPP and precipitation gradually increased from south to north. Spring temperatures had the greatest beneficial impact on forestlands; summer climatic factors and drought had little effect on shrublands; the autumn climate exhibited small differences in its influence of each plant type; and winter temperatures had the greatest positive effect on grasslands. No time lag effect was found between any of the four vegetation types and precipitation. A one-month lag was found between cultivated lands and temperature; a two-month lag was found between forestlands and temperature; and a one-month lag was found between forestlands and drought and between shrublands and drought. The results can provide a scientific foundation for the sustainable development and management of ecosystems.

Toxicity of historically metal(loid)-contaminated soils to Folsomia candida under the influence of climate change alterations

Global warming is drastically altering the climate conditions of our planet. Soils will be among the most affected components of terrestrial ecosystems, especially in contaminated areas. In this study we investigated if changes in climate conditions (air temperature and soil moisture) affect the toxicity of historically metal(loid)-contaminated soils to the invertebrate Folsomia candida, followed by an assessment of its recovery capacity. Ecotoxicity tests (assessing survival, reproduction) were performed in field soils affected by metal(loid)s under different climate scenarios, simulated by individually changing air temperature or soil moisture conditions. The scenarios tested were: standard conditions (20°C + 50% soil water holding capacity-WHC); increased air temperature (daily fluctuation of 20-30°C + 50% WHC); soil drought (20°C + 25% WHC); soil flood (20°C + 75% WHC). Recovery potential was assessed under standard conditions in clean soil. Increased temperature was the major climate condition negatively affecting collembolans performance (decreased survival and reproduction), regardless of metal(loid) contamination. Drought and flood conditions presented less pronounced effects. When it was possible to move to the recovery phase (enough juveniles in exposure phase), F. candida was apparently able to recover from the exposure to metal(loid) contamination and/or climate alterations. The present study showed that forecasted climate alterations in areas already affected by contamination should be considered to improve environmental risk assessment.

COVID-19 pandemic over 2020 (withlockdowns) and 2021 (with vaccinations): similar effects for seasonality and environmental factors

How is the dynamics of Coronavirus Disease 2019 (COVID-19) in 2020 with an health policy of full lockdowns and in 2021 with a vast campaign of vaccinations? The present study confronts this question here by developing a comparative analysis of the effects of COVID-19 pandemic between April-September 2020 (based upon strong control measures) and April-September 2021 (focused on health policy of vaccinations) in Italy, which was one of the first European countries to experience in 2020 high numbers of COVID-19 related infected individuals and deaths and in 2021 Italy has a high share of people fully vaccinated against COVID-19 (>89% of population aged over 12 years in January 2022). Results suggest that over the period under study, the arithmetic mean of confirmed cases, hospitalizations of people and admissions to Intensive Care Units (ICUs) in 2020 and 2021 is significantly equal (p-value<0.01), except fatality rate. Results suggest in December 2021 lower hospitalizations, admissions to ICUs, and fatality rate of COVID-19 than December 2020, though confirmed cases and mortality rates are in 2021 higher than 2020, and likely converging trends in the first quarter of 2022. These findings reveal that COVID-19 pandemic is driven by seasonality and environmental factors that reduce the negative effects in summer period, regardless control measures and/or vaccination campaigns. These findings here can be of benefit to design health policy responses of crisis management considering the growth of COVID-19 pandemic in winter months having reduced temperatures and low solar radiations ( COVID-19 has a behaviour of influenza-like illness). Hence, findings here suggest that strategies of prevention and control of infectious diseases similar to COVID-19 should be set up in summer months and fully implemented during low-solar-irradiation periods (autumn and winter period).

Detection and correlation analysis of shellfish pathogens in Dadeng Island, Xiamen

Food poisoning is caused by pathogenic bacteria in water and aquatic products, especially bivalves (e.g., oysters, clams), which can bioaccumulate pathogenic bacteria. Polluted water and aquatic products thus pose a serious threat to human health and safety. In this study, the types of pathogenic bacteria in water samples and shellfish collected from the Dadeng offshore area in Xiamen were examined. We also analyzed the relationships between dominant pathogens and major climate and water quality parameters. Our objective was to provide reference data that may be used to help prevent bacterial infections and to improve aquatic food hygiene in Xiamen and its surrounding areas to safe levels, thus ensuring the health of Xiamen residents. We found that the main pathogenic bacteria were Vibrio and Bacillus, with the dominant pathogen being Vibrio parahaemolyticus. Physical and chemical indexes (water temperature, salinity, pH, dissolved oxygen, and turbidity) of water bodies and the 3-day accumulated rainfall were found to be important factors affecting the occurrence and abundance of V. parahaemolyticus.

Chemotaxonomic variation in volatile component contents and their correlation between climate factors in Chinese prickly ash peels (Zanthoxylum bungeanum Maxim.)

In this study, we analyzed the characteristics of volatile compounds of Chinese prickly ash peels with different climate conditions and their correlation. The data revealed that the contents of limonene and linalool in peels from southwest and northwest regions were higher, and the aroma was stronger, while the contents of β-myrcene and (E)-ocimene in them from north, east and central China were higher, and the spicy flavor was heavier. Hierarchical cluster analysis demonstrated that the classification had geographical continuity. Through the correlation analysis and path analysis, it was found that the contents of volatile compounds were closely related to the climatic factors. The influence of wind speed and annual average temperature on volatile substances was greater than that of annual average precipitation and annual sunshine duration. This enriched the effect of climatic factors on the accumulation of volatile substances, and promoted the agriculture practices in area having similar climate conditions.

Human and climate drivers of global biomass burning variability

Biomass burning is one of the most critical factors impacting vegetation and atmospheric trends, with important societal implications, particularly when extreme weather conditions occur. Trends and factors of burned area (BA) have been analysed at regional and global scales, but little effort has been dedicated to study the interannual variability. This paper aimed to better understand factors explaining this variation, under the assumption that the more human control of fires the more frequently they occur, as burnings will be less dependent of weather cycles. Interannual variability of BA was estimated from the coefficient of variation of the annual BA (BA_CV) estimated from satellite data at 250 m, covering the period from 2001 to 2018. These data and the explanatory variables were resampled at 0.25-degree resolution for global analysis. Relations between this variable and explanatory factors, including human and climate drivers, were estimated using Random Forest (RF) and generalized additive models (GAM). BA_CV was negatively related to BA_Mean, implying that areas with higher average BA have lower variability as well. Interannual BA variability decreased when maximum temperature (TMAX) and actual and potential evapotranspiration (AET, PET) increased, cropland and livestock density increased and the human development index (HDI) values decreased. GAM models indicated interesting links with AET, PET and precipitation, with negative relation with BA_CV for the lower ranges and positive for the higher ones, the former indicating fuel limitations of fire activity, and the latter climate constrains. For the global RF model, TMAX, AET and HDI were the main drivers of interannual variability. As originally hypothesised, BA_CV was more dependent on human factors (HDI) in those areas with medium to large BA occurrence, particularly in tropical Africa and Central Asia, while climatic factors were more important in boreal regions, but also in the tropical regions of Australia and South America.

Effects of climate variables on the incidence of scorpion stings in Iran for five years

Background:

Although scorpionism is recorded worldwide, some regions such as Iran present a higher incidence. Due to the great prevalence of scorpion stings in Khuzestan province, southwestern Iran, the present study examined the relationship between different climate parameters and the scorpion sting rate in this area from April 2010 to March 2015.

Methods:

In this cross-sectional descriptive-analytical study, we considered all scorpion sting cases recorded in the Department of Infectious Diseases, Ahvaz Jundishapur University of Medical Sciences. Data were analyzed using statistics, frequency distribution and Pearson’s correlation coefficient.

Results:

A total of 104,197 cases of scorpion stings was recorded from 2010 to 2015. The cumulative incidence of scorpion sting was 2.23%. The spatial distribution of scorpion stings showed that most cases occurred in the Dehdez district (4,504 scorpion stings/100,000 inhabitants) and the Masjed Soleyman county (4,069 scorpion stings/100,000 inhabitants). A significant association was found between climate factors (temperature, evaporation rate, sunshine duration, humidity, and precipitation) and the scorpion sting rate. An increase in rainfall and humidity coincided with a reduction in scorpion stings whereas an increase in temperature, evaporation, and sunshine duration was accompanied by a growth of scorpion stings. No significant correlation was found between wind velocity/direction and the incidence rate of stings. Moreover, the seasonal peak incidence of scorpion stings was recorded in summer (an average of 8,838 cases) and the lowest incidence was recorded during winter (an average of 1,286 cases). The annual trend of scorpion sting cases decreased during the period from 2010 to 2015.

Conclusion:

Climate variables can be a good index for predicting the incidence of scorpion stings in endemic regions. Since they occur mostly in the hot season, designing preventive measures in the counties and districts with a high incidence of scorpion stings such as Dehdez and Masjed Soleyman can minimize mortality and other burdens.

Spatiotemporal variations of global terrestrial vegetation climate potential productivity under climate change

Evaluating the climate potential productivity (CPP) of terrestrial vegetation is crucial to ascertain the threshold of vegetation productivity, to maximize the utilization of regional climate resources, and to fully display the productivity application level. In this study, the maximum net primary productivity (NPPmax) representing the highest possible productivity of vegetation was calculated using the FLUXNET maximum gross primary productivity (GPPmax) from 177 flux towers. The relationships between NPPmax and a set of climate variables were established using the classification and regression tree (CART) modeling framework. The CART algorithm was used to upscale the CPP to the global scale under the current climate baseline (1980-2018) and future climate scenarios. The spatiotemporal variations in CPP over the globe were analyzed and the impacts of climate factors on it were assessed. The results indicate that global CPPs range from 0 to 2000 g C/m². The tropical rainforest area is the region with the highest CPP, whereas the lowest CPP occurs in arid/semiarid areas. These two regions were identified as the areas with the largest CPP reductions in the future. The findings reveal that CPP shows signs of productivity saturation and that future climate is not conducive to the increases in vegetation productivity in these regions. The increases in average annual temperature, minimum temperature, and solar radiation are beneficial to CPP increase in most parts of the globe under climate change. However, the negative contribution of maximum temperature increase and precipitation reduction to CPP is higher than the positive contribution of the above three rising factors to CPP in tropical and arid/semiarid areas. Our study is important to aid in creating targeted policies for future sustainable development, resource allocation, and vegetation management.

Effect of diurnal temperature range on emergency room visits for acute upper respiratory tract infections

Background

An acute upper respiratory tract infection (URI) is the most common disease worldwide, irrespective of age or sex. This study aimed to evaluate the short-term effect of diurnal temperature range (DTR) on emergency room (ER) visits for URI in Seoul, Korea, between 2009 and 2013.

Methods

Daily ER visits for URI were selected from the National Emergency Department Information System, which is a nationwide daily reporting system for ER visits in Korea. URI cases were defined according to International Classification of Diseases, 10^thRevision codes J00–J06. The search for DTR effects associated with URI was performed using a semi-parametric generalized additive model approach with log link.

Results

There were 529,527 ER visits for URI during the study period, with a daily mean of 290 visits (range, 74–1942 visits). The mean daily DTR was 8.05 °C (range, 1.1–17.6 °C). The cumulative day (lag 02) effect of DTR above 6.57 °C per 1 °C increment was associated with a 1.42% (95% confidence interval [CI] 0.04–2.82) increase in total URI. Children (≤ 5 years of age) were affected by DTR above 6.57 °C per 1 °C, with 1.45% (95% CI 0.32–2.60) at lag 02, adults (19–64 years) with 2.77% (95% CI 0.39–5.20) at lag 07. When the DTR (lag02) was 6.57 °C to 11.03 °C, the relative risk was significant at 6.01% (95% CI 2.45–9.69) for every 1 °C increase in youth subjects aged for 6 to 18 years.

Conclusions

DTR was associated with a higher risk for ER visits for URI. In addition, the results suggested that the lag effects and relative risks of DTR on URI were quite different according to age.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12199-021-00974-w.

Spatiotemporal variability of COVID-19 pandemic in relation to air pollution, climate and socioeconomic factors in Pakistan

Information on the spatiotemporal variability of respirable suspended particulate pollutant matter concentrations, especially of particles having size of 2.5 μm and climate are the important factors in relation to emerging COVID-19 cases around the world. This study aims at examining the association between COVID-19 cases, air pollution, climatic and socioeconomic factors using geospatial techniques in three provincial capital cities and the federal capital city of Pakistan. A series of relevant data was acquired from 3 out of 4 provinces of Pakistan (Punjab, Sindh, Khyber Pakhtunkhwa (KPK) including the daily numbers of COVID-19 cases, PM2.5 concentration (μgm-3), a climatic factors including temperature (°F), wind speed (m/s), humidity (%), dew point (%), and pressure (Hg) from June 1 2020, to July 31 2020. Further, the possible relationships between population density and COVID-19 cases was determined. The generalized linear model (GLM) was employed to quantify the effect of PM2.5, temperature, dew point, humidity, wind speed, and pressure range on the daily COVID-19 cases. The grey relational analysis (GRA) was also implemented to examine the changes in COVID-19 cases with PM2.5 concentrations for the provincial city Lahore. About 1,92, 819 COVID-19 cases were reported in Punjab, Sindh, KPK, and Islamabad during the study period. Results indicated a significant relationship between COVID-19 cases and PM2.5 and climatic factors at p < 0.05 except for Lahore in case of humidity (r = 0.175). However, mixed correlations existed across Lahore, Karachi, Peshawar, and Islamabad. The R2 value indicates a moderate relationship between COVID-19 and population density. Findings of this study, although are preliminary, offers the first line of evidence for epidemiologists and may assist the local community to expedient for the growth of effective COVID-19 infection and health risk management guidelines. This remains to be seen.

Characteristics of Norovirus Food Poisoning Outbreaks in Korea in the 2000s

ABSTRACT

Norovirus food poisoning outbreaks in Korea (South) appeared in the 2000s and have been increasing since then. We aimed to investigate the epidemiological features of norovirus food poisoning outbreaks in Korea from 2002 to 2017, on the basis of official food poisoning statistics and publically reliable reports, and to find any associations with climate factors. Norovirus was the most common cause of food poisoning among known causative substances in Korea during the study period. More than one-third of the outbreaks occurred in group meal service facilities, including school lunch programs. A few of these facilities used groundwater contaminated with noroviruses to wash or cook food, which contributed to outbreaks. Norovirus occurrences showed strong seasonality: cold and relatively dry winter air may help norovirus to flourish. Both norovirus genotypes GI and GII that are infectious to humans were detected, with GII becoming more prevalent than GI. According to our correlation analysis in connection with climate factors, average temperatures, the highest and lowest temperatures, precipitation, the number of rain days, and humidity showed a significant negative correlation with a monthly norovirus occurrence (P < 0.05). The lowest temperature and average temperature had higher coefficients of correlation, -0.377 and -0.376, respectively. The norovirus outbreaks in Korea showed complex etiological characteristics, although more prevailed in wintertime, and are now a major public health problem. The use of groundwater in group meal service settings is a public health issue, as well as a norovirus concern; therefore, groundwater used in food service facilities and businesses should be treated for safety.

HIGHLIGHTS

Spatial Risk Analysis of Batrachochytrium dendrobatidis, A Global Emerging Fungal Pathogen

Chytridiomycosis, a leading cause for the global decline in the number of amphibians, is caused by the fungal pathogen Batrachochytrium dendrobatidis. In this study, global distribution data of B. dendrobatidis were collected from January 2009 to May 2019. Space-time scan statistics and the maximum entropy (MaxEnt) model were used to analyze the epidemic trends and aggregation of the pathogen, and predict B. dendrobatidis distribution through its relationships with climate factors, wind speed, and solar radiation. The results of space-time scan statistics show seven clusters of data for the distribution of B. dendrobatidis. The time was mainly concentrated in 2009, 2013, 2015, and 2016, and the regions were primarily concentrated in southeastern Canada, southwestern France, Nigeria, Cameroon, eastern Brazil, southeastern Brazil, central Madagascar, and central and eastern Australia. MaxEnt showed that annual precipitation had the largest contribution percentage in the model, and annual mean temperature highly influenced the distribution of B. dendrobatidis. The global high-risk areas of B. dendrobatidis distribution were mainly observed in western Canada, southern Brazil, Chile, the United Kingdom, Japan, the Republic of Korea, eastern South Africa, eastern Madagascar, southeastern Australia, and southern China.

Temporal and climate characteristics of respiratory syncytial virus bronchiolitis in neonates and children in Sousse, Tunisia, during a 13-year surveillance

This study established the correlation between respiratory syncytial virus (RSV) bronchiolitis and climate factors in the area of Sousse, Tunisia, during 13 years (2003-2015), from neonates and children <= 5 years old and hospitalized in Farhat Hached University-Hospital of Sousse. The meteorological data of Sousse including temperature, rainfall, and humidity were obtained. RSV detection was carried out with the direct immunofluorescence assay. The impact of climate factors on viral circulation was statistically analyzed. From 2003 to 2015, the total rate of RSV bronchiolitis accounted for 34.5% and peaked in 2007 and 2013. RSV infection was higher in male cases and pediatric environment (p<0.001) and was detected in 47.3% of hospitalizations in intensive care units. The epidemic of this pathogen started in October and peaked in January (41.6%). When the infectivity of RSV was at its maximum, the monthly average rainfall was high (31 mm) and the monthly average temperature and the monthly average humidity were at their minimum (11 °C and 66%, respectively). RSV activity was negatively correlated with temperature (r = - 0.78, p = 0.003) and humidity (r = - 0.62, p = 0.03). Regression analysis showed that the monthly average temperature fits into a linear model (R² = 61%, p < 0.01). No correlation between RSV activity and rainfall was observed (p = 0.48). The meteorological predictions of RSV outbreaks with specific Tunisian climate parameters will help in determining the optimal timing of appropriate preventive strategies. In the area of Sousse, preventive measures should be enhanced since October especially, when the temperature is around 11 °C and humidity is above 60%.

The community composition variation of Russulaceae associated with the Quercus mongolica forest during the growing season at Wudalianchi City, China

BACKGROUND

Most species of the Russulaceae are ectomycorrhizal (ECM) fungi, which are widely distributed in different types of forest ecology and drive important ecological and economic functions. Little is known about the composition variation of the Russulaceae fungal community aboveground and in the root and soil during the growing season (June-October) from a Quercus mongolica forest. In this study, we investigated the changes in the composition of the Russulaceae during the growing season of this type of forest in Wudalianchi City, China.

METHODS

To achieve this, the Sanger sequencing method was used to identify the Russulaceae aboveground, and the high-throughput sequencing method was used to analyze the species composition of the Russulaceae in the root and soil. Moreover, we used the Pearson correlation analysis, the redundancy analysis and the multivariate linear regression analysis to analyze which factors significantly affected the composition and distribution of the Russulaceae fungal community.

RESULTS

A total of 56 species of Russulaceae were detected in the Q. mongolica forest, which included 48 species of Russula, seven species of Lactarius, and one species of Lactifluus. Russula was the dominant group. During the growing season, the sporocarps of Russula appeared earlier than those of Lactarius. The number of species aboveground exhibited a decrease after the increase and were significantly affected by the average monthly air temperature (r = -0.822, p = 0.045), average monthly relative humidity (r = -0.826, p = 0.043), monthly rainfall (r = 0.850, p = 0.032), soil moisture (r = 0.841, p = 0.036) and soil organic matter (r = 0.911, p = 0.012). In the roots and soils under the Q. mongolica forest, the number of species did not show an apparent trend. The number of species from the roots was the largest in September and the lowest in August, while those from the soils were the largest in October and the lowest in June. Both were significantly affected by the average monthly air temperature (r2 = 0.6083, p = 0.040) and monthly rainfall (r2 = 0.6354, p = 0.039). Moreover, the relative abundance of Russula and Lactarius in the roots and soils showed a linear correlation with the relative abundance of the other fungal genera.

Variability of soil carbon and nitrogen stocks after conversion of natural forest to plantations in Eastern China

Forest plantation, either through afforestation or reforestation, has been suggested to reverse and mitigate the process of deforestation. However, uncertainties remain in the potential of plantation forest (PF) to sequestrate carbon (C) and nitrogen (N) compared to natural forest (NF). Soil C and N stocks require a critical and updated look at what is happening especially in the context of increasing rate of land use change and climate change. The current study was conducted in China’s Eastern forest to estimate soil C and N stocks in six depth layers (0–10, 10–20, 20–40, 40–60, 60–80 and 80–100 cm) and two forest types (NF and PF) at four sites along climate factors gradient. The results showed that the overall mean soil C and N amounts to a depth of 20 cm ranged from 2.6 ± 1.1 Mg ha⁻¹ to 38.6 ± 23.1 Mg ha⁻¹, and soil nitrogen stock ranged from 0.2 ± 0.1 Mg ha⁻¹ to 3.3 ± 1.5 Mg ha⁻¹. Moreover, a loss of C stock was observed at Qingyuan (QY) by −7%, Dinghushan (DH) by −26%, Jianfengling (JF) by −13% while that of N stock was observed at QY (−8%), DH (−19%) and JF (−12%) at both depth layers. These results indicate that NFs have a better capacity to accumulate soil C and N. The soil C and N decreased from the southeast to the northeast and increased from tropical to temperate mixed forests zone in the eastern part of the study area. The C and N stock mainly occurred in the topsoil and decreased significantly with depth. Moreover, soil C and N stocks increased with age of plantation. This study provides an overview of the current spatial distribution and soil stocks of C and N, as well as the effects of environmental factors on soil C and N stocks. It also indicated that, although mean annual temperature and mean annual precipitation are the key factors affecting the variations in soil C and N, their vertical and horizontal distribution differed in various aspects.

The complex associations of climate variability with seasonal influenza A and B virus transmission in subtropical Shanghai, China

Most previous studies focused on the association between climate variables and seasonal influenza activity in tropical or temperate zones, little is known about the associations in different influenza types in subtropical China. The study aimed to explore the associations of multiple climate variables with influenza A (Flu-A) and B virus (Flu-B) transmissions in Shanghai, China. Weekly influenza virus and climate data (mean temperature (MeanT), diurnal temperature range (DTR), relative humidity (RH) and wind velocity (Wv)) were collected between June 2012 and December 2018. Generalized linear models (GLMs), distributed lag non-linear models (DLNMs) and regression tree models were developed to assess such associations. MeanT exerted the peaking risk of Flu-A at 1.4 °C (2-weeks' cumulative relative risk (RR): 14.88, 95% confidence interval (CI): 8.67-23.31) and 25.8 °C (RR: 12.21, 95%CI: 6.64-19.83), Flu-B had the peak at 1.4 °C (RR: 26.44, 95%CI: 11.52-51.86). The highest RR of Flu-A was 23.05 (95%CI: 5.12-88.45) at DTR of 15.8 °C, that of Flu-B was 38.25 (95%CI: 15.82-87.61) at 3.2 °C. RH of 51.5% had the highest RR of Flu-A (9.98, 95%CI: 4.03-26.28) and Flu-B (4.63, 95%CI: 1.95-11.27). Wv of 3.5 m/s exerted the peaking RR of Flu-A (7.48, 95%CI: 2.73-30.04) and Flu-B (7.87, 95%CI: 5.53-11.91). DTR ≥ 12 °C and MeanT <22 °C were the key drivers for Flu-A and Flu-B, separately. The study found complex non-linear relationships between climate variability and different influenza types in Shanghai. We suggest the careful use of meteorological variables in influenza prediction in subtropical regions, considering such complex associations, which may facilitate government and health authorities to better minimize the impacts of seasonal influenza.

Investigation of the relationship between burned areas and climate factors in large forest fires in theÇanakkaleregion

Fires pose a serious threat to the forests that lay on the western and southern coastline of Turkey that start with North Aegean coasts and end with the provincial boundaries of Hatay. Çanakkale, a western province of Turkey, is located in the North Aegean boundary and its topography (Dardanelles Strait), climate, and vegetation cover combine to form an inviting recipe to forest fires. Although the province is located in a transitional zone in terms of climate and vegetation, each year it witnesses highly dry and hot fire seasons. Thus, large forest fires occur periodically. In this research, the relationship between the large periodic fires (larger than 100 ha) and the climate data was investigated, with a particular focus on the most severe 8 fire seasons from 1969 to 2007. We established that there is a relationship between 1977, 1985, and 1986 fire seasons and the climate data for the corresponding periods. The remaining 5 seasons in which conflagrations occurred were also found to coincide with the days with high daily severity indices (DSR). These are 1969, 1977, 1985, 1987, and 2008. Additionally, 2008 was determined as the year with the highest fire risk, followed by year 1969.

Rate and factors for scabies recurrence in children in Saudi Arabia: a retrospective study

Background

Despite the fact that several scabies outbreaks emerged in schools in Saudi Arabia in 2018, no study has investigated the risk of scabies recurrence among children in Saudi Arabia. This study aimed to estimate the rate of scabies recurrence and identify factors that were associated with an increased risk of recurrence among children.

Methods

This is a multi-center retrospective study of children (age < 14 years) who were diagnosed between May 20, 2015 and September 12, 2018 with one or multiple recurrent scabies at the Ministry of National Guard Health Affairs (MNGHA) hospitals and clinics in Saudi Arabia. Data were obtained from an electronic health system, BestCare database.

Results

A sample of 264 children analyzed (mean age of 6.7 years) resulted in a cumulative number of 316 scabies diagnoses in which 86 (27.2%) experienced scabies recurrence (at least once). Independent factors associated with a high risk of scabies recurrence: older children (adjusted hazard ratio [aHR], 1.036; 95% CI, 1.002–1.072; P = 0.039), female gender (aHR, 1.734; 95% CI, 1.329–2.262; P = 0.001), Western region of Saudi Arabia (aHR, 1.548; 95% CI, 1.115–2.151; P = 0.009), and 2nd tertile season [May to August] (aHR, 2.368; 95% CI, 1.706–3.288; P = 0.001).

Conclusions

The study demonstrated that the recurrence rate of scabies among children is high. Older children, the female gender, the Western region of Saudi Arabia, and the seasonality were independently associated with an increased risk of scabies recurrence. High temperature and low humidity should be explored as leading factors for scabies infestations in Saudi Arabia. Findings derived from this study may be useful for clinicians and governments in optimizing clinical management of scabies cases and contacts.

Wind speed predicts population dynamics of the eriophyid mite Floracarus perrepae on invasive Old World climbing fern (Lygodium microphyllum) in a shade house colony

Lygodium microphyllum is one of the most noxious invasive plants in Florida, USA, smothering native vegetation in cypress swamps, pine flatwoods, and Everglades tree islands and altering fire regimes. The eriophyid mite Floracarus perrepae was introduced from Australia to help control L. microphyllum infestations. While F. perrepae exhibits high population growth rates in its native range, its population dynamics in Florida are unknown, particularly the dynamics that occur within the leaf roll galls the mite induces on the margins of leaves. Here, we monitored a shade house colony of F. perrepae in south Florida for 2 years to identify seasonal patterns and potential climate drivers of within-gall mite density. Gall dissections of mite-infested colony plants were performed monthly. Mite density within galls exhibited two cycles per year: a strong cycle that boomed in spring and busted in summer, and a weak cycle that moderately increased mite density in fall and declined in winter. Climate variables, particularly those related to wind speed, were positively associated with higher mite density. Our study sheds light on the within-gall dynamics of F. perrepae and suggests that the highest within-gall mite densities occur in the spring and fall.

Carabid community structure in northern China grassland ecosystems: Effects of local habitat on species richness, species composition and functional diversity

Background

Most carabid beetles are particularly sensitive to local habitat characteristics. Although in China grasslands account for more than 40% of the national land, their biodiversity is still poorly known. The aim of this paper is to identify the main environmental characteristics influencing carabid diversity in different types of grassland in northern China.

Methods

We investigated the influence of vegetation (plant biomass, cover, density, height and species richness), soil (bulk density, above ground litter, moisture and temperature) and climate (humidity, precipitation and temperature) on carabid community structure (species richness, species composition and functional diversity—measured as body size, movement and total diversity) in three types of grasslands: desert, typical and meadow steppes. We used Canonical correspondence analysis to investigate the role of habitat characteristics on species composition and eigenvector spatial filtering to investigate the responses of species richness and functional diversities.

Results

We found that carabid community structure was strongly influenced by local habitat characteristics and particularly by climatic factors. Carabids in the desert steppe showed the lowest richness and functional diversities. Climate predictors (temperature, precipitation and humidity) had positive effects on carabid species richness at both regional and ecosystem levels, with difference among ecosystems. Plant diversity had a positive influence on carabid richness at the regional level. Soil compaction and temperature were negatively related to species richness at regional level. Climatic factors positively influenced functional diversities, whereas soil temperature had negative effects. Soil moisture and temperature were the most important drivers of species composition at regional level, whereas the relative importance of the various environmental parameters varied among ecosystems.

Discussion

Carabid responses to environmental characteristics varied among grassland types, which warns against generalizations and indicates that management programs should be considered at grassland scale. Carabid community structure is strongly influenced by climatic factors, and can therefore be particularly sensitive to ongoing climate change.

The climatic factors affecting dengue fever outbreaks in southern Taiwan: an application of symbolic data analysis

BACKGROUND

Dengue fever is a leading cause of severe illness and hospitalization in Taiwan. This study sought to elucidate the linkage between dengue fever incidence and climate factors.

RESULTS

The result indicated that temperature, accumulated rainfall, and sunshine play an important role in the transmission cycles of dengue fever. A predictive model equation plots dengue fever incidence versus temperature, rainfall, and sunshine, and it suggests that temperature, rainfall, and sunshine are significantly correlated with dengue fever incidence.

CONCLUSIONS

The data suggests that climate factors are important determinants of dengue fever in southern Taiwan. Dengue fever viruses and the mosquito vectors are sensitive to their environment. Temperature, rainfall and sunshine have well-defined roles in the transmission cycle. This finding suggests that control of mosquito by climatic factor during high temperature seasons may be an important strategy for containing the burden of dengue fever.

Distribution of Haemagogus and Sabethes Species in Relation to Forest Cover and Climatic Factors in the Chapada Dos Guimarães National Park, State of Mato Grosso, Brazil

Members of the genera Haemagogus and Sabethes are the most important biological vectors of the wild yellow fever virus (WYF) in the forested areas of the Americas. The ecologies of Haemagogus janthinomys, Hg. leucocelaenus, Sabethes chloropterus, and Sa. glaucodaemon were studied in a forest of the Chapada dos Guimarães National Park, state of Mato Grosso, Brazil, to evaluate the influence of climatic factors (temperature and relative humidity [RH]) on their abundance. We also examined the association of climate with landscape structure on species distribution patterns throughout the seasons of the year. Multiple stepwise regressions showed that RH was most likely to influence the density of mosquito populations. A multidimensional scaling (MDS) was used to evaluate the effects of forest cover on the composition of mosquito populations at different radii (100-, 250-, and 1,000-m-radius buffer areas). The MDS provided 2 dimensions with values that indicated a higher similarity in the composition of culicid populations between sites 1 and 3, while site 2 was separate from the others in the ordination space. Site 2 had a much higher forest cover ratio at 100-m radius compared with sites 1 and 3. We found a possible relationship between the forest cover and the composition of the mosquito populations only in the 100-m radius. These results enabled us to infer that RH directly favored the activity of mosquito populations and that the forest cover located closest to the sampling site may influence the species composition. Since mosquito abundance was higher in the sites with lower local forest cover, forest fragmentation may be a key factor on the presence of WYF vector.

The weekly associations between climatic factors and Plasmodium vivax and Plasmodium falciparum malaria in China, 2005-2014

Background

Meteorological factors play a crucial role in malaria transmission, but limited evidence is available from China. This study aimed to estimate the weekly associations between meteorological factors and Plasmodium vivax and Plasmodium falciparum malaria in China.

Methods

The Distributed Lag Non-Linear Model was used to examine non-linearity and delayed effects of average temperature, rainfall, relative humidity, sunshine hours, wind speed and atmospheric pressure on malaria.

Results

Average temperature was associated with P. vivax and P. falciparum cases over long ranges of lags. The effect was more immediate on P. vivax (0-6 weeks) than on P. falciparum (1-9 weeks). Relative humidity was associated with P. vivax and P. falciparum over 8-10 weeks and 5-8 weeks lag, respectively. A significant effect of wind speed on P. vivax was observed at 0-2 weeks lag, but no association was found with P. falciparum. Rainfall had a decreasing effect on P. vivax, but no association was found with P. falciparum. Sunshine hours were negatively associated with P. falciparum, but the association was unclear for P. vixax. However, the effects of atmospheric pressure on both malaria types were not significant at any lag.

Conclusions

Our study highlights a substantial effect of weekly climatic factors on P. vivax and P. falciparum malaria transmission in China, with different lags. This provides an evidence base for health authorities in developing a malaria early-warning system.

The climate impact on female acute pyelonephritis in Taiwan: A population-based study

OBJECTIVE

Urinary tract infection (UTI) is the main reason of community-acquired infection which causes large losses in social economy. The individual as well as climate factors make changes on the incidence. Acute pyelonephritis (APN) is one of the most serious UTI in female. The object of our study is to analyze whether climate factors will have effect on the incidence of female APN in Taiwan.

MATERIALS AND METHODS

This study consisted of 14,568 female patients with APN from 2001 to 2013 in Taiwan and patients with repeated APN were excluded. The monthly climate data was collected from the Central Weather Bureau. The available monthly climate data included highest, lowest, and average level of temperatures, humidity, rainfall, total rain days, and sunshine hours.

RESULTS

The total incidence of female APN was 23.44 each 10,000 populations. The incidence of APN was positively correlated with temperature (r = 0.66), sunshine hours (r = 0.45), rainfall (r = 0.42), rain days (r = 0.29), and humidity (r = 0.23) per month. There is the strongest correlation between the average monthly temperature and the incidence of APN (β = 0.54). The correlation with the incidence of APN was also followed by rain days (β = 0.28) and humidity (β = 0.27).

CONCLUSION

There is a significant expression on the incidence of female APN affected by seasonality and climate parameters. The monthly average temperature has the strongest correlation with female APN. The results of this research may facilitate the potential preventive strategies on female APN.

The ectomycorrhizal fungal communities associated with Quercus liaotungensis in different habitats across northern China

Quercus liaotungensis is a major tree species in deciduous broad-leaved forests in northern China. In this study, we investigated ectomycorrhizal (ECM) communities associated with Q. liaotungensis from five typical habitats across northern China. We used internal transcribed spacer-polymerase chain reaction and DNA sequencing to identify ECM fungi, and we detected 220 operational taxonomic units. In general, at the regional scale, the dominant ECM lineages were /tomentella-thelephora, /cenococcum, /russula-lactarius, and /inocybe. Analysis of variance demonstrated significant differences in alpha diversity among these ECM communities, and the ECM fungal richness was positively correlated with elevation and soil organic matter. Analysis of similarity and a nonmetric multidimensional scaling analysis revealed that there were significant differences in community composition, and the geographical distance was correlated with the ECM fungal communities. Among the environmental factors we studied, soil parameters and climate factors were the primary direct driving factors of the ECM fungal communities. Our study primarily advances our understanding of environmental factors affecting ECM fungal communities at regional scale.

Seasonal patterns of dengue fever and associated climate factors in 4 provinces in Vietnam from 1994 to 2013

BACKGROUND

In Vietnam, dengue fever (DF) is still a leading cause of hospitalization. The main objective of this study was to evaluate the seasonality and association with climate factors (temperature and precipitation) on the incidences of DF in four provinces where the highest incidence rates were observed from 1994 to 2013 in Vietnam.

METHODS

Incidence rates (per 100,000) were calculated on a monthly basis from during the study period. The seasonal-decomposition procedure based on loess (STL) was used in order to assess the trend and seasonality of DF. In addition, a seasonal cycle subseries (SCS) plot and univariate negative binomial regression (NBR) model were used to evaluate the monthly variability with statistical analysis. Lastly, a generalized estimating equation (GEE) was used to assess the relationship between monthly incidence rates and weather factors (temperature and precipitation).

RESULTS

We found that increased incidence rates were observed in the second half of each year (from May through December) which is the rainy season in each province. In Hanoi, the final model showed that 1 °C rise of temperature corresponded to an increase of 13% in the monthly incidence rate of DF. In Khanh Hoa, the final model displayed that 1 °C increase in temperature corresponded to an increase of 17% while 100 mm increase in precipitation corresponded to an increase of 11% of DF incidence rate. For Ho Chi Minh City, none of variables were significant in the model. In An Giang, the final model showed that 100 mm increase of precipitation in the preceding and same months corresponded to an increase of 30% and 22% of DF incidence rate.

CONCLUSION

Our findings provide insight into understanding the seasonal pattern and associated climate risk factors.

A stage structured mosquito model incorporating effects of precipitation and daily temperature fluctuations

An outbreak of dengue fever in Guangdong province in 2014 was the most serious outbreak ever recorded in China. Given the known positive correlation between the abundance of mosquitoes and the number of dengue fever cases, a stage structured mosquito model was developed to investigate the cause of the large abundance of mosquitoes in 2014 and its implications for outbreaks of the disease. Data on the Breteau index (number of containers positive for larvae per 100 premises investigated), temperature and precipitation were used for model fitting. The egg laying rate, the development rate and the mortality rates of immatures and adults were obtained from the estimated parameters. Moreover, effects of daily fluctuations of temperature on these parameters were obtained and the effects of temperature and precipitation were analyzed by simulations. Our results indicated that the abundance of mosquitoes depended not only on the total annual precipitation but also on the distribution of the precipitation. The daily mean temperature had a nonlinear relationship with the abundance of mosquitoes, and large diurnal temperature differences can reduce the abundance of mosquitoes. In addition, effects of increasing precipitation and temperature were interdependent. Our findings suggest that the large abundance of mosquitoes in 2014 was mainly caused by the distribution of the precipitation. In the perspective of mosquito control, our results reveal that it is better to clear water early and spray insecticide between April and August in case of limited resources.

Dynamic Relations between Incidence of Zoonotic Cutaneous Leishmaniasis and Climatic Factors in Golestan Province, Iran

BACKGROUND

Zoonotic Cutaneous Leishmaniasis (ZCL), an important public health problem in Iran, is sensitive to climate conditions. This study aimed to examine dynamic relations between the climate factors and incidence of ZCL in Golestan Province, northern Iran during 2010-2012.

METHODS

Data of monthly climatic factors, including temperature variables, relative humidity variables, evaporation, total rainfall, and number of freezing and rainy days together with monthly ZCL incidence were used. Spear-man rank correlation was carried out to explain associations between the monthly ZCL incidence rate and climate factors at 0, 1, 2, 3 and 4 months lagged periods. Pearson's correlation analysis was conducted to examine the type and strength of relationships between the spatially averaged climate factors and ZCL incidence rate in district level. Stepwise multiple regression was used to find the best combination of independent climatic variables, which predict the ZCL incidence.

RESULTS

Spearman correlation analysis indicated that the highest correlations between climate factors and monthly ZCL incidence were established when the climate time-series lagged the ZCL incidence series, especially two month prior to disease incidence. Based on the results of the both Spearman rank correlation and Pearson correlation analyses, ZCL incidences in Golestan Province tend to be more prevalent in areas with higher temperature, lower relative humidity, lower total rainfall, higher evaporation and lower number of rainy days. The results of stepwise regression analysis indicated that minimum temperature, mean humidity, and rainfall had considerable effect on ZCL incidence.

CONCLUSION

Climate factors are major determinants of ZCL incidence rate in Golestan Province and such climate conditions provide favourable conditions for propagation and transmission of ZCL in this endemic area.