Explore the vast range of titles available.

Background Mosquitoes play an absolute role in the spread of epidemic arbovirus diseases. Worldwide, Aedes aegypti and Aedes albopictus are the main vectors responsible for the spread of these mosquito-borne diseases. Aedes vexans , a mosquito species native to China, also carries mosquito-borne viruses, such as dengue fever virus and Japanese encephalitis virus, but research on this mosquito has been inadequate. Mapping the potential distribution range of and monthly change in the distribution of these three Aedes species is of particular importance for mosquito surveillance, eradication and disease control. Methods Monitoring data were collected for the three Aedes species in China. Long-term temperature and precipitation data (2001–2021) and land cover data were used to represent various climate and environmental conditions. An ecological niche model was developed using a maximum entropy modeling method to predict the current optimum habitat areas for the three Aedes species and to obtain important variables influencing their monthly distribution. Results The distribution model for the three Aedes species performed well, with an area under the receiver operating characteristic curve value of 0.991 for Ae. aegypti , 0.928 for Ae. albopictus and 0.940 for Ae. vexans . Analysis of the distribution change and mapping of the optimum habitat range for each Aedes species for each month demonstrated that temperature, precipitation and construction land were important factors influencing the distribution of these three Aedes species. Conclusions In China, Aedes aegypti is mainly concentrated in a few tropical regions and along the Yunnan border; Aedes albopictus is widely distributed throughout most of the country, except for the arid and semi-arid regions of northwest China; and Aedes vexans is mainly found in the northern regions. Our results provide a basis for the timing and location of surveillance efforts for high-priority mosquitoes. Graphical abstract

Aim/Objective: Our initiative in cooperating with Regional Pharmacovigilance Officers (RPVOs) aims to improve the distribution and availability of aRMMs in local hospitals. Also, we aim to raise awareness about approved aRMMs, improve the aRMMs implementation, and communicate with Healthcare Professionals (HCPs). Methods: At the beginning of 2023, the SFDA met with RPVOs to explain to them the initiative's objectives. We have established three Key Performance Indicators: ensuring access to approved aRMMs by HCPs in the hospital, ensuring the availability of approved aRMMs in the hospital, and conducting one-to-one educational visits to HCPs to increase their knowledge and improve implementation of aRMMs. Educational visits included pre- and post-visit surveys and discussions. The Corrective actions were carried out in accordance with the desired outcomes. Results: The number of HCPs who received aRMMs experienced a significant rise, reaching 6,492 HCPs in 2023. This noteworthy increase can be attributed to the implementation of a monthly distribution system, wherein the approved ARMMs were transmitted electronically to the RPVOs via email. Upon evaluation, it was determined that the availability of aRMMs in hospitals stood at about 70%. To enhance this figure in the future, we have initiated a corrective plan that involves redistributing aRMMs within hospitals and establishing direct communication channels between the Qualified Person Responsible for Pharmacovigilance in a pharmaceutical company and RPVOs to address any issues of missing aRMMs promptly. Regarding educational visits, the RPVOs successfully conducted 100 visits to HCPs, surpassing the initial target by 100%. During these visits, HCPs found that aRMMs materials are useful and they expressed high satisfaction with the visits. Conclusion: Our project enhances HCP awareness of approved aRMMs at local hospitals as well as their accessibility and availability. Furthermore, Educational visits have been effective in enhancing HCP understanding of aRMMs and they should be conducted more frequently. Further efforts are needed to improve the regular distribution of aRMMs by pharmaceutical companies.

A humid tropical forest disturbance alert using Sentinel-1 radar data is presented for the Congo Basin. Radar satellite signals can penetrate through clouds, allowing Sentinel-1 to provide gap-free observations for the tropics consistently every 6-12 days at 10 m spatial scale. In the densely cloud covered Congo Basin, this represents a major advantage for the rapid detection of small-scale forest disturbances such as subsistence agriculture and selective logging. Alerts were detected with latest available Sentinel-1 images and results are presented from January 2019 to July 2020. We mapped 4 million disturbance events during this period, totalling 1.4 million ha with nearly 80% of events smaller than 0.5 ha. Monthly distribution of alert totals varied widely across the Congo Basin countries and can be linked to regional differences in wet and dry season cycles, with more forest disturbances in the dry season. Results indicated high user's and producer's accuracies and the rapid confirmation of alerts within a few weeks. Our disturbance alerts provide confident detection of events larger than or equal to 0.2 ha but do not include smaller events, which suggests that disturbance rates in the Congo Basin are even higher than presented in this study. The new alert product can help to better study the forest dynamics in the Congo Basin with improved spatial and temporal detail and near real-time detections, and highlights the value of dense Sentinel-1 time series data for large-area tropical forest monitoring. The research contributes to the Global Forest Watch initiative in providing timely and accurate information to support a wide range of stakeholders in sustainable forest management and law enforcement. The alerts are available via the https://www.globalforestwatch.org and http://radd-alert.wur.nl.

Aim Large marine predators, such as cetaceans and sharks, play a crucial role in maintaining biodiversity patterns and ecosystem function, yet few estimates of their spatial distribution exist. We aimed to determine the species richness of large marine predators and investigate their fine‐scale spatiotemporal distribution patterns to inform conservation management. Location The Hauraki Gulf/Tīkapa Moana/Te Moananui‐ā‐Toi, Aotearoa/New Zealand. Methods We conducted a replicate systematic aerial survey over 12 months. Flexible machine learning models were used to explore relationships between large marine predator occurrence (Bryde's whales, common and bottlenose dolphins, bronze whaler, pelagic and immature hammerhead sharks) and environmental and biotic variables, and predict their monthly distribution and associated spatially explicit uncertainty. Results We revealed that temporally dynamic variables, such as prey distribution and sea surface temperature, were important for predicting the occurrence of the study species and species groups. While there was variation in temporal and spatial distribution, predicted richness peaked in summer and was the highest in coastal habitats during that time, providing insight into changes in distributions over time and between species. Main Conclusions Temporal changes in distribution are not routinely accounted for in species distribution studies. Our approach highlights the value of multispecies surveys and the importance of considering temporally variable abiotic and biotic drivers for understanding biodiversity patterns when informing ecosystem‐scale conservation planning and dynamic ocean management.

The paper presents two aspects concerned with the mercury air emission inventory from coal-fired public power and energy plants: an uncertainty analysis, using Monte Carlo simulation (Journal of the American Statistical Association, 44(247), 335–341 1949 ) and the monthly distributions applying the Denton-Cholette approach (Dagum & Cholette 2006 ). The analysis determines uncertainty about the estimates mercury air emission from 1990 to 2012 including the development of air pollution control (APC) technologies in the Polish public power and energy sector, also the monthly distributions in comparison with previously obtained results (Hławiczka 2008 ). The uncertainty of mercury (Hg) content in fuel is 31.6% for hard coal and 42.4% for brown coal. The confidence interval for the estimated emission changed from [kg] (16,082.2; 16,242.2) in 1990 to (10,525.9;10,671.1) in 2012. However, the Denton-Cholette approach overestimates the emissions for the warmer periods of the year, but it could, however, in our view, be applied to attain the monthly distributions.

There are clear signs that milk production growth is leveling off, and recently even declining, in China. Heat stress is one of the main reasons for the recent reduction in milk production. In this study, we computed the change in milk production as a result of heat stress in major milk production areas in China. We constructed a temperature–humidity index (THI) spatial layer to understand the monthly distribution of heat and moisture. We documented specific areas in northern China where cattle were at high risk to heat stress in specific months. THI values exceeded the threshold above which milk production declines during months of June, July, and August. Especially during July, the THI value was higher than the production threshold in recent years (2008 to 2016) and in projected future scenarios (2050 and 2070). THI-based milk yield losses were up from 0.7 to about 4 kg per cow per day in July 2016. These losses are projected to increase from 1.5 to 6.5 kg in 2050 and 2 to 7.2 kg in 2070 (representing production losses between 15 and 50%). These results suggest that climate change will have significant consequences for the dairy sector in major milk-producing areas in China. Our results are useful in identifying areas susceptible to heat stress where adaptive livestock management practices are needed to prevent significant production decreases.

The trend analysis approach is adopted for the prediction of future climatological behavior and climate change impact on agriculture, the environment, and water resources. In this study, the innovative trend pivot analysis method (ITPAM) and trend polygon star concept method were applied for precipitation trend detection at 11 stations located in the Soan River basin (SRB), Potohar region, Pakistan. Polygon graphics of total monthly precipitation data were created and trends length and slope were calculated separately for arithmetic mean and standard deviation. As a result, the innovative methods produced useful scientific information and helped in identifying, interpreting, and calculating monthly shifts under different trend behaviors, that is, increase in some stations and decrease in others of precipitation data. This increasing and decreasing variability emerges from climate change. The risk graphs of the total monthly precipitation and monthly polygonal trends appear to show changes in the trend of meteorological data in the Potohar region of Pakistan. The monsoonal rainfall of all stations shows a complex nature of behavior, and monthly distribution is uneven. There is a decreasing trend of rainfall in high land stations of SRB with a significant change between the first dataset and the second dataset in July and August. It was examined that monsoon rainfall is increasing in lowland stations indicating a shifting pattern of monsoonal rainfall from highland to lowland areas of SRB. The increasing and decreasing trends in different periods with evidence of seasonal variations may cause irregular behavior in the water resources and agricultural sectors.

Tracking data of marine predators are increasingly used in marine spatial management. We developed a spatial dataset with estimates of the monthly distribution of six pelagic seabird species breeding in the Northeast Atlantic. The dataset is based on year-round global location sensor (GLS) tracking data of 2356 adult seabirds from 2006-2019 from a network of seabird colonies, data describing the physical environment, and data on seabird population sizes. Tracking and environmental data were combined in monthly species distribution models (SDMs). Cross-validations were used to assess the transferability of models between years and breeding locations. The analyses showed that birds from colonies close to each other (<500 km apart) used the same nonbreeding habitats, while birds from distant colonies (>1000 km) used colony-specific, and in many cases, non-overlapping habitats. Based on these results, the SDM from the nearest model colony was used to predict the distribution of all seabird colonies lying within a species-specific cut-off distance (400-500 km). The uncertainties in predictions were estimated by cluster bootstrap sampling. The resulting dataset consists of 4692 map layers, each layer predicting the densities of birds from a given species, colony and month across the North Atlantic. The dataset represents the annual distribution of 23.5 million adult pelagic seabirds, or 87% of the Northeast Atlantic breeding population of the study species. We show how the dataset can be used in population and spatial management applications, including the detection of population-specific nonbreeding habitats and identifying populations influenced by marine protected areas.

Habitat selection studies are designed to generate predictions of species distributions or inference regarding general habitat associations and individual variation in habitat use. Such studies frequently involve either individually indexed locations gathered across limited spatial extents and analyzed using resource selection functions (RSFs) or spatially extensive locational data without individual resolution typically analyzed using species distribution models. Both analytical methodologies have certain desirable features, but analyses that combine individual‐ and population‐level inference with flexible non‐linear functions may provide improved predictions while accounting for individual variation. Here, we describe how RSFs can be fit using hierarchical generalized additive models (HGAMs) using widely available software, providing a means to explore individual variation in habitat associations and to generate species distribution maps. We used GPS tracking data from golden eagles Aquila chrysaetos from across eastern North America with four environmental predictors to generate monthly distribution models. We considered three model structures that assumed different amounts of individual variation in the functional relationship between predictors and habitat use and used k‐fold cross‐validation to compare model performance. Models accounting for individual variability in shape and smoothness of functional responses performed best. Eagles exhibited the least amount of individual variation in response to land cover variables during winter months, with most individuals more closely adhering to the population‐level trend. During the summer months, eagles exhibited more substantial individual variation in shape and smoothness of the functional relationships, suggesting some need to account for individual variation in eagle habitat use for both inferential and predictive purposes, during this time of year. Because they allow users to blend flexible functions with random effects structures and are well‐supported by a variety of software platforms, we believe that HGAMs provide a useful addition to the suite of analyses used for modeling habitat associations or predicting species distributions.

We developed a remote sensing approach based on multi‐satellite observations, which provides an unprecedented estimate of monthly distribution and area of land‐surface open water over the whole globe. Results for 1993 to 2007 exhibit a large seasonal and inter‐annual variability of the inundation extent with an overall decline in global average maximum inundated area of 6% during the fifteen‐year period, primarily in tropical and subtropical South America and South Asia. The largest declines of open water are found where large increases in population have occurred over the last two decades, suggesting a global scale effect of human activities on continental surface freshwater: denser population can impact local hydrology by reducing freshwater extent, by draining marshes and wetlands, and by increasing water withdrawals. Key Points The monthly extent of global land surface water is estimated by satellites A large variability of the surface water extent is evidenced at global scale The effect of human activities on continental surface water is observed