Variations of floral temperature in changing weather conditions.

Floral temperature is a flower characteristic that has the potential to impact the fitness of flowering plants and their pollinators. Likewise, the presence of floral temperature patterns, areas of contrasting temperature across the flower, can have similar impacts on the fitness of both mutualists. It is currently poorly understood how floral temperature changes under the influence of different weather conditions, and how floral traits may moderate these changes. The way that floral temperature changes with weather conditions will impact how stable floral temperatures are over time and their utility to plants and pollinators. The stability of floral temperature cues is likely to facilitate effective plant-pollinator interactions and play a role in the plant's reproductive success. We use thermal imaging to monitor how floral temperatures and temperature patterns of four plant species (Cistus 'snow fire' and 'snow white', Coreopsis verticillata and Geranium psilostemon) change with several weather variables (illumination, temperature; windspeed; cloud cover; humidity and pressure) during times that pollinators are active. All weather variables influenced floral temperature in one or more species. The directionality of these relationships was similar across species. In all species, light conditions (illumination) had the greatest influence on floral temperatures overall. Floral temperature and the extent to which flowers showed contrasting temperature patterns were influenced predominantly by light conditions. However, several weather variables had additional, lesser, influences. Furthermore, differences in floral traits, pigmentation and structure, likely resulted in differences in temperature responses to given conditions between species and different parts of the same flower. However, floral temperatures and contrasting temperature patterns that are sufficiently elevated for detection by pollinators were maintained across most conditions if flowers received moderate illumination. This suggests the presence of elevated floral temperature and contrasting temperature patterns are fairly constant and may have potential to influence plant-pollinator interactions across weather conditions.

Particle-ozone complex pollution under diverse synoptic weather patterns in the Yangtze River Delta region: Synergistic relationships and the effects of meteorology and chemical compositions.

There is considerable academic interest in the particle-ozone synergistic relationship (PO) between fine particulate matter (PM2.5) and ozone (O3). Using various synoptic weather patterns (SWPs), we quantitatively assessed the variations in the PO, which is relevant to formulating policies aimed at controlling complex pollution in the air. First, based on one-year sampling data from March 2018 to February 2019, the SWPs classification of the Yangtze River Delta (YRD) was conducted using the sum-of-squares technique (SS). Five dominant SWPs can be found in the YRD region, including the Aleutian low under SWP1 (occurring 45 % of the year), a tropical cyclone under SWP2 (21 %), the tropical cyclone and western Pacific Subtropical High (WPSH) under SWP3 (15.4 %), the WPSH under SWP4 (6.9 %), and a continental high pressure under SWP5 (3.1 %). The phenomenon of a "seesaw" between PM2.5 and O3 concentrations exhibited significant spatial heterogeneity, which was influenced by meteorological mechanisms. Second, the multi-linear regression (MLR) model and the partial correlation (PCOR) analysis were employed to quantify the effects of dominant components and meteorological factors on the PO. Meteorological variables could collectively explain only 33.0 % of the PM2.5 variations, but 58.0 % for O3. O3 promoted each other with low concentrations of PM2.5 but was inhibited by high concentrations of PM2.5. High relative humidity (RH) was conducive to the generation of PM2.5 secondary components and enhanced the radiative effects of aerosols and the negative correlation of PO. In addition, attention should be paid to assessing the combined effects of precursor levels, weather, and chemical reactions on the particle-ozone complex pollution. The control of O3 pollutants should be intensified in summer, while the focus should be on reducing PM2.5 pollutants in winter. Prevention and control measures need to reflect the differences in weather conditions and pollution characteristics, with a focus on RH and secondary components of PM2.5.

Bt corn and cotton planting may benefit peanut growers by reducing aflatoxin risk.

Decades of studies have shown that Bt corn, by reducing insect damage, has lower levels of mycotoxins (fungal toxins), such as aflatoxin and fumonisin, than conventional corn. We used crop insurance data to infer that this benefit from Bt crops extends to reducing aflatoxin risk in peanuts: a non-Bt crop. In consequence, we suggest that any benefit-cost assessment of how transgenic Bt crops affect food safety should not be limited to assessing those crops alone; because the insect pest control offered by Bt crops affects the food safety profile of other crops grown nearby. Specifically, we found that higher Bt corn and Bt cotton planting rates in peanut-growing areas of the United States were associated with lower aflatoxin risk in peanuts as measured by aflatoxin-related insurance claims filed by peanut growers. Drought-related insurance claims were also lower: possibly due to Bt crops' suppression of insects that would otherwise feed on roots, rendering peanut plants more vulnerable to drought. These findings have implications for countries worldwide where policies allow Bt cotton but not Bt food crops to be grown: simply planting a Bt crop may reduce aflatoxin and drought stress in nearby food crops, resulting in a safer food supply through an inter-crop "halo effect."

Using Support Vector Machines to Classify Road Surface Conditions to Promote Safe Driving.

Accurate detection of road surface conditions in adverse winter weather is essential for traffic safety. To promote safe driving and efficient road management, this study presents an accurate and generalizable data-driven learning model for the estimation of road surface conditions. The machine model was a support vector machine (SVM), which has been successfully applied in diverse fields, and kernel functions (linear, Gaussian, second-order polynomial) with a soft margin classification technique were also adopted. Two learner designs (one-vs-one, one-vs-all) extended their application to multi-class classification. In addition to this non-probabilistic classifier, this study calculated the posterior probability of belonging to each group by applying the sigmoid function to the classification scores obtained by the trained SVM. The results indicate that the classification errors of all the classifiers, excluding the one-vs-all linear learners, were below 3%, thereby accurately classifying road surface conditions, and that the generalization performance of all the one-vs-one learners was within an error rate of 4%. The results also showed that the posterior probabilities can analyze certain atmospheric and road surface conditions that correspond to a high probability of hazardous road surface conditions. Therefore, this study demonstrates the potential of data-driven learning models in classifying road surface conditions accurately.

Enhancing primary healthcare nurses' preparedness for climate-induced extreme weather events.

BACKGROUND: Climate Change is causing frequent and sever extreme weather events globally, impacting human health and well-being. Primary healthcare (PHC) nurses' are at the forefront of addressing these challenges and must be prepared. PURPOSE: This scoping revieww explored literature on the preparedness of the PHC nursing workforce for extreme weather events and identify gaps in knowledge and practice. METHODS: Using Arksey and O'Malley's framework, a comprehensive search was conducted across PubMed, Scopus, CINHAL, Web of Sciences, and ProQuest, on studies from 2014-2024, addressing PHC nurses' preparedness. DISCUSSION: Nine studies were identified and highlighted a need for preparedness training and facility-based preparedness plans. Key themes included prioritizing regional networks, clinical leadership, service delivery, health information, health workforce, medical products and technologies, and financing. CONCLUSION: Strengthening PHC nurses' resilience against extreme weather requires targeted professional development, mental health support, comprehensive planning, and collaborative efforts. Future strategies should enhance PHC nurses' capacity through training, support, and policy development.

Association of genomic prediction for twin pregnancies with the incidence of double ovulation in primiparous dairy cows.

Twin pregnancies compromise the health and well-being of dairy cattle. A recent genomic prediction model for twin pregnancies has been developed based on twin calving or abortion. However, the incidence of double ovulation is significantly higher than that of twin births. This study aimed to evaluate whether genomic prediction values for twin pregnancies are associated with the incidence of double ovulation in primiparous dairy cows. Factors influencing the double ovulation rate were analysed using binary logistic regression on 676 cows: 475 (70.3%) inseminated at spontaneous estrus and 201 following one of two different estrus synchronization protocols for fixed-time artificial insemination (FTAI). The odds ratio for double ovulations was 0.92 (p = .002) per unit increase in prediction value and 2 (p = .01) for cows subjected to an FTAI protocol. Our findings suggest that genomic prediction values for twin pregnancies can effectively identify the risk of double ovulation at the herd level.

Process-Informed Subsampling Improves Subseasonal Rainfall Forecasts in Central America.

Subseasonal rainfall forecast skill is critical to support preparedness for hydrometeorological extremes. We assess how a process-informed evaluation, which subsamples forecasting model members based on their ability to represent potential predictors of rainfall, can improve monthly rainfall forecasts within Central America in the following month, using Costa Rica and Guatemala as test cases. We generate a constrained ensemble mean by subsampling 130 members from five dynamic forecasting models in the C3S multimodel ensemble based on their representation of both (a) zonal wind direction and (b) Pacific and Atlantic sea surface temperatures (SSTs), at the time of initialization. Our results show in multiple months and locations increased mean squared error skill by 0.4 and improved detection rates of rainfall extremes. This method is transferrable to other regions driven by slowly-changing processes. Process-informed subsampling is successful because it identifies members that fail to represent the entire rainfall distribution when wind/SST error increases.

Source-sink relationships during grain filling in wheat in response to various temperature, water deficit and nitrogen deficit regimes.

Grain filling is a critical process for improving crop production under adverse conditions caused by climate change. Here, using a quantitative method, we quantified post-anthesis source-sink relationships of a large data set to assess the contribution of remobilized pre-anthesis assimilates to grain growth for both biomass and nitrogen. The data set came from 13 years' semi-controlled field experimentation, in which six bread wheat genotypes were grown at plot scale under contrasting temperature, water, and nitrogen regimes. On average, grain biomass was ~10% higher than post-anthesis aboveground biomass accumulation across regimes and genotypes. Overall, the estimated relative contribution (%) of remobilized assimilates to grain biomass became increasingly significant with increasing stress intensity, ranging from virtually nil to 100%. This percentage was altered more by water and nitrogen regimes than by temperature, indicating the greater impact of water or nitrogen regimes relative to high temperatures under our experimental conditions. Relationships between grain nitrogen demand and post-anthesis nitrogen uptake were generally insensitive to environmental conditions, as there was always significant remobilization of nitrogen from vegetative organs, which helped to stabilize the amount of grain nitrogen. Moreover, variations in the relative contribution of remobilized assimilates with environmental variables were genotype-dependent. Our analysis provides an overall picture of post-anthesis source-sink relationships and pre-anthesis assimilate contributions to grain filling across (non-)environmental factors, and highlights that designing wheat adaption to climate change should account for complex multi-factor interactions.

[Transport Characteristics and Potential Source Areas of PM2.5 in Yangquan City Under Different Pollution Levels].

Based on the PM2.5 monitoring data, NCEP data, and the meteorological data of the weather situation analysis at the corresponding time in Yangquan City from 2020 to 2022, using the HYSPLIT4 backward trajectory model, multi-station potential source contribution factor analysis （MS-PSCF） and trajectory density analysis （TDA） were introduced to study the differentiation and classification of PM2.5 transport channels and potential sources in Yangquan City. The results showed that： ① The PM2.5 pollution in Yangquan was mainly concentrated in Yangquan and Pingding, whereas the pollution in Yuxian was relatively light. The proportion of days with different pollution levels and the average and maximum values of PM2.5 concentration in Yangquan and Pingding were significantly higher than those in Yuxian, and the distribution characteristics of PM2.5 were closely related to the local special terrain. ② The amount of PM2.5 pollution and the concentration of PM2.5 in different pollution levels were the highest in light wind weather. The influence of east-west regional transport on PM2.5 pollution times and PM2.5 concentration of Yangquan and Pingding was obvious, and the contribution of east wind was significant. The influence of local pollution sources was the main factor in the moderate pollution weather in Yuxian County. ③ There were four main ground conditions for the generation and maintenance of moderate or above pollution weather： warm low pressure type （22%）, high pressure front （bottom） type （54%）, high pressure back type （14%）, and pressure equalization field （10%）. High pressure front （bottom） type was the main ground situation causing the increase in PM2.5 concentration. There were two types of upper air conditions, namely, flat westerly flow type （78%） and northwest flow type （22%）. The upper westerly flow type was the main upper air condition that caused the increase in PM2.5 concentration. ④ The results of transport channels and potential source areas of PM2.5 with different pollution levels obtained by MS-PSCF and TDA were consistent. The main transport channels of PM2.5 were the northeast, southeast, and northwest channels, whereas the northeast and southeast channels were short-distance transport routes, which were the main routes causing the increase in PM2.5 concentration. The northwest channel was consistent with the northwest dust transport channel, belonging to long-distance transmission. The main potential source areas of PM2.5 pollution were located in the central and western parts of Hebei and the southeast part of Hebei, the northeast part of Henan and its junction with the southwest part of Shandong, and the southeast part of Shanxi.

Assessment of Heat Exposure and Health Outcomes in Rural Populations of Western Kenya by Using Wearable Devices: Observational Case Study.

BACKGROUND: Climate change increasingly impacts health, particularly of rural populations in sub-Saharan Africa due to their limited resources for adaptation. Understanding these impacts remains a challenge, as continuous monitoring of vital signs in such populations is limited. Wearable devices (wearables) present a viable approach to studying these impacts on human health in real time. OBJECTIVE: The aim of this study was to assess the feasibility and effectiveness of consumer-grade wearables in measuring the health impacts of weather exposure on physiological responses (including activity, heart rate, body shell temperature, and sleep) of rural populations in western Kenya and to identify the health impacts associated with the weather exposures. METHODS: We conducted an observational case study in western Kenya by utilizing wearables over a 3-week period to continuously monitor various health metrics such as step count, sleep patterns, heart rate, and body shell temperature. Additionally, a local weather station provided detailed data on environmental conditions such as rainfall and heat, with measurements taken every 15 minutes. RESULTS: Our cohort comprised 83 participants (42 women and 41 men), with an average age of 33 years. We observed a positive correlation between step count and maximum wet bulb globe temperature (estimate 0.06, SE 0.02; P=.008). Although there was a negative correlation between minimum nighttime temperatures and heat index with sleep duration, these were not statistically significant. No significant correlations were found in other applied models. A cautionary heat index level was recorded on 194 (95.1%) of 204 days. Heavy rainfall (>20 mm/day) occurred on 16 (7.8%) out of 204 days. Despite 10 (21%) out of 47 devices failing, data completeness was high for sleep and step count (mean 82.6%, SD 21.3% and mean 86.1%, SD 18.9%, respectively), but low for heart rate (mean 7%, SD 14%), with adult women showing significantly higher data completeness for heart rate than men (2-sided t test: P=.003; Mann-Whitney U test: P=.001). Body shell temperature data achieved 36.2% (SD 24.5%) completeness. CONCLUSIONS: Our study provides a nuanced understanding of the health impacts of weather exposures in rural Kenya. Our study's application of wearables reveals a significant correlation between physical activity levels and high temperature stress, contrasting with other studies suggesting decreased activity in hotter conditions. This discrepancy invites further investigation into the unique socioenvironmental dynamics at play, particularly in sub-Saharan African contexts. Moreover, the nonsignificant trends observed in sleep disruption due to heat expose the need for localized climate change mitigation strategies, considering the vital role of sleep in health. These findings emphasize the need for context-specific research to inform policy and practice in regions susceptible to the adverse health effects of climate change.

Quantifying the pollution changes and meteorological dependence of airborne trace elements coupling source apportionment and machine learning.

Airborne trace elements (TEs) present in atmospheric fine particulate matter (PM2.5) exert notable threats to human health and ecosystems. To explore the impact of meteorological conditions on shaping the pollution characteristics of TEs and the associated health risks, we quantified the variations in pollution characteristics and health risks of TEs due to meteorological impacts using weather normalization and health risk assessment models, and analyzed the source-specific contributions and potential sources of primary TEs affecting health risks using source apportionment approaches at four sites in Shandong Province from September to December 2021. Our results indicated that TEs experience dual effects from meteorological conditions, with a tendency towards higher TE concentrations and related health risks during polluted period, while the opposite occurred during clean period. The total non-carcinogenic and carcinogenic risks of TEs during polluted period increased approximately by factors of 0.53-1.74 and 0.44-1.92, respectively. Selenium (Se), manganese (Mn), and lead (Pb) were found to be the most meteorologically influenced TEs, while chromium (Cr) and manganese (Mn) were identified as the dominant TEs posing health risks. Enhanced emissions of multiple sources for Cr and Mn were found during polluted period. Depending on specific wind speeds, industrialized and urbanized centers, as well as nearby road dusts, could be key sources for TEs. This study suggested that attentions should be paid to not only the TEs from primary emissions but also the meteorology impact on TEs especially during pollution episodes to reduce health risks in the future.

Oceanic seabirds chase tropical cyclones.

In late summer and autumn, the passage of intense tropical cyclones can profoundly perturb oceanic and coastal ecosystems. Direct negative effects on individuals and marine communities can be dramatic, especially in the coastal zone,1,2,3,4 but cyclones can also enhance pelagic primary and secondary production.5,6,7,8,9 However, cyclone impacts on open ocean marine life remain poorly understood. Here, we investigate their effects on the foraging movements of a wide-ranging higher predator, the Desertas petrel (Pterodroma deserta), in the mid-latitude North Atlantic during hurricane season. Contrary to previously studied pelagic seabirds in tropical and mid-latitude regions,10,11 Desertas petrels did not avoid cyclones by altering course, nor did they seek calmer conditions within the cyclone eye. Approximately one-third of petrels tracked from their breeding colony interacted with approaching cyclones. Upon encountering strong winds, the birds reduced ground speed, likely by spending less time in flight. A quarter of birds followed cyclone wakes for days and over thousands of kilometers, a behavior documented here for the first time. Within these wakes, tailwind support was higher than along alternative routes. Furthermore, at the mesoscale (hours-weeks and hundreds of kilometers), sea surface temperature dropped and surface chlorophyll sharply increased, suggesting direct effects on ocean stratification, primary production, and therefore presumably prey abundance and accessibility for surface-feeding petrels. We therefore hypothesize that cyclone wakes provide both predictably favorable wind conditions and foraging opportunities. As such, cyclones may have positive net effects on the demography of many mid-latitude pelagic seabirds and, likely, other marine top-predators.

Impacts of climate change on water-related mosquito-borne diseases in temperate regions: A systematic review of literature and meta-analysis.

Mosquito-borne diseases are a known tropical phenomenon. This review was conducted to assesses the mechanisms through which climate change impacts mosquito-borne diseases in temperate regions. Articles were searched from PubMed, Scopus, Web of Science, and Embase databases. Identification criteria were scope (climate change and mosquito-borne diseases), region (temperate), article type (peer-reviewed), publication language (English), and publication years (since 2015). The WWH (who, what, how) framework was applied to develop the research question and thematic analyses identified the mechanisms through which climate change affects mosquito-borne diseases. While temperature ranges for disease transmission vary per mosquito species, all are viable for temperate regions, particularly given projected temperature increases. Zika, chikungunya, and dengue transmission occurs between 18 - 34°C (peak at 26 - 29°C). West Nile virus establishment occurs at monthly average temperatures between 14 - 34.3°C (peak at 23.7 - 25°C). Malaria establishment occurs when the consecutive average daily temperatures are above 16°C until the sum is above 210°C. The identified mechanisms through which climate change affects the transmission of mosquito-borne diseases in temperate regions include: changes in the development of vectors and pathogens; changes in mosquito habitats; extended transmission seasons; changes in geographic spread; changes in abundance and behaviours of hosts; reduced abundance of mosquito predators; interruptions to control operations; and influence on other non-climate factors. Process and stochastic approaches as well as dynamic and spatial models exist to predict mosquito population dynamics, disease transmission, and climate favorability. Future projections based on the observed relations between climate factors and mosquito-borne diseases suggest that mosquito-borne disease expansion is likely to occur in temperate regions due to climate change. While West Nile virus is already established in some temperate regions, Zika, dengue, chikungunya, and malaria are also likely to become established over time. Moving forward, more research is required to model future risks by incorporating climate, environmental, sociodemographic, and mosquito-related factors under changing climates.

Incorporating weather in counts and trends of migrating Common Nighthawks.

Effective conservation planning for species of concern requires long-term monitoring data that can accurately estimate population trends. Supplemental or alternative methods for estimating population trends are necessary for species that are poorly sampled by traditional breeding bird survey methods. Counts of migrating birds are commonly used to assess raptor population trends and could be useful for additional taxa that migrate diurnally and are difficult to monitor during the breeding season. In North America, the Common Nighthawk (Chordeiles minor) is challenging to detect during comprehensive dawn surveys like the North American Breeding Bird Survey and is considered a species of conservation concern because of steep population declines across its range. We conducted standardized evening counts of migrating Common Nighthawks at a fixed survey location along western Lake Superior each autumn from 2008 to 2022. To document peak migration activity, counts spanned ~3 hours each evening from mid-August to early September for a mean of 19.4 ± 2.4 days. These count data were then used to assess the effects of weather on daily counts and high-count days and to calculate population trends over this 15-year period. We used generalized linear mixed effects models to determine the relationship between daily counts and high-count days (i.e., ≥1000 migrating nighthawks) and weather variables. Additionally, using our 15-year dataset, we calculated a geometric mean passage rate that accounted for annual differences in weather to estimate count trends. Annual counts averaged ~18,000 (min = 2514, max = 32,837) individuals and high-count days occurred 56 times throughout the course of the study. Model results indicated lighter, westerly winds and warmer temperatures were associated with higher daily counts and greater probability of a large migratory flight. Results from the trend analyses suggest stable or non-significantly increasing trends for Common Nighthawks during this monitoring period; however, the trend models explained a relatively low percentage of the variation in the counts. Results from a power analysis suggest that continued monitoring efforts and adjustments with weather covariates will be necessary to effectively use visible migration count data to estimate Common Nighthawk trends. Establishing annual monitoring programs that use standardized visual counts to document Common Nighthawk migration at key sites across North America may provide supplemental information useful for population trend estimates of this species. Therefore, we advocate for the use of visible migration counts to monitor Common Nighthawks in North America and emphasize the value of long-term monitoring efforts.

Des données de suivi à long terme permettant de calculer avec précision les tendances démographiques sont garantes d'une planification réussie de la conservation d'espèces préoccupantes. Des méthodes complémentaires ou alternatives d'estimation des tendances démographiques sont nécessaires dans le cas d'espèces mal échantillonnées par les méthodes traditionnelles de relevé d'oiseaux nicheurs. Le dénombrement d'oiseaux migrateurs est couramment utilisé pour évaluer la tendance des populations de rapaces et pourrait servir pour d'autres taxons qui migrent de jour et sont difficiles à suivre pendant la saison de nidification. En Amérique du Nord, l'Engoulevent d'Amérique (Chordeiles minor) est difficile à détecter au cours de relevés généraux réalisés à l'aube, tel le Relevé des oiseaux nicheurs d'Amérique du Nord (BBS), et est considéré comme une espèce dont la conservation est préoccupante en raison de la baisse marquée de ses effectifs dans toute son aire de répartition. Nous avons effectué des comptages en soirée standardisés d'engoulevents en migration à un site fixe localisé du côté ouest du lac Supérieur, chaque automne de 2008 à 2022. Afin de caractériser le pic d'activité migratoire, les comptages ont duré ~3 heures chaque soir de la mi-août au début de septembre, durant 19,4 ± 2,4 jours en moyenne. Ces données ont ensuite servi pour évaluer l'effet des conditions météorologiques sur les comptages quotidiens et les jours d'activité migratoire élevée et calculer la tendance démographique au cours de ces 15 ans. Nous avons utilisé des modèles linéaires généralisés à effets mixtes pour déterminer la relation entre les comptages quotidiens et les jours d'activité migratoire élevée (c.-à-d. ≥1000 engoulevents) et les variables météorologiques. En outre, en utilisant notre jeu de données sur 15 ans, nous avons calculé la moyenne géométrique du taux de passage tenant compte des différences météorologiques annuelles afin d'estimer la tendance des comptages. La moyenne des comptages annuels était de ~18 000 (min = 2514, max = 32 837) individus et nous avons observé 56 cas d'activité migratoire élevée au cours de l'étude. Les résultats du modèle ont indiqué que des vents plus légers et de l'ouest, et des températures plus chaudes étaient associés à des comptages quotidiens plus élevés et à une plus grande probabilité d'une activité migratoire importante. Les résultats de l'analyse des tendances indiquent que les engoulevents ont montré une tendance stable ou en augmentation non significative au cours de cette période de suivi; cependant, les modèles de tendances n'ont expliqué qu'un pourcentage relativement faible de la variation des comptages. Les résultats d'une analyse de puissance révèlent que des suivis réguliers et des ajustements avec des covariables météorologiques seront nécessaires pour utiliser efficacement les données de comptages visuels réalisés en migration afin d'estimer la tendance de l'Engoulevent d'Amérique. La mise en place d'un programme de suivis annuels fondés sur des comptages visuels standardisés destiné à caractériser la migration de l'Engoulevent d'Amérique sur des sites clés en Amérique du Nord pourrait fournir des informations supplémentaires utiles pour estimer les tendances démographiques de cette espèce. Par conséquent, nous préconisons l'utilisation de comptages visuels en migration pour suivre l'Engoulevent d'Amérique en Amérique du Nord et soulignons la valeur de suivis à long terme.

Contrasting seasonal responses to wind in migrating songbirds on a globally important flyway.

During spring migration, nocturnal migrants attempt to minimize their travel time to reach their breeding grounds early. However, how they behave and respond to unfavourable conditions during their springtime travels is much less understood. In this study, we reveal the effects of atmospheric factors on nocturnal bird migration under adverse conditions during spring and autumn, based on one of the most detailed bird migration studies globally, using radar data from 13 deployments over a period of seven years (2014-2020) in the Levant region. Using ERA5 reanalysis data, we found that migratory birds maintain similar ground speeds in both autumn and spring migrations, but during spring, when encountering unfavourable winds, they put more effort into maintaining their travel speed by increasing self-powered airspeed by 18%. Moreover, we report for the first time that spring migrants showed less selectivity to wind conditions and migrated even under unfavourable headwind and crosswind conditions. Interestingly, we discovered that temperature was the most important weather parameter, such that warm weather substantially increased migration intensities in both seasons. Our results enhance our understanding of bird migration over the Levant region, one of the world's largest and most important migration flyways, and the factors controlling it. This information is essential for predicting bird migration, which-especially under the ongoing anthropogenic changes-is of high importance.

Investigating the relationship between extreme weather and cryptosporidiosis and giardiasis in Colorado: A multi-decade study using distributed-lag nonlinear models.

Environmentally-mediated protozoan diseases like cryptosporidiosis and giardiasis are likely to be highly impacted by extreme weather, as climate-related conditions like temperature and precipitation have been linked to their survival, distribution, and overall transmission success. Our aim was to investigate the relationship between extreme temperature and precipitation and cryptosporidiosis and giardiasis infection using monthly weather data and case reports from Colorado counties over a twenty-one year period. Data on reportable diseases and weather among Colorado counties were collected using the Colorado Electronic Disease Reporting System (CEDRS) and the Daily Surface Weather and Climatological Summaries (Daymet) Version 3 dataset, respectively. We used a conditional Poisson distributed-lag nonlinear modeling approach to estimate the lagged association (between 0 and 12-months) between relative temperature and precipitation extremes and the risk of cryptosporidiosis and giardiasis infection in Colorado counties between 1997 and 2017, relative to the risk found at average values of temperature and precipitation for a given county and month. We found distinctly different patterns in the associations between temperature extremes and cryptosporidiosis, versus temperature extremes and giardiasis. When maximum or minimum temperatures were high (90th percentile) or very high (95th percentile), we found a significant increase in cryptosporidiosis risk, but a significant decrease in giardiasis risk, relative to risk at the county and calendar-month mean. Conversely, we found very similar relationships between precipitation extremes and both cryptosporidiosis and giardiasis, which highlighted the prominent role of long-term (>8 months) lags. Our study presents novel insights on the influence that extreme temperature and precipitation can have on parasitic disease transmission in real-world settings. Additionally, we present preliminary evidence that the standard lag periods that are typically used in epidemiological studies to assess the impacts of extreme weather on cryptosporidiosis and giardiasis may not be capturing the entire relevant period.

Coarse particulate air pollution and mortality in a multidrug-resistant tuberculosis cohort.

RATIONALE: The association between ambient coarse particulate matter (PM2.5-10) and mortality in multi-drug resistant tuberculosis (MDR-TB) patients has not yet been studied. The modifying effects of temperature and humidity on this association are completely unknown. OBJECTIVES: To evaluate the effects of long-term PM2.5-10 exposures, and their modifications by temperature and humidity on mortality among MDR-TB patients. METHODS: A Chinese cohort of 3469 MDR-TB patients was followed up from diagnosis until death, loss to follow-up, or the study's end, averaging 2567 days per patient. PM2.5-10 concentrations were derived from the difference between PM10 and PM2.5. Cox proportional hazard models estimated hazard ratios (HRs) per 3.74 µg/m3 (interquartile range, IQR) exposure to PM2.5-10 and all-cause mortality for the full cohort and individuals at distinct long-term and short-term temperature and humidity levels, adjusting for other air pollutants and potential covariates. Exposure-response relationships were quantified using smoothed splines. RESULTS: Hazard ratios of 1.733 (95% CI, 1.407, 2.135) and 1.427 (1.114, 1.827) were observed for mortality in association with PM2.5-10 exposures for the full cohort under both long-term and short-term exposures to temperature and humidity. Modifying effects by temperature and humidity were heterogenous across sexes, age, treatment history, and surrounding environment measured by greenness and nighttime light levels. Nonlinear exposure-response curves suggestes a cumulative risk of PM2.5-10-related mortality starting from a low exposure concentration around 15 µg/m3. CONCLUSION: Long-term exposure to PM2.5-10 poses significant harm among MDR-TB patients, with effects modified by temperature and humidity. Immediate surveillance of PM2.5-10 is crucial to mitigate the progression of MDR-TB severity, particularly due to co-exposures to air pollution and adverse weather conditions.

Determinants of macaques' space use: A test for the ecological constraints model using GPS collars.

As a central topic in Behavioral Ecology, animal space use involves dynamic responses to social and ecological factors. We collared 22 rhesus macaques (Macaca mulatta) from six groups on Neilingding Island, China, and collected 80,625 hourly fixes over a year. Using this high-resolution location data set, we quantified the macaques' space use at the individual level and tested the ecological constraints model while considering various environmental and human interfering factors. As predicted by the ecological constraints model, macaques in larger groups had longer daily path lengths (DPLs) and larger home ranges. We found an inverted U-shape relationship between mean daily temperatures and DPLs, indicating that macaques traveled farther on mild temperature days, while they decreased DPLs when temperatures were too high or too low. Anthropogenic food subsidies were positively correlated to DPLs, while the effect of rainfall was negative. Macaques decreased their DPLs and core areas when more flowers and less leaves were available, suggesting that macaques shifted their space use patterns to adapt to the seasonal differences in food resources. By applying GPS collars on a large number of individuals living on a small island, we gained valuable insights into within-group exploitation competition in wild rhesus macaques.

Biomineralization for Reducing and Controlling Sand-Dust Storms.

The sand-dust weather and sand-dust storms have become a serious environmental disaster worldwide. It is an important challenge to develop technologies for desert sand solidification in order to prevent and control sand-dust weather. The biomineralization technology for solidifying desert sands has been a novel method for reinforced soils in recent years. The biomineralization solidification sand field tests are completed at the Wuma Highway solidification section in the Tengger Desert. The superiority of the biomineralization for solidifying sands is verified by measuring the water storage capacity of different reinforcement zones including bare sand zone, plant zone, biomineralization solidifying sand zone, and biomineralization combined plant solidifying sand zone. Simultaneously, the molecular dynamics calculation analysis is used to verify the role of biomineralization solidifying sands in preventing sand-dust storms. All results demonstrate that the biomineralization solidification sand method is effective for controlling and preventing sandstorm disasters.