Climatic Variables Research Articles

A time-to-event analysis for temperature dependence of seed germination in four conifers: Ecological niche and environmental gradients

Seed germination is a vital process in the plant life cycle. It directly influences seedling establishment and subsequently determines population persistence and species distribution. Germination is highly sensitive to climatic conditions, and germination traits may vary among species and populations within the same species. This study aims to improve our understanding of intraspecific and interspecific variation in temperature dependence of seed germination and how this variation relates to the environment where seeds were collected. We focused on four conifer species (Pinus contorta, Picea engelmannii, Pinus ponderosa, and Pseudotsuga menziesii) and seeds collected at 20 sites spanning the elevation range of each species in the Western United States. We conducted seed germination tests at temperatures from 5 to 40°C in 5°C intervals and tracked germination for 30 days. We used the log-logistic time-to-event model and Gaussian function to fit germination data and analyze their temperature dependence. The climate variables at the seed-collecting sites were summarized by long-term normals (1971–2000) covering most seed-collection years. Climate variables were correlated and thus were combined via Principal Component Analysis (PCA) into several uncorrelated principal components (PC scores) that describe climate at the seed collection sites. The estimated optimal temperature (To), shape coefficient (b), and maximum value at optimal temperature (GPmax) of germination percentages and median germination rates (GR50) were then fit to linear models to determine whether they varied with species identity and the climate-derived PC scores. Results showed that seed germination traits from different species exhibit climate gradients that are correlated with their ecological niche. However, seeds from different elevations within the same species had variable germination traits irrespective of environmental gradients. We also found that variation in germination was more significant near upper and lower temperature limits. Including inter- and intra-specific temperature dependence of seed germination would improve predictions of population dynamics and range shifts under climate warming and could inform forest management, seed transfer, and assisted migration practices.

Impacts of meteorological conditions on the plummeting surface-reaching solar radiation over a sub-tropical station – Pune, India

The unprecedented rise of solar energy as a dominant power source is a remarkable feat, considering our historical struggle to establish its widespread adoption. However, the dynamics of solar radiation reaching the Earth's surface are greatly influenced by meteorological conditions such as clouds, aerosols, and wind speed. This study investigates the detailed changes in surface-reaching solar radiation within the subtropical region Pune from 1993 to 2022. By employing analytical methodologies including linear regression, wavelet coherence, and quantitative analysis, our study reveals a compelling and alarming pattern of plummeting solar radiation over Pune (also known as solar dimming). The total drop is ∼1.04 Wm−2 year−1 (0.21 % year−1), with a more steep decline registered from 2018 to 2022, providing an astonishing rate of ∼6.01 Wm−2 year−1 (1.21 % year−1). Moreover, wavelet coherence transform revealed an antiphase coherence between solar radiation and both clouds and aerosols, suggesting their substantial influence over a longer timeframe. Extensive analysis utilizing observational data is conducted to find the contributing elements to the significant decline during 2018 to June 2022. Analysis of solar radiation statistics showed that clouds led to a substantial reduction of ∼30.18 % compared to conditions with clear-skies. Similarly, the aerosols have a comparable effect, reducing radiation by ∼17.75 % compared to days characterized by low or negligible aerosol loading for the same period. Furthermore, a through statistical analysis investigates a notable anticorrelation (ρ = −0.64) between wind speed and solar radiation. These findings underscore the imperative of incorporating climatic variables while studying solar dimming trends and devising sustainable renewable energy strategies to ensure the long-term viability of solar energy source.

Drivers of late Holocene ice core chemistry in Dronning Maud Land: the context for the ISOL-ICE project

Abstract. Within the framework of the Isotopic Constraints on Past Ozone Layer in Polar Ice (ISOL-ICE) project, we present initial ice core results from the new ISOL-ICE ice core covering the last millennium from high-elevation Dronning Maud Land (DML) and discuss the implications for interpreting the stable isotopic composition of nitrogen in ice core nitrate (δ15N(NO3-)) as a surface ultra-violet radiation (UV) and total column ozone (TCO) proxy. In the quest to derive TCO using δ15N(NO3-), an understanding of past snow accumulation changes, as well as aerosol source regions and present-day drivers of their variability, is required. We therefore report here the ice core age–depth model, the snow accumulation and ice chemistry records, and correlation analysis of these records with climate variables over the observational era (1979–2016). The ISOL-ICE ice core covers the last 1349 years from 668 to 2017 CE ± 3 years, extending previous ice core records from the region by 2 decades towards the present and shows excellent reproducibility with those records. The extended ISOL-ICE record of last 2 decades showed a continuation of the methane sulfonate (MSA−) increase from ∼ 1800 to present while there were less frequent large deposition events of sea salts relative to the last millennium. While our chemical data do not allow us to distinguish the ultimate (sea ice or the open ocean) source of sea salt aerosols in DML winter aerosol, our correlation analysis clearly suggests that it is mainly the variability in atmospheric transport and not the sea ice extent that explains the interannual variability in sea salt concentrations in DML. Correlation of the snow accumulation record with climate variables over the observational era showed that precipitation at ISOL-ICE is predominately derived from the South Atlantic with onshore winds delivering marine air masses to the site. The snow accumulation rate was stable over the last millennium with no notable trends over the last 2 decades relative to the last millennium. Interannual variability in the accumulation record, ranging between 2 and 20 cm a−1 (w.e.), would influence the ice core δ15N(NO3-) record. The mean snow accumulation rate of 6.5±2.4 cm a−1 (w.e.) falls within the range suitable for reconstructing surface mass balance from ice core δ15N(NO3-), highlighting that the ISOL-ICE ice core δ15N(NO3-) can be used to reconstruct either the surface mass balance or surface UV if the ice core δ15N(NO3-) is corrected for the snow accumulation influence, thereby leaving the UV imprint in the δ15N(NO3-) ice core record to quantify natural ozone variability.

A Coupled Statistical and Deterministic Model for Forecasting Climate-Driven Dengue Incidence in Selangor, Malaysia.

The mosquito-borne dengue virus remains a major public health concern in Malaysia. Despite various control efforts and measures introduced by the Malaysian Government to combat dengue, the increasing trend of dengue cases persists and shows no sign of decreasing. Currently, early detection and vector control are the main methods employed to curb dengue outbreaks. In this study, a coupled model consisting of the statistical ARIMAX model and the deterministic SI-SIR model was developed and validated using the weekly reported dengue data from year 2014 to 2019 for Selangor, Malaysia. Previous studies have shown that climate variables, especially temperature, humidity, and precipitation, were able to influence dengue incidence and transmission dynamics through their effect on the vector. In this coupled model, climate is linked to dengue disease through mosquito biting rate, allowing real-time forecast of dengue cases using climate variables, namely temperature, rainfall and humidity. For the period chosen for model validation, the coupled model can forecast 1-2weeks in advance with an average error of less than 6%, three weeks in advance with an average error of 7.06% and four weeks in advance with an average error of 8.01%. Further model simulation analysis suggests that the coupled model generally provides better forecast than the stand-alone ARIMAX model, especially at the onset of the outbreak. Moreover, the coupled model is more robust in the sense that it can be further adapted for investigating the effectiveness of various dengue mitigation measures subject to the changing climate.

Soil properties constrain forest understory plant distributions along an elevation gradient.

Projections of spatial biodiversity dynamics under climate warming are often based on models including only climate variables, and when non-climatic factors (e.g. soil) are included, data are often at much coarser spatial resolutions than those experienced by plants. Field studies along elevation gradients permit the gathering of detailed soil data, while still covering a wide climatic gradient. Here, an intensive field survey of four spring forest herbs along an elevation gradient showed that soil properties had substantial impacts on the occurrence/abundance of all species, and that soil effects were more pronounced at higher elevations. For Trillium erectum and Claytonia caroliniana, very infrequent occurrences at high elevation were strongly associated with rare microsites with high pH or nutrients. In a seven-year transplant experiment with T. erectum, we found that individuals grew to much smaller sizes at high than low elevation, suggesting that environmental factors rather than dispersal limitation constrain the species' upper range limit, despite substantial warming in recent decades. Our study demonstrates that soil factors interact strongly with climate to determine plant range limits along climatic gradients. Unsuitable soils for plants at high elevations or latitudes may represent an important constraint on future plant migration and biodiversity change. This article is part of the theme issue 'Ecological novelty and planetary stewardship: biodiversity dynamics in a transforming biosphere'.

Modeling dominant height growth of teak plantations in the Caribbean region of Colombia

Identifying sites with adequate biological productivity is a critical factor in ensuring timber production and the profitability of forest-based investments. The productivity of forest sites is influenced by climatic, edaphic and topographic variables, as well as by silvicultural practices. Site index is a phytocentric method widely used to assess site productivity and its estimation is based on dominant height growth modeling. Teak is the fifth most planted forest species in Colombia, and its importance is associated with high economic returns and profitability. This study aims to model dominant height growth using the generalized algebraic difference approach for teak plantations established in the Caribbean region of Colombia. The Lundqvist-Korf model, in which the correlation of the residuals was handled with a continuous autoregressive specification of the first order, resulted in a satisfactory statistical estimation of the dominant height growth. The results indicate that in the Caribbean region of Colombia, productive sites for the establishment of teak plantations can be found as productive as in some tropical American countries and better than some sites in Asian countries. This suggests a potential for the expansion of teak plantations and forest-based investments in Colombia.

Spatial patterns and climatic drivers of phylogenetic structure of regional liverwort assemblages in China.

Latitudinal diversity gradients have been intimately linked to the tropical niche conservatism hypothesis, which posits that there has been a strong filter due to the challenges faced by ancestral tropical lineages to adapt to low temperatures and colonize extra-tropical regions. In liverworts, species richness is higher towards the tropics, but the centers of diversity of the basal lineages are distributed across extra-tropical regions, pointing to the colonization of tropical regions by phylogenetically clustered assemblages of species of temperate origin. Here, we test this hypothesis through analyses on the relationship between macroclimatic variation and phylogenetic diversity in Chinese liverworts. Phylogenetic diversity metrics and their standardized effect sizes for liverworts in each of the 28 regional floras at the province level in China were related to latitude and six climate variables using regression analysis. We conducted variation partitioning analyses to determine the relative importance of each group of climatic variables. We find that the number of species decreases with latitude, whereas phylogenetic diversity shows the reverse pattern, and that phylogenetic diversity is more strongly correlated with temperature-related variables, compared with precipitation-related variables. We interpret the opposite patterns observed in phylogenetic diversity and species richness in terms of a more recent origin of tropical diversity coupled with higher extinctions in temperate regions.

Spring Meteorological Drought over East Asia and Its Associations with Large-Scale Climate Variations

East Asia is a region that is highly vulnerable to drought disasters during the spring season, as this period is critical for planting, germinating, and growing staple crops such as wheat, maize, and rice. The climate in East Asia is significantly influenced by three large-scale climate variations: the Pacific Decadal Oscillation (PDO), the El Niño–Southern Oscillation (ENSO), and the Indian Ocean Dipole (IOD) in the Pacific and Indian Oceans. In this study, the spring meteorological drought was quantified using the standardized precipitation evapotranspiration index (SPEI) for March, April, and May. Initially, coupled climate networks were established for two climate variables: sea surface temperature (SST) and SPEI. The directed links from SST to SPEI were determined based on the Granger causality test. These coupled climate networks revealed the associations between climate variations and meteorological droughts, indicating that semi-arid areas are more sensitive to these climate variations. In the spring, PDO and ENSO do not cause extreme wetness or dryness in East Asia, whereas IOD does. The remote impacts of these climate variations on SPEI can be partially explained by atmospheric circulations, where the combined effects of air temperatures, winds, and air pressure fields determine the wet/dry conditions in East Asia.

A unified dataset for pre-processed climate indicators weighted by gridded economic activity

Although high-resolution gridded climate variables are provided by multiple sources, the need for country and region-specific climate data weighted by indicators of economic activity is becoming increasingly common in environmental and economic research. We process available information from different climate data sources to provide spatially aggregated data with global coverage for both countries (GADM0 resolution) and regions (GADM1 resolution) and for a variety of climate indicators (total precipitations, average temperatures, average SPEI). We weigh gridded climate data by population density, night-time light intensity, cropland, and concurrent population count – all proxies of economic activity – before aggregation. Climate variables are measured daily, monthly, and annually, covering (depending on the data source) a time window from 1900 (at the earliest) to 2023. We pipeline all the preprocessing procedures in a unified framework, and we validate our data through a systematic comparison with those employed in leading climate impact studies.

Soundscape mapping: understanding regional spatial and temporal patterns of soundscapes incorporating remotely-sensed predictors and wildfire disturbance

Increased environmental threats require proper monitoring of animal communities to understand where and when changes occur. Ecoacoustic tools that quantify natural acoustic environments use a combination of biophony (animal sound) and geophony (wind, rain, and other natural phenomena) to represent the natural soundscape and, in comparison to anthropophony (technological human sound) can highlight valuable landscapes to both human and animal communities. However, recording these sounds requires intensive deployment of recording devices and storage and interpretation of large amounts of data, resulting in large data gaps across the landscape and periods in which recordings are absent. Interpolating ecoacoustic metrics like biophony, geophony, anthropophony, and acoustic indices can bridge these gaps in observations and provide insight across larger spatial extents and during periods of interest. Here, we use seven ecoacoustic metrics and acoustically-derived bird species richness across a heterogeneous landscape composed of densely urbanized, suburban, rural, protected, and recently burned lands in Sonoma County, California, U.S.A., to explore spatiotemporal patterns in ecoacoustic measurements. Predictive models of ecoacoustic metrics driven by land-use/land-cover, remotely-sensed vegetation structure, anthropogenic impact, climate, geomorphology, and phenology variables capture landscape and daily differences in ecoacoustic patterns with varying performance (avg. R 2 = 0.38 ± 0.11) depending on metric and period-of-day and provide interpretable patterns in sound related to human activity, weather phenomena, and animal activity. We also offer a case study on the use of the data-driven prediction of biophony to capture changes in soniferous species activity before (1–2 years prior) and after (1–2 years post) wildfires in our study area and find that biophony may depict the reorganization of acoustic communities following wildfires. This is demonstrated by an upward trend in activity 1–2 years post-wildfire, particularly in more severely burned areas. Overall, we provide evidence of the importance of climate, spaceborne-lidar-derived forest structure, and phenological time series characteristics when modeling ecoacoustic metrics to upscale site observations and map ecoacoustic biodiversity in areas without prior acoustic data collection. Resulting maps can identify areas of attention where changes in animal communities occur at the edge of human and natural disturbances.

Predicting the potential distribution of Dendrolimus punctatus and its host Pinus massoniana in China under climate change conditions

IntroductionDendrolimus punctatus, a major pest endemic to the native Pinus massoniana forests in China, displays major outbreak characteristics and causes severe destructiveness. In the context of global climate change, this study aims to investigate the effects of climatic variations on the distribution of D. punctatus and its host, P. massoniana. MethodsWe predict their potential suitable distribution areas in the future, thereby offering a theoretical basis for monitoring and controlling D. punctatus, as well as conserving P. massoniana forest resources. By utilizing existing distribution data on D. punctatus and P. massoniana, coupled with relevant climatic variables, this study employs an optimized maximum entropy (MaxEnt) model for predictions. With feature combinations set as linear and product (LP) and the regularization multiplier at 0.1, the model strikes an optimal balance between complexity and accuracy.ResultsThe results indicate that the primary climatic factors influencing the distribution of D. punctatus and P. massoniana include the minimum temperature of the coldest month, annual temperature range, and annual precipitation. Under the influence of climate change, the distribution areas of P. massoniana and its pests exhibit a high degree of similarity, primarily concentrated in the region south of the Qinling−Huaihe line in China. In various climate scenarios, the suitable habitat areas for these two species may expand to varying degrees, exhibiting a tendency to shift toward higher latitude regions. Particularly under the high emission scenario (SSP5-8.5), D. punctatus is projected to expand northwards at the fastest rate. DiscussionBy 2050, its migration direction is expected to closely align with that of P. massoniana, indicating that the pine forests will continue to be affected by the pest. These findings provide crucial empirical references for region-specific prevention of D. punctatus infestations and for the rational utilization and management of P. massoniana resources.

Analyzing vegetation health dynamics across seasons and regions through NDVI and climatic variables

This study assesses the relationships between vegetation dynamics and climatic variations in Pakistan from 2000 to 2023. Employing high-resolution Landsat data for Normalized Difference Vegetation Index (NDVI) assessments, integrated with climate variables from CHIRPS and ERA5 datasets, our approach leverages Google Earth Engine (GEE) for efficient processing. It combines statistical methodologies, including linear regression, Mann–Kendall trend tests, Sen's slope estimator, partial correlation, and cross wavelet transform analyses. The findings highlight significant spatial and temporal variations in NDVI, with an annual increase averaging 0.00197 per year (p < 0.0001). This positive trend is coupled with an increase in precipitation by 0.4801 mm/year (p = 0.0016). In contrast, our analysis recorded a slight decrease in temperature (− 0.01011 °C/year, p < 0.05) and a reduction in solar radiation (− 0.27526 W/m2/year, p < 0.05). Notably, cross-wavelet transform analysis underscored significant coherence between NDVI and climatic factors, revealing periods of synchronized fluctuations and distinct lagged relationships. This analysis particularly highlighted precipitation as a primary driver of vegetation growth, illustrating its crucial impact across various Pakistani regions. Moreover, the analysis revealed distinct seasonal patterns, indicating that vegetation health is most responsive during the monsoon season, correlating strongly with peaks in seasonal precipitation. Our investigation has revealed Pakistan's complex association between vegetation health and climatic factors, which varies across different regions. Through cross-wavelet analysis, we have identified distinct coherence and phase relationships that highlight the critical influence of climatic drivers on vegetation patterns. These insights are crucial for developing regional climate adaptation strategies and informing sustainable agricultural and environmental management practices in the face of ongoing climatic changes.

A Multi-Temporal Analysis on the Dynamics of the Impact of Land Use and Land Cover on NO2 and CO Emissions in Argentina for Sustainable Environmental Management

This study presents an analysis of NO2 and CO emissions in Argentina, utilizing remote sensing data. This research aims to determine the spatiotemporal distribution of NO2 and CO emissions from 2019 to 2021. It examines the influence of land use and cover on NO2 and CO emissions using various climatic, anthropic, and natural indicators. The year with the highest CO and NO2 concentration was 2020. NO2 exhibited the highest concentrations in built-up urban areas and croplands, notably impacting the capital city and the northern region of Buenos Aires province. Also, CO concentration was influenced by anthropic variable distances to national route, mining extraction, power plants, airports, and urban index (UI). They were also influenced by climatic and natural variables (Palmer drought index, vapor pressure, maximum environment temperature, wind speed, DEM, humidity, and normalized difference vegetation index (NDVI)) for the different uses and land covers. NO2 concentrations were influenced by anthropic (distance to airports, service stations, open dumpsites, power plants, and factories), climatic, and natural variables (Palmer drought index, vapor pressure, wind speed, and DEM) for the different uses and land cover. This research supports sustainable environmental management by guiding the development of effective emission mitigation strategies for improved community health and well-being.

Regional divergent evolution of vegetation greenness and climatic drivers in the Sahel-Sudan-Guinea region: nonlinearity and explainable machine learning

IntroductionThe vegetation dynamics of the Sahel-Sudan-Guinea region in Africa, one of the largest transition zones between arid and humid zones, is of great significance for understanding regional ecosystem changes. However, a time-unvarying trend based on linear assumption challenges the overall understanding of vegetation greenness evolution and of tracking a complex ecosystem response to climate in the Sahel-Sudan-Guinea region.MethodsThis study first applied the ensemble empirical mode decomposition (EEMD) method to detect the time-varying trends in vegetation greenness based on normalized difference vegetation index (NDVI) data in the region during 2001–2020, and then identified the dominant climatic drivers of NDVI trends by employing explainable machine learning framework.ResultsThe study revealed an overall vegetation greening but a significant nonlinear spatio-temporal evolution characteristic over the region. Trend reversals, i.e., browning-to-greening and greening-to-browning, were dominant in approximately 60% of the study area. The browning-to-greening reversal was primarily observed in the southern Sahel, Congo Basin north of the Equator, and East Africa, with a breakpoint around 2008, while the greening-to-browning reversal was mainly observed in West Africa, with a breakpoint around 2011. The sustained greening primarily took place in northern Sahel, Central African Republic and South Sudan; while sustained browning clustered in central West Africa and Uganda, mainly in agricultural lands. Furthermore, the combination of Random Forest (RF) algorithm and the SHapley Additive exPlanations (SHAP) method could robustly model and reveal the relationships between the observed trends in NDVI and in climatic variables, also detected by applying EEMD. The results suggested that air temperature and precipitation were the most important climatic drivers controlling the NDVI trends across the Sahel-Sudan-Guinea region. The NDVI trends were more likely to have negative correlations with solar radiation and vapor pressure deficit in arid areas, while they could have positive correlations in humid areas. The study also found that large-scale climate changes induced by sea surface temperature (SST) anomalies had strong relationships with trend reversals in vegetation greenness at a sub-continental scale. These findings advanced the understanding of the impacts of climatic drivers on vegetation greenness evolution in the Sahel-Sudan-Guinea region.

Nutritional quality of kabuli and desi chickpea in Argentina: Effects of environment

AbstractThe environment to which a crop is exposed during the growing season has a significant impact on seed composition. The objectives of this study were to (1) estimate the contribution of genotype (G), environment (E), and their interaction (G × E) to the agronomic features and seed quality of chickpea and (2) identify climatic variables that significantly affect chickpea nutritional composition. A total of 10 pre‐commercial and commercial cultivars of Cicer arietinum L. were evaluated in 15 environments in Argentina. Average plant height ranged between 44.7 and 55.2 cm, 100‐seed weight was 38 and 27 g, and grain yield was 750 and 1500 kg ha−1 for kabuli and desi chickpea, respectively. The results obtained from the variance component analysis showed statistically significant (p &lt; 0.05) contributions of the effects of G, E, and G × E interaction on the nutritional quality of chickpea seeds. Protein, carbohydrates, and mineral content were mostly affected by E, whereas oil content was G‐dependent. Only tocopherols were affected by G × E interaction. The total phenotypic variance is mostly composed of environmental effects captured during the seed filling period. High mean daily air temperature had a negative effect on carbohydrates and increased protein and mineral content. The fatty acid profile and gamma tocopherol contents were affected by accumulated precipitation and evapotranspiration. Air humidity was negatively correlated with protein content and iodine value. Results from this research are useful for breeders to broaden the genetic background of chickpea genotypes and for farmers to identify climatic conditions that impact grain quality.

The snow cover is more important than other climatic variables on the prediction of vegetation dynamics in the Pyrenees (1981-2014)

Abstract The dynamics of the mountain vegetation is governed by multiple climatic drivers including temperature, precipitation, radiation and snow cover variability. However, in the Mediterranean environment, little is known about the relative importance of each variable. In this study we assess how different snowpack indices (the maximum annual accumulation, the length of the snow season, and the melt-out date) and key climate variables (precipitation, temperature and shortwave solar radiation) control the interannual variability of the maximum Normalized Difference Vegetation Index (peak NDVI) in the Pyrenees. We use a 33 year long remote sensing dataset (1981-2014) to build a statistical model relating the annual peak NDVI with snow and climate variables. &amp;#xD;In elevated areas characterized by a well developed seasonal snowpack the melt-out date was the most important climatic variable for predicting the annual peak NDVI. However, at lower elevations where snow presence is ephemeral, shortwave solar radiation was the most important variable. This change in the relative importance of climatic variables occurs around 1300 m a.s.l. The results do not show a significant contribution of maximum snow accumulation, suggesting that indicators of snow presence (i.e. melt-out date or snow season duration), which are significantly easier to obtain than snow mass indicators from remote sensing, could be used to model the influence of the snowpack on peak NDVI at regional scale. &amp;#xD;

Fine-scale adaptive divergence and population genetic structure of Aedes aegypti in Metropolitan Manila, Philippines

BackgroundThe adaptive divergence of Aedes aegypti populations to heterogeneous environments can be a driving force behind the recent expansion of their habitat distribution and outbreaks of dengue disease in urbanized areas. In this study, we investigated the population genomics of Ae. aegypti at a regional scale in Metropolitan Manila, Philippines.MethodsWe used the Pool-Seq double digestion restriction-site association DNA sequencing (ddRAD-Seq) approach to generate a high number of single nucleotide polymorphisms (SNPs), with the aim to determine local adaptation and compare the population structure with 11 microsatellite markers. A total of 217 Ae. aegypti individuals from seven female and seven male populations collected from Metropolitan Manila were used in the assays.ResultsWe detected 65,473 SNPs across the populations, of which 76 were non-neutral SNPs. Of these non-neutral SNPs, the multivariate regression test associated 50 with eight landscape variables (e.g. open space, forest, etc.) and 29 with five climate variables (e.g. air temperature, humidity, etc.) (P-value range 0.005–0.045) in female and male populations separately. Male and female populations exhibited contrasting spatial divergence, with males exhibiting greater divergence than females, most likely reflecting the different dispersal abilities of male and female mosquitoes. In the comparative analysis of the same Ae. aegypti individuals, the pairwise FST values of 11 microsatellite markers were lower than those of the neutral SNPs, indicating that the neutral SNPs generated via pool ddRAD-Seq were more sensitive in terms of detecting genetic differences between populations at fine-spatial scales.ConclusionsOverall, our study demonstrates the utility of pool ddRAD-Seq for examining genetic differences in Ae. aegypti populations in areas at fine-spatial scales that could inform vector control programs such as Wolbachia-infected mosquito mass-release programs. This in turn would provide information on mosquito population dispersal patterns and the potential barriers to mosquito movement within and around the release area. In addition, the potential of environmental adaptability observed in Ae. aegypti could help population control efforts.Graphical

Nocturnal heat exposure and stroke risk.

In recent decades, nighttime temperatures have increased faster than daytime temperatures. The increasing prevalence of nocturnal heat exposure may pose a significant risk to cardiovascular health. This study investigated the association between nighttime heat exposure and stroke risk in the region of Augsburg, Germany, and examined its temporal variations over 15 years. Hourly meteorological parameters, including mean temperature, relative humidity, and barometric pressure, were acquired from a local meteorological station. A data set was obtained consisting of 11 037 clinical stroke cases diagnosed during warmer months (May to October) between the years 2006 and 2020. The average age of cases was 71.3 years. Among these cases, 642 were identified as haemorrhagic strokes, 7430 were classified as ischaemic strokes, and 2947 were transient ischaemic attacks. A time-stratified case-crossover analysis with a distributed lag non-linear model was used to estimate the stroke risk associated with extreme nighttime heat, as measured by the hot night excess (HNE) index after controlling for the potential confounding effects of daily maximum temperature and other climatic variables. Subgroup analyses by age group, sex, stroke subtype, and stroke severity were performed to identify variations in susceptibility to nighttime heat. Results suggested a significant increase in stroke risk on days with extreme nighttime heat (97.5% percentile of HNE) (odds ratio 1.07, 95% confidence interval 1.01-1.15) during the full study period. When comparing the results for 2013-20 with the results for 2006-12, there was a significant increase (P < .05) in HNE-related risk for all strokes and specifically for ischaemic strokes during the more recent period. Furthermore, older individuals, females, and patients with mild stroke symptoms exhibited a significantly increased vulnerability to nighttime heat. This study found nocturnal heat exposure to be related to elevated stroke risk after controlling for maximum daytime temperature, with increasing susceptibility between 2006 and 2020. These results underscore the importance of considering nocturnal heat as a critical trigger of stroke events in a warming climate.

Contrasting water use strategies to climate warming in white birch and larch in a boreal permafrost region.

The effects of rising atmospheric CO2 concentrations (Ca) with climate warming on intrinsic water-use efficiency (iWUE) and radial growth in boreal forests are still poorly understood. We measured tree-ring cellulose δ13C, δ18O, and tree-ring width in Larix dahurica (larch) and Betula platyphylla (white birch), and analyzed their relationships with climate variables in a boreal permafrost region of northeast China over past 68years covering a pre-warming period (1951-1984; base period) and a warm period (1985-2018; warm period). We found that white birch but not larch significantly increased their radial growth over the warm period. The increased iWUE in both species was mainly driven by elevated Ca but not climate warming. White birch but not larch showed significantly positive correlations between tree-ring δ13C, δ18O and summer maximum temperature as well as vapor pressure deficit in the warm period, suggesting a strong stomatal response in the broad-leaved birch to temperature changes. The climate warming-induced radial growth enhancement in white birch is primarily associated with a conservative water use strategy. In contrast, larch exhibits a profligate water use strategy. It implies an advantage for white birch over larch in the warming permafrost regions.

Rain and potential evapotranspiration are the main drivers of yield for wheat and barley in southern Australia: insights from 12 years of National Variety Trials

Context Water is widely assumed to be the factor most limiting the growth of annual crops in rainfed environments, but this is rarely tested at sub-continental scale. Aims Our study aimed to determine the key environmental and management variables influencing the yield of wheat and barley in the grain-production regions of southern Australia, using data from National Variety Trials. Methods We used generalised additive models to determine the importance of climatic and management variables on wheat and barley grain yield. We determined the effects of the best one, two or three variables and their interactions. Key results The aridity index, defined as the ratio of cumulative rainfall to potential evapotranspiration, was the single strongest determinant of grain yield for both crops. Model performance was further improved by separating the aridity index into pre-seasonal and seasonal components. Interestingly, other variables that might be expected to influence yield, such as nitrogen fertilisation and extreme temperatures, had relatively minor effects. A comparison between data collected over two 6-year periods showed that there had been yield gains and increased water-use efficiency with time, especially in wetter environments. Conclusions Our findings illustrate the importance of water availability for grain production in this region and suggest opportunities for benchmarking and yield prediction through use of readily available climate data. Implications Our study reinforces the importance of factors such as water-use efficiency and drought tolerance as goals for cultivar development and agronomic research in wheat and barley. It also highlights the potential of National Variety Trial data as a resource for understanding grain production systems and climate resilience. Further work could explore the value of additional variables and improved weather data.