Patterns Of Temporal Variation Research Articles

Quantifying Mineral Dust Emissions on the Tibetan Plateau With a Modified Dust Source Map

AbstractThe region surrounding the Tibetan Plateau (TP) is widely considered a primary global dust source, with mineral dust comprising a significant proportion of aerosols over the TP. Current research on TP dust has mainly focused on transport from the surrounding deserts, with little focus on dust emissions from the TP's interior. The erodibility factor used by the WRF‐Chem (ERODDEF) is 0 for the TP, so the model cannot simulate the dust emissions inside the plateau. Thus, we constructed a high‐resolution erodibility data set (ERODSDS) based on a reliable dust source distribution and intensity map. Based on the modified EROD map, the WRF‐Chem model was used to simulate dust emissions and direct radiative forcing on the TP in 2018. With the modified EROD map, WRF‐Chem can well simulate the temporal variation and spatial pattern of mineral dust on the plateau, which greatly improves the model's dust emissions simulation accuracy on the TP.

Exploring real-time patient monitoring and data analytics with IoT-based smart healthcare monitoring

This research study endeavours to explore the dynamic temporal variations in patient health parameters, specifically heart rate, temperature, and Systolic Blood Pressure (SBP), within the context of a synthetic patient dataset. The analysis of heart rate data revealed distinct patterns of temporal variation, characterized by cyclic fluctuations within each hour and an overall increasing trend over the observation period. These findings Highlights the influence of circadian rhythms, potential associations with physical activity and stress, and the broader physiological implications for patient health. The examination of temperature data exhibited analogous cyclic patterns and an increasing trend, prompting considerations of circadian rhythms and external factors such as environmental temperature and physiological health. Conversely, SBP data demonstrated fluctuations without a discernible trend, highlighting the complex nature of blood pressure regulation and its susceptibility to a myriad of physiological and external influences. This study provides a foundational understanding of patient health parameter dynamics and emphasizes the need for more extensive research to elucidate the underlying mechanisms and clinical implications. The insights gleaned from this investigation hold the potential to inform healthcare practitioners and researchers in the realms of patient monitoring and personalized healthcare, ultimately enhancing the quality of patient care and decision-making in clinical settings.

Patrones de cambios cromáticos en juveniles y adultos de la castañeta Nexilosus latifrons (Cichliformes: Pomacentridae)

Patterns of temporal chromatic and shape variations of live specimens of Nexilosus latifrons were studied. Observations were made on specimens captured in intertidal pools in the coastal area of Arica, northern Chile, which were kept in captivity for 16 months. Five developmental stages, in which there were noticeable changes in shape and colouration, were described. Findings obtained primarily to improve species identification are discussed, as well as to prevent removal of juvenile specimens from the environment and improve captive breeding practices.

Statistical Analysis of Temporal Variations in Patterns of Rainfall and Temperature in the Klang Valley

Malaysia is in the equatorial zone and experiences hot and humid climates throughout the year. During the rainy season, floods may occur in some parts of Malaysia, including the Klang Valley. During the flood occurrences, the residents may need to leave their houses and move to relief shelters. This disaster may happen because of the continuous heavy downpour in the area. Both incidents can be connected to climate change, and one way to investigate this is to examine differences in daily rainfall and daily temperature patterns through time. Climate change is inevitable and one of the causes is known as 'anthropogenic' in which it is caused by human activities. Geological records can provide evidence of climate change. However, there has been little statistical research on climate change in the Klang Valley. Even official organisations like the Department of Irrigation and Drainage Malaysia (DID) and the National Water Research Institute of Malaysia (NAHRIM) have not conducted this area of study. The goal of this study is to evaluate climate change in the Klang Valley using statistical analysis of temporal rainfall patterns. The data collection and statistical analysis are two critical parts of this study. Rainfall and average temperature data were obtained from their respective departments and websites. These data were sorted and filtered accordingly to get a satisfactory value for the analysis. Then, the correlation test and analysis of variance were conducted to find the relation between these data points. The results of this analysis were used to determine whether the variations in rainfall patterns in the Klang Valley are caused by climate change or otherwise. In addition to this, the statistical outputs indicate that there is a statistically significant finding in both data sets in which the decreasing temperatures may result in increasing rainfall and warmer conditions may lessen the rainfall. Surprisingly, the findings contradicted the expected outcome of this study, and it is believed that external factors are the primary contributors to these findings.

(Digital Presentation) Dynamic Thermal Detection of Water Electrolyzer with Dual-Layer Characteristic Temperature Based on IR

As the prevalence of water electrolysis systems continues to expand, increased attention has been placed on the dynamic thermal detection of the electrolyzer. Presently, the scope of dynamic thermal detection for water electrolysis systems is limited to solely the temperatures at the inlet and outlet. This study propounds a novel dual-layer characteristics temperature (DLCT) model for the purpose of water electrolyzer temperature monitoring. The DLCT model addresses two significant issues of the dynamic thermal detection of the WEE: characteristic temperature extraction difficulty and signal perturbation caused by auxiliary factors. The first layer of multi-gaussian distribution (MGD) regression in the DLCT model quantifies the sectional temperature of the electrolyzer surface and produces numerous potential characteristic temperatures (CT). These temperatures are subsequently assigned to each section and are then conveyed to the second layer of linear regression, which provides an incisive quantized temperature variation pattern for the electrolyzer surface temperature distribution. Importantly, the DLCT model requires no supplementary modifications to the water electrolysis system or the installation of temperature sensors within or on the surface. By employing the DLCT model during dynamic operation, the ability to monitor the temperature of the electrolyzer comprehensively is improved, the temperature distribution of the electrolyzer can be quantized and richer insights regarding the surface temperature distribution can be gained, promoting better thermal uniformity, as well as pin-pointing the potential hot spot. Hence this method can also be used for structure design to produce better uniformity. The dynamic temperature detection results under different operating conditions are analyzed, and it can be concluded that the operating parameters, especially the lye circulation flow, can greatly influence the temperature distribution uniformity of the electrolyzer significantly. In conclusion, the DLCT model effectively handles the two primary challenges in dynamic thermal detection of water electrolysis systems by providing global and sectional characteristic temperature readings, enabling electrolyzer to be monitored holistically. So that future improvement regarding both structural and operational optimization can be conducted in improving the thermal uniformity. Figure 1

Understanding temporal variability across trophic levels and spatial scales in freshwater ecosystems.

A tenet of ecology is that temporal variability in ecological structure and processes tends to decrease with increasing spatial scales (from locales to regions) and levels of biological organization (from populations to communities). However, patterns in temporal variability across trophic levels and the mechanisms that produce them remain poorly understood. Here we analyzed the abundance time series of spatially structured communities (i.e., metacommunities) spanning basal resources to top predators from 355 freshwater sites across three continents. Specifically, we used a hierarchical partitioning method to disentangle the propagation of temporal variability in abundance across spatial scales and trophic levels. We then used structural equation modeling to determine if the strength and direction of relationships between temporal variability, synchrony, biodiversity, and environmental and spatial settings depended on trophic level and spatial scale. We found that temporal variability in abundance decreased from producers to tertiary consumers but did so mainly at the local scale. Species population synchrony within sites increased with trophic level, whereas synchrony among communities decreased. At the local scale, temporal variability in precipitation and species diversity were associated with population variability (linear partial coefficient, β = 0.23) and population synchrony (β = -0.39) similarly across trophic levels, respectively. At the regional scale, community synchrony was not related to climatic or spatial predictors, but the strength of relationships between metacommunity variability and community synchrony decreased systematically from top predators (β = 0.73) to secondary consumers (β = 0.54), to primary consumers (β = 0.30) to producers (β = 0). Our results suggest that mobile predators may often stabilize metacommunities by buffering variability that originates at the base of food webs. This finding illustrates that the trophic structure of metacommunities, which integrates variation in organismal body size and its correlates, should be considered when investigating ecological stability in natural systems. More broadly, our work advances the notion that temporal stability is an emergent property of ecosystems that may be threatened in complex ways by biodiversity loss and habitat fragmentation.

Can the transition of multiple cropping systems affect the cropland change?

CONTEXTIntensification of cropland can achieve food security by increasing agricultural production pre-unit yield, thereby slowing down cropland expansion. However, it is also possible to accelerate the cropland expansion due to the increasing net benefits of cropland. The transition of multiple cropping system (MCS) caused by climate change is an important strategy for cropland intensification, its impact on cropland changes needs further in-depth discussion. OBJECTIVEThis research aims to address the following two questions: (1) The transition patterns of the MCS in China against the background of climate change; (2) The impact of the MCS's transition on the change rate of cropland and grain production capacity. METHODSBased on meteorological observation data, the Global Agro-Ecological Zones (GAEZ) model is utilized to map the China's MCS to analyze the temporal and spatial variation patterns of MCS under the comprehensive influence of various meteorological factors. On this basis, we investigate the change of cropland area and grain production capacity under the transition of MCS. RESULTS AND CONCLUSIONSThe results indicate that in the past 20 years, the increase of cropping intensity in China has led to 0.42Mha expansion in the multiple cropping area. Temporally, the probability of maintaining the original cropping system (2000) in 2020 was greater than the probability of transiting (83% > 17%). These transitions occurred more frequently between cropping systems with small gradation differences, and the probability of cross-gradation changes decreased with increasing cropping intensity. Spatially, the MCS exhibited a decreasing trend with increasing distance from the coastline. Although regardless of whether the MCS transiting or maintaining, the cropland area would reduce, the increase in cropping intensity would reduce the cropland area more than the decrease (6.48% > 2.66%). Moreover, we found that the decreased cropping intensity was beneficial to slow down the cropland reduction (4.35%; 2.93%; 5.95% > 2.66%), while increased cropping intensity accelerated the cropland reduction (1.56%; 4.35%; 2.93%; 5.95% < 6.48%). This result was further validated by grid-based statistical regression results (p < 0.05). In regions where the MCS transited, 96% of the cropland decrease was occupied by construction land, and 76% of the cropland expansion was due to the conversion of grassland. Among them, when the cropping intensity increased, although the cropland area reduced even more, the grain production capacity would actually increase by 40.29%, and when the planting intensity decreased, the grain production capacity would decrease by 10.55%. SIGNIFICANCEIncreasing the cropping intensity of cropland through multiple cropping can increase the comprehensive grain yield under the condition of limited cropland resources. This strategy holds significant promise for bolstering China's food security and fostering sustainable agricultural development.

Reinterpreting Global GRACE Trends Based on Century‐Long GRACE‐REC Data

AbstractAssessing changes in freshwater availability accurately is crucial for societal development. Previous studies have examined long‐term variations in basin‐scale terrestrial water storage (TWS) using Gravity Recovery and Climate Experiment (GRACE) mission data. However, different basins exhibit distinct spatial and temporal TWS variation patterns. To better interpret the TWS trends in each basin during the GRACE era (2003–2016), this study proposes a novel criterion based on a century‐long GRACE‐REC data set. This criterion assesses the trends in GRACE TWS (TrendG), precipitation‐induced trends (TrendPI), and non‐precipitation‐induced trends (TrendNPI) over the GRACE period. By calculating upper and lower bound values for long‐term climate‐driven TWS trends using GRACE‐REC data, an indicator is provided to evaluate the range of TWS trend variations in a basin under natural conditions. Results reveal that among the 266 global basins analyzed in this study, the trends (TrendG, TrendPI, or TrendNPI) in 115 basins exceed the maximum or minimum water storage trends associated with natural climate variability. This includes 20 large basins, 34 medium basins, and 61 small basins, indicating significant TWS changes during the GRACE period. Furthermore, we analyze the driving mechanisms of TWS trends in the 20 large basins using multi‐source data. The mechanisms identified through this method align well with both our analysis and previous studies, confirming the reliability of this approach for assessing TWS trends.

Year effects drive beta diversity, but unevenly across plant community types.

Year of establishment can be a critical driver of plant communities with the establishment stage of community development particularly susceptible to factors including ambient rain, temperature, and other temporally variable drivers (e.g., seed and seedling predators). However, while year effects have been shown to drive community structure at local (patch) scales, it is yet unexplored how these within-patch effects scale up to drive landscape-level patterns of biodiversity. These dynamics are likely to be critical but are overlooked in many systems including those with high-frequency disturbance regimes or active management. Here we leveraged a series of field-based grassland mesocosms established identically at three sites across 5 years, and each monitored for 4-8 years. We compared the strength of these temporal and spatial drivers (year effects and site effects) on consequent patterns of spatial and temporal variability (beta diversity and turnover) between plots seeded with native perennial species versus those seeded with nonnative annual species. The composition of plots seeded with perennial species showed strong effects of planting year and consequently exhibited higher beta diversity within sites (across mesocosms established in five different years within sites), while plots seeded with annual species had higher between-site variation but low beta diversity within sites. Plots with annual species were also more temporally variable than plots with perennial species. These findings have important implications for our understanding of key drivers of biodiversity across landscapes. Specifically, we showed that variable trajectories in community composition generated by site and year effects during establishment can promote beta diversity across landscapes dominated by perennial species, but are considerably less impactful in annual-dominated systems. These findings further our understanding of the importance of assembly dynamics on landscape-scale patterns of diversity, and have important management implications for restoration efforts.

Grapevine bacterial communities display compartment-specific dynamics over space and time within the Central Valley of California

BackgroundPlant organs (compartments) host distinct microbiota which shift in response to variation in both development and climate. Grapevines are woody perennial crops that are clonally propagated and cultivated across vast geographic areas, and as such, their microbial communities may also reflect site-specific influences. These site-specific influences along with microbial differences across sites compose ‘terroir’, the environmental influence on wine produced in a given region. Commercial grapevines are typically composed of a genetically distinct root (rootstock) grafted to a shoot system (scion) which adds an additional layer of complexity via genome-to-genome interactions.ResultsTo understand spatial and temporal patterns of bacterial diversity in grafted grapevines, we used 16S rRNA amplicon sequencing to quantify soil and compartment microbiota (berries, leaves, and roots) for grafted grapevines in commercial vineyards across three counties in the Central Valley of California over two successive growing seasons. Community composition revealed compartment-specific dynamics. Roots assembled site-specific bacterial communities that reflected rootstock genotype and environment influences, whereas bacterial communities of leaves and berries displayed associations with time.ConclusionsThese results provide further evidence of a microbial terroir within the grapevine root systems but also reveal that the microbiota of above-ground compartments are only weakly associated with the local soil microbiome in the Central Valley of California.

Viewing China's escalating heatwaves through the lens of complex networks

Heatwaves are increasingly frequent and severe, prompting international research concerns. This study utilized the Visibility Graph to construct complex networks (CNs) of heatwaves in China. From a CN perspective, we analyzed the spatial and temporal variability and hazard patterns of heatwaves, with a focus on their reliability and advantages. Our findings indicate that heatwaves in China have exhibited a consistent overall increasing trend since 1961, with a more significant rise observed in the past 30 years. Notably, there are substantial variations in the changes and severity of heatwaves experienced in different regions and decades, with southern China experiencing higher heatwaves hazards compared to central China where they are relatively lower. The utilization of CNs has demonstrated remarkable advantages in anomaly detection, change phase delineation, analysis of connections between meteorological stations, and selection of representative values. And, CNs enable the incorporation of both heatwave changes and severity in hazard zoning. The results of this study provide new methods and perspectives for researching climate extremes, and they establish a solid foundation for more comprehensive and accurate heatwave hazard prevention in China.

Study on modelling simulation and distribution characteristics of residual stress during robotic weld end grinding of high strength modulated steel

Welding technology is widely used in the aerospace, construction, machinery, and rail transportation industries. After the welding process is completed, the weld seam is typically ground to improve the assembly accuracy and surface integrity of the structural part. However, residual stresses on the surface after grinding often affect the product performance and service life of structural components. Therefore, the formation mechanism and distribution of residual stresses after the robotic weld end grinding of high–strength modulated steel welds were investigated. First, a local contact geometric model of the grinding wheel and weld was established, and a grinding -force contact model was built based on this model. Next, a model of the heat-flow distribution in robotic end grinding was analysed, and the magnitudes of the heat flow in the contact and weld-width directions were calculated for the contact area. The results of these calculations were similar to the actual heat-flow distribution. Then, based on a theoretical analysis, the finite element method was used with a birth–death element to establish a force-thermal coupled residual stress prediction model, which could simulate the material removal process during actual grinding. Finally, the accuracies and reliabilities of the theoretical and simulation models were experimentally verified. The maximum error between the maximum simulated temperature and experimentally measured value was 10 %. The minimum error was 3 % when using the same process parameters, and the residual stress error was approximately 15 %. Based on the simulations, the variations in the temperature and residual stresses with time and position were explored. The highest weld-contact surface temperature of 417.75 °C was found when grinding using process parameters ap = 0.2 mm, vs = 23.55 m/s, vw = 20 mm/s. The residual tensile stresses could be up to +553 MPa in the longitudinal direction and −161 MPa in the transverse direction. The temperature in the zone of demarcation between the weld and base material was 212.8 °C, with maximum residual tensile stresses in both the longitudinal and transverse directions, of up to 352 MPa. The analysis revealed that the magnitude of the residual stresses was consistent with the temperature variation pattern, and that the grinding depth had the greatest influence on the residual stress distribution.

Temperature variation generates interspecific synchrony but spatial asynchrony in survival for freshwater fish communities.

Identifying environmental drivers of demographic variation is key to predicting community-level impacts in response to global change. Climate conditions can synchronize population trends and can occur both spatially for populations of the same species, and across multiple species within the same local community. The aim of this study was to investigate patterns of temporal variation in survival for freshwater fish communities in two geographically close but isolated sites and to understand the amount of variation accounted for by abiotic covariates including metrics of water temperature and stream flow. Using mark-recapture data, we estimated bi-monthly apparent survival in a Bayesian Cormack-Jolly-Seber framework. The model included random effects to quantify temporal variance to understand species synchrony with the rest of the fish community and between sites. Study species included bluehead chub (Nocomis leptocephalus), creek chub (Semotilus atromaculatus), and striped jumprock (Moxostoma rupiscartes) in the southeastern USA. Results showed that survival varied over time and periods of low survival were associated with higher mean water temperature. However, temporal patterns of survival differed among species and between sites, where survival was synchronous among species within a site but asynchronous between sites for the same species despite their spatial proximity. Study streams differed in summer thermal regimes, which resulted in contrasting summer survival patterns, suggesting sensitivity of these fishes to warming. We found that interspecific synchrony was greater than spatial synchrony, where regional drivers such as temperature may interact with local habitat leading to differences in survival patterns at fine spatial scales. Finally, these findings show that changes in the timing and magnitude of environmental conditions can be critical in limiting vital rates and that some populations may be more resilient to climate variation than others.

Climate change does not equally affect temporal patterns of natural selection on reproductive timing across populations in two songbird species.

Climate change has led to changes in the strength of directional selection on seasonal timing. Understanding the causes and consequences of these changes is crucial to predict the impact of climate change. But are observed patterns in one population generalizable to others, and can spatial variation in selection be explained by environmental variation among populations? We used long-term data (1955-2022) on blue and great tits co-occurring in four locations across the Netherlands to assess inter-population variation in temporal patterns of selection on laying date. To analyse selection, we combine reproduction and adult survival into a joined fitness measure. We found distinct spatial variation in temporal patterns of selection which overall acted towards earlier laying, and which was due to selection through reproduction rather than through survival. The underlying relationships between temperature, bird and caterpillar phenology were however the same across populations, and the spatial variation in selection patterns is thus caused by spatial variation in the temperatures and other habitat characteristics to which birds and caterpillars respond. This underlines that climate change is not necessarily equally affecting populations, but that we can understand this spatial variation, which enables us to predict climate change effects on selection for other populations.

Fingerprinting Low-Frequency Last Millennium Temperature Variability in Forced and Unforced Climate Models

Abstract Constraining unforced and forced climate variability impacts interpretations of past climate variations and predictions of future warming. However, comparing general circulation models (GCMs) and last millennium Holocene hydroclimate proxies reveals significant mismatches between simulated and reconstructed low-frequency variability at multidecadal and longer time scales. This mismatch suggests that existing simulations underestimate either external or internal drivers of climate variability. In addition, large differences arise across GCMs in both the magnitude and spatial pattern of low-frequency climate variability. Dynamical understanding of forced and unforced variability is expected to contribute to improved interpretations of paleoclimate variability. To that end, we develop a framework for fingerprinting spatiotemporal patterns of temperature variability in forced and unforced simulations. This framework relies on two frequency-dependent metrics: 1) degrees of freedom (≡N) and 2) spatial coherence. First, we use N and spatial coherence to characterize variability across a suite of both preindustrial control (unforced) and last-millennium (forced) GCM simulations. Overall, we find that, at low frequencies and when forcings are added, regional independence in the climate system decreases, reflected in fewer N and higher coherence between local and global mean surface temperature. We then present a simple three-box moist-static-energy-balance model for temperature variability, which is able to emulate key frequency-dependent behavior in the GCMs. This suggests that temperature variability in the GCM ensemble can be understood through Earth’s energy budget and downgradient energy transport, and allows us to identify sources of polar-amplified variability. Finally, we discuss insights the three-box model can provide into model-to-model GCM differences. Significance Statement Forced and unforced temperature variability are poorly constrained and understood, particularly that at time scales longer than a decade. Here, we identify key differences in the time scale–dependent behavior of forced and unforced temperature variability using a combination of numerical climate models and principles of downgradient energy transport. This work, and the spatiotemporal characterizations of forced and unforced temperature variability that we generate, will aid in interpretations of proxy-based paleoclimate reconstructions and improve mechanistic understanding of variability.

Predictive modelling of heating and cooling degree hour indexes for residential buildings based on outdoor air temperature variability

Heating and cooling degree hours (HDH and CDH) are weather-based technical indexes designed to describe the need for energy requirements of buildings. Their calculation is the simplest method to estimate energy demand, providing the pattern of internal temperature variations in a building in response to weather conditions. The aim of the study is HDH and CDH prediction for Wrocław, Poland, based on outdoor air temperature using machine learning methods: artificial neural networks and support vector regression (ANN and SVR). The key issues raise in the study are: a detailed analysis of the most significant temperature lags (from 1 to 24 past hours) serving as predictors for modelling and an assessment of the impact of the database clustering on its accuracy. The best results are obtained with the clustering approach. The best predictor is the outdoor temperature observed 1 and 24 h before forecast demand (R2 = 0.981 and 0.904 for heating degree and cooling degree hours indices, respectively). Models with the highest quality are created using ANN, and the lowest with SVR. Prediction of heating/cooling degree hour indices provides building demand in advance, does not require knowledge about its characteristics, and expresses the possible impact of regional climate modifications.

Spatial and temporal variation patterns of summer grazing trajectories of Sunit sheep

GPS is an important tool for real-time monitoring of individual livestock. Analysis of the spatial and temporal changes in the grazing trajectory of Sunit sheep provided a scientific basis for the rational use of Inner Mongolia desert grassland resources and optimal breeding of livestock. In this study, GPS collars were used to track Sunit sheep in the Inner Mongolia desert grassland. These data were combined with ground measurement data and spatial remote sensing data to investigate the spatial and temporal variation patterns of sheep grazing tracks. The results showed that the distance travelled by sheep in June was significantly different from that in July and August (p < 0.05). The effective daily foraging time was mainly concentrated in the time ranges of 7:00–10:00 and 15:00–18:00 in June and August, and in the time ranges of 7:00–9:00 and 16:00–19:00 in July, for more active grazing behavior in the morning and evening hours. The degree of nuclear density of foraging track points was significantly different from the edible forage biomass (p < 0.05), with the average daily walking distance negatively correlated with the total grass biomass (R2 = 0.49). Overall, the results of this study confirm that GPS-positioning technology can be applied to study the spatial and temporal variation patterns of sheep and suggest new strategies for more effective grassland management in this region.

Investigating Spatial Variations of Compound Heat–Precipitation Events in Guangdong, China through a Convection-Permitting Model

Compound heat–precipitation events exert significant impacts on severe weather occurrences. Intense vertical air movement, driving vigorous convection, primarily contributes to the formation of extreme precipitation. Nevertheless, such compound events’ temporal and spatial variation patterns at convection-permitting resolutions remain inadequately explored. This study assesses the performance of the Convection-Permitting Model (CPM) against a model of convection parameterization while investigating the spatial dynamics of compound heat–precipitation events in Guangdong, China. Our findings indicate that the CPM exhibits heightened reliability and precision in simulating temperature and precipitation patterns, especially in extreme precipitation simulation, which would be highly underestimated without a convection-permitting process. Projections from the CPM reveal that, across historical and future periods, the occurrence frequency and fraction of T-P events (instances of extreme heat followed by extreme precipitation) surpass those of P-T events (occurrences of extreme precipitation followed by extreme heat). For T-P events, the CPM exhibits better capability in capturing high-frequency occurrence areas, whereas the results of the relatively low-resolution model show less distinct spatial variations. Both types of events exhibit noticeable upward trends yearly within each period. By the close of this century, the provincial average frequency of P-T events is anticipated to decrease from 20.32 times to 14.55 times. In contrast, the frequency of T-P events is projected to increase from 87.7 times to 101.38 times. These projected changes underscore the shifting dynamics of compound heat–precipitation events in the study region.

Water depth and productivity regulate methane (CH4) emissions from temperate cascade reservoirs in northern China

Reservoirs are important sources of emissions of the greenhouse gas methane, but the temporal and spatial variability of methane emissions and the underlying mechanisms driving methane flux in cascade reservoirs remain poorly understood. In this study, we used floating chambers to study methane emission fluxes and their temporal and spatial variations in three temperate cascade reservoirs with different nutrient levels and morphologies in northern China. We measured the methane production rate, sediment properties and water quality to identify key factors driving methane emissions. The results showed that the three cascade reservoirs were persistent sources of atmospheric methane during the ice-free period, and the methane flux ranged from 1.01 to 107.17 µmol m-2h−1, with a mean of 11.17 µmol m-2h−1. The mean methane flux of the eutrophic reservoir was more than 2 and 4 times that of the two mesotrophic reservoirs. Overall, methane flux showed high spatial and temporal variability within and among reservoirs, and the three reservoirs showed different patterns of spatial and temporal variability. Statistical analysis showed that the three reservoirs had different key drivers, indicating that the methane emissions of the three reservoirs were regulated by different underlying mechanisms. When all three reservoirs were pooled, we found that Chl-a was the most important driver of methane emissions. Our results suggest that to obtain an accurate estimate of whole-reservoir methane flux, different reservoir zones and seasons must be considered. Our results also highlight that regional and global methane budget upscaling should include considerations of reservoir diversity (different climatic zones, morphology and trophic levels).

Temporal Variation in Selection Influences Microgeographic Local Adaptation.

AbstractEcological heterogeneity can lead to local adaptation when populations exhibit fitness trade-offs among habitats. However, the degree to which local adaptation is affected by the spatial and temporal scale of environmental variation is poorly understood. A multiyear reciprocal transplant experiment was performed with populations of the annual plant Leptosiphon parviflorus living on adjacent serpentine and nonserpentine soil. Local adaptation over this small geographic scale was observed, but there were differences in the temporal variability of selection across habitats. On serpentine soil, the local population had a consistently large survival advantage, presumably as a result of the temporal stability in selection imposed by soil cation content. In contrast, a fecundity advantage was observed for the sandstone population on its native soil type but only in the two study years with the highest rainfall. A manipulative greenhouse experiment demonstrated that the fitness advantage of the sandstone population in its native soil type depends critically on water availability. The temporal variability in local adaptation driven by variation in precipitation suggests that continued drought conditions have the potential to erode local adaptation in these populations. These results show how different selective factors can influence spatial and temporal patterns of variation in fitness trade-offs.