Large-scale Patterns Research Articles

Soil moisture determines the consistency of organic matter decomposition in field and lab test patterns

Litter decomposition serves as the primary process for soil organic matter formation and renewal. However, few studies have been conducted to validate field large-scale litter decomposition patterns. In this study, cotton strips and Solanum nigrum were used to explore the decomposition differences between standardized litter and conventional litter, along with the release of heavy metal Cadmium (Cd). The indoor characteristics of organic matter decomposition were further verified. After 6 months of Cd pretreatment, litter was placed on the surface of soil collected from 15 sites across the China for 3 months of lab tests. The results indicated that the decomposition rates of both litter types were regulated by soil moisture and generally showed a trend of deceleration from east to west, consistent with the field decomposition pattern. In addition, the mass loss of cotton strip was significantly lower than S. nigrum due to the higher Cd concentration. With the decomposition process, both litter types showed a decreasing trend in Cd concentration, especially the cotton strip. Interestingly, our study found soil factors had the highest relative influence on decomposition. A higher soil microbial moisture use efficiency in the mid-latitude region during litter decomposition may be attributed to the adaptability of microorganisms. Our research demonstrated the consistency of lab test patterns with the field experiments, emphasizing the distinctions between standardized and conventional litter. This study not only revealed the influence mechanism of soil moisture and Cd on litter decomposition rate but also emphasized the important role of microorganisms in the litter decomposition process, which provided a new perspective for a deeper understanding of the ecosystem carbon cycle and heavy metal pollution.

Imperial Study on Convolutional Layer in CNN for Data Mining

Convolutional Neural Networks (CNNs) have become a cornerstone in the field of data mining, particularly for tasks involving large-scale image and pattern recognition. This paper presents an imperial study on the efficacy of convolutional layers within CNN architectures in data mining applications. We analyze the impact of varying convolutional layer configurations, such as kernel size, stride, and depth, on performance metrics including accuracy, computational cost, and feature extraction quality. By conducting a series of experiments across different datasets, we explore how these configurations influence the model's ability to generalize and detect complex patterns in structured and unstructured data. Our findings indicate that optimizing the convolutional layers significantly improves the efficiency of CNNs for data mining tasks, offering insights into best practices for architectural design in such applications. This study provides valuable guidelines for leveraging CNNs in the realm of data mining, pushing the boundaries of automated data analysis and knowledge discovery

Enhanced flight performance and adaptive evolution of Mesozoic giant cicadas.

Insects have evolved diverse ecological flight behaviors and adaptations that played a key role in their large-scale evolutionary patterns. However, the evolution of their flight performance is poorly understood because reconstructing flight abilities of extinct insects is highly challenging. Here, we propose an integrated approach to reveal the evolution of flight performance of Palaeontinidae (giant cicadas), a Mesozoic arboreal insect clade with large bodies and wings. Our analyses unveil a faunal turnover from early to late Palaeontinidae during the latest Jurassic-earliest Cretaceous, accompanied by a morphological adaptive shift and remarkable improvement in flight abilities including increased flight speed and enhanced maneuverability. The adaptive aerodynamic evolution of Palaeontinidae may have been stimulated by the rise of early birds, supporting the hypothesis of an aerial evolutionary arms race (Air Race) between Palaeontinidae and birds. Our results provide a potential example of predator-induced morphological and behavioral macroevolution and contribute to our understanding of how powered flight has shaped animal evolution.

Continental sampling reveals core bacterial and environmentally driven fungal leaf endophytes in Heuchera.

Endophytic plant-microbe interactions range from mutualistic relationships that confer important ecological and agricultural traits to neutral or quasi-parasitic relationships. In contrast to root-associated endophytes, the role of environmental and host-related factors in the acquisition of leaf endophyte communities at broad spatial and phylogenetic scales remains sparsely studied. We assessed endofoliar diversity to test the hypothesis that membership in these microbial communities is driven primarily by abiotic environment and host phylogeny. We used a broad geographic coverage of North America in the genus Heuchera L. (Saxifragaceae), representing 32 species and varieties across 161 populations. Bacterial and fungal communities were characterized using 16S and ITS amplicon sequencing, respectively, and standard diversity metrics were calculated. We assembled environmental predictors for microbial diversity at collection sites, including latitude, elevation, temperature, precipitation, and soil parameters. Assembly patterns differed between bacterial and fungal endophytes. Host phylogeny was significantly associated with bacteria, while geographic distance was the best predictor of fungal community composition. Species richness and phylogenetic diversity were consistent across sites and species, with only fungi showing a response to aridity and precipitation for some metrics. Unlike what has been observed with root-associated microbial communities, in this system microbes show no relationship with pH or other soil factors. Overall, this work improves our understanding of the large-scale patterns of diversity and community composition in leaf endophytes and highlights the relative significance of environmental and host-related factors in driving different microbial communities within the leaf microbiome.

New constraints to subduction zone arc and backarc mantle temperatures: a test of the corner flow model

The heat source for the hot volcanic arc of subduction zones and the uniformly warm backarc is not well understood, particularly where backarc extension is absent. In the widely studied corner flow model, the traction of the underthrusting plate drags down the overlying viscous mantle, and the induced shallower seaward flow brings in heat from the landward and deeper asthenosphere. However, earlier comparisons with observations, especially backarc heat flow, gave poor agreement. There are several new constraints to the temperatures and structures above the underthrusting plate and in the backarc, especially those in western North America, for comparison with model predictions. Seismic receiver functions map horizontal boundaries, including the lithosphere-asthenosphere boundary (LAB). Seismic shear wave tomography gives mantle velocity-depth that provides an approximate proxy for temperature. Attenuation of seismic wave speed indicates high temperature or partial melting. The presence of recent backarc volcanics and their geochemistry give the depth and temperature of the LAB. From these constraints in western North America, the LAB reflects ponding of partial melt, is consistently at a depth of 65±3 km and temperature of 1350±25°C, and overlies nearly constant temperature (adiabatic) asthenosphere across the backarc from Mexico to Alaska. These depth and temperature estimates are greatly different from corner flow model predictions. An alternative hypothesis more consistent with these constraints is vigorous small-scale convection in the backarc asthenosphere. Whether or how this convection coexists with the large-scale flow patterns inferred from the interpretation of seismic anisotropy analyses, and to what degree it prevails in the arc-forearc region, deserve further research.

Major moisture shifts in inland Northeast Asia during the last millennium

Abstract Previous paleoenvironmental data synthesis indicates that arid central Asia (“westerlies Asia”) and mid-latitude East Asia (“monsoonal Asia”) show anti-phased moisture variations over the last millennium. However, there are very few records from inland Northeast Asia, which obscures the spatial extent of or the boundary between the two domains and hinders the assessment of climate change impacts and consequences across the region. Here, we present a multi-proxy record that combines peat properties, plant macrofossils, and isotopic ratios of Sphagnum moss cellulose from a unique precipitation-fed peatland in northern Northeast China to fill this critical data gap. The results show major centennial-scale moisture anomalies at this site, with drier and wetter conditions during the Medieval Warm Period and Little Ice Age, respectively, which resemble the pattern of moisture changes in “westerlies Asia”. During the period of rapid anthropogenic warming, the site is much drier, with isotopic evidence for threshold-like summer desiccation of peat-forming Sphagnum mosses. This study provides the long-term context and identifies the large-scale pattern of moisture variability in an inland region home to carbon-rich peatlands, forests, and permafrost soils, and highlights their potential vulnerability to future warming-enhanced drying that can be transmitted widely through atmospheric teleconnection.

Multi-scale drought variability over West Africa and the associated large-scale circulation patterns

This study addresses the critical research gap in understanding regional drought dynamics in West Africa (WA) using advanced multivariate wavelet coherence (MWC) techniques, revealing critical insights into the complex interplay between large-scale climate indices and regional drought patterns. Utilizing the Standardized Precipitation Evapotranspiration Index (SPEI) and self-calibrating Palmer Drought Severity Index (scPDSI), we examined drought characteristics across WA from 1950 to 2018. Our analysis revealed significant drying trends across all seasons, with the Arid zone experiencing the most severe and prolonged drought (158 months) from August 1981 to September 1994, affecting 79.77% of the area. The entire WA region faced its most extensive drought during this period, impacting over 60% of the study area. MWC analysis demonstrated enhanced drought predictability when combining climate indices, particularly the Southern Oscillation Index with the Atlantic Multidecadal Oscillation, yielding high Average Wavelet Coherence values of 0.52 (SPEI) and 0.53 (scPDSI), with substantial Percentage Area of Significant Coherence of 40.19% and 43.68%, respectively. These findings highlight the crucial role of oceanic-atmospheric interactions in modulating drought conditions across WA. Our results provide valuable insights for improving drought forecasting and management strategies in the region, emphasizing the need for integrated approaches that consider multiple climate indices and their combined effects on drought dynamics.

Arctic climate response to European radiative forcing: a deep learning study on circulation pattern changes

Abstract. Heterogeneous radiative forcing in mid-latitudes, such as that exerted by aerosols, has been found to affect the Arctic climate, though the mechanisms remain debated. In this study, we leverage deep learning (DL) techniques to explore the complex response of the Arctic climate system to local radiative forcing over Europe. We conducted sensitivity experiments using the Max Planck Institute Earth System Model (MPI-ESM1.2) coupled with atmosphere–ocean–land-surface components. Large-scale circulation patterns can mediate the impact of the forcing on Arctic climate dynamics. We employed a DL-based clustering approach to classify large-scale atmospheric circulation patterns. To enhance the analysis of how these patterns impact the Arctic climate, the poleward moist static energy transport (PMSET) associated with the atmospheric circulation patterns was incorporated as an additional similarity metric in the clustering process. Furthermore, we developed a novel method to analyze the circulation patterns' contributions to various climatic parameter anomalies. Our findings indicate that the negative radiative forcing over Europe alters existing circulation patterns and their occurrence frequency without introducing new ones. Specifically, our analysis revealed that while the regional radiative forcing alters the occurrence frequencies of the circulation patterns, these changes are not the primary drivers of the forcing's impact on the Arctic parameters. Instead, it is the shifts in the mean spatial characteristics of the atmospheric circulation patterns, induced by the forcing, that predominantly determine the effects on the Arctic climate. Our methodology facilitates the uncovering of complex, nonlinear interactions within the climate system, capturing nuances that are often obscured in broader seasonal anomaly analyses. This approach enables a deeper understanding of the dynamics driving observed climatic anomalies and their links to specific atmospheric circulation patterns.

RASCAL v1.0: an open-source tool for climatological time series reconstruction and extension

Abstract. The reduction of in situ observations over the last few decades poses a potential risk of losing important information in regions where local effects dominate the climatology. Reanalyses face challenges in representing climatologies with highly localized effects, especially in regions with complex orography. Empirical downscaling methods offer a cost-effective and easier-to-implement alternative to dynamic downscaling methods and can partially overcome the aforementioned limitations of reanalyses by taking into account the local effects through statistical relationships. This article introduces RASCAL (Reconstruction by AnalogS of ClimatologicAL time series), an open-source Python tool designed to extend time series and fill gaps in observational climate data, especially in regions with limited long-term data and significant local effects, such as mountainous areas. Employing an object-oriented programming style, RASCAL's methodology effectively links large-scale circulation patterns with local atmospheric features using the analog method in combination with principal component analysis (PCA). The package contains routines for preprocessing observations and reanalysis data, generating reconstructions using various methods, and evaluating the reconstruction's performance in reproducing the time series of observations, statistical properties, and relevant climatic indices. Its high modularity and flexibility allow fast and reproducible downscaling. The evaluations carried out in central Spain, in mountainous and urbanized areas, demonstrate that RASCAL performs better than the ERA20C and ERA20CM reanalysis, as expected, in terms of R2, standard deviation, and bias. When analyzing reconstructions against observations, RASCAL generates series with statistical properties, such as seasonality and daily distributions, that closely resemble observations. This confirms the potential of this method for conducting robust climate research. The adaptability of RASCAL to diverse scientific objectives is also highlighted. However, as with any other method based on empirical training, this method requires the availability of sufficiently long-term data series. Furthermore, it is susceptible to disruption caused by changes in land use or urbanization processes that might compromise the homogeneity of the training data. Despite these limitations, RASCAL's positive outcomes offer opportunities for comprehensive climate variability analyses and potential applications in downscaling short-term forecasts, seasonal predictions, and climate change scenarios. The Python code and the Jupyter Notebook for the reconstruction validation are publicly available as an open project.

From Polar Day to Polar Night: A Comprehensive Sun and Star Photometer Study of Trends in Arctic Aerosol Properties in Ny-Ålesund, Svalbard

The climate impact of Arctic aerosols, like the Arctic Haze, and their origin are not fully understood. Therefore, long-term aerosol observations in the Arctic are performed. In this study, we present a homogenised data set from a sun and star photometer operated in the European Arctic, in Ny-Ålesund, Svalbard, of the 20 years from 2004–2023. Due to polar day and polar night, it is crucial to use observations of both instruments. Their data is evaluated in the same way and follows the cloud-screening procedure of AERONET. Additionally, an improved method for the calibration of the star photometer is presented. We found out, that autumn and winter are generally more polluted and have larger particles than summer. While the monthly median Aerosol Optical Depth (AOD) decreases in spring, the AOD increases significantly in autumn. A clear signal of large particles during the Arctic Haze can not be distinguished from large aerosols in winter. With autocorrelation analysis, we found that AOD events usually occur with a duration of several hours. We also compared AOD events with large-scale processes, like large-scale oscillation patterns, sea ice, weather conditions, or wildfires in the Northern Hemisphere but did not find one single cause that clearly determines the Arctic AOD. Therefore the observed optical depth is a superposition of different aerosol sources.

Integrated mid-infrared sensing and ultrashort lasers based on wafer-level Td-WTe2 Weyl semimetal

There is an urgent need for infrared (IR) detection systems with high-level miniaturization and room-temperature operation capability. The rising star of two-dimensional (2D) semimetals with extraordinary optoelectronic properties can fulfill these criteria. However, the formidable challenges with regard to large-scale patterning and substrate-selective requirements limit material deposition options for device fabrication. Here, we report a convenient and straightforward eutectic-tellurization transformation method for the wafer-level synthesis of 2D type-II Weyl semimetal WTe2. The non-cryogenic WTe2/Si Schottky junction device displays an ultrawide detection range covering 10.6 μm with a high detectivity of ∼109 Jones in the mid-infrared (MIR) region and a short response time of 1.3 μs. The detection performance has surpassed most reported IR sensors. On top of that, on-chip device arrays based on Schottky junction display an outstanding MIR imaging capability without cryogenic cooling, and 2D WTe2 Weyl semimetal can serve as a saturable absorber for stable Q-switched and mode-locked laser operation applications. Our work offers a viable route for wafer-scale vdW preparation of 2D semimetals, showcasing their intriguing potential in on-chip integrated MIR detection systems and ultrafast laser photonics.

An interpretable hybrid framework combining convolution latent vectors with transformer based attention mechanism for rolling element fault detection and classification

Failure of industrial assets can cause financial, operational and safety hazards across different industries. Monitoring their condition is crucial for successful and smooth operations. The colossal volume of sensory data generated and acquired throughout industrial operations supports real-time condition monitoring of these assets. Leveraging digital technologies to analyze acquired data creates an ideal environment for applying advanced data-driven machine learning techniques, such as convolutional neural networks (CNNs) and vision transformer (ViT) to detect faults and classify, enabling accurate prediction and timely maintenance of industrial assets. In this paper, we present a novel hybrid framework based on the local feature extraction ability of CNN with comprehensive understanding of transformer within a global context. The proposed method leverages the complex weight-sharing properties of CNNs and ability of transformers to understand the larger context of spatial relationships in large-scale patterns, making it applicable to datasets of varying sizes. Preprocessing methods such as data augmentation are used to train the model on the Case Western Reserve University (CWRU) dataset in order to increase generalization through computational efficiency. An average fault classification accuracy of 99.62% is accomplished over all three fault classes with an average time-to-fault detection of 38.4 ms. MFPT fault dataset is used to further validate the method with an accuracy of 99.17% for outer race and 99.26% for inner race. Moreover, the proposed framework can be modified to accommodate alternative convolutional models.

Process-Oriented Compound Long-Duration Dry and Hot Events in China: Atmospheric Conditions, Moisture, and Heat Budget Analyses

Abstract Summertime compound dry and hot events pose severe threats to agricultural production and human health, especially those with a long duration. Considering the joint evolution of such hazards in space and time, spatiotemporal compound long-duration dry and hot (SLDDH) events in China during 1961–2022 are identified with a process-oriented method. Here, we investigate the associated large-scale atmospheric circulation patterns and the physical processes causing their precipitation and temperature anomalies. In all regions of China, this persistent dry–hot compound extreme is accompanied by anomalous high pressure systems along with enhanced descending motion, increased net surface solar radiation, and decreased water vapor flux convergence. Moisture budget diagnosis shows that precipitation deficits during the SLDDH events are produced primarily by the suppressed vertical moisture advection associated with the dynamical contribution of anomalous subsidence, while the thermodynamic process due to the anomaly in atmospheric moisture content makes a small contribution. Horizontal temperature advection generally plays a negative role in sustaining SLDDH events, while it helps trigger the events in North China. In most regions, adiabatic warming due to abnormal subsidence plays a dominant role in determining the near-surface high temperatures during the long-lasting warm and dry periods, whereas diabatic heating has a cooling or small effect therein. However, in some northern areas such as North China and northern Xinjiang, hot extremes during SLDDH events arise from a combination of diabatic heating and adiabatic warming. This study thus quantifies and reveals the crucial factors leading to the severity of compound dry and hot events.

Differences in the impact of intense ENSO+ in Bahia Magdalena (SW of Baja California, Mexico) in the context of climate change

The Bahia Magdalena-Bahia Almejas (BM-BA) complex is the largest coastal lagoon system in the Mexican Pacific; it is in a transitional zone between temperate and tropical regions. BM-BA has a high biodiversity and is an important source of fishing resources; however, its productivity diminishes during ENSO+ events. This work aimed to compare the impact of the three most intense ENSOs (1982-1983, 1997-1998, and 2015-2016) on their environmental and biological characteristics in the context of global warming. Since 1970, this region has been warming, and the chlorophyll-a is diminishing; however, short-term environmental variations associated with basin-scale processes have modified these trends. The comparison showed that the equatorial influence on the 1997-1998 ENSO was more evident, while the North Pacific conditions and other large-scale environmental processes influenced the other ENSOs: in 1982-1983, the warm signal continued after the ENSO+ ended, and in 2015-2016, it was present before the ENSO+ onset due to the MWH The Blob and probably global warming. High temperatures at the basin scale altered large-scale wind patterns, weakening local upwelling and diminishing the nutrient input. The Equatorial and North Pacific influence on BM varies over time (as expected in a transition zone like BM); however, in recent years, North Pacific warming associated with climate change has probably gained relevance and modulated the effect of ENSO+.

Addressing large scale patterns of no-flow events in rivers: An in-depth analysis with Achelous software

Establishment of hydrological criteria that could serve as guidelines for addressing intermittency is not an easy task. However, efforts in the last years are yielding promising advancements in this direction. Scientists have been working to unravel the complexities of intermittency dynamics. In this study, we aimed to investigate the characteristics of naturally intermittent water systems by exploring the occurrence of no-flow events. A hydrological model was employed to generate streamflow data. Our analysis encompassed a thorough examination of nineteen flow regime metrics, estimated across 2064 subbasins, with 190 of these meeting the criterion for intermittency. A custom Python library was deployed to automate the quantification of no-flow events, aligning with the concept of critical thresholds. Using the probabilistic t-distributed Stochastic Neighbor Embedding technique to capture complex patterns, three clusters were emerged. The first one was characterized by a low probability of no-flow events and a small number of no-flow events per year, the second cluster was abundant in no-flow events and demonstrated a tendency towards longer annual recession time scales. The third cluster stands out due to the significant variance in the duration of no-flow events. Concerning the time variability of the no-flow events, we concluded that they predominantly occurred during August. Both long- and short-term quantification of no-flow events should be under consideration so as to harmonize the naturally intermittent waterways with the water use requirements and the potential consequences of not meeting them. Future research should prioritize the investigation of hydroecology and ecohydrology in relation to streamflow dynamics and ecosystem interactions. By doing so, we can elevate our comprehension of how intermittent water systems function and their significance within the broader ecological context.

Toward a Functional Trait Approach to Bee Ecology.

Functional traits offer an informative framework for understanding ecosystem functioning and responses to global change. Trait data are abundant in the literature, yet many communities of practice lack data standards for trait measurement and data sharing, hindering data reuse that could reveal large-scale patterns in functional and evolutionary ecology. Here, we present a roadmap toward community data standards for trait-based research on bees, including a protocol for effective trait data sharing. We also review the state of bee functional trait research, highlighting common measurement approaches and knowledge gaps. These studies were overwhelmingly situated in agroecosystems and focused predominantly on morphological and behavioral traits, while phenological and physiological traits were infrequently measured. Studies investigating climate change effects were also uncommon. Along with our review, we present an aggregated morphological trait dataset compiled from our focal studies, representing more than 1600 bee species globally and serving as a template for standardized bee trait data presentation. We highlight obstacles to harmonizing this trait data, especially ambiguity in trait classes, methodology, and sampling metadata. Our framework for trait data sharing leverages common data standards to resolve these ambiguities and ensure interoperability between datasets, promoting accessibility and usability of trait data to advance bee ecological research.

Long-term geomagnetic activities and stratospheric winter temperature

In this research, impact of long term solar forcing on stratospheric winter temperature is checked. The 11- year sunspot activity and geomagnetic indices (AE, Kp, Dst) are used as an indicator for solar forcing, as geomagnetic activity indices show good correlation with solar variability. To understand the impact of solar forcing through high latitude, stratospheric winter (November to March) time North Polar Region (60N–90N) temperature anomalies are considered. The findings showed that temperature changes in the stratosphere are significantly correlated with solar activity, as evidenced by a significant positive correlation between the 11-year moving mean of stratospheric (10 hPa) temperature anomalies and sunspot number. Approximately from 1970 to 2000, the North Polar Region saw positive anomalous stratospheric winter temperatures. During the same time, the geomagnetic activity also showed a substantial increase. The year-to-year correlation between stratospheric pole temperature and geomagnetic activity is significant (about 0.5). The Empirical Mode Decomposition analysis reveals a highly significant correlation (around 0.9) between the long-term component of stratospheric winter temperature (IMF-4) and the long-term component of geomagnetic activity (IMF-3 and IMF-4). One of the reasons for the increase in lower stratospheric temperature is an increase in ozone concentration during the same period when geomagnetic activity is higher. Empirical orthogonal function (EOF) and correlation analysis of stratospheric winter temperature with large-scale circulation patterns are also carried out. The spatial correlation is checked for stratospheric winter temperature at North Pole and lower atmospheric levels (250 hPa and 850 hPa) followed by pre-monsoon and monsoon season. This study includes statistical analysis, however, also highlights the necessity of in-depth dynamical analysis to improve our understanding of how solar activity impacts Earth's atmospheric layers, which may be helpful in predicting the weather and climate.

Atmospheric patterns associated with summer sub-daily rainfall extremes in western Europe

Large-scale atmospheric circulations are a significant driver of rainfall extremes. However, little attention has so far been devoted to understanding how large-scale circulation patterns influence sub-daily rainfall extremes. Using a gauge-based sub-daily rainfall dataset, we investigate the relationship between large-scale circulations and 3-hour extremes (defined here as ≥ 40 mm rainfall in 3 h) for western Europe. A set of 30 weather patterns (WPs) developed by the UK Met Office and reanalysis data of geopotential height at 500 hPa (z500) are used to represent large-scale atmospheric conditions. Strong associations with 3-hour extremes are found for a small number of WPs: over 50% of 3-hour rainfall extremes across Western Europe occur with just 5 WPs. Composites of z500 reveal the WPs resulting in southerly or south-westerly flow along the leading edge of a trough, accompanied by a ridge to the east or northeast, are most favourable for sub-daily rainfall extremes, with a statistically significant difference between the atmospheric conditions on WP days with a 3-hour extreme rainfall event compared to WP non-event days. Given that large-scale circulations are predictable much further in advance than individual extreme rainfall events, these identified relationships could therefore have important implications for forecasting, aiding in the early identification of periods with increased risk of short-duration rainfall extremes.

Intraspecific variation in leaf morphology of three widespread woody species along climatic gradients

Abstract The variation and plasticity of leaf morphology plays a pivotal role in the response to environmental changes for plant individuals. Discovering the large-scale pattern of such variation can reveal plants' general adaptive strategies. We analyzed leaf morphology of three widespread woody species in the northern hemisphere using specimen data from the iDigBio and GBIF databases, to investigate the variations in the individual mean traits, in the inter- and intra-individual variability of traits, and in the allometry between traits, along climatic gradients. We found that larger and wider leaves were associated with warmer, wetter and low-sunlight habitats, while smaller but wider leaves are linked to higher wind speed, indicating the response of leaf morphology to multiple climate stresses. The inter-individual variation in leaf area was smaller in colder and windier conditions, suggesting the trait convergence among individuals under environmental filtering, while the intra-individual variation in leaf relative width was smaller in warmer habitats, indicating the similar growth optimum of leaves within one individual in more favorable conditions. Finally, the allometric exponent between leaf length (X-axis) and width (Y-axis) became greater under lower solar radiation and higher wind speed, while the squared correlation coefficient (r2) indicating phenotypic integration showed a decoupling trend under colder conditions, indicating that climate affected the variation tendency of leaf relative width during leaf enlargement. These results reveal the common patterns of leaf morphology responding to climate variation spatially and underscore the necessity to consider inter- and intra-individual variability when examining plant responses to environmental changes.

Investigation on spectroscopy characteristics of different metamorphic degrees of coal-based graphite

To gain insights into the spectroscopy characteristics from coal to graphite, we investigated different metamorphic degrees of coal-based graphite which were collected from Hunan Province China. In this paper, by means of X-ray diffraction (XRD), scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), and Raman spectroscopy, graphite with different metamorphism degrees has been characterized to explore the evolution of macromolecular structure of organic matter during graphitization. The results show that with the increase of metamorphism degree, the 002-diffraction peak of the series of samples gradually shrinks and narrows, and the peak intensity becomes stronger, indicating that the microcrystalline structure gradually becomes regular and ordered. As the degree of graphitization increase, the uniformity of particle size in coal samples observed gradually increases, and the morphology becomes more regular, transitioning from disordered and irregular shapes to a structured large-scale flake pattern. The crystallinity improves, and the massive coal particles gradually coalesce into large plate crystals, with the inter-particle pores gradually closing. The graphite structure becomes increasingly evident. The FTIR spectra show that as the degree of graphitization increases, the peak at 1,581 cm−1 corresponding to C=C vibrations gradually intensifies. Some inert functional groups are retained throughout the graphitization process. The pores between coal particles gradually close, and the morphology of graphite particles becomes more regular and ordered. Additionally, during the graphitization process, structures similar to carbon nanotubes may develop. Throughout the structural transformation from coal macromolecules to graphite crystals, the size of the sp2 planar domains in single-layer graphene increases, and the lattice structure of carbon atoms gradually enlarges. These findings contribute to a comprehensive understanding of the properties and characteristics of coal-derived graphite, and can provide theoretical reference and basis for the metallogenic mechanism of coal-derived graphite and the efficient utilization of coal.