Groundwater nitrate contamination in intensive croplands often persists despite strong short-term weather variability, suggesting that near-surface signals may not translate directly to aquifers. We tested this across a four-site south-to-north transect in Henan Province, China, under a wheat-maize rotation. For each sampling event, we quantified antecedent hydroclimatic windows from daily meteorological data, and measured depth-integrated (0-50 cm) soil inorganic N stocks, soil N-cycling enzyme activities, groundwater NO3--N, and nitrate isotopes (δ15N-NO3- and δ18O-NO3-). Principal component analysis summarized hydroclimatic variability into two dominant gradients: a dry-frequency axis (PC1) and an evaporative-demand axis (PC2). Drier antecedent conditions (higher dry-frequency scores) were consistently associated with greater 0-50 cm soil NO3- storage and coordinated changes in enzyme indices related to N turnover; the dry-frequency gradient accounted for 41% of the variance in soil NO3--N stocks. In contrast, groundwater nitrate showed weak short-term coupling across all window lengths, with only 19% of NO3- variability explained and no significant contemporaneous associations with short-term soil indicators or hydroclimate axes. Isotope patterns showed strong site- and stage-dependent scatter: groundwater δ15N-NO3- varied widely (3.5-17.5‰) while δ18O-NO3- remained narrow (7.0-8.9‰), consistent with mixing among multiple nitrate sources and transport lags that buffer legacy signals in the aquifer. These results help explain why groundwater nitrate remains poorly predictable from short-term surface indicators, and they support management strategies that prioritize sustained reductions in long-term N surplus and monitoring designs that account for subsurface storage and time lags.

Background Global climate change is rapidly impacting biodiversity and threatening the sustainable use of medicinal plant species by reducing their availability and increasing harvest uncertainty. Understanding the adaptive genetic variation and genetic vulnerability of medicinal plants under climate change is crucial for effective germplasm management, cultivation, and breeding efforts. In this study, we assessed the genetic differentiation, local adaptation, and genomic vulnerability of the medicinal plant Isodon rubescens (Hemsl.) H. Hara, with the goals of elucidating the impacts of geographic and environmental factors on its genetic structure and identifying at-risk populations for informed conservation and breeding under climate change. Results We applied restriction site-associated DNA sequencing (RAD-seq) to 17 populations of I. rubescens spanning its central and peripheral ranges, including the Taihang and Qinling-Funiu Mountains. The analysis revealed two distinct genetic groups: one in the Taihang Mountains and the other in the Qinling-Funiu Mountains. Significant patterns of isolation by distance (IBD), environment (IBE), and resistance (IBR) were detected, alongside high niche differentiation. We identified 456 candidate adaptive SNPs, some linked to genes involved in stress responses and biosynthesis. Precipitation was a key environmental driver of local adaptation. Populations in the northern Taihang Mountains and southern Funiu Mountains showed higher genomic vulnerability, indicating a greater risk of maladaptation. Conclusion Our findings demonstrate that geographic isolation and environmental factors, particularly precipitation, are key drivers of genetic differentiation and local adaptation in I. rubescens . The identified genomic vulnerability pinpoints specific populations at high risk under climate change. These insights provide a crucial genetic basis for formulating targeted conservation strategies and developing climate-resilient breeding programs for this medicinal species.

IntroductionGeographic variation markedly influences Atractylodes chinensis medicinal quality, driven by rhizosphere processes regulating atractylodin accumulation. Understanding soil-microbe-phytochemical interactions is essential for targeted cultivation to stabilize bioactive compound content.MethodsThis study investigated the relationships among rhizosphere soil properties, microbial communities, and atractylodin content in A. chinensis from three geographic origins including high-content (HC; E118°48′55″, N41°21′40″), medium-content (MC; E115°31′30″, N40°29′36″), and low-content (LC; E117°40′22″, N41°20′24″). Analyses encompassed soil chemical properties (nutrients, trace elements), enzyme activities, and the composition of bacterial and fungal communities, alongside their correlations with atractylodin accumulation.ResultsSignificant inter-regional differences in soil properties were observed. MC soils exhibited the highest levels of organic matter, total nitrogen, and hydrolyzable nitrogen. LC soils contained elevated concentrations of trace elements (Zn, Cu, Fe, Mn) and total potassium. In contrast, HC soils possessed the highest magnesium content. Activities of soil phosphatases (alkaline phosphatase and neutral phosphatase) also differed significantly among sites. Microbial α-diversity peaked in MC soils. Distinct β-diversity patterns clearly differentiated the microbial communities of all three regions. In addition, bacterial community composition showed a stronger association with soil chemical parameters than fungal communities. The relative abundances of Methylomirabilota (bacteria) and Mortierellomycota (fungi) correlated positively with atractylodin content, whereas Actinobacteriota (bacteria) abundance correlated negatively.DiscussionThese findings elucidate key ecological mechanisms driving variation in the medicinal quality of A. chinensis and provide practical insights for optimizing cultivation practices, including soil management and cultivar selection, to standardize quality and enhance stability of its medicinal value.

The forest ecosystem is a significant pool for capturing atmospheric mercury (Hg) deposition, with most Hg accumulating in forest soils. As secondary forests now dominate global forest cover, they are particularly sensitive to changes in ambient temperature. However, the impact of these changes on Hg dynamics in secondary forests remains poorly understood. Here, we quantified Hg inputs, outputs, and mass balances in two secondary forests in China, each with different ambient temperatures. We found that elevated ambient temperature (similar to 1.0 degrees C) advanced the germination of leaves by 2-3 days and extended the growing season by approximately one week, resulting in increased litterfall biomass by 1.18 Mg hm(-2) yr(-1) and a thicker litterfall layer by 0.22 cm over 34 years. This temperature rise also facilitated Hg methylation within forest and enhanced methylmercury (MeHg) export, heightening the potential risk of MeHg exposure to surrounding ecosystems. Additionally, higher ambient temperature not only increased soil Hg emissions (2.75 mu g m(-2) yr(-1)) but also led to significant Hg deposition via litterfall (9.26 mu g m(-2) yr(-1)), resulting in a net annual Hg deposition of 6.88 mu g m(-2) yr(-1). This net Hg deposition accumulated in the topsoil, increasing the Hg pool by 0.51 mg m(-2) in organic and 0-10 cm mineral soil horizons. Our findings suggest that even a similar to 1.0 degrees C temperature rise could enhance the role of secondary forests as atmospheric Hg sink by 45.10 %. Therefore, the impact of ongoing climate warming on Hg cycling and pools in forests should receive increased attention and warrants further research.

In recent decades, large ensemble simulation (LENS) or super-large ensemble simulation (SLENS) experiments with climate models, including the simulation of both the historical and future climate, have been increasingly exploited in the fields of climate change, climate variability, climate projection, and beyond. This paper provides an overview of LENS in climate systems. It delves into its definition, initialization, significance, and scientific concerns. Additionally, its development history and relevant theories, methods, and primary fields of application are also reviewed. Conclusions obtained from single-model LENS can be more robust compared with those from ensemble simulations with smaller numbers of members. The interactions among model biases, forced responses, and internal variabilities, which serve as the added value in LENS, are highlighted. Finally, we put forward the future trajectory of LENS with climate or Earth system models (ESMs). Super-large ensemble simulation, high-resolution LENS, LENS employing ESMs, and combining LENS with artificial intelligence, will greatly promote the study of climate and related applications.

Microbial resource limitation and metabolic processes synergistically regulate soil carbon dynamics through multiple mechanisms. Elevation gradient shapes soil microbial resource limitation patterns, yet how microbial metabolic processes adapt to resource limitation along elevation gradient remains poorly understood. In this study, we collected soil samples at five altitudinal sites (860, 1230, 1360, 1510, and 1810 m), to analyze soil microbial resource limitation and metabolic activities. The results showed that elevation gradient significantly affected soil properties, enzyme activities, microbial biomass, and related stoichiometries (p < 0.05). With the increase in altitude, vector length (VL) decreased from 1.62 to 1.58, while vector angle (VA) increased from 25.47 degrees to 33.02 degrees, indicating microbial communities generally exhibited co-limitation by carbon and nitrogen, whereas the intensity of co-limitation alleviated with increasing altitude. This pattern was primarily attributed to adaptive stoichiometric adjustments of microbial extracellular enzymes involved in nutrient acquisition. Meanwhile, simulation models of microbial metabolic processes showed that both organic carbon decomposition rate (M) and microbial respiration rate (Rm) significantly increased with elevation (p < 0.05). Specifically, M rose from 11.66 % day- 1 to 16.18 % day(-1), and Rm increased from 251.46 mmol C m(-3) day(-1) to 1089.71 mmol C m(-3) day(-1). Random Forest results indicated soil enzyme stoichiometric ratios as major factors influencing soil microbial metabolism. Through integrating resource limitation theory with microbial metabolic simulation models, we have revealed the adaptive strategies of high-altitude microorganisms to enhance carbon turnover by alleviating resource limitation. This study provides a scientific foundation for predicting and managing forest ecosystems affected by resource limitations.

Context Chufa (Cyperus esculentus L. var. sativus Boeck) is an emerging oil crop with significant economic, nutritional, and ecological value. However, few studies have examined the photosynthetic physiological function of chufa, C4 type plant, especially regarding the differences among the three tuber types. Methods This study investigated the photosynthetic characteristics and photosynthate allocation in 8 accessions of 3 types of tubers (round-tuber, large-tuber, and long-tuber). The field tests were conducted over two years in 2022 and 2023 to explore differences in photosynthetic efficiency and agronomic traits. Besides, one accession with the highest yield was selected from each tuber type for further study on the photosynthate allocation using 13C labeling. Results Field tests revealed significant differences among the three tuber types. The long-tuber type of chufa exhibited the lowest photosynthetic capacity, stomatal conductance, tillering number, leaf weight, and 1000-tuber weight, whereas it had a high oil content. The large-tuber type had the largest tuber size and the highest 1000-tuber weight. The round-tuber type showed the highest photosynthetic capacity, gas exchange index, plant height, and yield. The short-term effect of photosynthate transport and distribution followed a similar trend across the three chufa types, with the rate of photosynthate transport being fastest during the tuber initiation stage. However, differences were observed in the long-term effect, indicating distinct distribution strategies among the chufa types, resulting in variations in agronomic traits. Conclusions This study revealed distinct differences in photosynthetic physiology and agronomic traits among the three tuber types of chufa. Additionally, it provided insights into the transport and distribution strategies of photosynthates in chufa across different growth stages and tuber types. Implications These findings offer valuable theoretical and practical insights for chufa cultivar breeding and cultivation management.

Rhododendron purdomii Rehder & E. H. Wilson (Ericaceae) is a threatened ornamental and medicinal shrub or small tree species primarily distributed in the Qinling-Daba Mountains of Central China. To facilitate its conservation and utilization, the complete chloroplast genome of Rh. purdomii was sequenced, assembled, and characterized. The cp genome exhibited a typical quadripartite structure with a total length of 208,062 bp, comprising a large single copy (LSC) region of 110,618 bp, a small single copy (SSC) region of 2606 bp, and two inverted repeat (IR) regions of 47,419 bp each. The overall GC content was 35.81%. The genome contained 146 genes, including 96 protein-coding genes, 42 transfer RNA genes, and 8 ribosomal RNA genes. Structure analysis identified 67,354 codons, 96 long repetitive sequences, and 171 simple sequence repeats. Comparative genomic analysis across Rhododendron species revealed hypervariable coding regions (accD, rps9) and non-coding regions (trnK-UUU-ycf3, trnI-CAU-rpoB, trnT-GGU-accD, rpoA-psbL, rpl20-trnC-GCA, trnI-CAU-rrn16, and trnI-CAU-rps16), which may serve as potential molecular markers for genetic identification. Phylogenetic reconstruction confirmed the monophyly of Rhododendron species and highlighted a close relationship between Rh. purdomii and Rh. henanense subsp. lingbaoense. These results provide essential genomic resources for advancing taxonomic, evolutionary, conservation, and breeding studies of Rh. purdomii and other species within the genus Rhododendron.

Rapid climate change is affecting biodiversity and threatening locally adapted species. Relict species are often confined to relatively narrow, discontinuous geographic ranges and provide excellent opportunities to study local adaptation and extinction. Understanding the adaptive genetic variation and genetic vulnerability of relict species under climate change is essential for their conservation and management efforts. Here, we applied a landscape genomics approach to investigate the population genetic structure and predict adaptive capacity to climatic change for Taiwania cryptomerioides Hayata, a vulnerable Tertiary relict tree species in China. We used restriction site-associated DNA sequencing on 122 individuals across 10 sampling sites. We found three genetic groups across the Chinese range of T. cryptomerioides: the southwest, central-eastern, and Taiwanese groups. We detected significant signals of isolation by environment and isolation by distance, with environment playing a more important role than geography in shaping spatial genetic variation in T. cryptomerioides. Moreover, some outliers were related to defense and stress responses, which could reflect the genomic basis of adaptation. Gradient forest (GF) analysis revealed that precipitation-related variables were important in driving adaptive variation in T. cryptomerioides. Ecological niche modeling and GF analysis revealed that the central-eastern populations were more vulnerable to future climate change than other populations, with range contractions and high genetic offsets, suggesting these populations may be at higher risk of decline or local extinction. These findings deepen our understanding of local adaptation and vulnerability to climate change in relict tree species and will guide conservation and restoration programs for T. cryptomerioides in the future.

Cornus officinalis Siebold & Zucc. is an important Chinese herbal medicine with traditional clinical applications, a long history of cultivation and high economic value in China. C. officinalis is distributed mainly in the Shaanxi, Henan, and Zhejiang provinces of China, which are also historically the main production areas, and which still provide 90% of the medicinal material of this species for the contemporary market. In this study, we investigated the main germplasm resources of C. officinalis across its distribution, based on fruit morphology, the concentrations of selected medicinally active ingredients, and molecular population genetic analysis. The results indicated that the C. officinalis populations sampled had abundant variation in fruit morphology and chemical component content, and showed high genetic diversity, as revealed by microsatellite markers. Clustering results based on morphology, active ingredient concentration and microsatellite markers supported the idea that populations from Zhejiang province were distinct from those in Shaanxi and Henan. The fruit and molecular data suggested that the Shaanxi and Henan populations were mixed, which could be attributed to their close geographical distance and frequent germplasm exchange. Most of the C. officinalis populations had relatively weak genetic differentiation from others sampled, and most of the individuals sampled showed extensive admixture. We suggest that artificial gene flow caused by intensive cultivation and widespread trading is responsible for the observed admixture of genetic components and blurred genetic boundaries between C. officinalis populations, especially in the Shaanxi and Henan populations. We also propose some suggestions for the efficient utilization and conservation of C. officinalis germplasm. These findings provide valuable information on the genetic resources of C. officinalis and offer guidelines for breeding programs and scientific management of C. officinalis.

Surface defect detection is an important step in ensuring product quality in various manufacturing industries. Existing methods have achieved significant results, but there are still challenges, such as the lack of real-time detection speed, low contrast between defects and background, and insufficient handling of defect details. To address these issues, we introduce a lightweight and efficient method called context-aware adaptive weighted attention network (CAWANet) for real-time surface defect segmentation. To handle the computational resource constraints when dealing with deep features, we introduce context-aware adaptive weighted convolution (CAWAConv), which aims to extract deep features while suppressing noise interference. This allows the model to remain lightweight without compromising its ability to recognize subtle defect characteristics. In addition, during the feature fusion stage, we propose the feature detail improvement (FDI) module to capture more complex defect details. The FDI module enhances the representation of defect-related information, further optimizing the segmentation results. We conducted experimental evaluations of CAWANet on three surface defect detection datasets: the magnetic tile, NEU-Seg, and MSD. The experimental results indicate that our proposed CAWANet strikes a favorable balance between accuracy and inference speed compared to other state-of-the-art methods. Our code will be available at https://github.com/ZGWzzu/CAWANet.

Understanding the genetic diversity and origin of plantations will support the genetic monitoring and provenance selection in restoration projects and help to enhance the adaptation and resilience of plantation forests under climate change. However, information on the origin and genetic variation for plantations with native tree species is inadequate. Taiwania cryptomerioides Hayata is a threatened tree species and has been used as an important tree species for plantation in montane areas of South China. Information on the genetic diversity and origin of the existing Taiwania plantations is needed to facilitate their further development. In this study, using 12 nuclear microsatellite markers, the genetic diversity and structure were investigated in seven previously assumed natural populations and 19 plantation populations of T. cryptomerioides in South China. The Taiwania plantations showed lower genetic diversity and closer genetic distance than natural populations, indicating that most plantations were established with a narrow genetic basis. The results revealed that the majority of Taiwania plantations originated from two areas of the species’ natural distribution: northwestern Yunnan and southeastern Guizhou. Interestingly, we found that part of plantations in western Yunnan might represent unique genetic resources. Finally, conservation strategies of germplasm resources and genetic guidelines for seed sourcing of T. cryptomerioides are recommended. This study could facilitate the sustainable development of Taiwania plantations and also serve as a valuable reference for plantation management in China and elsewhere. We suggest that genetic monitoring of plantation forests should be considered in future restoration programs.

The infrageneric taxonomy system, species delimitation, and interspecies systematic relationships of Leontopodium remain controversial and complex. However, only a few studies have focused on the molecular phylogeny of this genus. In this study, the characteristics of 43 chloroplast genomes of Leontopodium and its closely related genera were analyzed. Phylogenetic relationships were inferred based on chloroplast genomes and nuclear ribosomal DNA (nrDNA). Finally, together with the morphological characteristics, the relationships within Leontopodium were identified and discussed. The results showed that the chloroplast genomes of Filago, Gamochaeta, and Leontopodium were well-conserved in terms of gene number, gene order, and GC content. The most remarkable differences among the three genera were the length of the complete chloroplast genome, large single-copy region, small single-copy region, and inverted repeat region. In addition, the chloroplast genome structure of Leontopodium exhibited high consistency and was obviously different from that of Filago and Gamochaeta in some regions, such as matk, trnK (UUU)-rps16, petN-psbM, and trnE (UUC)-rpoB. All the phylogenetic trees indicated that Leontopodium was monophyletic. Except for the subgeneric level, our molecular phylogenetic results were inconsistent with the previous taxonomic system, which was based on morphological characteristics. Nevertheless, we found that the characteristics of the leaf base, stem types, and carpopodium base were phylogenetically correlated and may have potential value in the taxonomic study of Leontopodium. In the phylogenetic trees inferred using complete chloroplast genomes, the subgen. Leontopodium was divided into two clades (Clades 1 and 2), with most species in Clade 1 having herbaceous stems, amplexicaul, or sheathed leaves, and constricted carpopodium; most species in Clade 2 had woody stems, not amplexicaul and sheathed leaves, and not constricted carpopodium.

The plant species in the mountainous regions might be relatively more vulnerable to climate change. Understanding the potential effects of climate change on keystone species, such as Rhododendron species in the subalpine and alpine ecosystems, is critically important for montane ecosystems management and conservation. In this study, we used the maximum entropy (MaxEnt) model, 53 distribution records, and 22 environmental variables to predict the potential impacts of climate change on the distribution of the endemic and vulnerable species Rhododendron purdomii in China. The main environmental variables affecting the habitat suitability of R. purdomii were altitude, temperature seasonality, annual precipitation, slope, and isothermality. Our results found suitable distribution areas of R. purdomii concentrated continuously in the Qinling-Daba Mountains of Central China under different climate scenarios, indicating that these areas could potentially be long-term climate refugia for this species. The suitable distribution areas of R. purdomii will expand under the SSP126 (2070s), SSP585 (2050s), and SSP585 (2070s) scenarios, but may be negatively influenced under the SSP126 (2050s) scenario. Moreover, the potential distribution changes of R. purdomii showed the pattern of northward shift and west–east migration in response to climate change, and were mainly limited to the marginal areas of species distribution. Finally, conservation strategies, such as habitat protection and assisted migration, are recommended. Our findings will shed light on biotic responses to climate change in the Qinling-Daba Mountains region and provide guidance for the effective conservation of other endangered tree species.

Surface defect inspection is an important task in industrial inspection. Deep learning-based methods have demonstrated promising performance in this domain. Nevertheless, these methods still suffer from misjudgment when encountering challenges such as low-contrast defects and complex backgrounds. To overcome these issues, we present a decision fusion network (DFNet) that incorporates the semantic decision with the feature decision to strengthen the decision ability of the network. In particular, we introduce a decision fusion module (DFM) that extracts a semantic vector from the semantic decision branch and a feature vector for the feature decision branch and fuses them to make the final classification decision. In addition, we propose a perception fine-tuning module (PFM) that fine-tunes the foreground and background during the segmentation stage. PFM generates the semantic and feature outputs that are sent to the classification decision stage. Furthermore, we present an inner-outer separation weight matrix to address the impact of label edge uncertainty during segmentation supervision. Our experimental results on the publicly available datasets including KolektorSDD2 (96.1% AP) and Magnetic-tile-defect-datasets (94.6% mAP) demonstrate the effectiveness of the proposed method.

Surface defect inspection is of great importance for industrial manufacture and production. Though defect inspection methods based on deep learning have made significant progress, there are still some challenges for these methods, such as indistinguishable weak defects and defect-like interference in the background. To address these issues, we propose a transformer network with multi-stage CNN (Convolutional Neural Network) feature injection for surface defect segmentation, which is a UNet-like structure named CINFormer. CINFormer presents a simple yet effective feature integration mechanism that injects the multi-level CNN features of the input image into different stages of the transformer network in the encoder. This can maintain the merit of CNN capturing detailed features and that of transformer depressing noises in the background, which facilitates accurate defect detection. In addition, CINFormer presents a Top-K self-attention module to focus on tokens with more important information about the defects, so as to further reduce the impact of the redundant background. Extensive experiments conducted on the surface defect datasets DAGM 2007, Magnetic tile, and NEU show that the proposed CINFormer achieves state-of-the-art performance in defect detection.

Surface defect inspection is a very challenging task in which surface defects usually show weak appearances or exist under complex backgrounds. Most high-accuracy defect detection methods require expensive computation and storage overhead, making them less practical in some resource-constrained defect detection applications. Although some lightweight methods have achieved real-time inference speed with fewer parameters, they show poor detection accuracy in complex defect scenarios. To this end, we develop a Global Context Aggregation Network (GCANet) for lightweight saliency detection of surface defects on the encoder-decoder structure. First, we introduce a novel transformer encoder on the top layer of the lightweight backbone, which captures global context information through a novel Depth-wise Self-Attention (DSA) module. The proposed DSA performs element-wise similarity in channel dimension while maintaining linear complexity. In addition, we introduce a novel Channel Reference Attention (CRA) module before each decoder block to strengthen the representation of multi-level features in the bottom-up path. The proposed CRA exploits the channel correlation between features at different layers to adaptively enhance feature representation. The experimental results on three public defect datasets demonstrate that the proposed network achieves a better trade-off between accuracy and running efficiency compared with other 17 state-of-the-art methods. Specifically, GCANet achieves competitive accuracy (91.79% $F_{\beta}^{w}$, 93.55% $S_\alpha$, and 97.35% $E_\phi$) on SD-saliency-900 while running 272fps on a single gpu.

Leontopodium R. Brown ex Cass.belongs to the tribe Gnaphalieae in the family Asteraceae; it comprises approximately 60 species worldwide, of which 40 are distributed in China. The morphological characteristics of the achene are relatively stable and can be used as taxonomic criteria for species classification within Asteraceae. In this study, scanning electron microscopy (SEM) was used to observe the achene micromorphological characteristics of 28 species and 1 variety of Chinese Leontopodium . The results showed that the achenes of Leontopodium species were elliptical or narrow elliptical, approximately 0.7–1.6-mm long × 0.1–0.6-mm wide. The surface ornamentation was reticulate or rippled with clavate twin hairs or smooth. The carpopodium base was constricted or unconstricted. Based on these characteristics, we provided a new key for Chinese Leontopodium taxa. The characteristics of the achene trichome, surface ornamentation, and carpopodium show important taxonomic value at different taxonomic levels, leading us to arrive at the following conclusions. 1. The shape of the achene trichome is valuable for the taxonomic delimitation of taxa between Leontopodium and related genera. 2. The achene surface ornamentation can be categorized into two types and is a taxonomic tool for classification at the section level within Leontopodium . 3. The characteristics of the carpopodium and trichome density can serve as important features under the section level. Thus, achene micromorphological characteristics provide significant morphological evidence that could play an important role in resolving various taxonomic problems within Leontopodium related Asteraceae.

AbstractSoil microbiota is associated with plant growth and nutrition. Investigation of plant–soil interaction is essential for revealing the changes of microbial dynamics in the soil. Vegetation types and human activities, such as agriculture, had severely affected soil microbial structure and function. In this study, 16S rRNA were analysed to identify microbial structures in the soil. The total organic carbon (TOC) and total nitrogen (TN) in these soil samples were also analysed. TOC and TN in these soil samples were different, which might be due to different vegetation types. The main phyla in these soil samples were Actinobacteria, Proteobacteria and Acidobacteria. Furthermore, the genera in these soil groups were highly diverse, and most of the bacteria could not be assigned to any known genus. This indicated the presence of novel bacterial genera in these soil samples. A fraction of the dominant operational taxonomic units in the soil microbiota was identified, several of which played functional roles in soil nutrition. The linkage between the soil microbiota, especially the dominant species, and soil nutrients was analysed in this study. The culturomics and other omics technologies would help to isolate some novel microorganisms, which might lead to the recovery of functional microbial agents for plant growth.

Lactuca L. is the central genus of Lactucinae (Cichorieae; Asteraceae), containing cultivated lettuce and its wild relatives. In this study, we used Scanning Electron Microscope, Plain Stereo Microscope and Automated Digital Microscope to observe, record and discuss the achene characters and surface micro-features ofLactuca (including taxa of ex-Pterocypsela Shih) and Notoseris Shih species. The taxon sampling consisted of fifteen globally distributed Lactuca species and Chinese originated Notoseris species. The results indicated that the morphological and micro-morphological features of achenes were of great importance to identify Lactuca species at the genus and species level. We conclude that two key features, the presence or absence of beak and the arrangement and shape of epidermal cells, can be used to distinguish Lactuca from Notoseris. The shape and margin of the achene body, the beak length and the number of ribs on either side of achene are key features to classify Lactuca species. The ornamentation of epidermal cells can also provide extra evidence to determine closely related Lactuca species. The interspecific relationships among the Lactuca species based on achene features are consistent with the results of previously molecular systematics of these species.