Experimental Center of Forestry in North China

Carya luodianensis Y.B. Yang & M.T. An (Juglandaceae), a new species from Luodian County, Guizhou Province, China, is described and illustrated based on morphological and molecular data. Morphologically, this species resembles C. kweichowensis but can be easily distinguished by its rusty brown buds, 5-7 leaflets, a greater number of lateral veins (12-21 pairs), pubescent anthers, and nut shell bearing four faint longitudinal ridges. Molecular phylogenetic analysis based on complete chloroplast genome sequences indicates that C. luodianensis is sister to C. kweichowensis. The plastome of C. luodianensis, with a total length of 175,255 bp, exhibits a typical quadripartite structure and consists of 78 protein-coding genes (PCGs), 30 transfer RNA (tRNA) genes, and four ribosomal RNA (rRNA) genes. Notably, a marked expansion of the inverted repeat (IR) region (40,956 bp) and contraction of the small single-copy (SSC) region (3553 bp) are detected, accompanied by the translocation of several genes from the SSC region to the IR regions, resulting in increased gene copy numbers and reduced GC content. Comparative plastome analyses further reveal the conservation and differentiation between C. luodianensis and its closely related species, particularly in genome size, gene order, and repeat sequence distribution. Collectively, the unique morphological traits, plastid genome features, and phylogenetic placement strongly support the recognition of C. luodianensis as a distinct new species and highlight its evolutionary divergence within Carya.

<strong class="journal-contentHeaderColor">Abstract.</strong> Gas fluxes passing through an eddy-covariance (EC) system's measurement volume reflect the outgassing rate of these molecules from an upwind area known as the "flux footprint". While sources/sinks of these molecules may be uniform over a flat field, their spatial contribution to the measured fluxes is not. Thus, understanding the contribution to measured fluxes and the spatial quantification of sources/sinks from the measured fluxes requires footprint analysis. Such analysis yields flux footprint characteristics, which commonly include upwind maximum footprint location, upwind fetch containing certain percentages of measured flux (70%, 80%, 90%), and the percent of flux from a user-defined upwind fetch of interest. These characteristics are included in the datasets of flux networks such as ChinaFlux, AmeriFlux, and FluxNet. Ideally, the characteristics are calculated in real-time and on-site by EC systems in the field, but this has often not been the case due to the calculations being computationally challenging. For field applications, this study develops the equations and algorithms for these characteristics from analytical crosswind-integrated flux footprint equations. The development shows that in-field computation is made feasible by the following means: using time-efficient algorithms, taking advantage of the nondimensional nature of the footprint equations of Kljun et al. (2015), implementing practical limits on numerical integration, and developing a differential-based estimation of boundary layer height for each EC interval. Accuracy of in field calculations is maintained by the selection of footprint equations based on boundary-layer conditions and considerations of integration methods and computation techniques. This computational approach may also be applied to footprint analyses over complex terrain, nonuniform sources/sinks, or in cases where other footprint equations are used. The most popular application of footprint analysis is to optimize the EC sensor height for maximization of measured fluxes from an area of interest. This optimization using the nondimensional footprint equations is discussed, which leads to a practical methodology. This work serves as a technical reference for users or developers of EasyFlux programs, widely used in Campbell Scientific EC systems globally.

<strong class="journal-contentHeaderColor">Abstract.</strong> Gas fluxes passing through an eddy-covariance (EC) system's measurement volume reflect the outgassing rate of these molecules from an upwind area known as the "flux footprint". While sources/sinks of these molecules may be uniform over a flat field, their spatial contribution to the measured fluxes is not. Thus, understanding the contribution to measured fluxes and the spatial quantification of sources/sinks from the measured fluxes requires footprint analysis. Such analysis yields flux footprint characteristics, which commonly include upwind maximum footprint location, upwind fetch containing certain percentages of measured flux (70%, 80%, 90%), and the percent of flux from a user-defined upwind fetch of interest. These characteristics are included in the datasets of flux networks such as ChinaFlux, AmeriFlux, and FluxNet. Ideally, the characteristics are calculated in real-time and on-site by EC systems in the field, but this has often not been the case due to the calculations being computationally challenging. For field applications, this study develops the equations and algorithms for these characteristics from analytical crosswind-integrated flux footprint equations. The development shows that in-field computation is made feasible by the following means: using time-efficient algorithms, taking advantage of the nondimensional nature of the footprint equations of Kljun et al. (2015), implementing practical limits on numerical integration, and developing a differential-based estimation of boundary layer height for each EC interval. Accuracy of in field calculations is maintained by the selection of footprint equations based on boundary-layer conditions and considerations of integration methods and computation techniques. This computational approach may also be applied to footprint analyses over complex terrain, nonuniform sources/sinks, or in cases where other footprint equations are used. The most popular application of footprint analysis is to optimize the EC sensor height for maximization of measured fluxes from an area of interest. This optimization using the nondimensional footprint equations is discussed, which leads to a practical methodology. This work serves as a technical reference for users or developers of EasyFlux programs, widely used in Campbell Scientific EC systems globally.

<strong class="journal-contentHeaderColor">Abstract.</strong> Gas fluxes passing through an eddy-covariance (EC) system's measurement volume reflect the outgassing rate of these molecules from an upwind area known as the "flux footprint". While sources/sinks of these molecules may be uniform over a flat field, their spatial contribution to the measured fluxes is not. Thus, understanding the contribution to measured fluxes and the spatial quantification of sources/sinks from the measured fluxes requires footprint analysis. Such analysis yields flux footprint characteristics, which commonly include upwind maximum footprint location, upwind fetch containing certain percentages of measured flux (70%, 80%, 90%), and the percent of flux from a user-defined upwind fetch of interest. These characteristics are included in the datasets of flux networks such as ChinaFlux, AmeriFlux, and FluxNet. Ideally, the characteristics are calculated in real-time and on-site by EC systems in the field, but this has often not been the case due to the calculations being computationally challenging. For field applications, this study develops the equations and algorithms for these characteristics from analytical crosswind-integrated flux footprint equations. The development shows that in-field computation is made feasible by the following means: using time-efficient algorithms, taking advantage of the nondimensional nature of the footprint equations of Kljun et al. (2015), implementing practical limits on numerical integration, and developing a differential-based estimation of boundary layer height for each EC interval. Accuracy of in field calculations is maintained by the selection of footprint equations based on boundary-layer conditions and considerations of integration methods and computation techniques. This computational approach may also be applied to footprint analyses over complex terrain, nonuniform sources/sinks, or in cases where other footprint equations are used. The most popular application of footprint analysis is to optimize the EC sensor height for maximization of measured fluxes from an area of interest. This optimization using the nondimensional footprint equations is discussed, which leads to a practical methodology. This work serves as a technical reference for users or developers of EasyFlux programs, widely used in Campbell Scientific EC systems globally.

Abstract. Gas fluxes passing through an eddy-covariance (EC) system's measurement volume reflect the outgassing rate of these molecules from an upwind area known as the "flux footprint". While sources/sinks of these molecules may be uniform over a flat field, their spatial contribution to the measured fluxes is not. Thus, understanding the contribution to measured fluxes and the spatial quantification of sources/sinks from the measured fluxes requires footprint analysis. Such analysis yields flux footprint characteristics, which commonly include upwind maximum footprint location, upwind fetch containing certain percentages of measured flux (70%, 80%, 90%), and the percent of flux from a user-defined upwind fetch of interest. These characteristics are included in the datasets of flux networks such as ChinaFlux, AmeriFlux, and FluxNet. Ideally, the characteristics are calculated in real-time and on-site by EC systems in the field, but this has often not been the case due to the calculations being computationally challenging. For field applications, this study develops the equations and algorithms for these characteristics from analytical crosswind-integrated flux footprint equations. The development shows that in-field computation is made feasible by the following means: using time-efficient algorithms, taking advantage of the nondimensional nature of the footprint equations of Kljun et al. (2015), implementing practical limits on numerical integration, and developing a differential-based estimation of boundary layer height for each EC interval. Accuracy of in field calculations is maintained by the selection of footprint equations based on boundary-layer conditions and considerations of integration methods and computation techniques. This computational approach may also be applied to footprint analyses over complex terrain, nonuniform sources/sinks, or in cases where other footprint equations are used. The most popular application of footprint analysis is to optimize the EC sensor height for maximization of measured fluxes from an area of interest. This optimization using the nondimensional footprint equations is discussed, which leads to a practical methodology. This work serves as a technical reference for users or developers of EasyFlux programs, widely used in Campbell Scientific EC systems globally.

The corolla of Rehmannia glutinosa typically exhibits a stable reddish-purple color, but a naturally occurring yellow-flowered variant has recently been identified. To clarify the molecular basis of flower color variant, metabolomics, transcriptomics, and variant analyses were integrated. Metabolomic profiling revealed that the yellow phenotype was associated with lower anthocyanin levels and higher carotenoid levels. Specifically, the decreased cyanidin-3-O-glucoside led to a loss of red, while increased lutein provided the basis for the yellow color. Transcriptomic analysis revealed a downregulation of anthocyanin biosynthetic genes, including CHS, CHI, F3H, DFR, and ANS, in the yellow-flowered variant, and three S6-subgroup R2R3-MYB genes, including the known anthocyanin activator RgMYB41 (gene-DH2020_015992), were downregulated. Variant analysis showed that A12S and G255E in the gene-DH2020_015992 transcription factor were predicted to markedly alter protein conformation and potentially impair regulatory function. Subcellular localization and transcriptional activation assays further supported the functional characterization of gene-DH2020_015992 as a transcription factor. Collectively, these findings suggest that flower color variation in R. glutinosa is driven by MYB-mediated repression of anthocyanin biosynthesis and by carotenoid accumulation. This study provides a comprehensive genetic explanation for flower color variation in R. glutinosa and offers a theoretical foundation for floral pigmentation in plants.

Endophytic microorganisms play important roles in plant health, but their diversity and functions can vary with host developmental stage. Camellia oleifera, a major oil-producing crop in China, offers a valuable system for exploring these dynamics. We compared endophytic microbial communities in young (5-year) and old (15-year) C. oleifera leaves using high-throughput 16 S rRNA and ITS sequencing, supported by culture-dependent isolation and inhibition assays. Diversity indices, taxonomic composition, and predicted bacterial functions were analyzed with QIIME2 and PICRUSt2. Ten fungal and nine bacterial strains were isolated and tested for antagonism against key leaf pathogens in confrontation assays (n = 3 replicates). A total of 607 bacterial and 778 fungal ASVs were identified. Young leaves contained greater bacterial richness and diversity (Observed ASVs, Shannon, Simpson indices, P < 0.05), while old leaves hosted higher fungal richness. Beta diversity (Bray–Curtis PCoA) showed clear separation of bacterial communities by leaf age, with weaker separation for fungi. Bacterial functional prediction revealed enrichment of carbohydrate metabolism and amino acid biosynthesis in young leaves, while secondary metabolism and stress response pathways were more prominent in old leaves. Among isolated strains, Coniochaeta velutina inhibited Colletotrichum gloeosporioides, Alternaria alternata, and Botryosphaeria dothidea by 67.4%, 54.8%, and 65.2%, respectively. The bacterium Burkholderia ambifaria achieved the highest inhibition rate of 87.2% against B. dothidea. These findings suggest that leaf age shapes endophytic microbial diversity and bacterial functional potential in C. oleifera. Moreover, C. velutina and B. ambifaria represent promising candidates for biocontrol applications. While limited by small sample size (n = 3 replicates), this exploratory study provides foundational insights into age-associated shifts in endophyte communities and their biocontrol potential. Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-31248-y.

ABSTRACT Soil organic carbon (SOC) is the largest carbon pool in terrestrial ecosystems and plays an important role in mitigating global climate change. Increasing atmospheric nitrogen (N) deposition could cause remarkable changes in SOC dynamics. Platycladus orientalis, one of the most important plantation species, has been widely planted in northern China. However, how N deposition affects SOC and its components in P. orientalis has not been observed. Therefore, in this study, we conducted a 2-yr N deposition experiment with annual N deposition of 30, 60, and 90 kg N in northern China to explore the effects of N deposition on SOC and its components. Results showed that regardless of the amount of N deposition, the total SOC content was not significantly affected because of unchanged vegetation carbon input and microbial decomposition of SOC. However, N deposition tended to increase particle organic carbon and its contribution to SOC, indicating that N deposition may increase labile carbon content to decrease SOC stability in P. orientalis plantation. Moreover, our study highlighted that a long-term N deposition experiment was required to improve our understanding of SOC dynamics to increase atmospheric N deposition.

In this paper, we propose a differential-flatness-based controller (DFBC) for precise trajectory tracking of tractor-trailers, particularly during reversing maneuvers, which are challenging due to unstable equilibrium points. The proposed controller leverages the differential flatness property of tractor-trailers, equivalently transforming the nonlinear kinematics into a brunovsky canonical form, allowing the application of linear control theory for control design. Compared to traditional linear quadratic regulator (LQR) controllers, the proposed DFBC method achieves higher precision and robustness in reversing maneuvers. We also showcase the performance of the proposed DFBC method through physical experiments conducted on our self-developed 1/10 scale autonomous tractor-trailer.