Dodders (Cuscuta spp., Convolvulaceae) are root- and leafless parasitic plants. The physiology, ecology, and evolution of these obligate parasites are poorly understood. A high-quality reference ...genome of Cuscuta australis was assembled. Our analyses reveal that Cuscuta experienced accelerated molecular evolution, and Cuscuta and the convolvulaceous morning glory (Ipomoea) shared a common whole-genome triplication event before their divergence. C. australis genome harbors 19,671 protein-coding genes, and importantly, 11.7% of the conserved orthologs in autotrophic plants are lost in C. australis. Many of these gene loss events likely result from its parasitic lifestyle and the massive changes of its body plan. Moreover, comparison of the gene expression patterns in Cuscuta prehaustoria/haustoria and various tissues of closely related autotrophic plants suggests that Cuscuta haustorium formation requires mostly genes normally involved in root development. The C. australis genome provides important resources for studying the evolution of parasitism, regressive evolution, and evo-devo in plant parasites.
Many plants use environmental cues, including seasonal changes of day length (photoperiod), to control their flowering time. Under inductive conditions, FLOWERING LOCUS T (FT) protein is synthesized ...in leaves, and FT protein is a mobile signal, which is able to travel to the shoot apex to induce flowering. Dodders (Cuscuta, Convolvulaceae) are root- and leafless plants that parasitize a large number of autotrophic plant species with varying flowering time. Remarkably, some dodder species, e.g., Cuscuta australis, are able to synchronize their flowering with the flowering of their hosts. Detailed sequence inspection and expression analysis indicated that the FT gene in dodder C. australis very likely does not function in activating flowering. Using soybean host plants cultivated under inductive and noninductive photoperiod conditions and soybean and tobacco host plants, in which FT was overexpressed and knocked out, respectively, we show that FT-induced flowering of the host is likely required for both host and parasite flowering. Biochemical analysis revealed that host-synthesized FT signals are able to move into dodder stems, where they physically interact with a dodder FD transcription factor to activate dodder flowering. This study demonstrates that FTs can function as an important interplant flowering signal in host–dodder interactions. The unique means of flowering regulation of dodder illustrates how regressive evolution, commonly found in parasites, may facilitate the physiological synchronization of parasite and host, here allowing the C. australis parasite to time reproduction exactly with that of their hosts, likely optimizing parasite fitness.
The analysis of spatiotemporal changes of landscape patterns is of great significance for forest protection. However, the selection of landscape metrics is often subjective, and existing composite ...landscape metrics rarely consider the effects of spatial correlation. A more objective approach to formulating composite landscape metrics involves proper weighting that incorporates spatial structure information into integrating individual conventional metrics selected for building a composite metric. This paper proposes an integrated spatial landscape index (ISLI) based on variogram modeling and entropy weighting. It was tested through a case study, which sought to analyze spatiotemporal changes in the landscape pattern in the Changbai Mountains over 30 years based on six global land-cover products with a fine classification system at 30 m resolution (GLC_FCS30). The test results confirm: (1) spatial structure information is useful for weighting conventional landscape pattern metrics when constructing ISLI as validated by correlation analysis between the incorporated conventional metrics and their variogram ranges. In terms of the range parameters of different land cover types, broadleaf forest and needleleaf forest have much larger range values than those of other land cover types; (2) DIVISION and PLAND, two of the conventional landscape metrics considered for constructing ISLI, were assigned the greatest weights in computing ISLI for this study; and (3) ISLI values can be used to determine the dominant landscape types. For the study area, ISLI values of broadleaf forests remained the largest until 2020, indicating that forest landscape characteristics were the most prominent during that period. After 2020, the dominance of needleleaf forest gradually increased, with its ISLI value reaching a maximum of 0.91 in 2025. Therefore, the proposed ISLI not only functions as an extension and complement to conventional landscape metrics but also provides more comprehensive information concerning landscape pattern dynamics.
Parameters of geomorphological characteristics are critical for research on yardangs. However, methods which are low-cost, accurate, and automatic or semi-automatic for extracting these parameters ...are limited. We present here semi-automatic techniques for this purpose. They are object-based image analysis (OBIA) and Canny edge detection (CED), using free, very high spatial resolution images from Google Earth. We chose yardang fields in Dunhuang of west China to test the methods. Our results showed that the extractions registered an overall accuracy of 92.26% with a Kappa coefficient of agreement of 0.82 at a segmentation scale of 52 using the OBIA method, and the exaction of yardangs had the highest accuracy at medium segmentation scales (138, 145). Using CED, we resampled the experimental image subset to a series of lower spatial resolutions for eliminating noise. The total length of yardang boundaries showed a logarithmically decreasing (R2 = 0.904) trend with decreasing spatial resolution, and there was also a linear relationship between yardang median widths and spatial resolutions (R2 = 0.95). Despite the difficulty of identifying shadows, the CED method achieved an overall accuracy of 89.23% with a kappa coefficient of agreement of 0.72, similar to that of the OBIA method at medium segmentation scale (138).
Dodder shoot parasites often connect different host plants. Dodder can transfer salt-induced long-distance systemic signals from one host to the adjacent dodder-connected hosts, leading to enhanced ...salt tolerance.
Abstract
The dodders (Cuscuta spp.) are a genus of shoot parasites. In nature, a dodder often simultaneously parasitizes two or more neighboring hosts. Salt stress is a common abiotic stress for plants. It is unclear whether dodder transmits physiologically relevant salt stress-induced systemic signals among its hosts and whether these systemic signals affect the hosts’ tolerance to salt stress. Here, we simultaneously parasitized two or more cucumber plants with dodder. We found that salt treatment of one host highly primed the connected host, which showed strong decreases in the extent of leaf withering and cell death in response to subsequent salt stress. Transcriptomic analysis indicated that 24 h after salt treatment of one cucumber, the transcriptome of the other dodder-connected cucumber largely resembled that of the salt-treated one, indicating that inter-plant systemic signals primed these dodder-connected cucumbers at least partly through transcriptomic reconfiguration. Furthermore, salt treatment of one of the cucumbers induced physiological changes, including altered proline contents, stomatal conductance, and photosynthetic rates, in both of the dodder-connected cucumbers. This study reveals a role of dodder in mediating salt-induced inter-plant signaling among dodder-connected hosts and highlights the physiological function of these mobile signals in plant–plant interactions under salt stress.
In China, ground-level ozone has shown an increasing trend and has become a serious ambient pollutant. An accurate spatiotemporal distribution of ground-level ozone concentrations (GOCs) is urgently ...needed. Generalized linear models (GLMs) and Bayesian maximum entropy (BME) models are practical for predicting GOCs. However, GLMs have limited capacity to capture temporal variations and can miss some short-term and regional patterns, while the performance of BME models may degrade in cases of sparse or imperfect monitoring networks. Thus, to predict nationwide 1 km monthly average GOCs for China, we designed a novel hybrid model containing three modules. (1) A GLM was established to accurately describe the variability in GOCs in the space domain. (2) A BME model incorporating GLM residuals was employed to capture the temporal variability of GOCs in detail. (3) A combination of GLM and BME models was developed based on the specific broad range of each submodel. According to the cross-validation results, the hybrid model exhibited superior performance, with coefficient of determination (R2) values of 0.67. The predictive performance of the large-scale and high-resolution hybrid model is superior to that in previous studies. The nationwide spatiotemporal variability of the GOCs derived from the hybrid model shows that they are valuable indicators for ground-level ozone pollution control and prevention in China.
Land cover information is vital for research and applications concerning natural resources and environmental modeling. Accuracy assessment is an important dimension in use and production of land ...cover information. GlobeLand30 is a relatively new global land cover information product with a fine spatial resolution of 30 m and is potentially useful for many applications. This paper describes the methods for and results from the first country-wide and statistically based accuracy assessment of GlobeLand30 2010 land cover dataset over China. For this, a total of 8400 validation sample pixels were collected based on a sampling design featuring two levels of stratification (ten geographical regions, each with nine or eight land-cover classes). Validation sample data with reference class labels were acquired from visual interpretation based on Google Earth high-resolution satellite images. Error matrices for individual regions and entire China were estimated properly based on the sampling design adopted, with the former aggregated to get the latter through suitable weighting. Results were obtained, with agreement at a sample pixel defined both as a match between the map (class) label and either the primary or alternate reference label therein and, more strictly, as a match between the map label and the primary reference label only. Based on the former definition of agreement, the overall accuracy of GlobeLand30 2010 land cover for China was assessed to be 84.2%. User’s accuracy and producer’s accuracy were both greater than 80% for cultivated land, forest, permanent snow and ice, and bareland, with user’s accuracy for water bodies estimated 94.2% (82.1% for wetland, 79.8% for artificial surface) and producer’s accuracy for grassland estimated 89.0%. These indicate that GlobeLand30 2010 depicts land cover circa 2010 in China quite accurately, although estimates of accuracy indicators based on the latter definition of agreement were lower as expected with an estimated national overall accuracy of 81.0%. Regional and class variations in accuracy were revealed and examined in the light of their associations with land cover distributions and patterns. Implications for use and production of GlobeLand30 land cover information were discussed, so were commonality and lack of it between GlobeLand30 and other fine-resolution land cover products.
Remote-sensing images of high spatial resolution (HSR) are valuable sources of fine-grained spatial information for various applications, such as urban surveys and governance. There is continuing ...research on positional errors in remote-sensing images and their impacts in geoprocessing and applications. This paper explores the combined use of multi-point geostatistics (MPS), machine learning—in particular, generalized additive modeling (GAM)—and computer-image correlation for characterizing positional errors in images—in particular, HSR images. These methods are employed because of the merits of MPS in being flexible for non-parametric and joint simulation of positional errors in X and Y coordinates, the merits of GAM in being capable of handling non-stationarity in-positional errors through error de-trending, and the merits of computer-image correlation in being cost-effective in furnishing the training data (TD) required in MPS. Procedurally, image correlation is applied to identify homologous image points in reference-test image pairs to extract image displacements automatically in constructing TD. To cope with the complexity of urban scenes and the unavailability of truly orthorectified images, visual screening is performed to clean the raw displacement data to create quality-enhanced TD, while manual digitization is used to obtain reference sample data, including conditioning data (CD), for MPS and test data for performance evaluation. GAM is used to decompose CD and TD into trends and residuals. With CD and TD both de-trended, the direct sampling (DS) algorithm for MPS is applied to simulate residuals over a simulation grid (SG) at 80 m spatial resolution. With the realizations of residuals and, hence, positional errors generated in this way, the means, standard deviation, and cross correlation in bivariate positional errors at SG nodes are computed. The simulated error fields are also used to generate equal-probable realizations of vertices that define some road centerlines (RCLs), selected for this research through interpolation over the aforementioned simulated error fields, leading to error metrics for the RCLs and for the lengths of some RCL segments. The enhanced georectification of the RCLs is facilitated through error correction. A case study based in Shanghai municipality, China, was carried out, using HSR images as part of generalized point clouds that were developed. The experiment results confirmed that by using the proposed methods, spatially explicit positional-error metrics, including means, standard deviation, and cross correlation, can be quantified flexibly, with those in the selected RCLs and the lengths of some RCL segments derived easily through error propagation. The reference positions of these RCLs were obtained through error correction. The positional accuracy gains achieved by the proposed methods were found to be comparable with those achieved by conventional image georectification, in which the CD were used as image-georectification control data. The proposed methods are valuable not only for uncertainty-informed image geolocation and analysis, but also for integrated geoinformation processing.
There are a variety of land cover products generated from remote-sensing images. However, misclassification errors in individual products and inconsistency among them undermine their utilities for ...research and other applications. While it is worth developing advanced pattern classifiers and utilizing the images of finer spatial, temporal, and/or spectral resolution for increased classification accuracy, it is also sensible to increase map classification accuracy through effective map fusion by exploiting complementarity among multi-source products over a study area. This paper presents a novel fusion method that works by weighting multiple source products based on their map-reference cover type transition probabilities, which are predicted using random forest for individual map pixels. The proposed method was tested and compared with three alternatives: consensus-based weighting, random forest, and locally modified Dempster–Shafer evidential reasoning, in a case study, over Shaanxi province, China. For this case study, three types of land cover products (GlobeLand30, FROM-GLC, and GLC_FCS30) of two nominal years (2010 and 2020) were used as the base maps for fusion. Reference sample data for model training and testing were collected following a robust stratified random sampling design that allows for augmenting reference data flexibly. Accuracy assessments show that overall accuracies (OAs) of fused land cover maps have been improved (1~9% in OAs), with the proposed method outperforming other methods by 2~8% in OAs. The proposed method does not need to have the base products’ classification systems harmonized beforehand, thus being robust and highly recommendable for fusing land cover products.
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