Search Results (12)

Disease caused by Plasmodiophora brassicae severely disrupts cruciferous crops by altering root physiology and rhizosphere ecology. While pathogen-induced shifts in rhizosphere microbiomes are documented, the mechanisms linking root exudate reprogramming to microbial community remodeling remain poorly understood. Here, we integrated untargeted metabolomics and 16S rRNA sequencing to investigate how root exudates reshape the rhizosphere microbiome of tumorous stem mustard (Brassica juncea var. tumida) through P. brassicae infection. Metabolomic profiling identified 1718 root exudate metabolites, with flavones (e.g., apigenin 7-O-β-D-rutinoside, VIP > 1.5) and phenolic derivatives (e.g., gastrodin) being selectively enriched in infected plants. P. brassicae infection significantly increased rhizobacterial richness (ACE index, p < 0.05) and restructured the community composition, marked by enrichment of Paenibacillus (LDA score > 3.0). Procrustes analysis revealed tight coupling between microbial community shifts and metabolic reprogramming (M² = 0.446, p = 0.005), while Spearman correlations implicated pathogen-induced metabolites like geniposidic acid in recruiting beneficial Paenibacillus. Our results reveal that plant hosts dynamically secrete defense-related root metabolites to remodel the rhizosphere microbiome in response to P. brassicae infection. This dual-omics approach elucidates a chemical dialogue mediating plant–microbe–pathogen interactions, offering novel insights for engineering disease-suppressive microbiomes through root exudate manipulation. Full article

Nighttime near-surface air temperature is a critical input for ecological, hydrological, and meteorological models and the Moderate Resolution Imaging Spectroradiometer (MODIS)-derived instantaneous nighttime near-surface air temperature (T_a) and daily minimum temperatures (T_min) can provide spatially continuous monitoring. The MOD07 Level-2 and MYD07 Level-2 atmospheric profile product provides air temperature at various altitude levels, facilitating a more direct estimation of T_a and T_min. However, previous validations mainly focused on daytime, with a lack of validation for nighttime. Therefore, this study comprehensively evaluated the MOD07 Level-2 and MYD07 Level-2 derived T_a by 2168 hourly meteorological measurements over 5000 m altitude spanning in China. Furthermore, a detailed evaluation of their capability to estimate T_min was also compared with MOD11 Level-2 and MYD11 Level-2 land surface temperature. Our results show that the highest available pressure method (HAP) estimated that, in instantaneous nighttime T_a, there was severe underestimation especially in high-altitude areas for both MOD07 (r = 0.95, Bias = −0.27 °C, and RMSE = 4.53 °C) and MYD07 data (r = 0.96, Bias = −0.17 °C, and RMSE = 3.73 °C). The adiabatic lapse rate (ALR) correction effectively reduced these errors, achieving optimal accuracy with MYD07 data (r = 0.97, Bias = −0.05 °C, and RMSE = 3.29 °C). However, the underestimation by the HAP method was still insufficient compared to T_min estimation by land surface temperature (LST). The LST method offers improved accuracy (r = 0.98, Bias = −0.16 °C, RMSE = 2.89 °C). In general, MYD-based estimations consistently outperformed MOD-based estimations. However, seasonal and elevational variability was observed in all methods, with errors increasing notably in mountainous areas (RMSE rapidly increases to 5 °C and above when the altitude exceeds 2000 m). These findings can provide practical guidance for selecting appropriate inversion methods according to terrain and season and support the development of more accurate air temperature products for a range of climate- and environmental-related applications. Full article

Wetlands are amongst Earth’s most dynamic and complex ecological resources, serving productive and biodiverse ecosystems. Enhancing the quality of wetland mapping through Earth observation (EO) data is essential for improving effective management and conservation practices. However, the achievement of reliable and accurate wetland mapping faces challenges due to the heterogeneous and fragmented landscape of wetlands, along with spectral similarities among different wetland classes. The present study aims to produce advanced 10 m spatial resolution wetland classification maps for four pilot sites on the Island of Newfoundland in Canada. Employing a comprehensive and multidisciplinary approach, this research leverages the synergistic use of optical, synthetic aperture radar (SAR), and light detection and ranging (LiDAR) data. It focuses on ecological and hydrological interpretation using multi-source and multi-sensor EO data to evaluate their effectiveness in identifying wetland classes. The diverse data sources include Sentinel-1 and -2 satellite imagery, Global Ecosystem Dynamics Investigation (GEDI) LiDAR footprints, the Multi-Error-Removed Improved-Terrain (MERIT) Hydro dataset, and the European ReAnalysis (ERA5) dataset. Elevation data and topographical derivatives, such as slope and aspect, were also included in the analysis. The study evaluates the added value of incorporating these new data sources into wetland mapping. Using the Google Earth Engine (GEE) platform and the Random Forest (RF) model, two main objectives are pursued: (1) integrating the GEDI LiDAR footprint heights with multi-source datasets to generate a 10 m vegetation canopy height (VCH) map and (2) seeking to enhance wetland mapping by utilizing the VCH map as an input predictor. Results highlight the significant role of the VCH variable derived from GEDI samples in enhancing wetland classification accuracy, as it provides a vertical profile of vegetation. Accordingly, VCH reached the highest accuracy with a coefficient of determination ($R^2$) of 0.69, a root-mean-square error (RMSE) of 1.51 m, and a mean absolute error (MAE) of 1.26 m. Leveraging VCH in the classification procedure improved the accuracy, with a maximum overall accuracy of 93.45%, a kappa coefficient of 0.92, and an F1 score of 0.88. This study underscores the importance of multi-source and multi-sensor approaches incorporating diverse EO data to address various factors for effective wetland mapping. The results are expected to benefit future wetland mapping studies. Full article

Ischemic stroke (IS) can be caused by perturbations of the gut–brain axis. An imbalance in the gut microbiota (GM), or dysbiosis, may be linked to several IS risk factors and can influence the brain through the production of different metabolites, such as short-chain fatty acids (SCFAs), indole and derivatives. This study examines ecological changes in the GM and its metabolic activities after stroke. Fecal samples of 10 IS patients were compared to 21 healthy controls (CTRLs). GM ecological profiles were generated via 16S rRNA taxonomy as functional profiles using metabolomics analysis performed with a gas chromatograph coupled to a mass spectrometer (GC-MS). Additionally fecal zonulin, a marker of gut permeability, was measured using an enzyme-linked immuno assay (ELISA). Data were analyzed using univariate and multivariate statistical analyses and correlated with clinical features and biochemical variables using correlation and nonparametric tests. Metabolomic analyses, carried out on a subject subgroup, revealed a high concentration of fecal metabolites, such as SCFAs, in the GM of IS patients, which was corroborated by the enrichment of SCFA-producing bacterial genera such as Bacteroides, Christensellaceae, Alistipes and Akkermansia. Conversely, indole and 3-methyl indole (skatole) decreased compared to a subset of six CTRLs. This study illustrates how IS might affect the gut microbial milieu and may suggest potential microbial and metabolic biomarkers of IS. Expanded populations of Akkermansia and enrichment of acetic acid could be considered potential disease phenotype signatures. Full article

Periodic flushing operations during moderate flood events (≤annual flood flow HQ${}_1$) are an approach to counteract problems caused by disturbed sediment continuity in rivers, which is possibly an effect of run-of-river hydropower plants (RoR-HPPs). Considering ecology, flood risk, technical, and economical reasons, discharge values of 0.7 × HQ${}_1$ are a good reference point for the initiation of gate operations. This work aimed to investigate the role of different gate opening actions on the effectiveness of such flushing measures. Physical model tests were performed, to capture bed load rates, together with 2D velocity measurements in the vicinity of two movable radial gates above a fixed weir. The length scale of the idealized model arrangement was 1:20, and a conveyor-belt sediment feeder was used to supply a heterogeneous sediment mixture. Velocities were acquired using 2D laser doppler velocimetry (LDV). Based on the LDV measurements, mean velocity profiles and Reynolds stresses were derived. The full opening of both radial gates led to the highest bed load mobility. While the flushing efficiency drastically decreased, even for slightly submerged gates, an asymmetrical gate opening initially led to the formation of a flushing cone in the vicinity of the weir, accompanied by temporarily high flushing efficiency. In conclusion, our results stress the importance of full drawdowns in successfully routing incoming bed load downstream of the HPP. However, the combination of an asymmetric gate opening followed by a full drawdown could be a promising approach to further improve the flushing efficiency of RoR-HPPs. Full article

The mining of mineral resources has caused serious damage to land and significant pressure on ecological environment. During the repairing of damaged land and degraded ecosystems, there have been many pieces of literature related to land reclamation and ecological restoration (LRER) that have emerged. To understand the progress and prospect of LRER research, it is necessary to sort out such pieces of literature, analyze the current research status, and forecast the future research directions. Here, Bibliometrix R-package was used to analyze 2357 articles, which were derived from the core database of Web of Science, to explore the development of LRER from 1990 to 2022. The results are as follows. (1) The annual scientific output results show that both the number of articles published on LRER and the number of articles annually citied were increasing gradually from 1990 to 2022. (2) High-frequency keyword analysis indicates that heavy metal (Cd, Pb) pollution remediation is a research hotspot. The cluster analysis (CA) and multiple correspondence analysis (MCA) show that there are two clusters in the current research of LRER, in which one surrounds heavy metal pollution and the other focuses on ecological restoration of mining areas. The two clusters correspond to the remediation and ecological restoration (rehabilitation) stages of stepwise ecological restoration, respectively. Thematic evolution analysis shows that, for more than 30 years, mine drainage and heavy metal pollution treatment, soil reconstruction (soil profile reconstruction, soil improvement), and vegetation restoration have been the focus of research. (3) Future research should focus on the relationship between mine ecological restoration and carbon sequestration and the relationship between ecological restoration and biodiversity in mine areas. In addition, LRER technology exchange, international cooperation, and industrialization are also main directions of development. Generally, in this study, metrology software (Bibliometrix R-package 3.1.4) from the literature was used to sort out the relevant literature on LRER over the past 30 years so as to provide reference for future research on LRER. Full article

Due to botanical tulips’ economic interest coupled with limited information regarding their seed germination, we investigated the effect of temperature on dormancy release and germination in two endangered local endemic tulip species of Greece (Tulipa hageri Heldr., T. orphanidea Heldr.). Their germination responses at five constant temperatures (5, 10, 15, 20, and 25 °C) were evaluated in growth chambers, while the type of seed dormancy and the temperature effect on its release were determined based on open-sourced, R-derived species-specific ecological profiles illustrating abiotic conditions of their wild habitats. The results indicated a range-specific temperature dependence in seed germination for both studied species with seed germination observed only in very low temperatures (5–10 °C). The seeds of both species after dispersal had an underdeveloped embryo. The existence of a complex morphophysiological seed dormancy was confirmed in both species by the significant embryo development only at 5 and 10 °C (almost doubled after 30 days) coupled with observed delay in germination only at low temperatures. Furthermore, to facilitate their cultivation and ex situ conservation, the germinated seeds were planted in pots to develop bulblets in greenhouse conditions resulting in bigger T. orphanidea bulblets compared to T. hageri. Full article

Spaceborne profiling lidar missions such as the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) are collecting direct elevation measurements, supporting the retrieval of vegetation attributes such as canopy height that are crucial in forest carbon and ecological studies. However, such profiling lidar systems collect observations along predefined ground tracks which limit the spatially complete mapping of forest canopy height. We demonstrate that the fusion of ICESat-2 along-track canopy height estimates and ancillary Landsat and LANDFIRE (Landscape Fire and Resource Management Planning Tools Project) data can enable the generation of spatially complete canopy height data at a regional level in the United States. We developed gradient-boosted regression models relating canopy heights with ancillary data values and used them to predict canopy height in unobserved locations at a 30 m spatial resolution. Model performance varied (R² = 0.44 − 0.50, MAE = 2.61–2.80 m) when individual (per month) Landsat data and LANDFIRE data were used. Improved performance was observed when combined Landsat and LANDFIRE data were used (R² = 0.69, MAE = 2.09 m). We produced a gridded canopy height product over our study area in eastern Texas, which agreed moderately (R² = 0.46, MAE = 4.38 m) with independent airborne lidar-derived canopy heights. Further, we conducted a comparative assessment with the Global Forest Canopy Height product, an existing 30 m spatial resolution canopy height product generated using GEDI (Global Ecosystem Dynamics Investigation) canopy height and multitemporal Landsat data. In general, our product showed better agreement with airborne lidar heights than the global dataset (R² = 0.19 MAE = 5.83 m). Major differences in canopy height values between the two products are attributed to land cover changes, height metrics used (98th in this study vs 95th percentile), and the inherent differences in lidar sampling and their geolocation uncertainties between ICESat-2 and GEDI. On the whole, our integration of ICESat-2 data with ancillary datasets was effective for spatially complete canopy height mapping. For better modeling performance, we recommend the careful selection of ICESat-2 datasets to remove erroneous data and applying a series of Landsat data to account for phenological changes. The canopy height product provides a valuable spatially detailed and synoptic view of canopy heights over the study area, which would support various forestry and ecological assessments at an enhanced 30 Landsat spatial resolution. Full article

Regolith, or unconsolidated materials overlying bedrock, exists as an active zone for many geological, geomorphological, hydrological and ecological processes. This zone and its processes are foundational to wide-ranging human needs and activities such as water supply, mineral exploration, forest harvesting, agriculture, and engineered structures. Regolith thickness, or depth-to-bedrock (DTB), is typically unavailable or restricted to finer scale assessments because of the technical and cost limitations of traditional drilling, seismic, and ground-penetrating radar surveys. The objective of this study was to derive a high-resolution (10 m²) DTB model for the province of New Brunswick, Canada as a case study. This was accomplished by developing a DTB database from publicly available soil profiles, boreholes, drill holes, well logs, and outcrop transects (n = 203,238). A Random Forest model was produced by modeling the relationships between DTB measurements in the database to gridded datasets derived from both a LiDAR-derived digital elevation model and photo-interpreted surficial geology delineations. In developing the Random Forest model, DTB measurements were split 70:30 for model development and validation, respectively. The DTB model produced an R² = 92.8%, MAE = 0.18 m, and RMSE = 0.61 m for the training, and an R² = 80.3%, MAE = 0.18 m, and RMSE = 0.66 m for the validation data. This model provides an unprecedented resolution of DTB variance at a landscape scale. Additionally, the presented framework provides a fundamental understanding of regolith thickness across a post-glacial terrain, with potential application at the global scale. Full article

Glacial lake formations are currently being observed in the majority of glacierized mountains in the world. Given the ongoing climate change and population increase, studying glacier ice thickness and bed topography is a necessity for understanding the erosive power of glacier activity in the past and lake formation in the future. This study uses the available information to predict potential sites for future lake formation in the Upper Rhône catchment located in the Southwestern Swiss Alps. The study integrates the latest available glacier outlines and high-quality digital elevation models (DEMs) into the Volume and Topography Automation (VOLTA) model to estimate ice thickness within the extent of the glaciers. Unlike the previous ice thickness models, VOLTA calculates ice thickness distribution based on automatically-derived centerlines, while optimizing the model by including the valley side drag parameter in the force equation. In this study, a total ice volume of 37.17 ± 12.26 km³ (1σ) was estimated for the Upper Rhône catchment. The comparison of VOLTA performance indicates a stronger relationship between measured and predicted bedrock, confirming the less variability in VOLTA’s results (r² ≈ 0.92) than Glacier Bed Topography (GlabTop) (r² ≈ 0.82). Overall, the mean percentage of ice thickness error for all measured profiles in the Upper Rhône catchment is around ±22%, of which 28 out of 42 glaciers are underestimated. By incorporating the vertical accuracy of free-ice DEM, we could identify 171 overdeepenings. Among them, 100 sites have a high potential for future lake formation based on four morphological criteria. The visual evaluation of deglaciated areas also supports the robustness of the presented methodology, as 11 water bodies were already formed within the predicted overdeepenings. In the wake of changing global climate, such results highlight the importance of combined datasets and parameters for projecting the future glacial landscapes. The timely information on future glacial lake formation can equip planners with essential knowledge, not only for managing water resources and hazards, but also for understanding glacier dynamics, catchment ecology, and landscape evolution of high-mountain regions. Full article

As China is suffering from severe fine particle pollution from dense industrialization and urbanization, satellite-derived aerosol optical depth (AOD) has been widely used for estimating particulate matter with an aerodynamic diameter less than 2.5 μm (PM_2.5). However, the correlation between satellite AOD and ground-level PM_2.5 could be influenced by aerosol vertical distribution, as satellite AOD represents the entire column, rather than just ground-level concentration. Here, a new column-to-surface vertical correction scheme is proposed to improve separation of the near-surface and elevated aerosol layers, based on the ratio of the integrated extinction coefficient within 200–500 m above ground level (AGL), using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP)) aerosol profile products. There are distinct differences in climate, meteorology, terrain, and aerosol transmission throughout China, so comparisons between vertical correction via CALIOP ratio and planetary boundary layer height (PBLH) were conducted in different regions from 2014 to 2015, combined with the original Pearson coefficient between satellite AOD and ground-level PM_2.5 for reference. Furthermore, the best vertical correction scheme was suggested for different regions to achieve optimal correlation with PM_2.5, based on the analysis and discussion of regional and seasonal characteristics of aerosol vertical distribution. According to our results and discussions, vertical correction via PBLH is recommended in northwestern China, where the PBLH varies dramatically, stretching or compressing the surface aerosol layer; vertical correction via the CALIOP ratio is recommended in northeastern China, southwestern China, Central China (excluding summer), North China Plain (excluding Beijing), and the spring in the southeast coast, areas that are susceptible to exogenous aerosols and exhibit the elevated aerosol layer; and original AOD without vertical correction is recommended in Beijing and the southeast coast (excluding spring), where the elevated aerosol layer rarely occurs and a large proportion of aerosol is aggregated in near-surface. Moreover, validation experiments in 2016 agreed well with our discussions and conclusions drawn from the experiments of the first two years. Furthermore, suggested vertical correction scheme was applied into linear mixed effect (LME) model, and high cross validation (CV) R² (~85%) and relatively low root mean square errors (RMSE, ~20 μg/m³) were achieved, which demonstrated that the PM_2.5 estimation agreed well with the measurements. When compared to the original situation, CV R² values and RMSE after vertical correction both presented improvement to a certain extent, proving that the suggested vertical correction schemes could further improve the estimation accuracy of PM_2.5 based on sophisticated model in China. Estimating PM_2.5 with better accuracy could contribute to a more precise research of ecology and epidemiology, and provide a reliable reference for environmental policy making by governments. Full article

Molecular analysis of the bacterial community structure associated with sludge processed by autothermal thermophilic aerobic digestion (ATAD), was performed using a number of extraction and amplification procedures which differed in yield, integrity, ability to amplify extracted templates and specificity in recovering species present. Interference to PCR and qPCR amplification was observed due to chelation, nuclease activity and the presence of thermolabile components derived from the ATAD sludge. Addition of selected adjuvant restored the ability to amplify community DNA, derived from the thermophilic sludge, via a number of primer sets of ecological importance and various DNA polymerases. Resolution of community profiles by molecular techniques was also influenced by the ATAD sludge extraction procedure as demonstrated by PCR-DGGE profiling and comparison of taxonomic affiliations of the most predominant members within 16S rRNA gene libraries constructed from ATAD DNA extracted by different methods. Several modifications have been shown to be necessary to optimize the molecular analysis of the ATAD thermal niche which may have general applicability to diversity recovery from similar environments. Full article