Highways provide key social and economic functions but generate a wide range of environmental consequences that are poorly quantified and understood. Here, we developed a before–during–after control-impact remote sensing (BDACI-RS) approach to quantify the spatial and temporal changes of environmental impacts during and after the construction of the Wujing Highway in China using three buffer zones (0–100 m, 100–500 m, and 500–1000 m). Results showed that land cover composition experienced large changes in the 0–100 m and 100–500 m buffers while that in the 500–1000 m buffer was relatively stable. Vegetation and moisture conditions, indicated by the normalized difference vegetation index (NDVI) and the normalized difference moisture index (NDMI), respectively, demonstrated obvious degradation–recovery trends in the 0–100 m and 100–500 m buffers, while land surface temperature (LST) experienced a progressive increase. The maximal relative changes as annual means of NDVI, NDMI, and LST were about −40%, −60%, and 12%, respectively, in the 0–100m buffer. Although the mean values of NDVI, NDMI, and LST in the 500–1000 m buffer remained relatively stable during the study period, their spatial variabilities increased significantly after highway construction. An integrated environment quality index (EQI) showed that the environmental impact of the highway manifested the most in its close proximity and faded away with distance. Our results showed that the effect distance of the highway was at least 1000 m, demonstrated from the spatial changes of the indicators (both mean and spatial variability). The approach proposed in this study can be readily applied to other regions to quantify the spatial and temporal changes of disturbances of highway systems and subsequent recovery.

Ecosystem services (ES) are directly affected by land use and land cover changes (LUCC); however, the impacts of extended period LUCC on ES are poorly explored. Here, we mapped the 1998–2019 annual land use and land cover in the Dongting Lake Region (China) and explored the spatiotemporal evolution of LUCC and landscape patterns (i.e., composition, shape, and aggregation) and their relationship with ES, including carbon storage, gross primary production (GPP), water conservation capacity, and crop yield in the region. The results showed a significant increase in forest areas and impervious surfaces and a decrease in croplands and bare lands with spatial heterogeneity. Carbon storage was strongly correlated with forest, cropland, waterbody, impervious surface, and bare land, and there was a nonlinear relationship between landscape patterns and ES. The trade-offs and synergies (correlations) among ES varied considerably, with crop yield being significantly synergistic with carbon stocks, GPP, or GPP with carbon stocks. This study revealed the nonlinear relationship between landscape patterns and ES, and the mechanism of landscape characteristics on ES. The findings can provide scientific support for regional land use planning, ES regulation, and landscape optimization in the lake region.

Alfalfa represents one of the most important legume forages, and it is also applied as an organic fertilizer to improve soil quality. However, this perennial plant is native to warmer temperate regions, and its valuable cold-acclimation-related regulatory mechanisms are still less known. In higher plants, the bHLH transcription factors play pleiotropic regulatory roles in response to abiotic stresses. The recently released whole genome sequencing data of alfalfa allowed us to identify 469 MsbHLHs by multi-step homolog search. Herein, we primarily identified 65 MsbHLH genes that significantly upregulated under cold stress, and such bHLHs were classified into six clades according to their expression patterns. Interestingly, the phylogenetic analysis and conserved motif screening of the cold-induced MsbHLHs showed that the expression pattern is relatively varied in each bHLH subfamily, this result indicating that the 65 MsbHLHs may be involved in a complex cold-responsive regulatory network. Hence, we analyzed the TFBSs at promoter regions that unraveled a relatively conserved TFBS distribution with genes exhibiting similar expression patterns. Eventually, to verify the core components involved in long-term cold acclimation, we examined transcriptome data from a freezing-tolerant species (cv. Zhaodong) in the field and compared the expression of cold-sensitive/tolerant subspecies of alfalfa, giving 11 bHLH as candidates, which could be important for further cold-tolerance enhancement and molecular breeding through genetic engineering in alfalfa.

Large river floodplain systems (LRFS) are among the most diverse and dynamic ecosystems. Accurately monitoring the dynamics of LRFS over long time series is fundamental and essential for their sustainable development. However, challenges remain because the spatial distribution of LRFS is never static due to inter- and intra-annual changes in environmental conditions. In this study, we developed and tested a methodological framework to re-construct the long-term wetland dynamics in Dongting Lake, China, utilizing an unsupervised machine-learning algorithm (UMLA) on the basis of MODIS (Moderate Resolution Imaging Spectroradiometer) EVI (Enhanced Vegetation Index) time series. Our results showed that the UMLA achieved comparable performance to the time-consuming satellite image segmentation method with a Kappa coefficient of agreement greater than 0.75 and an overall accuracy over 85%. With the re-constructed annual wetland distribution maps, we found that 31.35% of wet meadows, one of most important ecological assets in the region, disappeared at an average rate of c.a. 1660 ha year⁻¹ during the past two decades, which suggests that the Dongting Lake is losing its ecological function of providing wintering ground for migratory water birds, and remediation management actions are urgently required. We concluded that UMLA offers a fast and cost-efficient alternative to monitor ecological responses in a rapidly changing environment.