Land use and land cover change is an important driving force for changes in ecosystem services. We defined several important human-induced land cover change processes such as Ecological Restoration Project, Cropland Expansion, Land Degradation, and Urbanization by the land use/land cover transition matrix method. We studied human-induced land cover changes in the Yellow River Basin from 1980 to 2015 and evaluated its impact on ecosystem service values by the benefit transfer method and elasticity coefficient. The results show that the cumulative area of human-induced land cover change reaches 65.71 million ha from 1980 to 2015, which is close to the total area of the Yellow River Basin. Before 2000, Ecological Restoration Project was the most important human-induced land cover change process. However, due to the large amount of cropland expansion and land degradation, the area of natural vegetation was reduced and the ecosystem value declined. Since 2000, due to the implementation of the “Grain for Green” program, the natural vegetation of upstream area and midstream area of Yellow River Basin has been significantly improved. This implies that under an appropriate policy framework, a small amount of human-induced land cover change can also improve ecosystem services significantly.

Indices of biotic integrity (IBIs) are widely used to assess aquatic ecosystem health. However, there are few studies on their relationships. Based on fish, macroinvertebrate and plankton survey data collected in the Ganjiang River system from 2016 to 2017, redundancy analysis (RDA) and canonical correspondence analysis (CCA) were used to analyze how the community structures of these organisms respond to environmental variables. The fish IBI (F-IBI), benthic macroinvertebrate IBI (B-IBI), and phytoplankton IBI (P-IBI) were applied to evaluate the health status of the aquatic ecosystem. A Kruskal–Wallis test (p < 0.05) and Spearman’s correlation coefficient analysis were performed to evaluate the spatiotemporal heterogeneity of the results. Our results suggested that the F-IBI-, B-IBI-, and P-IBI-based assessments indicated good, fair, and healthy Ganjiang River system ecosystem health statuses, respectively, and significant differences existed among these indices (p < 0.05). The main environmental factors affecting F-IBI, B-IBI, and P-IBI were different. At the temporal scale, the F-IBI and B-IBI were stable, while the P-IBI fluctuated obviously. The consistency between the F-IBI and B-IBI results was better than that between each of these indices and the P-IBI results, and the consistency was better on a larger scale. These research results show that comprehensive assessments based on multiple groups rather than a single group can better characterize the impacts of environmental pressures on water ecosystems.

The deliberate introduction of the beneficial gene in crop plants through transgenic technology can provide enormous agricultural and economic benefits. However, the impact of commercialization of these crops on the ecosystem particularly on belowground soil biodiversity is still uncertain. Here, we examined and compared the effects of a non-transgenic maize cultivar and an insect-resistant transgenic maize cultivar genetically engineered with cry1Ah gene from Bacillus thuringiensis, on the rhizosphere bacterial community using 16S rDNA amplicon sequencing and soil metabolome profile using UPLC/MS analysis at six different growth stages. We found no significant differences in bacterial community composition and diversity at all growth stages between the two cultivars. The analysis of bacterial beta-diversity showed an evident difference in community structure attributed to plant different growth stages but not to the plant type. In contrast, the soil metabolic profile of transgenic maize differed from that of the non-transgenic plant at some growth stages, and most of the altered metabolites were usually related to the metabolism but not to the plant-microbe interaction related pathways. These results suggest that genetic modification with the cry1Ah gene-altered maize soil metabolism but had no obvious effect on the rhizosphere bacterial community.

Analysis of the relationships among ecosystem services (ESs) can help ensure that benefits from ecosystems are consistent over time. This study explored the spatial and temporal patterns of water supply (WS), grain supply (GS), carbon storage (CS), water conservation (WC), soil conservation (SC), and habitat quality (HQ) in the Wujiang River Basin (WJRB) from 2000 to 2020 and the trade-off/synergy relationships of ESs. The ESs in the WJRB are downstream > midstream > upstream in space, with the greatest increase and decrease in the upstream and midstream temporal dimensions, respectively. The WS, WC, and SC underwent a trend shift in 2005 due to climatic influences, whereas GS and HQ underwent a trend shift in 2010 due to human social development. GS formed a trade-off with other ESs in the spatial pattern, whereas WS formed a trade-off with CS and WC in the temporal dimension. Adjusting the GS spatial pattern reduces the trade-off between ESs in the spatial pattern, allowing for focusing on monitoring soil and water erosion-prone areas to prevent extensive soil erosion during heavy precipitation years; this reduces the trade-off between ESs in the time dimension in the WJRB. This provides a theoretical basis for achieving high-quality WJRB development.

Accurate land cover (LC) datasets are the basis for global environmental and climate change studies. Recently, numerous open-source annual LC datasets have been created due to advances in remote sensing technology. However, the agreements and sources of error that affect the accuracy of current annual LC datasets are not well understood, which limits the widespread use of these datasets. We compared four annual LC datasets, namely the CLCD, MCD12Q1, CCI-LC, and GLASS-LC, in the Yellow River Basin (YRB) to identify their spatial and temporal agreement for nine LC classes and to analyze their sources of error. The Mann–Kendall test, Sen’s slope analysis, Taylor diagram, and error decomposition analysis were used in this study. Our results showed that the main LC classes in the four datasets were grassland and cropland (total area percentage > 80%), but their trends in area of change were different. For the main LC classes, the temporal agreement was the highest between the CCI-LC and CLCD (0.85), followed by the MCD12Q1 (0.21), while the lowest was between the GLASS-LC and CLCD (−0.11). The spatial distribution of area for the main LC classes was largely similar between the four datasets, but the spatial agreement in their trends in area of change varied considerably. The spatial variation in the trends in area of change for the cropland, forest, grassland, barren, and impervious LC classes were mainly located in the upstream area region (UA) and the midstream area region (MA) of the YRB, where the percentage of systematic error was high (>68.55%). This indicated that the spatial variation between the four datasets was mainly caused by systematic errors. Between the four datasets, the total error increased along with landscape heterogeneity. These results not only improve our understanding of the spatial and temporal agreement and sources of error between the various current annual LC datasets, but also provide support for land policy making in the YRB.