The coupling and coordination relationship between ecology and the economy in the Yellow River Basin is a hot topic in sustainable development research. Said research has important guiding significance for the ecological security and comprehensive development of the Yellow River Basin. Taking the Yellow River Basin as the object of our study, based on the data of the economy, energy consumption data, ecology data and water resources data, we construct an indicator system of the economic development and ecological status of the Yellow River Basin and use the principal component analysis method to calculate the economic development and ecological status index. Then, we use the evaluation method, the coupling degree model and the coupling coordination degree model to analyze the time and space evolution trends of economic development and ecological state, coupling degree and coupling coordination degree. The results show that: (1) From 2000 to 2018, the economic development index of the Yellow River Basin rose steadily; the ecological status index showed a slow rise and then a downward trend. (2) The degree of coupling between economic development and ecological state has been considered as intensity coupling after 2005. The coupling trend slowly increased and then decreased, indicating that the interaction effect between the economy and ecology was first significantly enhanced and then slowly weakened. (3) The degree of coupling coordination increased from 0.2994 to 0.6266 and then decreased to 0.5917, reflecting the continuous improvement of the relationship between the regional economy and the ecological environment and the trend toward coordination. From 2015 to 2018, due to the gradual increase in the difference between economic development and ecological conditions, the coupling and coordination between the two decreased. Studies have shown that ecological construction and protection should be strengthened to ease the contradiction between the economy and ecology and achieve coordinated development.

Habitat suitability assessment is critical for wildlife population conservation and management planning. The MaxEnt model is widely used in species habitat suitability modeling. In order to investigate the habitat status of the black-necked crane in the Zoige grassland wetland ecological function area, this study evaluated the habitat suitability of the black-necked crane using the MaxEnt model with 152 occurrence records and 13 environmental variables. Based on the Akaike information criterion corrected for the small sample size, the best optimal parameter combination (feature class LQPHT, regularization multiplier 3.0) was selected. The results show that the Maxent model had good accuracy with an area under the curve (AUC) value of 0.895. Distance to roads, average summer precipitation, distance to lakes, and altitude are the dominant environmental variables affecting the potential distribution of black-necked cranes, with the contribution rates of 31.6%, 29.8%, 14.3%, and 8.2%, respectively. The suitable habitat area of black-necked cranes is mainly distributed in and around the Zoige Wetland National Nature Reserve, accounting for 46.49% of the Zoige Grassland Wetland National Key Ecological Function Zone. The potential distribution area has a tendency to spread to Hongyuan County in the south of the functional zone, and the unsuitable habitat is mainly distributed in the high-altitude area in the southwest of the functional zone. This study recommends focusing on the distribution area of black-necked cranes around Zoige Wetland National Nature Reserve and Hongyuan County to improve conservation strategies and strengthen protection efforts.

Global climate change and disturbances from human activities lead to habitat loss and changes in habitat quality, resulting in a reduction in biodiversity. The continental coastline of China distributes some cities with highly developed economies and coastal wetlands with rich biodiversity, and both economic development and biodiversity conservation are important topics. In order to clarify the spatial distribution of bird biodiversity in coastal areas, based on the MaxEnt model and GIS spatial analysis, the distribution data of 488 species of birds and 15 environmental variables were used to simulate the suitable distribution areas of birds, and to analyze the spatial distribution and hotspots of bird biodiversity in coastal areas. The main findings of this study are as follows. (1) A total of 488 species in 249 genera of 21 orders and 81 families of birds were involved in the modeling of coastal areas. The main environmental factors affecting the potential distribution of birds in general are: land use, monthly mean diurnal temperature range, and precipitation of the driest month. (2) High value areas of bird richness are distributed in different locations in the four sea areas: In the Bohai Rim region, they are mainly distributed in the Liaohe Estuary Wetland in Liaoning Province, the vicinity of the Yongdingxin River and Haihe River in Tianjin, the eastern part of Tianjin, and the Yellow River and Yellow River Delta Wetland in Shandong Province; In the coastal area of the Yellow Sea, they are primarily found in Kunyu Mountain National Nature Reserve, Laoshan Provincial Nature Reserve in Shandong Province, and Yancheng Wetland Rare Birds National Nature Reserve in Jiangsu Province; In the coastal areas of the East China Sea, they are mainly found at the mouth of the Yangtze River, at the national nature reserve of Dongtan birds on Chongming Island, along the southeastern coast of Zhejiang Province, near the Min River and along the coast of Quanzhou in Fujian Province; In the coastal areas of the South China Sea, they are mainly distributed in the Pearl River Delta wetlands of Guangdong Province, the southeastern coastal areas of the Guangdong-Hong Kong-Macao Greater Bay Area, and the Shiwanda Mountain National Nature Reserve in Guangxi Province. (3) The spatial trends in the distribution of hotspots of birds of national priority protection in coastal areas are generally consistent, but more concentrated than the distribution of all birds. (4) After the high-value areas of bird richness were superimposed with protected areas, it was found that the intersecting area accounted for a small proportion of the protected area, and many areas near the inland had low bird richness. Finally, the findings provide references for bird biodiversity conservation and planning in coastal areas.

Accurate high-resolution soil moisture mapping is critical for surface studies as well as climate change research. Currently, regional soil moisture retrieval primarily focuses on a spatial resolution of 1 km, which is not able to provide effective information for environmental science research and agricultural water resource management. In this study, we developed a quantitative retrieval framework for high-resolution (250 m) regional soil moisture inversion based on machine learning, multisource data fusion, and in situ measurement data. Specifically, we used various data sources, including the normalized vegetation index, surface temperature, surface albedo, soil properties data, precipitation data, topographic data, and soil moisture products from passive microwave data assimilation as input parameters. The soil moisture products simulated based on ground model simulation were used as supplementary data of the in situ measurements, together with the measured data from the Maqu Observation Network as the training target value. The study was conducted in the Zoige region of the Tibetan Plateau during the nonfreezing period (May–October) from 2009 to 2018, using random forests for training. The random forest model had good accuracy, with a correlation coefficient of 0.885, a root mean square error of 0.024 m³/m³, and a bias of −0.004. The ground-measured soil moisture exhibited significant fluctuations, while the random forest prediction was more accurate and closely aligned with the field soil moisture compared to the soil moisture products based on ground model simulation. Our method generated results that were smoother, more stable, and with less noise, providing a more detailed spatial pattern of soil moisture. Based on the permutation importance method, we found that topographic factors such as slope and aspect, and soil properties such as silt and sand have significant impacts on soil moisture in the southeastern Tibetan Plateau. This highlights the importance of fine-scale topographic and soil property information for generating high-precision soil moisture data. From the perspective of inter-annual variation, the soil moisture in this area is generally high, showing a slow upward trend, with small spatial differences, and the annual average value fluctuates between 0.3741 m³/m³ and 0.3943 m³/m³. The intra-annual evolution indicates that the monthly mean average soil moisture has a large geographical variation and a small multi-year linear change rate. These findings can provide valuable insights and references for regional soil moisture research.