Despite the Yangtze River Basin (YRB)’s abundant land and forestry resources, there is still a dearth of research on forecasting habitat quality changes resulting from various geographic and environmental factors that drive landscape transformations. Hence, this study concentrates on the YRB as the focal area, with the aim of utilizing the Patch Landscape Upscaling Simulation model (PLUS) and the habitat quality model to scrutinize the spatial distribution of landscape patterns and the evolution of HQ under four scenarios: the natural development scenario (NDS), farmland protection scenario (CPS), urban development scenario (UDS), and ecological protection scenario (EPS), spanning from the past to 2030. Our results show that (1) from 2000 to 2020, the construction land in the YRB expanded at a high dynamic rate of 47.86% per year, leading to a decrease of 32,776 km² in the cultivated land area; (2) the UDS had the most significant expansion of construction land, followed by the NDS, CPS, and EPS, which had higher proportions of ecologically used land such as forests and grasslands; (3) from 2000 to 2020, the HQ index ranged from 0.211 to 0.215 (low level), showing a slight upward trend, with the most drastic changes occurring in the low-level areas (−0.49%); (4) the EPS had the highest HQ (0.231), followed by the CPS (0.215), with the CPS increasing the HQ proportion of the lower-level areas by 2.64%; (5) and in addition to government policies, NDVI, DEM, GDP, and population were also significant factors affecting landscape pattern and changes in habitat quality.

Water temperature, as one of the important water environment impact factors, has a significant impact on the survival and development of aquatic organisms. We selected water temperature data (1959–2017) from four key hydrological stations in the Yangtze River: Cuntan, Yichang, Hankou, and Datong. We analyzed the characteristics and variability of the Yangtze River hydrothermal regime by the Mann-Kendall method, wavelet analysis, and by the IHA-RVA method to analyze hydrothermal regime variations of the Yangtze River, and the response mechanisms of fish to variations in hydrothermal regimes were explored. The results show that (1) The annual average water temperature of the Yangtze River is warming, and Cuntan Station, Yichang Station, Hankou Station, and Datong Station have a sudden increase in temperature in 2002, 1995, 2003, and 2004, and the periodicity analysis demonstrates that Cuntan, Yichang, and Datong stations all have main periods of 24~32 years; (2) The overall variations of 32 hydrothermal indicators at Cuntan Station and Yichang Station reached 65% and 61%, which are close to the height variation; (3) With the construction of the Three Gorges Dam, the stagnant cooling effect caused the arrival date of the upper reproduction water temperature of “The four major fish species” to be delayed by about 23 days, and the stagnant heat effect caused the arrival date of the Chinese sturgeon reproduction upper limit water temperature to be delayed by about 30 days.

Potential vegetation distribution is an important study in environmental sciences. We utilized the Mixed Least Squares–Total Least Squares (MLS-TLS) method and the Signal Mode Decomposition method and the Ecological Niche model to identify the inter-correlations of internal climate change factors and constructed an environmental factor response regression model. We identified the resonance periods and trend relationships among climate factors (temperature, precipitation, and evapotranspiration) and found that the evapotranspiration of the watershed interferes with the correlation between temperature and precipitation on a five-year scale. The specific change degree of extreme climate indicators in the region was quantified by the Range of Variability Approach, among which the precipitation indicators were all below 33% (low change). There were significant differences between the key bioclimatic variables and Aspect of the development of suitable vegetation habitats. The difference between the Aspect and average daily air temperature is the main contributor to the spatial distribution of vegetation, and the mutual contribution is 76.19%. Our regression model can effectively simulate the potential distribution of vegetation (r = 0.854). Compared to the MaxEnt model, our regression model can quantitatively and intuitively provide suitable habitat values for Cryptomeria fortunei at any given location in the basin. Under future scenarios (2021–2040), suitable habitat for Cryptomeria fortunei in the eastern and western regions of the basin is projected to deteriorate further. The research results can provide some help for policymakers to eliminate the potential adverse effects of future climate change on regional ecology.