Spatiotemporal Variation and Influence Factors of Habitat Quality in Loess Hilly and Gully Area of Yellow River Basin: A Case Study of Liulin County, China

Abstract

China has set up ecological protection and high-quality development of the Yellow River Basin as its national strategy. However, the fragile natural ecosystem and intensive human disturbances pose challenges to it. This study evaluates habitat quality change and analyzes its drivers in a representative county of this region, aiming to provide scientific basis for ecological protection and sustainable development. We took Liulin, a representative county of middle Yellow River Basin as the study area and evaluated the spatiotemporal variation of habitat quality from 2000 to 2020 with the InVEST model. Further, the influencing factors of habitat quality pattern were explored using GeoDetector, and their gradient ranges dominating the habitat quality change were determined by gradient analysis. The results showed that: (1) Areas of low and medium-low habitat quality grades were distributed interactively in the whole county; medium grade areas were scattered in the northeast and southwest parts of the county; and medium-high and high grades area were distributed sporadically along the Yellow River and its branches. (2) Habitat quality of the county almost unchanged from 2000 to 2010. However, from 2010 to 2020, with the rapid expansion of construction land (increased by 9.62 times), the area proportion of medium, medium-high, and high habitat quality grades decreased from 7.01% to 5.31%, while that of low and medium-low habitat quality grades increased from 92.99% to 94.69%. (3) The habitat quality was influenced by multiple natural-human factors. The main influencing factor was land use, followed by elevation. (4) Most changes of habitat quality occurred in areas with lower elevation, gentler slope, and higher vegetation coverage, which were affected by intensive human activities. These results suggest that in future land use policy making and, the construction land expansion in Liulin County should be restricted and differentiated ecological protection and restoration strategies should be implemented in areas with different habitat quality.

Study of Spatiotemporal Changes and Driving Factors of Habitat Quality: A Case Study of the Agro-Pastoral Ecotone in Northern Shaanxi, China

“…Arable land and construction land have reduced biodiversity in comparison to their original habitats due to human influence. Different from the results of Zhang et al in Liulin County, Shanxi Province of China [47], our results show that the driving force of precipitation and vegetation cover on the habitat quality of the agro-pastoral ecotone in northern Shaanxi is stronger than the driving force of elevation and slope. Because the study area is in the transition zone between semi-arid areas, precipitation is significant for the local ecological environment.…”

Impact of land use change on habitat quality and regional biodiversity capacity: Temporal and spatial evolution and prediction analysis

“…The research data was derived from a raster data set from 2000, 2010, and 2020 with a resolution of 30 m provided by the Resource and Environmental Science Center of China Academy of Sciences (Zhang X. et al, 2022). According to the land use planning of Suzhou, the area is divided into six types: cultivated lands, forests, grasslands, water bodies, construction lands, and bare lands (Figure 2).…”

“…We selected the relevant influencing factors (social economy, land use, and natural environment) from the four landscape patterns (Qi and Wu, 2005;Weber et al, 2018;Clairmont et al, 2021;Zhang X. et al, 2022). As a characteristic of the landscape's spatial structure, landscape patterns represent spatial heterogeneity.…”

Impact of Land Use Change on the Habitat Quality Evolution in Three Gorges Reservoir Area, China

“…It is currently the most frequently applied model and is widely used for HQ assessment, spatial and temporal variability, and future prediction in cities [ 22 ], watersheds [ 23 ], nature reserves [ 24 ], and coastal zones [ 25 ]. In addition, in studies related to the effects of land use change on HQ, the models such as contribution index [ 26 ], bivariate spatial autocorrelation [ 27 ], ordinary least square (OLS) [ 28 ], geodetector [ 29 ], geographically weighted regression (GWR) [ 30 ], multi-scale geographically weighted regression (MGWR) [ 31 ] were mainly used to quantify and assess the relationship between land use change and habitat quality change. Among them, the multi-scale geographically weighted regression model (MGWR) can not only analyze the spatial response relationships at different scales [ 32 ] but also better reveal the spatial response mode of land use change on habitat quality, providing an important means to explore the quantitative relationship between land use change and HQ in space.…”