Spatio-temporal evolution and driving forces of habitat quality in Guizhou Province

Abstract

This study aimed to analyze spatio-temporal changes in habitat quality in Guizhou Province during the 1990–2018 period and identify factors influencing habitat quality. Land-use data for the period were used to evaluate spatio-temporal variations in habitat quality using the InVEST model, and factors influencing habitat quality were analyzed using GeoDetector. According to the results, cultivated land and forestland decreased by 0.48% and 0.88%, respectively, during the study period. Grassland, water, and construction land areas increased, with construction land increasing the most (0.92%) followed by water area (0.37%). The main land-use changes included conversion of cultivated land to forestland, grassland, and construction land. The average habitat quality index for Guizhou Province changed from 0.633 to 0.627 over the 1990–2018 period, showing an overall downward trend. The distribution pattern of habitat quality was spatially "high in the north, south, and, east, and low in the west". High habitat quality areas were mainly located in the western part of Guizhou Province, whereas low habitat quality areas were located in the central region. Land-use was the major factor influencing the spatio-temporal variations in habitat quality, and the interactive effect between any two factors was stronger than that of a single factor. Natural factors and human factors co-dominate the temporal-spatial changes of habitat quality.

Research on habitat quality in arid urban agglomerations: Influencing mechanisms and multi‐scenario simulations

“…Variations in HQ across different areas result from a combination of factors, and the analysis conducted using the PLS‐SEM model reveals that changes in LULC strongly affect HQ (Figures 6 and 7 and Table 8), which is consistent with previous research conclusions (Xie & Zhang, 2023; Zhang et al, 2020). The geographically weighted regression (GWR) (Brunsdon et al, 1996; Lu, Ge, et al, 2023; Lu, Hu, et al, 2023) technique was used to explore the spatially varying relationships between HQ changes and LULC changes.…”