Monitoring spatio-temporal dynamics of habitat quality in Nansihu Lake basin, eastern China, from 1980 to 2015

Abstract

Rapid industrial development, local urban expansion and intensive agricultural activities directly impact habitat quality and biodiversity. Effective monitoring tools for biodiversity could help assess trade-offs between biodiversity conservation and economic development. Habitat quality is the ability of the ecosystem to support conditions that are suitable for species and is regarded as proxy for biodiversity. This research used the InVEST-Habitat Quality model to monitor the spatio-temporal dynamics of habitat quality in the Nansihu Lake basin from 1980 to 2015; additionally, this research analyzed the potential factors that impacted habitat quality. The results indicated serious habitat degradation in the basin, especially in lake areas, from 1980 to 2015, and the spatial variation in habitat quality decreased extensively, especially from 2005 to 2015. Decreased values of habitat quality in lake areas during past decades suggested that the conservation strategies to natural reserve of lake is not effective. Overall, the habitat quality of most parts of the basin was very low, ranging from 0.10 to 0.20. In addition, only small regions possessed relatively higher habitat quality values, such as hilly areas and lake areas. The spatial patterns of habitat quality were influenced by the physical geographical factors and socio-economic activities. The patterns of habitat quality were significantly related to the physical geographical factors, including DEM (r = 0.50, p < 0.01), slope (r = 0.42, p < 0.01) and NDVI (r = 0.42, p < 0.01), as well as the socio-economic factors, such as land-use intensity (r = −0.88, p < 0.01) and population density (r = −0.32, p < 0.01). The distribution of gross domestic product (GDP) was not correlated with habitat quality. As a surrogate for biodiversity, this approach could be applied for the continuous monitoring of biodiversity in other areas, especially areas that lack biodiversity distribution data; thus, this method can serve as a useful tool for decision makers.