Abstract
Understanding changes in habitat quality and the driving forces of these changes at landscape scales is a critical part of effective ecosystem management. The present study investigated spatiotemporal habitat quality dynamics and related driving forces from 2005 to 2015 in the upper basin of Miyun Reservoir in North China by constructing an effective framework integrated InVEST and binary logistic regression models. This framework expanded the driving force analysis into an assessment of changes in habitat quality and intuitively verified the effectiveness of relevant environmental policies. The proposed method of combining the equidistant random sampling method and the method of introducing spatial lag variables in logistic regression equation can effectively solve spatial autocorrelation with a large enough number of sampling points. Overall, habitat quality improved during the study period. Spatially, a concentrated loss of habitat occurred in the southeastern part of the basin between the reservoir and mountainous areas, while other areas gradually recovered. Driving force analysis showed that lower elevation mountain land, gentle slopes, locations near rural land or roads, larger areas of grain cultivation, and areas with little population change had a higher likelihood of having changed in habitat quality in the upper basin of Miyun Reservoir. These results suggested that the present policy of protecting the ecosystem had a positive effect on improving habitat quality. In the future, the human activity management related to habitat quality needs to be strengthened. The present study would provide a reference for land use policy formulation and biodiversity conservation.
Highlights
The extent and quality of habitat conditions are often used as proxies for biodiversity [1]
The objectives of the present study were (1) to quantitatively assess habitat quality in the upper basin of Miyun Reservoir using the spatially explicit habitat quality module of the Integrated Valuation of Environmental Services and Tradeoffs (InVEST) model while using data covering 2005 to 2015, and (2) to identify the main forces driving the changes in habitat quality with an improved logistic regression model in the upper basin of this reservoir
Effective ecosystem management will require an understanding of how habitat quality is changing over space and time and of the driving forces of habitat quality change
Summary
The extent and quality of habitat conditions are often used as proxies for biodiversity [1]. Under the influence of climate change and ecosystem management policies (such as protection of natural forest, and other environmental development projects), assessing changes in habitat quality and exploring their driving forces is a critical need, especially in important ecologically sensitive areas such as drinking water sources. The model-based analysis methods mainly investigate the distribution of species and their environmental conditions to determine habitat suitability. The InVEST model can analyze impacts of land use and land management on species habitat quality [12]. The InVEST model has successfully been used in many land use change studies to assess the effects of the results of change on habitat quality [1,8,12,14,15]. Most previous studies have focused on evaluating changes in habitat quality but have not explored their driving forces
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