基于地理加权回归模型探究环境异质性对秦岭大熊猫空间利用的影响

环境异质性对野生动物分布的影响具有明显的空间不均匀性。传统分析中多采用经典线性回归模型来量化野生动物分布与环境变量之间的关系,难以准确反映物种-环境关系的空间异质特征。地理加权回归(GWR)是近年来提出的一种新的空间分析方法,通过将空间结构嵌入线性回归模型中,以此来探测空间关系的非均匀性。以秦岭大熊猫为例,应用GWR模型分析大熊猫空间分布与环境异质性特征之间的潜在关系,并同经典的全局最小二乘回归法(OLS)进行比较。结果表明,GWR模型的AIC、R~2和校正R~2均显著优于OLS模型,GWR模型的局部回归系数估计能够更加深刻地揭示大熊猫空间分布与环境变量间的复杂空间关系,且GWR模型能够为物种的科学保护提供更加有效的理论支撑。因此,GWR模型可为探究物种-环境关系的空间异质特征提供一种新的方法,在物种栖息地选择与利用研究中具有一定的应用前景。

Using geographically weighted regression to explore the effects of environmental heterogeneity on the space use by giant pandas in Qinling Mountains

The effects of environmental heterogeneity on the distribution of wildlife are obviously uneven over space. Traditional approaches, such as classical linear regression model, are unable to accurately depict the spatial variations in species-environment relationship. Geographically weighted regression (GWR) is a newly proposed spatial regression method that shows promise in detecting spatial variability of environmental relationship through embedding spatial structure into linear regression model. Taking giant pandas (Ailuropoda melanoleuca) in Qinling Mountains as an example, we used the GWR method to analyze the potential relationship between the spatial distribution of giant pandas and environmental heterogeneity. We also compared the results of GWR with those of the classical global ordinary least squares regression (OLS). The results show that AIC, R² and adjust R² of GWR model were significantly better than those of OLS model. Local regression coefficients of GWR model can reveal the complex spatial relationship between the spatial distribution of giant pandas and environmental variables, and provide more effective theoretical support for the scientific protection of species. Therefore, the GWR is an effective tool for exploring the spatial heterogeneity of species-environment relationship, which would have a wide application prospect in the research of species habitat selection and utilization.