Application of the MaxEnt model in improving the accuracy of ecological red line identification: A case study of Zhanjiang, China

Abstract

China's Ecological protection Red Lines (ERLs) policy has proven effective in protecting ecological resources, restoring ecosystems and promoting regional ecological civilization. However, the existing methods for identifying high-value ecological conservation land are insufficiently less precise and quantitative, which affects the implementation of the ERL policy. In this study, the Maximum Entropy (MaxEnt) model was introduced into the identification work of ERLs. This paper began with an assessment of the ecological status of Zhanjiang based on the methodology for assessing Ecosystem Services (ESs) function in the “Guidelines for the Delineation of Ecological Protection Red Lines”. On this basis, the MaxEnt model was used to predict the extent of potential high-value ecological protection land through sample training and quantify the contribution of impact factors common to ecological planning work. The following conclusions were obtained: The regional ecosystem of Zhanjiang had a good function of soil and water conservation services and a poor function of windbreak and sand-fixation services. The importance of ESs was strongest in western Leizhou and the southeast coastal region of Xuwen and weakest in Wuchuan and central Xuwen. Among the contributions of influencing factors to identify high-value ecological land, natural factors were the dominant influencing factors, and anthropogenic factors were the secondary influencing factors. Meteorological factors represented by dryness made the greatest information contribution to identifying high-value ecological protection land at 42.0%, followed by surface vegetation biomass (represented by NPP) at 37.1%. The remaining impact factors contributed less than 10.0%, and the distance from the road factor contributed 0 in this prediction. The MaxEnt model, when applied to the delineation of ERLs, has been proven to be effective in improving the accuracy of identifying high-value ecological land and discovering areas with conservation value that traditional assessment methods have overlooked. It will help to provide a reasonable basis for the identification of ERLs and promote the implementation of ERL policy.