Identification of Ecological Risk “Source-Sink” Landscape Functions of Resource-Based Region: A Case Study in Liaoning Province, China

Abstract

Ecological risk assessment plays an important role in ecosystem management and conservation. Conventional landscape-level assessment can only estimate the ecological risk level. It does not define ecological risk types, resulting in a lack of targeted regulation methods. This study establishes a model for identifying ecological risk-related “source-sink” landscape functions according to (1) “source-sink” landscape theory, (2) the responses of landscape types to ecological risks, and (3) the key influences on ecological risk. Four ecological risk “source-sink” landscape functions were mapped as a grid to understand their distribution. Natural and human activity factors were analyzed to determine their effects. After comprehensively considering the ecological risk levels, types of ecological risk, “source-sink” landscape functions, and their influencing factors, six principles and twenty-four targeted regulation strategies were proposed. Take the Liaoning province, China, as an example. The results prove that more than 80% of the grids were affected by the ecological risk “sink” landscape function for different and multiple ecological risks in the study area. Landscapes with the “source” function were mainly located in central cities and coastal areas. About 65% of the grids with “sink” landscape functions had medium, moderate-high, and high ecological risks. More than 75% of the grids with “source” landscape functions had medium, moderate-low, and low ecological risks. Local terrain features, vegetation, and climate were closely related to the “source” or “sink” landscape function of a grid. The land use type converted to artificial surface had the highest driving effects (q value) on multiple ecological risk “source-sink” landscape functions, and had a significant difference between other factors. The driving effects of land use type converted to artificial surface and road network density gradually increased with the risk level. The influences of GDP and population density gradually weakened with the level. The influence of interaction between any two factors was stronger than the influence of a single factor on ecological risk. The proposed assessment model can help to identify specific ecological risk at the grid level, and combined with the regulation strategy, the scientific basis can be provided for the regulation and management of different ecological risks.