Incorporating Landscape Scaling Relations into Catchment Classification for Optimizing Ecological Management

The landscape scaling relation challenges catchment ecological management; however, how the scaling relations change among naturally and anthropogenically differentiated catchments is still unknown. In this study, approximately 1500 soil samples were determined; more than 800 households were surveyed; and the landscape pattern was investigated in 120 sub-catchments of a subtropical Chinese urbanizing agricultural catchment. A scalogram and a coefficient of variation of the commonly used landscape metrics were estimated among various grain sizes, to quantify the Strength of Landscape Scale Effects (SSE) among sub-catchments. Natural and anthropogenic determinants for the SSE were determined. Then, the determinants incorporating landscape scaling relation were applied to classify the sub-catchments through the k-means clustering analysis. The SSE presented different spatial heterogeneity across the 120 sub-catchments and was not expectedly related to the scaling relation over the entire catchment, especially for the Contagion index and Shannon’s Evenness Index. The SSE were significantly related to natural and anthropogenic factors including the soil sand content, the population density, the relief ratio, and the ratio of arable land to woodland. The four factors combing with landscape scaling relations contributed to the four gratifying convergent categories for the 120 sub-catchments. Category I with a large relief and less anthropogenic disturbance had higher spatially non-stationary relationship, while categories II, III, and IV, with varying degrees of relatively small relief and strong intensities of anthropogenic disturbance, had a lower spatial heterogeneity of the landscape scaling relation. The results implied that category I was required to strengthen environmental protection of spatial differences, and categories II, III, and IV could ignore the landscape scale effects and even upscaling management to save management resources when carrying out ecological management within. Our findings could minimize uncertainty in ecological planning and provide opportunities for the application of multiple-scale management.