Spatial planning constraints will mitigate the fragmentation trajectory of natural and semi-natural landscapes: A case of Lushan City, China

Context The biodiversity of ecosystems is under severe threat from landscape fragmentation resulting from rapid urbanization. To understand the future trajectory of landscape patterns, it is imperative to examine the impact of current spatial planning constraint policies on the preservation of natural and semi-natural landscapes, as well as the promotion of ecosystem services and sustainability. Methods We employed a Patch-generating Land Use Simulation (PLUS) model to simulate and predict the land use and landscape pattern alterations in Lushan City under two distinct scenarios: "Planning Constraints (PC)" and "Natural Development (ND)". Subsequently, we identified an appropriate Landscape Fragmentation Index (LFI) that effectively captures the essence of fragmentation. To determine the optimal scale, we adopted an experimental approach using both the Moving Window (MW) method and the semi-variance function. By constructing a spatiotemporal sequence of LFI and the following trend analysis, we selected the Potential Fragmentation Areas (PFA) with significant tendencies toward landscape fragmentation. Results The spatial planning constraints 1) would prevent the encroachment of construction land into 2.14 km 2 of cropland, 0.21 km 2 of forest, and 0.13 km 2 of grassland; 2) shift the highly fragmented area from the northeastern portion of Lushan to the planned area defined by the development boundary; 3) will mitigate and decelerate the trend of landscape fragmentation in natural and semi-natural landscapes, decrease PFA by 7.74 km 2 and preserve 15.61 km 2 of natural landscapes. Conclusions Spatial planning constraints have effectively controlled the expansion of the construction land. This control mechanism has greatly protected natural and semi-natural landscapes and ensured the conservation of habitats. Moreover, it provides an opportunity to incorporate landscape fragmentation risk considerations into future eco-management optimization.