城市化背景下景观格局对天津市生态系统服务供需匹配的影响

揭示城市化驱动下景观格局指数变化与生态系统服务供需匹配的交互关系,对于优化景观生态规划以及促进城市生态系统的可持续发展具有重要作用。以天津市为例,运用GIS空间分析、双变量空间自相关与随机森林回归等方法,评估了碳储量、生境质量与空气净化服务供需匹配度的空间分布特征,分析了景观格局指数沿城市化梯度的变化趋势,进而探讨了景观格局指数与生态系统服务供需匹配度的交互关系。结果表明:(1)天津市碳储量、生境质量与空气净化服务的供给和需求呈现出明显的空间错位关系,城市化综合发展水平对典型生态系统服务的供需匹配度具有显著负面影响;(2)景观水平上的景观格局指数沿城乡梯度呈现非线形的变化趋势,最大斑块指数、Shannon多样性指数以及斑块密度等多个指数在城市化综合发展水平20%-30%左右出现明显拐点;类型水平上的建设用地最大斑块指数与斑块内聚力指数等呈现增长态势,而林地与耕地的最大斑块指数等持续降低;(3)景观格局指数对生态系统服务供需匹配度的解释力度在类型水平上的贡献整体大于景观水平。研究结果可为天津市景观生态规划的管理与决策提供有益参考,有助于城市生态系统服务供需关系的协调与平衡。

Influence of landscape pattern on ecosystem service supply-demand mismatch in Tianjin within the context of urbanization

Ecosystem services serve as crucial linkages connecting biological processes and human well-being, providing a scientific basis for a comprehensive understanding of environmental issues and sustainability challenges. However, unprecedented urbanization inevitably disrupts the balance of urban ecosystem services, elevating urban fragility and risk exposure. The emergence of landscape patterns reflects the intricate interaction between humans and nature, visually illustrating the impact of urbanization on ecological processes. Therefore, it is imperative to uncover the effects of changes in landscape patterns on the ecosystem services supply-demand mismatches within the urbanization context. This understanding plays a critical role in optimizing landscape ecological planning and promoting the sustainable development of urban ecosystems. Taking Tianjin as an example, this study conducted a comprehensive analysis employing GIS spatial analysis, bivariate spatial autocorrelation, and random forest regression methods. Specifically, we quantified spatial distribution of three selected ecosystem services, including carbon storage, habitat quality, and air purification. In addition, we analyzed the changing trends of landscape pattern indices along the urbanization gradient, followed by an exploration of the interactive relationship between landscape pattern indices and the ecosystem services supply-demand mismatches. The results showed that: (1) there is a significant spatial mismatches between the supply and demand of carbon storage, habitat quality, and air purification. Meanwhile, the comprehensive level of urbanization had a significantly negative impact on ecosystem services supply-demand mismatches. (2) At the landscape level, the landscape pattern indices such as the largest patch index, Shannon diversity index, and patch density exhibited a non-linear trend along the urbanization gradient, with a notable inflection point at approximately 20% to 30% of the comprehensive urban development level. At the class level, the largest patch index and patch cohesion index of construction land showed a growing trend, while the largest patch index of forest and farmland continued to decline. (3) The explanatory power of landscape patten indices on ecosystem services supply-demand mismatches contributed more at the class level than at the landscape level. In conclusion, this study focused on analyzing the impact of landscape pattern changes on ecosystem services supply-demand mismatches within the urbanization gradient context. Our findings provided valuable insights for developing targeted optimization strategies to address the supply-demand mismatches of ecosystem services in municipal areas, thereby promoting urban resilience and sustainability. The multi-step approach employed in this study contributes to urban planning and decision-making, ensuring effective implementation of the ecosystem service concept in practice.