基于成本收益分析的生境网络优化——以苏锡常地区白鹭为例

A cost-benefit analysis approach to habitat network optimization: a case study of the little egret( Egretta garzetta) in the Su-Xi-Chang area

引入经济学成本收益分析方法对于生境网络优化保护具有重要现实意义。以苏锡常地区白鹭生境网络优化为例,通过分别构建研究区生境网络保护成本与提升收益测算体系、模拟多种情景和优化方案、成本收益分析的方法,实现了满足不同约束条件的优化方案。结果表明:(1)最大收益累积情景下的方案在总收益最高的基础上,实现了保护总成本较低的目的;最佳效益累积情景下的方案在研究区土地资源有限和保护总成本最低的基础上,实现了优化成效最佳的目的;(2)最佳效益累积情景下的方案在实现成本最低的基础上,兼顾到了优化成效的最大化,适宜于土地资源稀缺且城市化快速发展地区;(3)有限效益累积情景作为最佳效益累积情景的基本模式,其优化方案在一定范围内可以兼顾到成本较低和成效较高。将经济学与生态学相结合的网络优化方法,综合考虑了研究区的生态、经济和社会等现实因素,极大的提高了网络优化方案操作的可行性,其思路和方法拓展了网络优化研究视角。但诸多实现因素也决定了成本与收益体系的构建还处于不断完善之中。

The economic approach of cost-benefit analysis plays an important role in analyzing biodiversity conservation, especially in rapidly urbanizing areas. In the existing Chinese academic literature, cost-benefit analyses have been applied in studies on habitat conservation, ecological land protection, and ecological networks. The present study addresses a gap in the research, by determining the effects of a cost-benefit analysis approach on habitat network optimization. Furthermore, this study uses a cross-disciplinary approach between methodologies in economics and ecology, to examine the impacts of the economic approach and ecological processes on network optimization methodology. As a case study, we focused on the Su- Xi-Chang area of the Yangtze River Delta Region, with the little egret （ Egretta garzetta ） as a regional representative species. We hypothesized that adding new habitat patches would improve network connectivity, and help with network optimization. The study assessed the size and the land use type of 35 identified habitat patches, which were the prior research results. The cost of avoiding the transfer from the current habitat into other land use types, especially construction land, was treated as the protect-cost, which was derived from local prices for land acquisition. From the perspective of network structure, the benefit of habitat network connectivity improvement was represented as a comprehensive indicator generated from three different indexes. These indexes included the number of newly added corridors, the betweenness, and the node degree. The protect-cost of each habitat patch could be matched to its corresponding benefit value for different purposes. The study design considered five different conditions and six scenarios. Scenario 1 was formed from the perspective of minimum cost accumulation, whereas scenarios 2 to 5 focused on maximum benefit accumulation, best effectiveness accumulation, and two limited effectiveness accumulations, respectively. Scenario 6 was the ideal condition meaning with all 35 habitat patches being added as the ideal result of habitat network optimization. The results indicate that （ 1 ） scenario 2 of maximum benefit accumulation resulted in network optimization with a lower total cost compared to scenario 6. Scenario 3 of best effectiveness accumulation resulted in network optimization with the least protected area, and associated cost. （2） The effects of the scenarios of best effectiveness accumulation, as well as the one of minimum cost accumulation （i.e., scenario 3 and scenario 1, respectively） were similar. However, the total cost of scenario 3 was only 74 percent of that of scenario 1. This indicates that scenario 3 of best effectiveness accumulation maximized effectiveness with the least cost, and was suitable for rapidly urbanizing areas with scarce land resources. （3） The effects of the two scenarios of limited effectiveness accumulation, （i.e., scenario 4 and scenario 5 ） were in between the effects of scenario 1 of minimum cost accumulation and scenario 2 of maximum benefit accumulation. This indicates that all scenarios of limited effectiveness accumulation would realize the aim of better effectiveness with lower cost. Furthermore, it indicated that scenario 3 of best effectiveness accumulation was gained under special circumstances. In economics, this phenomenon is referred to as the marginal benefit. Overall, the combined method detailed here, along with ecological, economic, and social factors, contributes to habitat network optimization, and highlights the methodology of network optimization.