基于不同网络构建方法的生境网络优化研究——以苏锡常地区白鹭为例

Habitat network optimization based on different network building methods:a case study of Egretta garzetta in the Su-Xi-Chang area

生境网络在支持物种长期存活中具有重要意义,由于受人类社会经济频繁活动的影响,迫切需要优化。选取城市化快速发展的苏锡常地区为研究区域,以湿地代表性鸟类白鹭为目标物种,利用2000、2010年土地利用/覆被数据,采用两种不同的网络构建方法,识别出恢复或新建生境节点或斑块,以及基于网络连接度并兼顾了集合覆盖问题遴选出重要新增生境节点,得到生境网络优化方案。结果表明:(1)恢复型生境节点对应的生境斑块面积在10—50 hm^2之间,以乔木林地为主;新建型生境节点对应的生境斑块是乔木林地和湖泊水库与河流兼而有之,均满足白鹭生境地类要求;优化的生境网络连接度较高、整体结构状况最好,兼顾了资源有限性下经济效益和生态效益最大化,与历史观测点实际情况较为吻合,可作为优化方案。(2)相同的生境斑块格局受生境格局时空变化、生态过程模拟方法及网络优化评判标准的影响,在采用不同网络构建方法时,会得到不同的网络结构及网络优化结果。采用时空格局变化与不同生态过程模拟相结合的网络优化方法,有助于分析格局-生态过程及网络结构变化情况,其思路为网络优化方法进一步深入研究提供了借鉴。

Habitat networks are important to the long-term survival of many species. However, due to frequent human disturbance, the habitat networks in most developing urban regions of China desperately need rebuilding or optimization. Identification of potential conservation areas is usually based on land use-and land cover data from a single year~ multiple years of data are rarely used. AdditionaLly, in majority of the studies, only one network building method is applied to optimize networks; for example the least-cost path method, the Spatial [.inks Tool or the LARCH model. Furthermore, studies which combine spatio-temporal analysis and different network building methods to optimize networks are rare. The aim of this study was to determine：how spatio-temporal analysis and different network building methods affect network optimization and how combining spatio-temporal analysis and different network building methods can contribute to the methodology of network optimization. In this case study, the Su-Xi-Chang area of the Yangtze River Delta Region was taken as the study area and Egretta garzetta was selected as a representative species of wild animal. Egretta garzetta distribution was identified and recorded in a grid using ArcGIS software along with land use/land cover data from 2010 and 2000. Potential corridors were identified using the least-cost path method and a 10 kin-radial-line path method, based on landuse/land cover data from 2010 and 2000, respectively. The potential corridors and habitats identified using these two methods and years were overlaid. Habitats that existed in 2000 but not in 2010, and were linked by both types of potential corridors were identified. These were named Rebuild Potential Habitats （RP habitats）. Habitats that did not exist in either 2000 or 2010, but were at the junctions of potential corridors were also identified ; these were named Newly-added Potential Habitats （NP habitats）. The landscape connectivity indices of RP habitats, NP habitats and their comprehensive values were calculated and arranged. The habitats that had higher comprehensive values were selected from the perspective of a Set Covering Problem. These selected habitats along with habitats from 2010 formed the optimized habitat network. 17 RP habitats and 18 NP habitats were identified. The identified potential habitats and the existing habitats from 2010 formed network scenarios Ⅱ , Ⅲ and Ⅳ, respectively. The RP habitats had areas of approximately 10--50 hectares. The land-use type for most RP habitats was arboreal forest. The land-use types for most NP habitats were arboreal forest, lakes, ponds, and rivers. These have suitable conditions for Egretta garzetta. There were 14 RP habitats and 12 NP habitats left after selection. The selected potential habitats and the existing habitats in 2010 formed network scenario V. Comparison of three network structure indices （α,β, and γ） for network scenarios Ⅱ , Ⅲ, Ⅳ, and V showed that scenario network V offered the maximum economic and ecological benefits from a limited land area. The network structure connectivity in 2010 could emulate that of 2000, even if RP habitats were rebuilt in the same pattern as 2010. This indicates that spatio-temporal changes had an obvious effect on ecological processes and patterns. Network structure is not necessarily optimized even if all RP habitats and NP habitats meet the conditions Of the different network construction methods. This suggested that the chosen ecological process model or network optimization criteria have important influences on the results. The method developed in this study was helpful in analyzing the relationships among spatio-temporal patterns, changes in network structure and ecological processes and patterns. Our analysis also highlights on the methodology of network structure optimization.