阻力赋值对景观连接模拟的影响

Effects of changing cost values on landscape connectivity simulation

景观连接度是研究景观结构和生态过程互馈关系的重要内容。在最小成本路径模拟中整合图论理论可有效辨识、评价斑块之间的潜在连接,近些年逐步应用于景观连接模拟、生态网络构建等研究中。理论上,模型的重要参数之一,生物体通过不同景观单元的阻力系数应根据观测与实验等实证研究获取,但大多数情况下简化为土地适宜性评价结合专家经验为土地利用/覆盖类型打分,存在一定主观性与不确定性。因此,设计了1个三因素(阻力赋值方式、景观粒度和景观整体破碎度)的析因实验,以SIMMAP2.0景观中性模型产生的8个模拟景观为对象,研究不同的景观格局下,阻力赋值方式对连接模拟的影响;探讨、总结经验赋值带来的不确定性。结果表明,这3个因素均对景观连接模拟产生显著影响,并存在一定交互作用;阻力赋值绝对大小不会对模拟产生影响;而赋值倾向性能够显著改变最小成本路径的空间位置,并且这种影响程度依赖于景观粒度大小,而与景观整体破碎度交互关系不显著。针对阻力赋值方式与景观结构特征交互作用下连接模拟的规律性变化,提出一些建议,以提高连接模拟的准确性。

Landscape connectivity is a critical concern for the study of interactive relationships between landscape structure and ecological processes. A combination of Least-cost Path （LcP） analysis and graph-theoretic techniques can provide a more efficient approach to identifying and assessing potential links in heterogeneous landscapes. This method has been increasingly used in landscape connectivity and ecological network simulation. In the modeling process, the evaluation of cost surfaces ideally should be based on field survey and/or experimental data; however, it is time-consuming and costly to collect these data. To simplify the process, many connectivity modeling studies rely entirely or in large part on collective expert knowledge together with land suitability assessment. Different experts may assign different values, affecting the reliability of network simulation. Therefore, this study was set up to examine how a range of cost values （ representing variation in expert opinion） influence landscape connectivity simulation and how this impact reacts to landscape spatial configuration. We designed a factorial experiment with three factors： cost values, spatial grain size, and landscape fragmentation. Firstly, artificial landscapes were generated in SIMMAP2.0. They comprised five land cover types with controlled area coverage： 20% habitat sources （S）, 40% unsuitable land （U）, and 5%, 15%, and 20% moderately suitable land types （A, B, and C respectively）. We then faetorially set two levels of fragmentation, a clumped configuration （P_simmap=0.575） and fragmented landscape （P_simmap=0.3）, and four levels of spatial grain size （ 1 m, 5 m, 10 m,and 20 m） in the factorial experiment. A total of 8 combinations were replicated 5 times for the simulation analysis. We generated 4 groups of cost value scenarios, Ⅰ ： equidistant ; Ⅱ ： close to cost values of habitat sources （ 1 assigned as cost value of habitat sources） ; Ⅲ ： close to cost values of the unsuitable landscape matrix ; and Ⅳ ： close to middle values. Each group scenario has 3 levels; for example, group IV has rankings of 1 to 10, 1 to 100, and 1 to 1000, respectively. Within each group, the values were assigned to test whether and to what extent the absolute values would change the spatial location of simulated LoPs. Between group scenarios, the cost values were set to investigate whether and to what extent the different scenarios would change the spatial location of simulated LoPs. Our study found that these three factors all have a significant impact on the pathways simulation, and, indeed, there are some interactions between factors. The spatial location of LoPs was insensitive to the absolute values or the range of cost values as long as the relative ranking scenarios remained constant. However, they were significantly affected by the four different group scenarios of cost values assigned to land use/cover types, and the degree of influence is highly dependent on the spatial grain size of the landscapes, but less so with landscape fragmentation. To deal with the uncertainty and subjectivity brought by cost values and their interactions with landscape structure factors, this study provided some suggestions for an appropriate selection of cost values in order to increase network modeling rigorousness.