Effects of changing cost values on landscape connectivity simulation

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 factorially 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 Lc Ps. 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 Lc Ps. Our study found that these three factors all