# Set up linear landscape disp_prob = 0.5*dgeom(0:(num_patches-1),prob=dispersal_probability_param) #disp_prob = 0.5*ddisclap(0:(num_patches-1),p=dispersal_probability_param) #disp_prob = c(0.25,0.125,0.0625,0.03,0.02,0.0125) #disp_prob = c(0.4,0.03,0.02,0.01,0.0001,0.00001)#rep(0.25,3) connectivity_matrix = matrix(0,num_patches,num_patches) diag(connectivity_matrix) = 2*disp_prob[1] for (index in 1:length(disp_prob)){ connectivity_matrix[row(connectivity_matrix) == col(connectivity_matrix)+index] = disp_prob[index+1] connectivity_matrix[row(connectivity_matrix) == col(connectivity_matrix)-index] = disp_prob[index+1] } connectivity_matrix = rbind(matrix(0,nrow=extra_patches,ncol=num_patches),connectivity_matrix) connectivity_matrix = rbind(connectivity_matrix,matrix(0,nrow=extra_patches,ncol=num_patches)) connectivity_matrix = cbind(matrix(0,ncol=extra_patches,nrow=num_patches+2*extra_patches),connectivity_matrix) connectivity_matrix = cbind(connectivity_matrix,matrix(0,ncol=extra_patches,nrow=num_patches+2*extra_patches)) #connectivity_matrix[1,1]=0.75 #connectivity_matrix[10,10]=0.75 #image(t(connectivity_matrix)) connectivity=connectivity_matrix #connectivity_matrix avg_disp_dist = sum(disp_prob*(0:(num_patches-1))) connectivity_matrix[is.na(connectivity_matrix)]=0