Here, we quickly show you how we created the dynamic landscapes from Tutorial 4 using the package NLMR (Sciaini et al. 2018). For the road scenario, we used a random cluster nearest-neighbour algorithm to generate the neutral landscape, and for the urbanisation scenario, we used the midpoint displacement algorithm.

# 1 Getting started

## 1.1 Create RS directory

First of all, load the required packages and set the relative path from your current working directory to the RS directory.

# load packages
library(purrr)
library(raster)
library(rasterVis)
library(SDMTools)
library(NLMR)
library(landscapetools)

# relative path from current working directory:
dirpath = "Tutorial_03/"

# 2 Create maps for road scenario

In this scenario, we envisioned a landscape mosaic with woodland patches and a grassland matrix. Over time, smaller woodland patches are being deforested and converted into grassland. Later, a corridor is cleaned from woodland patches and a road is built through the landscape.

## 2.1 Random clusters neutral landscape model

We create a landscape with four different land cover types using random cluster neutral landscape simulator from the NLMR package (Sciaini et al. 2018). As the simulator is stochastic, your landscape will look different every time you run the algorithm and also different from the landscape shown here. To obtain the same landscape as shown in this example, you have to set the seed for the random number generator. Maybe you also want to play around with some landscape settings.

We then save the landscape as ascii map in the Inputs folder of our working directory so that we can use it for running RangeshiftR simulations.

# Set this seed if you want to obtain the same landscape as shown here:
set.seed(54321)

r <- nlm_randomcluster(235,125, p=0.5, neighbourhood = 4, ai = c(0.12, 0.72, 0.08, 0.08), rescale = F)
show_landscape(r)

# write to ASCII:
r@file@nodatavalue <- -9999
writeRaster(r, format="ascii", filename = paste0(dirpath, "Inputs/map_01"), datatype = 'INT1S', overwrite = F)

Here, we chose the most abundance land cover type as the grassland matrix and all other land cover types as suitable woodland habitat patches. Then, we assign patch numbers and save the numbered patches as ascii.

rp <- r
rp[values(r)==2] <- 0
rp[values(r) %in% c(1,3,4)] <- 1
patches <- ConnCompLabel(rp)
writeRaster(patches, format="ascii", filename = paste0(dirpath, "Inputs/patches_01"), datatype = 'INT1S', overwrite = F)

## 2.2 Dynamic landscape maps: remove patches and build road

We create three more maps with increasing landscape conversion.

### 2.2.1 First step: remove small patches (with 5 or less cells)

First, smaller woodland patches are removed and converted to grassland matrix.

rm_patches <- as.integer(names(table(values(patches))[table(values(patches))<5]))

r1 <- r
r1[values(patches) %in% rm_patches] <- 2
patches1 <- patches
patches1[values(patches1) %in% rm_patches] <- 0
writeRaster(r1, format="ascii", filename = paste0(dirpath, "Inputs/map_02"), datatype = 'INT1S', overwrite = F)
writeRaster(patches1, format="ascii", filename = paste0(dirpath, "Inputs/patches_02"), datatype = 'INT1S', overwrite = F)

### 2.2.2 Second step: remove a corridor of smaller patches

Second, a corridor is cleared from woodland. For this, we arbitrarily define a horizontal line across the landscape and remove all patches that touch this line.

corridor <- c(40,45)
rm_patches <- as.integer(names(table(patches1[corridor[1]:corridor[2],])))

r2 <- r1
r2[values(patches) %in% rm_patches] <- 2
patches2 <- patches1
patches2[values(patches2) %in% rm_patches] <- 0
writeRaster(r2, format="ascii", filename = paste0(dirpath, "Inputs/map_03"), datatype = 'INT1S', overwrite = F)
writeRaster(patches2, format="ascii", filename = paste0(dirpath, "Inputs/patches_03"), datatype = 'INT1S', overwrite = F)

Last, we simulate a road that is being build through the cleared corridor. This road is generated as B-spline between the middle points of closest patches above and below the corridor.

ext <- extent(r2)
x <- seq(ext@xmax-ext@xmin)
upper <- as.matrix(r2)[ext@ymin:corridor[1],]
y_up <- apply(upper,2,FUN=function(x){
temp <- which(x!=2)
if(length(temp)>0) return(temp[length(temp)])
else return(ext@ymin)
})
lower <- as.matrix(r2)[corridor[2]:ext@ymax,]
y_lo <- apply(lower,2,FUN=function(x){
temp <- which(x!=2)
if(length(temp)>0) return(temp[1]+corridor[2])
else return(ext@ymax)
})
y <- (y_lo-y_up)/2+y_up

road <- loess.smooth(x, y, degree = 2, span = .25, evaluation = length(x))

r3 <- r2
for(col in 1:ncol(r3)){
r3[build:(build+1),col] <- 5
}
writeRaster(r3, format="ascii", filename = paste0(dirpath, "Inputs/map_04"), datatype = 'INT1S', overwrite = F)
# patches are the same as in the previous step

Now, we can plot all landscape maps as time series:

spplot(stack(r,r1,r2,r3))

# 3 Create maps for land abandonment scenario

In this scenario, we envisioned an urbanised landscape that gets abandoned over time with grassland and woodland expanding. The habitat maps should contain the following land cover types:

1. urban
2. sub-urban
3. grassland
4. woodland

## 3.1 Midpoint displacement neutral landscape model

We can create a landscape with smooth transitions between different landcover types using the midpoint displacement algorihtm the NLMR package (Sciaini et al. 2018). Again, this simulator is stochastic and will produce different landscapes every time you run it, unless you set a seed for the random number generator.

# Set this seed if you want to obtain the same landscape as shown here:
set.seed(765)

r <- nlm_mpd(ncol = 513, nrow = 513, roughness = 0.65, rand_dev = 2)
## Warning in nlm_mpd(ncol = 513, nrow = 513, roughness = 0.65, rand_dev = 2):
## nlm_mpd changes the dimensions of the RasterLayer if even ncols/nrows are
## choosen.
show_landscape(r)

## 3.2 Reclassify rasters

The midpoint displacement algorithm outputs a continuous map ranging 0-1. Now, we want to slice this landscape at different values to receive discrete patches of different land cover types. This can be done using a reclassification matrix. As the size of these patches should change dynamically over time, we need several reclassification matrices that define the thresholds for different time steps. This will require a little number crunching.

For convenience, we define a function that creates the required reclassification matrices. As arguments it takes the thresholds between 0-1 for each land cover type for the first and the last landscapes, and the desired number of time steps.

# Reclassification matrices
create_rclmatrices <- function(minmax_matrix,nr_dyntimes){
rcl_list <- list()
Nclasses <- nrow(minmax)
delta <- (minmax[,2]-minmax[,1])/(nr_dyntimes-1)
rcl_list[[1]] <- matrix(c(0,minmax[1:(Nclasses-1),1],minmax[,c(1,3)]),
ncol=3, byrow=FALSE)
if(nr_dyntimes>1){
for(i in 2:nr_dyntimes){
temp <- rcl_list[[i-1]][,2]+delta
rcl_list[[i]] <- matrix(c(0,temp[1:Nclasses-1],temp,minmax[,3]),
ncol=3, byrow=FALSE)
}
}
return(rcl_list)}

Now, we define the thresholds (upper bounds) of four land cover classes for the first and the last time step, and create reclassification matrices from them:

# Columns: (1) min / (2) max upper bounds, (3) class ID
minmax <- matrix(c(0.35,0.20,1,
0.50,0.35,2,
0.80,0.65,3,
1.00,1.00,4),
ncol=3, byrow=TRUE)

rcl_list <- create_rclmatrices(minmax,nr_dyntimes=4)

Now, we use the reclassify() function in the raster package to reclassify the continuous landscape map into a time series of land cover maps:

r_ts <- lapply(rcl_list, FUN = reclassify, x=r, include.lowest=TRUE)
r_ts <- stack(r_ts)
spplot(r_ts, col.regions=hcl.colors(nrow(minmax), "Terrain 2", rev=T), at = c(0,minmax[,3]))

## 3.3 Save maps

Write rasters to ASCII files for use in RangeshiftR simulations:

#assign a number to this landscape
land_nr <- 1
for (step in 1:length(rcl_list)) {
r_ts[[step]]@file@nodatavalue <- -9999
writeRaster(r_ts[[step]], format="ascii", datatype = 'INT1S',
filename = paste0(dirpath,"Inputs/habitat",land_nr,"_",step),
overwrite = F)
}

# References

Sciaini, Marco, Matthias Fritsch, Cedric Scherer, and Craig Eric Simpkins. 2018. “NLMR and Landscapetools: An Integrated Environment for Simulating and Modifying Neutral Landscape Models in R.” Methods in Ecology and Evolution 9 (11): 2240–8. https://doi.org/10.1111/2041-210x.13076.