## Introduction

This tutorial shows the basics of how to use the package to calculate and visualize several metrics using map based inputs. It utilizes the package’s built-in data, which is the same example data used in Fletcher et al. (2019).

## Libraries

# First step is to load the libraries. Not all of these libraries are stricly
# needed; some are used for convenience and visualization for this tutorial.

# "Load" the data. In this case we are using data built into the package.
# In practice, users will likely load raster data using the raster() function
# from the raster package.
res_data <- samc::example_split_corridor$res abs_data <- samc::example_split_corridor$abs
init_data <- samc::example_split_corridor$init # To make things easier for plotting later, convert the matrices to rasters res_data <- samc::rasterize(res_data) abs_data <- samc::rasterize(abs_data) init_data <- samc::rasterize(init_data) # Plot the data and make sure it looks good. The built-in data is in matrices, # so we use the raster() function to help with the plotting. Note that when # matrices are used by the package, it sets the extents based on the number of # rows/cols. We do the same thing here when converting to a raster, otherwise # the default extents will be (0,1) for both x and y, which is not only # uninformative, but can result in "stretching" when visualizing datasets # based non-square matrices. plot(res_data, main = "Example Resistance Data", xlab = "x", ylab = "y", col = viridis(256)) plot(abs_data, main = "Example Absorption Data", xlab = "x", ylab = "y", col = viridis(256)) plot(init_data, main = "Example Occupancy Data", xlab = "x", ylab = "y", col = viridis(256)) ## Create the samc Object # Setup the details for our transition function rw_model <- list(fun = function(x) 1/mean(x), # Function for calculating transition probabilities dir = 8, # Directions of the transitions. Either 4 or 8. sym = TRUE) # Is the function symmetric? # Create a samc-class object using the resistance and absorption data. We use the # recipricol of the arithmetic mean for calculating the transition matrix. Note, # the input data here are matrices, not RasterLayers. samc_obj <- samc(res_data, abs_data, model = rw_model) # Print out the samc object and make sure everything is filled out. Try to # double check some of the values, such as the nrows/ncols of the landscape # data. The dimensions of the matrix (slot p) should be the number of non-NA # cells in your data +1. In this case, our data has 2624 non-NA cells, so the # matrix should be 2625 x 2625 str(samc_obj) #> Formal class 'samc' [package "samc"] with 12 slots #> ..@ data :Formal class 'samc_data' [package "samc"] with 3 slots #> .. .. ..@ f :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots #> .. .. .. .. ..@ i : int [1:21612] 0 1 115 116 117 0 1 2 116 117 ... #> .. .. .. .. ..@ p : int [1:2625] 0 5 11 17 23 29 35 41 47 53 ... #> .. .. .. .. ..@ Dim : int [1:2] 2624 2624 #> .. .. .. .. ..@ Dimnames:List of 2 #> .. .. .. .. .. ..$ : NULL
#>   .. .. .. .. .. ..$: NULL #> .. .. .. .. ..@ x : num [1:21612] 1 -0.226 -0.138 -0.163 -0.104 ... #> .. .. .. .. ..@ factors : list() #> .. .. ..@ t_abs: num [1:2624] 2e-04 2e-04 2e-04 2e-04 2e-04 ... #> .. .. ..@ c_abs: num[0 , 0 ] #> ..@ conv_cache: NULL #> ..@ model :List of 4 #> .. ..$ fun :function (x)
#>   .. .. ..- attr(*, "srcref")= 'srcref' int [1:8] 2 24 2 44 24 44 2 2
#>   .. .. .. ..- attr(*, "srcfile")=Classes 'srcfilecopy', 'srcfile' <environment: 0x563e5451e848>
#>   .. ..$dir : num 8 #> .. ..$ sym : logi TRUE
#>   .. ..\$ name: chr "RW"
#>   ..@ source    : chr "SpatRaster"
#>   ..@ map       :S4 class 'SpatRaster' [package "terra"]
#>   ..@ crw_map   : NULL
#>   ..@ names     : NULL
#>   ..@ clumps    : int 1
#>   ..@ override  : logi FALSE
#>   ..@ solver    : chr "direct"
#>   ..@ threads   : num 1
#>   ..@ .cache    :<environment: 0x563e581ebb90>

## Basic Analysis

# Convert the initial state data to probabilities
init_prob_data <- init_data / sum(values(init_data), na.rm = TRUE)

# Calculate short- and long-term mortality metrics and long-term dispersal
short_mort <- mortality(samc_obj, init_prob_data, time = 4800)
long_mort <- mortality(samc_obj, init_prob_data)
long_disp <- dispersal(samc_obj, init_prob_data)
#>
#> Performing setup. This can take several minutes... Complete.
#> Calculating matrix inverse diagonal...
#>
Computing: 50%  (~10s remaining)
Computing: 100% (done)
#>
Complete
#> Diagonal has been cached. Continuing with metric calculation...

## Visualization

# Create rasters using the vector result data for plotting.
short_mort_map <- map(samc_obj, short_mort)
long_mort_map <- map(samc_obj, long_mort)
long_disp_map <- map(samc_obj, long_disp)

# Plot the mortality and dispersal results
plot(short_mort_map, main = "Short-term Mortality", xlab = "x", ylab = "y", col = viridis(256))
plot(long_mort_map, main = "Long-term Mortality", xlab = "x", ylab = "y", col = viridis(256))
plot(long_disp_map, main = "Long-term Dispersal", xlab = "x", ylab = "y", col = viridis(256))