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<div id="header">
<h1 class="title toc-ignore">Tutorial 2: Landscape-scale connectivity,
matrix permeability and dispersal behaviour</h1>
</div>
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<p>In this second example, <code>RangeShiftR</code> is used at the
landscape scale to model functional connectivity of a woodland network
for a hypothetical woodland species. The aims are:</p>
<ul>
<li>to illustrate how the platform can be used to investigate
connectivity issues as well as species spatial dynamics at local and
landscape scales;</li>
<li>to show how the platform can run a model as patch-based;</li>
<li>to show how additional complexity in the population dynamics and
dispersal behaviour can be incorporated;</li>
<li>and to show how the connectivity analyses can be dependent upon the
type of model and on the modelled dispersal behaviour.</li>
</ul>
<p>We want to reproduce Figure 3 of <span class="citation">Bocedi et al.
(2014)</span>. To this end, we run four different scenarios:</p>
<ol style="list-style-type: lower-alpha">
<li>Explicit sexual model. Constant per-step mortality probability of
<em>0.01</em>; individuals settle only if at least one individual of the
opposite sex is present in the patch (Figure 3b in the paper).</li>
<li>As in (a), but with different settlement rules. Females settle in
suitable patches, while males will settle only if at least one female is
present in the patch (Figure 3c in the paper).</li>
<li>Only-female model. Constant per-step mortality probability of
<em>0.01</em>; females settle in suitable patches (Figure 3d in the
paper).</li>
<li>As in (a), but with habitat-specific per-step mortality (Figure 3e
in the paper).</li>
</ol>
<p><span class="citation">Bocedi et al. (2014)</span> defined the
measures ‘final probability of occupancy’ and the ‘mean time to first
colonisation’ to illustrate the connectivity between the initial patch
and the rest of the woodland network. These measures allow rapidly
assessing the effects of landscape characteristics and species movement
abilities on connectivity and, importantly, also on the population
dynamics. Note that both measures represent multi-generation
connectivity.</p>
<div id="getting-started" class="section level1" number="1">
<h1><span class="header-section-number">1</span> Getting started</h1>
<div id="create-a-rs-directory" class="section level2" number="1.1">
<h2><span class="header-section-number">1.1</span> Create a RS
directory</h2>
<p>We need to set up the folder structure again with the three
sub-folders named ‘Inputs’, ‘Outputs’ and ‘Output_Maps’.</p>
<pre class="r"><code>library(RangeShiftR)
library(terra)
library(RColorBrewer)
library(viridis)
library(grid)
library(gridExtra)
# relative path from working directory:
dirpath = "Tutorial_02/"
dir.create(paste0(dirpath,"Inputs"), showWarnings = TRUE)
dir.create(paste0(dirpath,"Outputs"), showWarnings = TRUE)
dir.create(paste0(dirpath,"Output_Maps"), showWarnings = TRUE)</code></pre>
<p>Copy the input files provided for exercise 2 into the ‘Inputs’
folder. The files can be downloaded <a
href="files/Tutorial2_Inputs.zip">here</a>.</p>
</div>
<div id="landscape-parameters" class="section level2" number="1.2">
<h2><span class="header-section-number">1.2</span> Landscape
parameters</h2>
<p>We use a typical British lowland, agricultural landscape having small
fragments of woodland, as used by Forest Research, UK, in Watts et
al. (2010). The landscape map has an extent of <em>10km</em> by
<em>6km</em> and a resolution of <em>10m</em>. Land-covers were
aggregated into seven categories (Figure 3a in <span
class="citation">Bocedi et al. (2014)</span>). Similar to tutorial 1,
the map, <em>landscape_10m_batch.txt</em>, is a raster map with codes
for different land-cover types. Land-covers were aggregated into seven
categories which have to be given as sequential integer numbers,
starting from one:</p>
<ul>
<li>1 = semi-natural broad-leaved woodland</li>
<li>2 = planted/felled broad-leaved and mixed woodland, shrubs and
bracken</li>
<li>3 = heathland, marshy grassland</li>
<li>4 = unimproved grassland, mire</li>
<li>5 = planted/felled coniferous woodland, semi-improved grassland,
swamp</li>
<li>6 = improved grasslands, arable, water</li>
<li>7 = roads, buildings</li>
</ul>
<pre class="r"><code>landsc <- terra::rast(paste0(dirpath, "Inputs/landscape_10m_batch.txt"))
# Plot land cover map and highlight cells with initial species distribution - option 2 with categorical legend:
landsc.f <- as.factor(landsc)
# add the land cover classes to the raster attribute table
rat <- levels(landsc.f)[[1]][-2]
rat[["landcover"]] <- c("semi-natural broad-leaved woodland", "planted/felled broad-leaved and mixed woodland", "heathland, marshy grassland", "unimproved grassland", "planted/felled coniferous woodland", "improved grasslands, arable, water", "roads, buildings")
levels(landsc.f) <- rat
plot(landsc.f, axes = F, col=brewer.pal(n = 7, name = "Spectral"))</code></pre>
<p><img src="tutorial_2_files/figure-html/unnamed-chunk-3-1.png" width="672" /></p>
<p>The second text file, <em>woodland_1ha_patchIDs.txt</em>, contains
the patch-matrix landscape. It has the same extent and resolution as the
land-type map, and each cell contains a unique patch ID that indicates
to which patch it belongs. Patch number <em>0</em> designates the matrix
patch, i.e. all unsuitable habitat.</p>
<pre class="r"><code>patch <- terra::rast(paste0(dirpath, "Inputs/woodland_1ha_patchIDs.txt"))
# We can have a glimpse at how many cells the different patches contain:
table(values(patch))</code></pre>
<pre><code>##
## 0 1 2 3 4 5 6 7 8 9 10
## 585734 287 232 243 996 240 238 181 141 990 162
## 11 12 13 14 15 16 17 18 19 20 21
## 221 311 207 594 694 118 137 172 245 361 423
## 22 23 24 25 26 27 28 29 30 31 32
## 349 145 1141 138 401 280 336 706 1919 249 154
## 33 34 35 36 37 38 39 40 41 42 43
## 166 524 215 1277 383 735 113 1008 447 125 100
## 44 45 46 47 48 49 50
## 547 116 225 675 189 110 301</code></pre>
<pre class="r"><code># Plot the patches in different colours:
plot(patch, axes=F, legend=F,
col = c('black',rep(brewer.pal(n = 12, name = "Paired"),5))
) </code></pre>
<p><img src="tutorial_2_files/figure-html/unnamed-chunk-4-1.png" width="672" /></p>
<p>The last text file, <em>patch30.txt</em>, is a map that specifies the
patches that contain the initial distribution of the species. In our
case, this is only the patch with ID <em>30</em>.</p>
<pre class="r"><code>patch30 <- terra::rast(paste0(dirpath, "Inputs/patch30.txt"))
# Look at initial patch:
plot(patch30, type="continuous")</code></pre>
<p><img src="tutorial_2_files/figure-html/unnamed-chunk-5-1.png" width="672" /></p>
<p>We are ready to set up the landscape parameter object with these
maps, their respective resolutions, and the demographic density
dependence for all land cover types. In contrast to tutorial 1, we will
use a stage-structured population model here (defined below), so that
the values in <code>K_or_DensDep</code> will be used as the parameter
<em>1/b</em> in the population dynamics (see
<code>?StageStructure</code>), describing the strength density
dependence (in fecundity, development and survival).</p>
<p>We choose to define only ‘semi-natural broad-leaved woodland’ (code
1) as suitable for our species.</p>
<pre class="r"><code>land <- ImportedLandscape(LandscapeFile = "landscape_10m_batch.txt",
PatchFile = "woodland_1ha_patchIDs.txt",
Resolution = 10,
Nhabitats = 7,
K_or_DensDep = c(10, rep(0,6)),
SpDistFile = "patch30.txt",
SpDistResolution = 10)</code></pre>
</div>
</div>
<div id="scenario-a-sexual-model-with-mate-finding"
class="section level1" number="2">
<h1><span class="header-section-number">2</span> Scenario a: sexual
model with mate finding</h1>
<div id="demographic-and-dispersal-parameters" class="section level2"
number="2.1">
<h2><span class="header-section-number">2.1</span> Demographic and
dispersal parameters</h2>
<p>We will simulate a sexual species with simple, two-staged structured
population dynamics. The parameters are chosen to be representative of
species having moderately high fecundity, high juvenile mortality and
low adult mortality. This is encoded in the following transition
matrix</p>
<pre class="r"><code>(trans_mat <- matrix(c(0, 1, 0, 0, 0.1, 0.4, 5, 0, 0.8), nrow = 3, byrow = F))</code></pre>
<pre><code>## [,1] [,2] [,3]
## [1,] 0 0.0 5.0
## [2,] 1 0.1 0.0
## [3,] 0 0.4 0.8</code></pre>
<p>The first row and column describe the juvenile stage, the others the
two adult stages. Juveniles will develop to the first adult stage at the
end of their first year with a probability of <em>1.0</em>, which allows
for juvenile dispersal before any mortality happens.</p>
<p>In order to add a stage-structure to our population dynamics, we use
the <code>StageStructure()</code> function within the demography module.
The reproduction type <em>1</em> denotes a simple sexual model,
i.e. mating is not explicitly modelled.</p>
<pre class="r"><code>stg <- StageStructure(Stages=3, # 1 juvenile + 2 adult stages
TransMatrix=trans_mat,
MaxAge=1000,
SurvSched=2,
FecDensDep=T)
demo <- Demography(StageStruct = stg,
ReproductionType = 1) # simple sexual model</code></pre>
<p>After reproduction, we allow only juveniles to disperse, and define a
density-dependent emigration probability. To do so, we enable the
options <code>DensDep=T</code> and <code>StageDep=T</code>, and in the
matrix <code>EmigProb</code> we set the parameters D<sub>0</sub> =
<em>0.5</em>, α = <em>10.0</em> and β = <em>1.0</em> for juveniles and
to zero for all adult stages.</p>
<p>To account for functional connectivity, we use a mechanistic movement
model which enables individuals to interact with the landscape and
determine their path according to what they can perceive in the
landscape. Therefore we will simulate movements with a stochastic
movement simulator (<code>SMS()</code>) where individuals move stepwise
(each step goes from one cell to a neighbouring cell) and the direction
chosen at each step is determined by the land cover costs (specified for
each land type), the species’ perceptual range (<code>PR</code>) and
directional persistence (<code>DP</code>). We set these parameters so
that individuals have a perceptual range of <em>50m</em>, use the
arithmetic mean method (the default) for calculating effective cost
(which tends to emphasise the avoidance of high-cost landscape
features), and tend to follow highly correlated paths within the
landscape. We also set a constant per-step mortality probability
(<code>StepMort</code>).</p>
<p>Once arrived in a new patch, an individual decides to settle or not
based on certain settlement rules. Finding suitable habitat is a
necessary condition in all cases. Additionally, we set mate availability
as requirement, i.e. there has to be at least one individual of the
opposite sex present in the patch to be considered suitable for
settlement.</p>
<pre class="r"><code>disp <- Dispersal(Emigration = Emigration(DensDep=T, StageDep=T,
EmigProb = cbind(0:2,c(0.5,0,0),c(10.0,0,0),c(1.0,0,0)) ),
Transfer = SMS(PR=5, DP=10, Costs = c(1,1,3,5,10,20,50), StepMort = 0.01),
Settlement = Settlement(FindMate = T) )</code></pre>
<p>We can visualise the defined processes by plotting some of the rates
and probabilities that we have parameterised:</p>
<pre class="r"><code>par(mfrow=c(1,2))
plotProbs(demo@StageStruct)
plotProbs(disp@Emigration)</code></pre>
<p><img src="tutorial_2_files/figure-html/unnamed-chunk-10-1.png" width="672" /></p>
</div>
<div id="initialisation-simulation" class="section level2" number="2.2">
<h2><span class="header-section-number">2.2</span> Initialisation &
simulation</h2>
<p>We choose to initialise our simulation in all initial patches
(specified in initial distribution map; in our case only patch
#<em>30</em>) at a density of <em>10</em> individuals per hectare, with
an equal number of individuals in stages 1 and 2 at their respective
minimum age.</p>
<pre class="r"><code># Population is initialised in Patch 30:
init <- Initialise(InitType = 1, # from loaded species distribution map
SpType = 0, # all suitable cells
InitDens = 2, # user-specified density
IndsHaCell = 10,
PropStages = c(0,0.5,0.5),
InitAge = 0)</code></pre>
<p>We set the simulation time to <em>100</em> years and <em>20</em>
replicates, and set the output types to write the files for population,
occupancy and range data every year.</p>
<pre class="r"><code>sim <- Simulation(Simulation = 0,
Replicates = 20,
Years = 100,
OutIntPop = 1,
OutIntOcc = 1,
OutIntRange = 1)</code></pre>
<p>As before, we need to stitch all modules together to a parameter
master. Within <code>RSsim()</code>, we can also set a seed for the
random number generator to make our results reproducible:</p>
<pre class="r"><code>s <- RSsim(batchnum = 3, land = land, demog = demo, dispersal = disp, simul = sim, init = init, seed = 324135)</code></pre>
<p>Run the simulation:</p>
<pre class="r"><code>RunRS(s, dirpath)</code></pre>
</div>
<div id="analyse-output" class="section level2" number="2.3">
<h2><span class="header-section-number">2.3</span> Analyse output</h2>
<p>To analyse the simulation output, we first plot the meta-population
results. Note here that - in contrast to the cell-based model from
exercise 1 - the plotted occupancy refers to occupied patches rather
than cells.</p>
<pre class="r"><code>par(mfrow=c(1,2))
plotAbundance(s, dirpath)
plotOccupancy(s, dirpath)</code></pre>
<p><img src="tutorial_2_files/figure-html/unnamed-chunk-15-1.png" width="672" /></p>
<p>In order to create occupancy maps, we first plot the landscape with
the suitable patches in green and the initial patch in red. This color
scheme was also used in Fig. 3a of <span class="citation">Bocedi et al.
(2014)</span>.</p>
<pre class="r"><code># We have initiated the population in the patch with ID=30. We highlight this in the map.
values(patch30)[values(patch30)<1] <- NA
values(patch)[values(patch)<1] <- NA
plot(landsc, axes=F,breaks=seq(.5,7.5,by=1),
col = rev(brewer.pal(n = 7, name = "Greys") ), legend=F)
plot(patch, axes=F, col="green4", legend=F, add=T)
plot(as.polygons(patch30, dissolve=T), col=NA, border='red',lwd=2, add=T)</code></pre>
<p><img src="tutorial_2_files/figure-html/unnamed-chunk-16-1.png" width="672" /></p>
<pre class="r"><code># Store underlying landscape map display for later:
bg <- function(main=NULL){
plot(landsc, axes=F, breaks=seq(.5,7.5,by=1), legend=F,
col = rev(brewer.pal(n = 7, name = "Greys") ), main=ifelse(is.null(main),"",main))
}
# as well as the extent of the landscape:
e <- ext(landsc)</code></pre>
<p>To reproduce Fig. 3b of <span class="citation">Bocedi et al.
(2014)</span>, we map the mean occupancy probability for each patch in
year <em>100</em> (left panel in the paper) as well as the mean time to
colonisation (right panel), both calculated over the <em>20</em>
replicates. We can use the built-in function
<code>ColonisationStats()</code> for this. It calculates the mean
occupancy probability of given years as well as the time to colonisation
for all replicates:</p>
<pre class="r"><code>col_stats_a <- ColonisationStats(s, dirpath, years = 100, maps = T)</code></pre>
<pre><code>## Warning: [rast] the first raster was empty and was ignored</code></pre>
<pre class="r"><code># mean occupancy probability in year 100
head(col_stats_a$occ_prob)</code></pre>
<pre><code>## patch 100
## 1 15 0.10
## 2 17 0.00
## 3 20 0.15
## 4 23 1.00
## 5 24 1.00
## 6 26 1.00</code></pre>
<pre class="r"><code># time to colonisation
head(col_stats_a$col_time)</code></pre>
<pre><code>## patch rep.0 rep.1 rep.2 rep.3 rep.4 rep.5 rep.6 rep.7 rep.8 rep.9 rep.10
## 1 15 80 NA NA NA NA NA NA NA NA NA NA
## 2 17 NA NA NA 91 NA NA NA NA 77 NA 63
## 3 20 NA NA NA 80 91 90 NA NA NA NA 29
## 4 23 12 50 51 37 36 57 56 49 38 86 35
## 5 24 23 36 32 21 19 25 19 18 26 34 24
## 6 26 2 5 3 9 1 4 1 2 3 3 7
## rep.11 rep.12 rep.13 rep.14 rep.15 rep.16 rep.17 rep.18 rep.19
## 1 NA 46 NA NA 96 NA NA NA NA
## 2 NA NA NA NA NA NA NA NA NA
## 3 NA NA NA 94 NA NA 99 93 92
## 4 45 27 71 38 33 46 61 49 65
## 5 29 6 54 14 12 33 51 35 46
## 6 1 1 5 1 4 1 3 2 2</code></pre>
<p>For mapping the results, we can use the optional raster output: If
enabled, the function <code>ColonisationStats()</code> returns a raster
stack with the mean occupancy probabilities of the given years as well
as a raster with the mean time to colonisation over all replicates. We
plot these maps on top of our landscape:</p>
<pre class="r"><code># map occupancy probability
mycol_occprob <- colorRampPalette(c('blue','orangered','gold'))
plot(col_stats_a$map_occ_prob, axes=F, range=c(0,1), col=mycol_occprob(10), type="continuous")</code></pre>
<p><img src="tutorial_2_files/figure-html/unnamed-chunk-19-1.png" width="672" /></p>
<pre class="r"><code># map occupancy probability on landscape background
bg()
plot(col_stats_a$map_occ_prob, axes=F, range=c(0,1), col=mycol_occprob(10), type="continuous", plg=list(x="bottom", ext=c(e$xmin+400, e$xmax-400, e$ymin-150, e$ymin-50)), add =T)</code></pre>
<p><img src="tutorial_2_files/figure-html/unnamed-chunk-19-2.png" width="672" /></p>
<pre class="r"><code># map colonisation time
mycol_coltime <- colorRampPalette(c('orangered','gold','yellow','PowderBlue','LightSeaGreen'))
plot(col_stats_a$map_col_time, axes=F, breaks=c(-9,seq(-9,100,length=11)), col=c('blue',mycol_coltime(20)), type="continuous")</code></pre>
<p><img src="tutorial_2_files/figure-html/unnamed-chunk-19-3.png" width="672" /></p>
<pre class="r"><code># map colonisation time on landscape background
bg()
plot(col_stats_a$map_col_time, axes=F, breaks=c(-9,seq(-9,100,length=11)), col=c('blue',mycol_coltime(20)), type="continuous", plg=list(x="bottom", ext=c(e$xmin+400, e$xmax-400, e$ymin-150, e$ymin-50)), add =T)</code></pre>
<p><img src="tutorial_2_files/figure-html/unnamed-chunk-19-4.png" width="672" /></p>
</div>
</div>
<div id="scenario-b-females-settle-independent-of-males"
class="section level1" number="3">
<h1><span class="header-section-number">3</span> Scenario b: females
settle independent of males</h1>
<p>This experiment was designed to provide an example of how the
dispersal behaviour of the species and the specification of settlement
rules can change the estimated connectivity of a habitat network. We
will relax the mating requirement a little by making it sex-dependent
and only setting it for males. This means that female dispersers will
settle in suitable patches regardless of males, while males settle only
when finding a female. Since settlement is now sex-dependent, we also
need to set the other parameters for both sexes.</p>
<pre class="r"><code># Change Settlement rules
disp_b <- Dispersal(Emigration = Emigration(DensDep=T, StageDep=T,
EmigProb = cbind(0:2,c(0.5,0,0),c(10.0,0,0),c(1.0,0,0)) ),
Transfer = SMS(PR=5, DP=10, Costs = c(1,1,3,5,10,20,50), StepMort = 0.01),
Settlement = Settlement(SexDep=T,
FindMate = cbind(c(0,1),c(F,T)),
Settle=cbind(c(0,1),c(1,1)),
MinSteps = cbind(c(0,1),c(0,0)),
MaxSteps = cbind(c(0,1),c(0,0)),
MaxStepsYear = cbind(c(0,1),c(0,0)))
)
# Update simulation
sim_b <- Simulation(Simulation = 1,
Replicates = 20,
Years = 100,
OutIntPop = 1,
OutIntOcc = 1,
OutIntRange = 1)
# Update parameter master
s_b <- s + disp_b + sim_b</code></pre>
<pre class="r"><code># run simulation
RunRS(s_b, dirpath)</code></pre>
<p>Now, let’s post-process the simulation results and plot the maps.</p>
<pre class="r"><code># Get colonisation stats
col_stats_b <- ColonisationStats(s_b, dirpath, years = 100, maps = T)</code></pre>
<pre><code>## Warning: [rast] the first raster was empty and was ignored</code></pre>
<pre class="r"><code># Map occupancy probabilities:
bg()
plot(col_stats_b$map_occ_prob, axes=F, range=c(0,1), col=mycol_occprob(11), type="continuous", plg=list(x="bottom", ext=c(e$xmin+400, e$xmax-400, e$ymin-150, e$ymin-50)), add =T)</code></pre>
<p><img src="tutorial_2_files/figure-html/unnamed-chunk-22-1.png" width="672" /></p>
<pre class="r"><code># map colonisation time + background
bg()
plot(col_stats_b$map_col_time, axes=F, breaks=c(-9,seq(-9,100,length=11)), col=c('blue',mycol_coltime(20)), type="continuous", plg=list(x="bottom", ext=c(e$xmin+400, e$xmax-400, e$ymin-150, e$ymin-50)), add =T)</code></pre>
<p><img src="tutorial_2_files/figure-html/unnamed-chunk-22-2.png" width="672" /></p>
<p>From both the visualisation and the results, we see that relaxing the
mate-finding rules substantially increased the number of occupied
patches, their probability of occupancy and the mean time to
colonisation. This results in higher functional connectivity of the
woodland network over <em>100</em> years.</p>
</div>
<div id="scenario-c-asexual-female-only-model" class="section level1"
number="4">
<h1><span class="header-section-number">4</span> Scenario c: asexual /
female-only model</h1>
<p>Here we change the demography module to represent a female-only
model. This change also has important consequences for the dispersal
process and potential implications for patterns of colonisation across a
landscape. Female-only models assume that males are not limiting, and
that the population dynamics are driven only by females. It also means
that sexes are not modelled explicitly and it is not possible to account
for behaviours like mate-finding in the settlement decisions; females
will settle in suitable habitat patches and then will automatically be
able to attempt reproduction.</p>
<p>The stage-structure of the model remains the same apart from
accounting for the female-only case. In particular, in female-only
models, we ignore the male part of the population and offspring.
Therefore, we set the fecundity of stage 3 to <em>2.5</em> instead of
<em>5.0</em> and the demographic density dependence <em>1/b</em>
(<code>K_or_DensDep</code>) to <em>5</em> instead of <em>10</em>.
Sex-dependent settlement options are no longer available.</p>
<pre class="r"><code># Change demographic density dependence to half its value of the sexual model
land_c <- ImportedLandscape(LandscapeFile = "landscape_10m_batch.txt",
PatchFile = "woodland_1ha_patchIDs.txt",
Resolution = 10,
Nhabitats = 7,
K_or_DensDep = c(5, rep(0,6)),
SpDistFile = "patch30.txt",
SpDistResolution = 10)
# Change demography settings
stg_c <- StageStructure(Stages=3,
TransMatrix=matrix(c(0, 1, 0, 0, 0.1, 0.4, 2.5, 0, 0.8), nrow = 3, byrow = F),
MaxAge=1000,
SurvSched=2,
FecDensDep=T)
demo_c <- Demography(StageStruct = stg_c,
ReproductionType = 0) # female-only model
# Remove settlement rules
disp_c <- Dispersal(Emigration = Emigration(DensDep=T, StageDep=T,
EmigProb = cbind(0:2,c(0.5,0,0),c(10.0,0,0),c(1.0,0,0)) ),
Transfer = SMS(PR=5, DP=10, Costs = c(1,1,3,5,10,20,50), StepMort = 0.01),
Settlement = Settlement()
)
# Update simulation
sim_c <- Simulation(Simulation = 2,
Replicates = 20,
Years = 100,
OutIntPop = 1,
OutIntOcc = 1,
OutIntRange = 1)
# parameter master
s_c <- RSsim(batchnum = 3, land = land_c, demog = demo_c, dispersal = disp_c, simul = sim_c, init = init, seed = 48263)</code></pre>
<pre class="r"><code>RunRS(s_c, dirpath)</code></pre>
<p>Process the output and plot the occupancy maps:</p>
<pre class="r"><code># Get colonisation stats
col_stats_c <- ColonisationStats(s_c, dirpath, years = 100, maps = T)</code></pre>
<pre><code>## Warning: [rast] the first raster was empty and was ignored</code></pre>
<pre class="r"><code># Map occupancy probabilities:
bg()
plot(col_stats_c$map_occ_prob, axes=F, range=c(0,1), col=mycol_occprob(11), type="continuous", plg=list(x="bottom", ext=c(e$xmin+400, e$xmax-400, e$ymin-150, e$ymin-50)), add =T)</code></pre>
<p><img src="tutorial_2_files/figure-html/unnamed-chunk-25-1.png" width="672" /></p>
<pre class="r"><code># Map colonisation time + background
bg()
plot(col_stats_c$map_col_time, axes=F, breaks=c(-9,seq(-9,100,length=11)), col=c('blue',mycol_coltime(20)), type="continuous", plg=list(x="bottom", ext=c(e$xmin+400, e$xmax-400, e$ymin-150, e$ymin-50)), add =T)</code></pre>
<p><img src="tutorial_2_files/figure-html/unnamed-chunk-25-2.png" width="672" /></p>
<p>As we see from the results, the asexual model without mate finding as
settlement rule leads to a drastic increase in the overall occupancy of
the habitat network after <em>100</em> years.</p>
</div>
<div id="scenario-d-habitat-specific-per-step-mortality"
class="section level1" number="5">
<h1><span class="header-section-number">5</span> Scenario d:
habitat-specific per-step mortality</h1>
<p>In this last simulation, we will demonstrate how
<code>RangeshiftR</code> can incorporate more complexity in the way that
movement is modelled. We relax the unrealistic assumption that the
per-step mortality is constant across all land-cover types, and assign
different mortality values to each habitat. To set up this simulation,
we use the parameters from scenario a) and only add a modified transfer
module. Here, we define <code>StepMort</code> as habitat-dependent by
providing a vector with mortality probabilities for each land cover
type.</p>
<pre class="r"><code># Update Transfer sub-module within the dispersal module
disp_d <- disp + SMS(PR=5, DP=10, Costs = c(1,1,3,5,10,20,50),
StepMort = c(0,0,0,0.01,0.01,0.02,0.05)
)
# Update simulation
sim_d <- Simulation(Simulation = 3,
Replicates = 20,
Years = 100,
OutIntPop = 1,
OutIntOcc = 1,
OutIntRange = 1)
# Use parameter master from a) and add new transfer module
s_d <- s + disp_d + sim_d</code></pre>
<p>Run the simulation:</p>
<pre class="r"><code>RunRS(s_d, dirpath)</code></pre>
<p>Process and map results:</p>
<pre class="r"><code># Get colonisation stats
col_stats_d <- ColonisationStats(s_d, dirpath, years = 100, maps = T)</code></pre>
<pre><code>## Warning: [rast] the first raster was empty and was ignored</code></pre>
<pre class="r"><code># Map occupancy probabilities:
bg()
plot(col_stats_d$map_occ_prob, axes=F, range=c(0,1), col=mycol_occprob(11), type="continuous", plg=list(x="bottom", ext=c(e$xmin+400, e$xmax-400, e$ymin-150, e$ymin-50)), add =T)</code></pre>
<p><img src="tutorial_2_files/figure-html/unnamed-chunk-28-1.png" width="672" /></p>
<pre class="r"><code># map colonisation time + background
bg()
plot(col_stats_d$map_col_time, axes=F, breaks=c(-9,seq(-9,100,length=11)), col=c('blue',mycol_coltime(20)), type="continuous", plg=list(x="bottom", ext=c(e$xmin+400, e$xmax-400, e$ymin-150, e$ymin-50)), add =T)</code></pre>
<p><img src="tutorial_2_files/figure-html/unnamed-chunk-28-2.png" width="672" /></p>
<p>We see that such small changes in the per-step mortality, in
interaction with the landscape structure, make a big difference in the
results, in this case decreasing the functional connectivity of the
network.</p>
</div>
<div id="scenario-comparison" class="section level1" number="6">
<h1><span class="header-section-number">6</span> Scenario
comparison</h1>
<p>Let’s plot all maps next to each other.</p>
<pre class="r"><code># Plot occupancy probabilities for all scenarios
par(mfrow=c(2,2))
bg(main="Scenario A")
plot(col_stats_a$map_occ_prob, axes=F, range=c(0,1), col=mycol_occprob(11), type="continuous", plg=list(x="bottom", ext=c(e$xmin+400, e$xmax-400, e$ymin-150, e$ymin-50)), add =T)
bg(main="Scenario B")
plot(col_stats_b$map_occ_prob, axes=F, range=c(0,1), col=mycol_occprob(11), type="continuous", plg=list(x="bottom", ext=c(e$xmin+400, e$xmax-400, e$ymin-150, e$ymin-50)), add =T)
bg(main="Scenario C")
plot(col_stats_c$map_occ_prob, axes=F, range=c(0,1), col=mycol_occprob(11), type="continuous", plg=list(x="bottom", ext=c(e$xmin+400, e$xmax-400, e$ymin-150, e$ymin-50)), add =T)
bg(main="Scenario D")
plot(col_stats_d$map_occ_prob, axes=F, range=c(0,1), col=mycol_occprob(11), type="continuous", plg=list(x="bottom", ext=c(e$xmin+400, e$xmax-400, e$ymin-150, e$ymin-50)), add =T)</code></pre>
<p><img src="tutorial_2_files/figure-html/unnamed-chunk-29-1.png" width="672" /></p>
<pre class="r"><code># Plot colonisation times for all scenarios
par(mfrow=c(2,2))
bg(main="Scenario A")
plot(col_stats_a$map_col_time, axes=F, breaks=c(-9,seq(-9,100,length=11)), col=c('blue',mycol_coltime(20)), type="continuous", plg=list(x="bottom", ext=c(e$xmin+400, e$xmax-400, e$ymin-150, e$ymin-50)), add =T)
bg(main="Scenario B")
plot(col_stats_b$map_col_time, axes=F, breaks=c(-9,seq(-9,100,length=11)), col=c('blue',mycol_coltime(20)), type="continuous", plg=list(x="bottom", ext=c(e$xmin+400, e$xmax-400, e$ymin-150, e$ymin-50)), add =T)
bg(main="Scenario C")
plot(col_stats_c$map_col_time, axes=F, breaks=c(-9,seq(-9,100,length=11)), col=c('blue',mycol_coltime(20)), type="continuous", plg=list(x="bottom", ext=c(e$xmin+400, e$xmax-400, e$ymin-150, e$ymin-50)), add =T)
bg(main="Scenario D")
plot(col_stats_d$map_col_time, axes=F, breaks=c(-9,seq(-9,100,length=11)), col=c('blue',mycol_coltime(20)), type="continuous", plg=list(x="bottom", ext=c(e$xmin+400, e$xmax-400, e$ymin-150, e$ymin-50)), add =T)</code></pre>
<p><img src="tutorial_2_files/figure-html/unnamed-chunk-29-2.png" width="672" /></p>
<pre class="r"><code># reset plot layout
par(mfrow=c(1,1))</code></pre>
</div>
<div id="dispersal-heatmap" class="section level1" number="7">