More realistic and complicated
simulations may require a more advanced setup. This vignette will cover
some of the more advanced / hidden features of the
metaRange
package.
The setup and processing of the environmental SDS
can
take quite some time, especially when the environmental data is large.
In order to save the user time, the method
set_time_layer_mapping()
allows the user to define a custom
mapping between the time steps and the layers of the environmental
raster. Use cases may be:
The default configuration is that each layer in the environment represents one time step of the simulation. I.e:
raster_file <- system.file("ex/elev.tif", package = "terra")
r <- rast(raster_file)
temperature <- scale(r, center = FALSE, scale = TRUE) * 10 + 273.15
precipitation <- r * 2
temperature <- rep(temperature, 10)
precipitation <- rep(precipitation, 10)
landscape <- sds(temperature, precipitation)
names(landscape) <- c("temperature", "precipitation")
To use the same environmental raster (i.e. the first one) for all time steps, one can use:
In the same way described above, one can also pick the specific layer that are used and shorten the simulation length.
To add a burn-in period of 10 time steps, where the environmental data is not updated, one could use the function as follows:
In metaRange
each species is described by its own
biologically relevant processes and traits. However, there may be cases
where the user wants to define a global variable or process that is
shared between all species, either to store intermediate results that
don’t belong to one species or processes to visualize or store output.
This can be done by using the add_globals()
function for
adding global variables and the add_process()
function with
no species
argument specified, for adding global processes.
The global variables and processes are accessible through the
globals
and processes
fields of the simulation
object itself, respectively. The benefit of using a global process is
that the self
keyword refers to the simulation object
itself, which allows for easier indexing across multiple species.
sim$add_species("species_one")
sim$add_species("species_two")
sim$add_globals(
mean_abundance_over_time = list(
"species_one" = c(),
"species_two" = c()
)
# ... more global variables
)
sim$globals$global_var
#> NULL
sim$add_process(
# Note the missing species argument
process_name = "global_process",
process_fun = function() {
# self = simulation object
# easy access to simulation functions
for (sp in self$species_names()) {
self$globals$mean_abundance_over_time[[sp]] <-
c(
self$globals$mean_abundance_over_time[[sp]],
mean(self[[sp]]$traits$abundance)
)
}
},
execution_priority = 1
)
sim$processes$global_process
#> Process name: global_process
#> PID: PID-11951bb8c1-global_process-simulation_448f0fd5
#> execution_priority: 1
#> execution_environment_label: simulation_448f0fd5
#> $fun: function ()
#> {
#> for (sp in self$species_names()) {
#> self$globals$mean_abundance_over_time[[sp]] <- c(self$globals$mean_abundance_over_time[[sp]],
#> mean(self[[sp]]$traits$abundance))
#> }
#> }
#> <environment: 0x555dcff8ed68>
With some study questions, it may not be desired to simulate all
species from the first time step. For example, when simulating invasion
dynamics, one may want to have a burn-in period without the invasive
species present and then introduce it after this point. Aditionally,
there may be the need to simulate a species for a period of time and
then remove it from the simulation (e.g. there is no point in
calculating the reproduction of a species that has gone extinct). To
accommodate this, metaRange
allows the user to manually add
and remove processes from the priority queue during the simulation.
The default behavior of add_process()
is to directly add
the process to the priority queue (so that is is executed in the first
time step). Setting the argument queue = FALSE
will add the
process to the simulation, but not to the priority queue. In that case,
the user has at any point during the simulation the option to add the
process to the priority queue using the enqueue()
method of
the priority queue.
sim <- create_simulation(landscape)
sim$set_time_layer_mapping(c(1:6))
sim$add_species(name = "species_1")
sim$add_process(
species = "species_1",
process_name = "invasion",
process_fun = function() {
message("Species invades!")
},
execution_priority = 1,
# Note the queue = FALSE argument
queue = FALSE
)
sim$add_process(
process_name = "activate_species_1",
process_fun = function() {
message(paste0("time step: ", self$get_current_time_step()))
# Note that when manually changing the queue,
# the changes will take place in the
# _next_ time step
# e.g. the following will lead to the process
# being first executed in time step 4)
if (self$get_current_time_step() == 3) {
message("Activating species 1")
for (pr in self$species_1$processes) {
self$queue$enqueue(pr)
}
}
},
execution_priority = 1
)
sim$begin()
#> time step: 1
#> time step: 2
#> time step: 3
#> Activating species 1
#> time step: 4
#> Species invades!
#> time step: 5
#> Species invades!
#> time step: 6
#> Species invades!
The dequeue()
function of the priority queue allows the
user to remove a process from the priority queue.
sim <- create_simulation(landscape)
sim$set_time_layer_mapping(c(1:6))
sim$add_species(name = "species_1")
sim$add_process(
species = "species_1",
process_name = "invasion",
process_fun = function() {
message("Species invades!")
},
execution_priority = 1,
)
sim$add_process(
process_name = "stop_invasion",
process_fun = function() {
message(paste0("time step: ", self$get_current_time_step()))
if (self$get_current_time_step() == 3) {
message("Extiction species 1")
for (pr in self$species_1$processes) {
# Here we are querying the process ID,
# which is a unique identifier for each process
# so that the priority queue knows what to remove
self$queue$dequeue(pr$get_PID())
}
}
},
execution_priority = 1
)
sim$begin()
#> Species invades!
#> time step: 1
#> Species invades!
#> time step: 2
#> Species invades!
#> time step: 3
#> Extiction species 1
#> time step: 4
#> time step: 5
#> time step: 6
To end the simulation safely, before the last time step, the user can
use the exit()
method of the simulation. This will end the
simulation at the end of the process it is called inside of. A possible
use case would be to conditionally end the simulation if all species are
extinct.
sim <- create_simulation(landscape)
sim$set_time_layer_mapping(c(1:6))
sim$add_species(name = "species_1")
sim$add_process(
species = "species_1",
process_name = "invasion",
process_fun = function() {
message("Species invades!")
},
execution_priority = 1,
)
sim$add_process(
process_name = "end_simualtion",
process_fun = function() {
message(paste0("time step: ", self$get_current_time_step()))
if (self$get_current_time_step() == 4) {
message("Ending simulation early")
self$exit()
}
},
execution_priority = 1
)
sim$begin()
#> Species invades!
#> time step: 1
#> Species invades!
#> time step: 2
#> Species invades!
#> time step: 3
#> Species invades!
#> time step: 4
#> Ending simulation early