plot.range.single.run: Generate summary plot of range dynamics from a single run of...

In KarelMokany/AdaptR: Species distribution modelling incorporating genetic adaptation

Description

Generate summary plot of range dynamics from a single run of AdpatR

Usage

## S3 method for class 'range.single.run'
plot(output.folder.path = getwd(), run.name)

Arguments

output.folder.path	The directory where the output files are stored
run.name	The name for the AdaptR simulation run

Value

summary plots of the entire distribution over time

Examples

## Complete example of the multiple steps in applying AdaptR using data example provided for Drosophila jambulina

# 1. Set the filepath to the example data set
filepath.data <- system.file("extdata", package="AdaptR")

# 2. Create text files to describe the file path to each input variable, and the output variable
write.table((file.path(filepath.data,"Tmax",paste0("Tmax",seq(1:159),".asc"))), file = file.path(filepath.data,"Tmax","Tmax_filenames.txt"), eol = "\n", row.names = FALSE, col.names = FALSE, quote=FALSE )
write.table((file.path(filepath.data,"Habitat",paste0("Habitat",seq(1:159),".asc"))), file = file.path(filepath.data,"Habitat","Habitat_filenames.txt"), eol = "\n", row.names = FALSE, col.names = FALSE, quote=FALSE )
write.table((file.path(filepath.data,"compact_grids",paste0("demo_compact_grids_T",seq(1:159)))), file = file.path(filepath.data,"compact_grids","demo_compact_grids_output_filenames.txt"), eol = "\n", row.names = FALSE, col.names = FALSE, quote=FALSE )

# 3. Run the grid compactor
CompactGrids(compactor.parameter.file.name = file.path(filepath.data,"species_inputs","Jambulina__grid_compactor_parameter_file.txt"),
             ncols = 100,
             nrows = 79,
             n.env.vars = 2,
             n.time.points = 159,
             raw.env.grids.name.files = c(file.path(filepath.data,"Tmax","Tmax_filenames.txt"),file.path(filepath.data,"Habitat","Habitat_filenames.txt")),
             output.env.name.file = file.path(filepath.data,"compact_grids","demo_compact_grids_output_filenames.txt"))

# 4. Generate a dispersal kernel for the drosophila example
filepath.data <- system.file("extdata", package="AdaptR")
Dispersal_Neighbourhood(radius=5, 
                       type="neg.power", 
                       params=c(1,1), 
                       output.name="Dispersal_relfile_L1_K1_rad5",
                       output.directory=file.path(filepath.data,"species_inputs"),
                       dispersal.plot=TRUE)  

# 5. Create text files to describe the file path to the compact grids
write.table(paste0("demo_compact_grids_T",rep(1:159, length.out=159)), file = file.path(filepath.data,"species_inputs","compact_series_names.txt"), eol = "\n", row.names = FALSE, col.names = FALSE, quote=FALSE )

# 6. Run the AdaptR model
AdaptR(run.name = "jambulina_test",
        parameter.file.name = file.path(filepath.data,"outputs","jambulina_test_parameters.txt"),
        ncols = 100,
        nrows = 79,
        output.folder.path = file.path(filepath.data,"outputs"),
        verbose.outputs = FALSE,
        n.time.points = 159,
        n.env.vars = 2,
        env.vars.names = c("MaxTemp", "Other_Maxent"),
        env.grids.folder.path = file.path(filepath.data,"compact_grids"),
        env.grids.name.file = file.path(filepath.data,"species_inputs","compact_series_names.txt"),
        species.initial.grid = file.path(filepath.data,"species_inputs","demo_jambulia_initial_distribution.asc"),
        minimum.survival.percentage = 5,
        resident.population.weighting = 1000,
        dispersal.neighbourhood.file = file.path(filepath.data,"species_inputs","Dispersal_relfile_L1_K1_rad5.dna"),
        species.location.file = file.path(filepath.data,"species_inputs","demo_locations_out.txt"),
        env.lower.thresholds = c(19.47,0.99),
        env.upper.thresholds = c(37.94547,1.01),
        env.low.adaptation = c(FALSE,FALSE),
        env.high.adaptation = c(TRUE,FALSE),
        adapt.limit = c(40,0),
        heritability = c(0.53,0),
        fitness.cost = c(0.05,0),
        adapt.threshold.grids = c(FALSE,FALSE),
        phenotypic.sd.grid = c(FALSE,FALSE),
        phenotypic.sd.value = c(1.106,0),
        plasticity = c(1.106,0))

# 7. Map the predictions of the model (at the last time point)
map.single.run(output.folder.path = file.path(filepath.data,"outputs"), 
               run.name = "jambulina_test")

# 8. Plot a summary of the model predictions over time
plot.range.single.run(output.folder.path = file.path(filepath.data,"outputs"), 
                      run.name = "jambulina_test")

KarelMokany/AdaptR documentation built on May 8, 2019, 4:48 p.m.