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bison_baseline.R
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bison_baseline.R
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# Set directories for script and general and K Cuts and Human Density data files, and results
SOURCE_DIR <- getwd()
DATA_DIR <- file.path(SOURCE_DIR, "data")
K_CUTS_DIR <- file.path(SOURCE_DIR, "k_cuts")
RESULTS_DIR <- file.path(SOURCE_DIR, "results")
# create results dir if needed
if (!dir.exists(RESULTS_DIR)) {
dir.create(RESULTS_DIR, recursive = TRUE)
}
# Parallel cores available on machine
# 128 max
parallel_cores <- 95
# Install paleopop and dependencies
library(poems)
library(paleopop)
library(raster)
library(purrr)
library(stringr)
#### Step 1: Create a simulation model template with a study region ####
##### Settings #####
parallel_cores <- 8
nsims <- 25
burn_in_steps <- 100
timesteps <- 2100 + burn_in_steps
#### Step 1: Static inputs ####
##### Region #####
region <- readRDS(file.path(DATA_DIR, "wisent_paleo_region.RDS"))
raster::plot(region$region_raster, main = "Wisent region (cell indices)",
colNA = "blue")
raster::plot(region$temporal_mask_raster()[[2000]], colNA = "blue")
##### Re-usable distance matrix #####
distance_matrix <- DispersalGenerator$new(region = region, dispersal_max_distance = 500, # in km
distance_scale = 1000)$calculate_distance_matrix()
summary(as.vector(distance_matrix))
##### Distance-based environmental correlation #####
# (via a compacted Cholesky decomposition)
env_corr <- SpatialCorrelation$new(region = region, amplitude = 0.99, breadth = 850, distance_scale = 1000)
compact_decomposition <- env_corr$get_compact_decomposition(distance_matrix = distance_matrix)
##### Friction raster #####
friction <- readRDS(file.path(DATA_DIR, "wisent_conductance_v2.RDS"))[region$region_indices,]
##### Human matrix #####
burn_in <- stack(replicate(100, raster(file.path(DATA_DIR, "humans_mean.grd"))))
humans_mean_raster <- raster::stack(burn_in, raster::stack(file.path(DATA_DIR, "humans_mean.grd")))
raster::plot(humans_mean_raster[[c(1,50,100,1000)]], main = "Human mean density",
xlab = "Longitude (degrees)", ylab = "Latitude (degrees)",
colNA = "blue")
raster::values(humans_mean_raster) <- raster::values(humans_mean_raster) # memory
humans_mean_matrix <- humans_mean_raster[region$region_indices]
humans_mean_matrix[!is.finite(humans_mean_matrix)] <- 0
burn_in <- stack(replicate(100, raster(file.path(DATA_DIR, "humans_sd.grd"))))
humans_sd_raster <- raster::stack(burn_in, raster::stack(file.path(DATA_DIR, "humans_sd.grd")))
raster::values(humans_sd_raster) <- raster::values(humans_sd_raster) # memory
humans_sd_matrix <- humans_sd_raster[region$region_indices]
humans_sd_matrix[!is.finite(humans_sd_matrix)] <- 0
rm(humans_mean_raster, humans_sd_raster); gc()
##### Population model template #####
model_template <- PaleoPopModel$new(
region = region,
time_steps = timesteps, # include burn-in
years_per_step = 10,
populations = region$region_cells,
# initial_abundance: generated
transition_rate = 1.0,
# standard_deviation: sampled
compact_decomposition = compact_decomposition,
# carrying_capacity: generated
density_dependence = "logistic",
# growth_rate_max: sampled
harvest = TRUE,
# harvest_max: sampled
harvest_g = 0.4, # constant
# harvest_z: sampled
# harvest_max_n: sampled
# human_density: generated
dispersal_target_k = 10,
# dispersal_data: generated
# abundance_threshold: sampled,
occupancy_threshold = 1, # this was set to 2 in the mammoth model
# niche_ref: sampled
results_selection = c("abundance", "harvested", "human_density")
)
#### Step 2: Dynamic inputs ####
##### Carrying-capacity generator #####
cuts <- list.files(K_CUTS_DIR) %>% stringr::str_split("_") %>% purrr::map(~.[3:4]) %>% purrr::map_chr(paste0, collapse = "_")
capacity_gen <- Generator$new(description = "capacity",
region = region,
generate_rasters = FALSE, # use but don't generate
burn_in_steps = burn_in_steps,
generative_requirements = list(
hs_matrix = "file",
initial_abundance = "function",
carrying_capacity = "function"),
# these come from the latin hypercube sampler
inputs = c("density_max", "niche_ref"),
outputs = c("initial_abundance", "carrying_capacity"))
# Here we tell the generator to import the HS file and save it as "hs_matrix"
capacity_gen$add_file_template("hs_matrix",
path_template = file.path(K_CUTS_DIR, "clim_suit_%s_21k-10BP_SCALED.RDS"),
path_params = "niche_ref",
file_type = "RDS")
# Here we subset the hs_matrix to have only the region cells, and we add the burn in.
# Also, we tell the generator to generate the carrying_capacity based on "density_max" and "hs_matrix".
capacity_gen$add_function_template("carrying_capacity",
function_def = function(params) {
hs_matrix <- params$hs_matrix[params$region$region_indices,]
hs_matrix[!is.finite(hs_matrix)] <- 0
# repeat the first timestep n times as a burn in
hs_matrix <- cbind(replicate(params$burn_in_steps, hs_matrix[, 1]), hs_matrix)
# round the density values
round(params$density_max*hs_matrix)
},
call_params = c("density_max", "hs_matrix", "burn_in_steps", "region"))
# Here we tell the generator what function to use to generate initial_abundance
# based on the carrying capacity of the first time step
capacity_gen$add_function_template("initial_abundance",
function_def = function(params) {
params$carrying_capacity[, 1]
},
call_params = c("carrying_capacity"))
system.time({test_capacity <- capacity_gen$generate(input_values = list(density_max = 250,
niche_ref = "0.7_101"))}) # ~4
raster::plot(region$raster_from_values(test_capacity$carrying_capacity[,1000]),
colNA = "blue")
##### Dispersal generator #####
known_dispersals <- c(205, 190, 190, 190, 280, 280, 190, 155, 150, 125, 155, 125, 135, 135, 255, 260, 190)
summary(known_dispersals)
b_lookup <- data.frame(d_max = -Inf, b = 0:300)
for (i in 2:300) {
b_lookup$d_max[i] <- which.max(exp(-1*(1:501)/b_lookup$b[i]) <= 0.19)
}
b_lookup$d_max[301] <- 501
dispersal_gen <- DispersalGenerator$new(
region = region,
dispersal_max_distance = 500,
# km
distance_classes = seq(10, 500, 10),
distance_scale = 1000,
# km
dispersal_function_data = b_lookup,
dispersal_friction = DispersalFriction$new(conductance = friction),
inputs = c("dispersal_p",
"dispersal_r"),
decimals = 3
)
dispersal_gen$distance_data <- readRDS(file = file.path(DATA_DIR, "dispersal_distance_data.RDS"))
system.time(test_dispersal <- dispersal_gen$generate(input_values =
list(dispersal_p = 0.5,
dispersal_r = 400))$dispersal_data)
##### Human density generator #####
human_threshold_mean <- quantile(humans_mean_matrix[humans_mean_matrix > 0], 0.95, na.rm = FALSE)
human_density_gen <- Generator$new(description = "Human Density Generator",
humans_abundance = humans_mean_matrix,
humans_var = humans_sd_matrix,
spatial_correlation = env_corr,
human_threshold = human_threshold_mean,
# temporal_correlation = 0,
# time_steps = timesteps,
generate_rasters = FALSE,
generative_requirements = list(p_window = 'function',
human_density = 'distribution'),
inputs = c("p"),
outputs = c("human_density"))
human_density_gen$add_function_template("p_window",
function_def = function(params) {
w <- params$p*10/100
p_lower <- params$p - w
p_upper <- params$p + w
p_lower <- ifelse(p_lower < 0, 0, p_lower)
p_upper <- ifelse(p_upper > 1, 1, p_upper)
return(c(p_lower, p_upper))
},
call_params = c("p"))
human_density_gen$add_distribution_template("human_density",
distr_type = "lognormal",
distr_params = list(mean = "humans_abundance", sd = "humans_var"),
sample = c("p_window"),#list(mid = "p", window = 0.0),
normalize_threshold = "human_threshold")
## test the generator
system.time(test_humans <- human_density_gen$generate(input_values = list(p=0.1)))
raster::plot(region$raster_from_values(test_humans[[1]]), main = "Human density",
colNA = "blue")
rm(test_capacity, test_dispersal, test_humans); gc()
#### Step 3: Simulation ####
sample_data <- read.csv(file.path(DATA_DIR, "selected_parameters.csv"))
sim_manager <- SimulationManager$new(sample_data = sample_data,
model_template = model_template,
generators = list(capacity_gen,
dispersal_gen,
human_density_gen),
parallel_cores = parallel_cores,
results_dir = RESULTS_DIR)
sim_manager$results_filename_attributes <- c("sample", "results")
run_output <- sim_manager$run()
run_output$summary