---
title: "Workflow example for the steppe bison"
author: "Global ChEC Lab"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Workflow example for the steppe bison}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
editor_options: 
  markdown: 
    wrap: 72
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

In this vignette, we demonstrate the new features that `paleopop` adds
to `poems` for modeling populations over paleo-ecological time scales
using the example of the steppe bison in Siberia. The steppe bison was
once distributed throughout the Northern Hemisphere. It became
regionally extinct in Siberia 9,500 years ago, and it is thought to have
gone completely extinct in Canada 500 years ago. We will model the range
dynamics of the steppe bison in Siberia from the Last Glacial Maximum
(21,000 years ago) until 9,000 years before present. Along the way, we
will learn how `paleopop` handles regions, model templates, simulations,
and results.

# Setup

Here we load `poems` and `paleopop` and set the number of parallel cores
for simulation and an output directory for results.

```{r setup}
library(poems)
library(paleopop)
parallel_cores <- 1
output_dir <- tempdir()
```

# Table of Contents

In this vignette, we will do the following:

1.  Create a PaleoRegion

2.  Generate dispersal rates and carrying capacities

3.  Create the model template

4.  Sample parameter space with Latin Hypercube Sampling

5.  Run the simulations

6.  Examine the results

# 1. Create a PaleoRegion

While `poems` implements a static region object, in `paleopop` we use
something different, because regions on paleo time scales are never
static. Oceans rise, continents collide, and the contours of our
spatially explicit landscape shift. In the case of the steppe bison in
Siberia, the sea level rises over time. `paleopop` includes a temporally
dynamic raster stack of Siberia we can use to build a `PaleoRegion`
object. The raster resolution is 2 by 2.

```{r siberia_raster, fig.align = "center", fig.width = 7, fig.height = 5}
library(raster)
raster::plot(paleopop::siberia_raster[[1]], main = "Siberia raster (LGM)",
             xlab = "Longitude (degrees)", ylab = "Latitude (degrees)",
             colNA = "blue")
raster::plot(paleopop::siberia_raster[[1001]], main = "Siberia raster (9000 BP)",
             xlab = "Longitude (degrees)", ylab = "Latitude (degrees)",
             colNA = "blue")
```

We can use this `RasterStack` object as a template to create a
`PaleoRegion` object, which will set the indices of occupiable cells at
each time step, and mask cells that are not occupiable at a time step
(in the case of this example, because those cells are now underwater.)
When we plot the `region_raster` in the `PaleoRegion` object, we see the
maximum extent of the region, showing all cells that are occupiable at
any time step. The `temporal_mask_raster` method generates a
`RasterStack` that marks occupiable cells at each time step with a 1.

```{r paleoregion, fig.align = "center", fig.width = 7, fig.height = 5}
region <- PaleoRegion$new(template_raster = paleopop::siberia_raster)
raster::plot(region$region_raster, main = "Bison region maximum extent (cell indices)",
             xlab = "Longitude (degrees)", ylab = "Latitude (degrees)",
             colNA = "blue")
```

# 2. Generate dispersal and carrying capacity

This part of the workflow is identical to `poems`: we create `Generator`
objects to dynamically generate dispersal between grid cells, initial
abundance, and carrying capacity at each point in space and time.

## 2.a. Generate dispersal matrix

We cut down on computational time during the simulations by generating
ahead of time a matrix that describes the potential dispersal rates
between every pairwise combination of cells in the study region.
Dispersal rates are calculated using a distance-based dispersal function
as well as, optionally, a conductance/friction landscape that maps out
barriers to dispersal, such as ice or mountains. (We will omit the
friction landscape for this example.)

```{r generate dispersal matrix}
dispersal_gen <- DispersalGenerator$new(region = region,
                                        dispersal_max_distance = 500, # km
                                        distance_classes = seq(10, 500, 10), # divide into bins
                                        distance_scale = 1000, # sets units to km
                                        dispersal_friction = DispersalFriction$new(), # empty
                                        inputs = c("dispersal_p", "dispersal_b"),
                                        decimals = 3)
dispersal_gen$calculate_distance_data(use_longlat = TRUE) # pre-calculate matrix of distances between cells
test_dispersal <- dispersal_gen$generate(input_values =
                                                       list(dispersal_p = 0.5,
                                                            dispersal_b = 200))$dispersal_data
head(test_dispersal[[1]])
```

## 2.b. Generate carrying capacity landscape

Here we will generate a dynamic landscape based on a raster stack of
habitat suitability values (from 0, totally unsuitable, to 1, ideally
suitable). `paleopop` has a spatiotemporally explicit habitat
suitability `RasterStack` for Siberia from 21,000 - 9,000 years BP.

```{r hs_raster, fig.align = "center", fig.width = 7, fig.height = 5}
raster::plot(bison_hs_raster[[1]], main = "Bison habitat suitability (LGM)",
             xlab = "Longitude (degrees)", ylab = "Latitude (degrees)",
             colNA = "blue")
```

However, PaleoPop simulations are not based on habitat suitability
values. Rather, they operate on initial populations and on carrying
capacities. Therefore, we need to enter these habitat suitability
landscapes into a generator that can translate them into an initial
abundance landscape and into a dynamic carrying capacity landscape.

To do this, we must create a `poems::Generator` object. Because every
simulation has different requirements, we must set our own inputs,
outputs, and generator functions to convert the inputs into the outputs.
Here, we will very simply use a maximum density parameter and multiply
it by the habitat suitability.

```{r carrying capacity generator, fig.align = "center", fig.width = 7, fig.height = 5}
# Convert the habitat suitability raster to matrix
bison_hs <- bison_hs_raster[region$region_indices]
# Create the generator
capacity_gen <- Generator$new(description = "Capacity generator",
                              example_hs = bison_hs, 
                              inputs = c("density_max"),
                              outputs = c("initial_abundance", "carrying_capacity"))
# indicate that initial abundance and carrying capacity are required
capacity_gen$add_generative_requirements(list(initial_abundance = "function",
                                              carrying_capacity = "function"))
# define custom generative functions
capacity_gen$add_function_template("carrying_capacity",
                                   function_def = function(params) {
                                     round(params$density_max*params$example_hs)
                                   },
                                   call_params = c("density_max", "example_hs"))
capacity_gen$add_function_template("initial_abundance",
                                   function_def = function(params) {
                                     params$carrying_capacity[,1]
                                   },
                                   call_params = c("carrying_capacity"))
# test run
test_capacity <- capacity_gen$generate(input_values = list(density_max = 2000))
raster::plot(region$raster_from_values(test_capacity$initial_abundance), main = "Initial abundance",
             xlab = "Longitude (degrees)", ylab = "Latitude (degrees)",
             colNA = "blue")
```

# 3. Create the model template

We create a model template for simulation using the `PaleoPopModel`
object. Let's check the required attributes for the object.

```{r paleopopmodel attributes}
model_template <- PaleoPopModel$new()
model_template$model_attributes # all possible attributes
model_template$required_attributes # required attributes to run simulations
```

Initial abundance, carrying capacity, and dispersal data will be
provided by the generators above. Here we will define an environmental
correlation matrix so there can be spatial autocorrelation in the
simulations.

```{r autocorrelation}
# Distance-based environmental correlation (via a compacted Cholesky decomposition)
env_corr <- SpatialCorrelation$new(region = region, amplitude = 0.4, breadth = 500)
correlation <- env_corr$get_compact_decomposition(decimals = 2)
```

Normally we would have spatiotemporally dynamic data on human density
over time, which the prey- and predator-dependent harvest function
translates into numbers of bison harvested. The harvest function in
`paleopop` is defined by `harvest_z`, which determines whether
harvesting is a Type II or Type III functional response, `harvest_g`, a
constant added to the denominator that reduces harvest rates, and
`harvest_max_n`, a ceiling on maximum prey density that can be
harvested.

In the interest of simplicity for this example, we will create a matrix
of constant low human density.

```{r human density}
human_density <- array(rep(0.1), c(913, 1001)) # rows = populations and columns = timesteps
```

Now we have all the components we need to build the model template, less
the model attributes that will be sampled via Latin Hypercube Sampling
later.

```{r model template}
model_template <- PaleoPopModel$new(
  simulation_function = "paleopop_simulator", # this is the default; made it explicit for the example
  region = region,
  # coordinates: you could use XY coordinates to define the region instead
  time_steps = 1001,
  years_per_step = 12, # generational length for bison
  populations = region$region_cells, # extracts number of population cells
  # initial_abundance: generated
  transition_rate = 1.0, # rate of transition between generations
  # standard_deviation: sampled
  compact_decomposition = correlation,
  # 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 = human_density,
  dispersal_target_k = 10, # minimum carrying capacity that attracts dispersers
  # dispersal_data: generated
  # abundance_threshold: sampled
  # initial_n: sampled
  occupancy_threshold = 1, # threshold: # of populations for the species to persist
  random_seed = 321,
  results_selection = c(
    # ema (expected minimum abundance), extirpation, occupancy, human_density,
    "abundance", "harvested"
    )
)
```

# 4. Sample model and generator parameters for each simulation

Here the workflow is the same as in `poems`: we sample along a range of
values so as to create a prior, and use these to parameterize different
simulations. Here, we will sample each range of values *uniformly* so as
to create an *uninformative* prior, but the LHS generator object offers
options for sampling along different distributions to create an
*informative* prior. For the sake of this example, we will sample 10
different parameter combinations, but it is advisable to run thousands
in order to fully sample the parameter space.

```{r latin hypercube sampling}
nsims <- 10 # adjust to run your own example if desired

lhs_generator <- LatinHypercubeSampler$new()
lhs_generator$set_uniform_parameter("standard_deviation", lower = 0, upper = sqrt(0.06))
lhs_generator$set_uniform_parameter("growth_rate_max", lower = log(1.31), upper = log(2.84))
lhs_generator$set_uniform_parameter("abundance_threshold", lower = 0, upper = 500, decimals = 0)
lhs_generator$set_uniform_parameter("harvest_max", lower = 0, upper = 0.35)
lhs_generator$set_uniform_parameter("harvest_z", lower = 1, upper = 2)
lhs_generator$set_uniform_parameter("density_max", lower = 500, upper = 3250) # alias for harvest_max_n
lhs_generator$set_uniform_parameter("dispersal_p", lower = 0.05, upper = 0.25) # for the dispersal generator
lhs_generator$set_uniform_parameter("dispersal_b", lower = 65, upper = 145) # for the dispersal generator

sample_data <- lhs_generator$generate_samples(number = nsims, random_seed = 123)
sample_data$sample <- c(1:nsims)
head(sample_data)
```

# 5. Run the simulations

Here we build a simulation manager to manage the sample data,
generators, and model template. The simulation manager will use the
`paleopop_simulator` function to run simulations.

```{r simulate}
sim_manager <- SimulationManager$new(sample_data = sample_data, 
                                     model_template = model_template,
                                     generators = list(capacity_gen,
                                                       dispersal_gen),
                                     parallel_cores = parallel_cores,
                                     results_dir = output_dir) 

sim_manager$results_filename_attributes <- c("sample", "results")

run_output <- sim_manager$run()
run_output$summary
```

The simulation log, `run_output`, can be examined for error messages and
failure indices if any of the simulations fail. There is also a detailed
simulation log created in the output directory.

# 6. Examine the results

We can explore the results of the simulations using the convenient
wrapper of the `PaleoPopResults` object. Each `PaleoPopResults` object
can hold the result of one simulation, and it can generate outputs that
were not specified in the results selection above, such as extirpation
times for each population.

```{r results}
results1 <- PaleoPopResults$new(
  results = readRDS(paste0(output_dir, "/sample_1_results.RData")),
  region = region, time_steps = 1001
  )
head(results1$extirpation)
```

We can also use a `PaleoPopResults` object as a kind of template for the
`ResultsManager`, an object that can calculate summary metrics for many
results all at once. As with the generators, we must define our own
custom metrics functions, because each *in silico* experiment calls for
a different set of metrics to summarize the outputs. We can use the
metrics generated by the `PaleoPopResults` object as the basis for our
custom summary metrics.

```{r results manager}
results_model <- PaleoPopResults$new(region = region, time_steps = 1001, trend_interval = 1:10)
metrics_manager <- ResultsManager$new(simulation_manager = sim_manager,
                                      simulation_results = results_model,
                                      generators = NULL) # don't need generators
metrics_manager$summary_metrics <- c("abundance_trend", "extinction_time")
metrics_manager$summary_functions <- list()

metrics_manager$summary_functions$extinction_time <- function(simulation_results) {
  extinction_time <- -12*(1001 - simulation_results$all$extirpation)-9001 # converts timestep to years BP
  if (is.na(extinction_time)) {
    extinction_time <- -9001 # if NA, then it survived to the end of the simulation
  }
  return(extinction_time)
}

metrics_manager$summary_functions$abundance_trend <- function(simulation_results) {
  abundance_trend <- simulation_results$all$abundance_trend
  return(abundance_trend) # this will use the trend interval specified above
}

gen_log <- metrics_manager$generate(results_dir = output_dir)
```

You can examine the log for the summary metrics calculation to browse
error messages for failed calculations.

Now that we've calculated some summary metrics, based on outputs from
`PaleoPopResults` like extinction time and abundance trend, we can
examine the simulation results. Here I show the simulated extinction times.

```{r histograms, fig.align = "center", fig.width = 7, fig.height = 5}
metrics_manager$summary_metric_data$extinction_time
```

Both of these summary metrics, abundance trend
over the first ten time steps and extinction time, could be a suitable
metric to converge toward a validation target.

From here, you may continue with the `poems` workflow, as explained in
the `poems`
[vignette](https://cran.r-project.org/web//packages/poems/vignettes/thylacine_example.pdf),
creating a `Validator` object for pattern-oriented modeling to validate
the results of the simulation.
