spread3: An alternative spread function, conceived for insects

In PredictiveEcology/SpaDES.tools: Additional Tools for Developing Spatially Explicit Discrete Event Simulation (SpaDES) Models

An alternative spread function, conceived for insects

Description

This is built with spread2() and is still experimental. This one differs from other attempts in that it treats the advection and dispersal as mathematical vectors that are added together. They are "rounded" to pixel centres.

Usage

spread3(
  start,
  rasQuality,
  rasAbundance,
  advectionDir,
  advectionMag,
  meanDist,
  dispersalKernel = "exponential",
  sdDist = 1,
  plot.it = 2,
  minNumAgents = 50,
  verbose = getOption("LandR.verbose", 0),
  saveStack = NULL,
  skipChecks = FALSE
)

Arguments

start	Raster indices from which to initiate dispersal
rasQuality	A raster with habitat quality. Currently, must be scaled from 0 to 1, i.e., a probability of "settling"
rasAbundance	A raster where each pixel represents the number of "agents" or pseudo-agents contained. This number of agents, will be spread horizontally, and distributed from each pixel that contains a non-zero non NA value.
advectionDir	A single number or RasterLayer in degrees from North = 0 (though it will use radians if all values are ⁠abs(advectionDir) > 2 * pi)⁠. This indicates the direction of advective forcing (i.e., wind).
advectionMag	A single number or RasterLayer in distance units of the rasQuality, e.g., meters, indicating the relative forcing that will occur. It is imposed on the total event, i.e., if the meanDist is 10000, and advectionMag is 5000, then the expected distance (i.e., ⁠63%⁠ of agents) will have settled by 15000 map units.
meanDist	A single number indicating the mean distance parameter in map units (not pixels), for a negative exponential distribution dispersal kernel (e.g., dexp). This will mean that ⁠63%⁠ of agents will have settled at this meanDist (still experimental).
dispersalKernel	One of either "exponential" or "weibull".
sdDist	A single number indicating the sd parameter of a two-parameter dispersalKernel. Defaults to 1, which is the same as the exponential distribution.
plot.it	Numeric. With increasing numbers above 0, there will be plots produced during iterations. Currently, only 0, 1, or 2+ are distinct.
minNumAgents	Single numeric indicating the minimum number of agents to consider all dispersing finished. Default is 50.
verbose	Numeric. With increasing numbers above 0, there will be more messages produced. Currently, only 0, 1, or 2+ are distinct.
saveStack	If provided as a character string, it will save each iteration as part of a rasterStack to disk upon exit.
skipChecks	Logical. If TRUE, assertions will be skipped (faster, but could miss problems)

Value

A data.table with all information used during the spreading

Examples

## these tests are fairly heavy, so don't run during automated tests
#########################################################
# Simple case, no variation in rasQuality, numeric advectionDir and advectionMag
#########################################################

  library(terra)

  origDTThreads <- data.table::setDTthreads(2L)
  origNcpus <- options(Ncpus = 2L)

  maxDim <- 10000
  ras <- terra::rast(terra::ext(c(0, maxDim, 0, maxDim)), res = 100, vals = 0)
  rasQuality <- terra::rast(ras)
  rasQuality[] <- 1
  rasAbundance <- terra::rast(rasQuality)
  rasAbundance[] <- 0
  # startPixel <- middlePixel(rasAbundance)
  startPixel <- sample(seq(terra::ncell(rasAbundance)), 30)
  rasAbundance[startPixel] <- 1000
  advectionDir <- 70
  advectionMag <- 4 * res(rasAbundance)[1]
  meanDist <- 2600

  # Test the dispersal kernel -- create a function
  plotDispersalKernel <- function(out, meanAdvectionMag) {
    out[, disGroup := round(distance / 100) * 100]
    freqs <- out[, .N, by = "disGroup"]
    freqs[, `:=`(cumSum = cumsum(N), N = N)]
    plot(freqs$disGroup, freqs$cumSum, # addTo = "CumulativeNumberSettled",
         main = "Cumulative Number Settled") # can plot the distance X number
    abline(v = meanAdvectionMag + meanDist)
    newTitle <- "Number Settled By Distance"
    plot(freqs$disGroup, freqs$N, # addTo = gsub(" ", "", newTitle),
         main = newTitle) # can plot the distance X number
    abline(v = meanAdvectionMag + meanDist)
    # should be 0.63:
    freqs[disGroup == meanAdvectionMag + meanDist, cumSum] / tail(freqs, 1)[, cumSum]
    mtext(side = 3, paste("Average habitat quality: ",
                          round(mean(rasQuality[], na.rm = TRUE), 2)),
          outer = TRUE, line = -2, cex = 2)
  }
  out <- spread3(rasAbundance = rasAbundance,
                 rasQuality = rasQuality,
                 advectionDir = advectionDir,
                 advectionMag = advectionMag,
                 meanDist = meanDist, verbose = 2,
                 plot.it = interactive())

  plotDispersalKernel(out, advectionMag)

  # The next examples are potentially time consuming; avoid on automated testing
  if (interactive()) {
    #########################################################
    ### The case of variable quality raster
    #########################################################
    rasQuality <- terra::rast(system.file("extdata", "rasQuality.tif",
                                          package = "SpaDES.tools"))
    terra::crs(rasQuality) <- system.file("extdata", "targetCRS.rds", package = "SpaDES.tools") |>
      readRDS() |>
      slot("projargs")
    mask <- rasQuality < 5
    rasQuality[mask[] %in% TRUE] <- 0
    # rescale so min is 0.75 and max is 1
    rasQuality[] <- rasQuality[] / (reproducible::maxFn(rasQuality) * 4) + 1 / 4
    rasAbundance <- terra::rast(rasQuality)
    rasAbundance[] <- 0
    startPixel <- sample(seq(ncell(rasAbundance)), 300)
    rasAbundance[startPixel] <- 1000
    advectionDir <- 75
    advectionMag <- 4 * res(rasAbundance)[1]
    meanDist <- 2600
    out <- spread3(rasAbundance = rasAbundance,
                   rasQuality = rasQuality,
                   advectionDir = advectionDir,
                   advectionMag = advectionMag,
                   meanDist = meanDist, verbose = 2,
                   plot.it = interactive())
    if (interactive()) {
      plotDispersalKernel(out, advectionMag)
    }

    ###############################################################################
    ### The case of variable quality raster, raster for advectionDir & advectionMag
    ###############################################################################
    maxDim <- 10000
    ras <- terra::rast(terra::ext(c(0, maxDim, 0, maxDim)), res = 100, vals = 0)
    rasQuality <- terra::rast(ras)
    rasQuality[] <- 1
    rasAbundance <- terra::rast(rasQuality)
    rasAbundance[] <- NA
    # startPixel <- middlePixel(rasAbundance)
    startPixel <- sample(seq(ncell(rasAbundance)), 25)
    rasAbundance[startPixel] <- 1000

    # raster for advectionDir
    advectionDir <- terra::rast(system.file("extdata", "advectionDir.tif",
                                            package = "SpaDES.tools"))
    crs(advectionDir) <- crs(rasQuality)
    # rescale so min is 0.75 and max is 1
    advectionDir[] <- advectionDir[] / (reproducible::maxFn(advectionDir)) * 180

    # raster for advectionMag
    advectionMag <- terra::rast(system.file("extdata", "advectionMag.tif",
                                            package = "SpaDES.tools"))
    crs(advectionMag) <- crs(rasQuality)
    # rescale so min is 0.75 and max is 1
    advectionMag[] <- advectionMag[] / (reproducible::maxFn(advectionMag)) * 600

    out <- spread3(rasAbundance = rasAbundance,
                   rasQuality = rasQuality,
                   advectionDir = advectionDir,
                   advectionMag = advectionMag,
                   meanDist = meanDist, verbose = 2,
                   plot.it = interactive())

    if (interactive()) {
      names(advectionDir) <- "Wind direction"
      names(advectionMag) <- "Wind speed"
      names(rasAbundance) <- "Initial abundances"
      terra::plot(c(advectionDir, advectionMag, rasAbundance))

      plotDispersalKernel(out, mean(advectionMag[]))
    }

    #########################################
    # save iterations to a stack to make animated GIF
    ########################################
    tmpStack <- tempfile(pattern = "stackToAnimate", fileext = ".tif")
    out <- spread3(rasAbundance = rasAbundance,
                   rasQuality = rasQuality,
                   advectionDir = advectionDir,
                   advectionMag = advectionMag,
                   meanDist = 2600, verbose = 2,
                   plot.it = interactive(), saveStack = tmpStack)

    ## This animates the series of images into an animated GIF
    if (require(animation, quietly = TRUE)) {
      out2 <- terra::rast(tmpStack)
      gifName <- file.path(tempdir(), "animation.gif")

      # Only works on some systems; may need to configure
      # Works on Windows without system adjustments
      if (identical(.Platform$OS.type, "windows"))
        saveGIF(interval = 0.1, movie.name = gifName, expr = {
          for (i in seq(length(names(out2)))) terra::plot(out2[[i]])
        })
    }
  }

  # clean up
  data.table::setDTthreads(origDTThreads)
  options(Ncpus = origNcpus)

PredictiveEcology/SpaDES.tools documentation built on April 18, 2024, 3:21 a.m.